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PKU-YuanGroup/OpenAI4S
Open-source AI agent for scientific research. Analyze data in Python/R with Claude, GPT, Gemini, and more.
History
Review weekly changes to SKILL.md before updating. Dates mark the weeks in which we observed each version; checks apply to the recorded commits.
View weekly changes
skills/admet_genetic/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/alphafold2/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/audit-dataset/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioprobench/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-alignment-alignment-io/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-alignment-alignment-trimming/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-alignment-files-alignment-amplicon-clipping/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-alignment-files-alignment-filtering/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-alignment-files-alignment-indexing/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-alignment-files-alignment-sorting/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-alignment-files-alignment-validation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-alignment-files-bam-statistics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-alignment-files-duplicate-handling/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-alignment-files-pileup-generation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-alignment-files-reference-operations/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-alignment-files-sam-bam-basics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-alignment-msa-parsing/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-alignment-msa-statistics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-alignment-multiple-alignment/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-alignment-pairwise-alignment/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-alignment-structural-alignment/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-alternative-splicing-differential-splicing/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-alternative-splicing-isoform-switching/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-alternative-splicing-long-read-splicing/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-alternative-splicing-outlier-splicing-detection/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-alternative-splicing-sashimi-plots/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-alternative-splicing-single-cell-splicing/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-alternative-splicing-splice-variant-prediction/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-alternative-splicing-splicing-qc/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-alternative-splicing-splicing-quantification/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-atac-seq-allele-specific-accessibility/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-atac-seq-atac-peak-calling/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-atac-seq-atac-qc/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-atac-seq-co-accessibility/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-atac-seq-consensus-peakset/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-atac-seq-deep-learning-atac/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-atac-seq-differential-accessibility/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-atac-seq-enhancer-gene-linking/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-atac-seq-footprinting/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-atac-seq-motif-deviation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-atac-seq-nucleosome-positioning/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-atac-seq-single-cell-atac/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-causal-genomics-colocalization-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-causal-genomics-effector-gene-prioritization/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-causal-genomics-fine-mapping/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-causal-genomics-genetic-correlation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-causal-genomics-genomic-sem/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-causal-genomics-heritability-partitioning/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-causal-genomics-mediation-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-causal-genomics-mendelian-randomization/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-causal-genomics-pleiotropy-detection/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-causal-genomics-proteome-mr-drug-target/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-causal-genomics-transcriptome-wide-association/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chemoinformatics-admet-prediction/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chemoinformatics-conformer-generation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chemoinformatics-covalent-design/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chemoinformatics-free-energy-calculations/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chemoinformatics-generative-design/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chemoinformatics-ml-docking-rescoring/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chemoinformatics-molecular-descriptors/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chemoinformatics-molecular-io/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chemoinformatics-molecular-standardization/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chemoinformatics-pharmacophore-modeling/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chemoinformatics-pose-validation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chemoinformatics-protac-degraders/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chemoinformatics-qsar-modeling/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chemoinformatics-reaction-enumeration/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chemoinformatics-retrosynthesis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chemoinformatics-scaffold-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chemoinformatics-shape-similarity/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chemoinformatics-similarity-searching/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chemoinformatics-substructure-search/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chemoinformatics-virtual-screening/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chip-seq-allele-specific-binding/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chip-seq-chip-deep-learning/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chip-seq-chipseq-qc/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chip-seq-chipseq-visualization/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chip-seq-chromatin-state-segmentation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chip-seq-cut-and-run-tag/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chip-seq-differential-binding/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chip-seq-motif-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chip-seq-peak-annotation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chip-seq-peak-calling/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chip-seq-spike-in-normalization/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-chip-seq-super-enhancers/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clinical-biostatistics-adaptive-designs/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clinical-biostatistics-bayesian-trials/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clinical-biostatistics-categorical-tests/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clinical-biostatistics-cdisc-data-handling/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clinical-biostatistics-effect-measures/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clinical-biostatistics-logistic-regression/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clinical-biostatistics-missing-data-sensitivity/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clinical-biostatistics-multiplicity-graphical/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clinical-biostatistics-power-and-sample-size/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clinical-biostatistics-subgroup-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clinical-biostatistics-survival-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clinical-biostatistics-trial-reporting/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clinical-databases-acmg-classification/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clinical-databases-clinvar-lookup/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clinical-databases-dbsnp-queries/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clinical-databases-gnomad-frequencies/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clinical-databases-hla-typing/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clinical-databases-msi-detection/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clinical-databases-myvariant-queries/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clinical-databases-pharmacogenomics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clinical-databases-polygenic-risk/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clinical-databases-somatic-signatures/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clinical-databases-tumor-mutational-burden/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clinical-databases-variant-prioritization/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clip-seq-ago-clip-mirna-targets/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clip-seq-binding-site-annotation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clip-seq-clip-alignment/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clip-seq-clip-deep-learning/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clip-seq-clip-motif-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clip-seq-clip-peak-calling/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clip-seq-clip-preprocessing/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clip-seq-clip-qc/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clip-seq-crosslink-site-detection/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clip-seq-differential-clip/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clip-seq-m6a-clip/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-clip-seq-stamp-antibody-free/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-comparative-genomics-ancestral-reconstruction/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-comparative-genomics-comparative-annotation-projection/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-comparative-genomics-gene-family-evolution/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-comparative-genomics-gene-tree-species-tree-reconciliation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-comparative-genomics-genome-distance-and-species-delineation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-comparative-genomics-hgt-detection/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-comparative-genomics-introgression-detection/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-comparative-genomics-ortholog-inference/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-comparative-genomics-pangenome-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-comparative-genomics-positive-selection/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-comparative-genomics-synteny-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-comparative-genomics-whole-genome-alignment/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-comparative-genomics-whole-genome-duplication/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-copy-number-allele-specific-copy-number/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-copy-number-cnv-annotation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-copy-number-cnv-visualization/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-copy-number-cnvkit-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-copy-number-copy-ratio-segmentation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-copy-number-focal-amplification-ecdna/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-copy-number-gatk-cnv/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-copy-number-germline-cnv-interpretation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-copy-number-hrd-scoring/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-copy-number-recurrent-cnv/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-copy-number-subclonal-copy-number/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-crispr-screens-bagel-essentiality/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-crispr-screens-base-editing-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-crispr-screens-batch-correction/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-crispr-screens-combinatorial-screens/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-crispr-screens-copy-number-correction/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-crispr-screens-crispresso-editing/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-crispr-screens-drugz-chemogenomic/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-crispr-screens-hit-calling/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-crispr-screens-in-vivo-screens/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-crispr-screens-jacks-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-crispr-screens-library-design/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-crispr-screens-mageck-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-crispr-screens-perturb-seq-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-crispr-screens-prime-editing-screens/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-crispr-screens-screen-qc/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-data-visualization-circos-plots/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-data-visualization-color-palettes/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-data-visualization-dimensionality-reduction-plots/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-data-visualization-distribution-plots/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-data-visualization-flow-and-transition-plots/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-data-visualization-forest-funnel-plots/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-data-visualization-genome-tracks/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-data-visualization-ggplot2-fundamentals/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-data-visualization-heatmaps-clustering/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-data-visualization-interactive-visualization/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-data-visualization-lollipop-protein-maps/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-data-visualization-manhattan-qq-locuszoom/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-data-visualization-matplotlib-fundamentals/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-data-visualization-multipanel-figures/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-data-visualization-network-visualization/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-data-visualization-oncoprint-mutation-matrices/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-data-visualization-sequence-logos/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-data-visualization-statistical-annotation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-data-visualization-upset-plots/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-data-visualization-volcano-and-ma-plots/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-database-access-batch-downloads/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-database-access-biomart-queries/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-database-access-blast-searches/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-database-access-ensembl-rest/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-database-access-entrez-fetch/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-database-access-entrez-link/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-database-access-entrez-search/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-database-access-geo-data/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-database-access-interaction-databases/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-database-access-local-blast/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-database-access-ncbi-datasets-cli/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-database-access-ortholog-inference/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-database-access-remote-homology/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-database-access-sra-data/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-database-access-uniprot-access/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-differential-expression-batch-correction/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-differential-expression-de-results/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-differential-expression-de-visualization/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-differential-expression-deseq2-basics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-differential-expression-edger-basics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-differential-expression-timeseries-de/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-ecological-genomics-biodiversity-metrics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-ecological-genomics-community-ecology/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-ecological-genomics-conservation-genetics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-ecological-genomics-edna-metabarcoding/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-ecological-genomics-landscape-genomics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-ecological-genomics-species-delimitation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-epidemiological-genomics-amr-surveillance/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-epidemiological-genomics-pathogen-typing/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-epidemiological-genomics-phylodynamics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-epidemiological-genomics-transmission-inference/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-epidemiological-genomics-variant-surveillance/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-epitranscriptomics-m6a-differential/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-epitranscriptomics-m6a-peak-calling/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-epitranscriptomics-m6anet-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-epitranscriptomics-merip-preprocessing/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-epitranscriptomics-modification-visualization/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-experimental-design-batch-design/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-experimental-design-multiple-testing/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-experimental-design-power-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-experimental-design-randomization-blocking/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-experimental-design-sample-size/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-expression-matrix-counts-ingest/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-expression-matrix-gene-id-mapping/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-expression-matrix-metadata-joins/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-expression-matrix-normalization/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-expression-matrix-sparse-handling/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-flow-cytometry-bead-normalization/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-flow-cytometry-clustering-phenotyping/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-flow-cytometry-compensation-transformation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-flow-cytometry-cytometry-qc/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-flow-cytometry-differential-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-flow-cytometry-doublet-detection/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-flow-cytometry-fcs-handling/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-flow-cytometry-gating-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-gene-regulatory-networks-coexpression-networks/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-gene-regulatory-networks-differential-networks/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-gene-regulatory-networks-grn-inference/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Instructions only
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-gene-regulatory-networks-multiomics-grn/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-gene-regulatory-networks-perturbation-simulation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-gene-regulatory-networks-scenic-regulons/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Network or broad shell
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Not checked yet
First observation → e509c8080f25
No earlier version was observed for comparison.
skills/bioskills/bio-genome-annotation-annotation-qc/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Not checked yet → After Local scripts
e509c8080f25 → ef85836a0b90
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
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skills/bioskills/bio-genome-annotation-annotation-transfer/SKILL.md
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skills/bioskills/bio-genome-annotation-eukaryotic-gene-prediction/SKILL.md
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skills/bioskills/bio-genome-annotation-functional-annotation/SKILL.md
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skills/bioskills/bio-genome-annotation-ncrna-annotation/SKILL.md
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skills/bioskills/bio-genome-annotation-prokaryotic-annotation/SKILL.md
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skills/bioskills/bio-genome-annotation-repeat-annotation/SKILL.md
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skills/bioskills/bio-genome-assembly-assembly-polishing/SKILL.md
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skills/bioskills/bio-genome-assembly-assembly-qc/SKILL.md
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skills/bioskills/bio-genome-assembly-contamination-detection/SKILL.md
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skills/bioskills/bio-genome-assembly-genome-profiling/SKILL.md
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skills/bioskills/bio-genome-assembly-hifi-assembly/SKILL.md
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skills/bioskills/bio-genome-assembly-long-read-assembly/SKILL.md
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skills/bioskills/bio-genome-assembly-metagenome-assembly/SKILL.md
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skills/bioskills/bio-genome-assembly-scaffolding/SKILL.md
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skills/bioskills/bio-genome-assembly-short-read-assembly/SKILL.md
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skills/bioskills/bio-genome-engineering-base-editing-design/SKILL.md
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skills/bioskills/bio-genome-engineering-grna-design/SKILL.md
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skills/bioskills/bio-genome-engineering-hdr-template-design/SKILL.md
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skills/bioskills/bio-genome-engineering-off-target-prediction/SKILL.md
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skills/bioskills/bio-genome-engineering-prime-editing-design/SKILL.md
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skills/bioskills/bio-genome-intervals-bed-file-basics/SKILL.md
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skills/bioskills/bio-genome-intervals-bedgraph-handling/SKILL.md
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skills/bioskills/bio-genome-intervals-bigwig-tracks/SKILL.md
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skills/bioskills/bio-genome-intervals-coverage-analysis/SKILL.md
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skills/bioskills/bio-genome-intervals-gtf-gff-handling/SKILL.md
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skills/bioskills/bio-genome-intervals-interval-arithmetic/SKILL.md
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skills/bioskills/bio-genome-intervals-overlap-significance/SKILL.md
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skills/bioskills/bio-genome-intervals-proximity-operations/SKILL.md
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skills/bioskills/bio-hi-c-analysis-compartment-analysis/SKILL.md
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skills/bioskills/bio-hi-c-analysis-contact-pairs/SKILL.md
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skills/bioskills/bio-hi-c-analysis-hic-data-io/SKILL.md
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skills/bioskills/bio-hi-c-analysis-hic-differential/SKILL.md
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skills/bioskills/bio-hi-c-analysis-hic-visualization/SKILL.md
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skills/bioskills/bio-hi-c-analysis-hichip-plac-loops/SKILL.md
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skills/bioskills/bio-hi-c-analysis-loop-calling/SKILL.md
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skills/bioskills/bio-hi-c-analysis-matrix-operations/SKILL.md
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skills/bioskills/bio-hi-c-analysis-tad-detection/SKILL.md
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skills/bioskills/bio-imaging-mass-cytometry-cell-segmentation/SKILL.md
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skills/bioskills/bio-imaging-mass-cytometry-data-preprocessing/SKILL.md
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skills/bioskills/bio-imaging-mass-cytometry-differential-analysis/SKILL.md
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skills/bioskills/bio-imaging-mass-cytometry-interactive-annotation/SKILL.md
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skills/bioskills/bio-imaging-mass-cytometry-phenotyping/SKILL.md
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skills/bioskills/bio-imaging-mass-cytometry-quality-metrics/SKILL.md
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skills/bioskills/bio-imaging-mass-cytometry-spatial-analysis/SKILL.md
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skills/bioskills/bio-immunoinformatics-epitope-prediction/SKILL.md
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skills/bioskills/bio-immunoinformatics-immunogenicity-scoring/SKILL.md
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skills/bioskills/bio-immunoinformatics-mhc-binding-prediction/SKILL.md
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skills/bioskills/bio-immunoinformatics-mhc-class-ii-prediction/SKILL.md
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skills/bioskills/bio-immunoinformatics-neoantigen-prediction/SKILL.md
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skills/bioskills/bio-immunoinformatics-tcr-epitope-binding/SKILL.md
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skills/bioskills/bio-liquid-biopsy-analytical-validation/SKILL.md
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skills/bioskills/bio-liquid-biopsy-cfdna-preprocessing/SKILL.md
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skills/bioskills/bio-liquid-biopsy-ctdna-mutation-detection/SKILL.md
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skills/bioskills/bio-liquid-biopsy-fragment-analysis/SKILL.md
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skills/bioskills/bio-liquid-biopsy-longitudinal-monitoring/SKILL.md
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skills/bioskills/bio-liquid-biopsy-methylation-based-detection/SKILL.md
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skills/bioskills/bio-liquid-biopsy-tumor-fraction-estimation/SKILL.md
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skills/bioskills/bio-long-read-sequencing-basecalling/SKILL.md
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View on GitHubAdd to my skills
Verified commit ef85836a0b900799d7d478593080a84d64a86d8c
git clone --filter=blob:none --no-checkout https://github.com/PKU-YuanGroup/OpenAI4S.git .hotskills/pku-yuangroup-openai4s && git -C .hotskills/pku-yuangroup-openai4s checkout --detach ef85836a0b900799d7d478593080a84d64a86d8c
# Copy each selected skill directory from .hotskills/pku-yuangroup-openai4s into .claude/skills/<skill-name>/git clone --filter=blob:none --no-checkout https://github.com/PKU-YuanGroup/OpenAI4S.git .hotskills/pku-yuangroup-openai4s && git -C .hotskills/pku-yuangroup-openai4s checkout --detach ef85836a0b900799d7d478593080a84d64a86d8c
# Copy each selected skill directory from .hotskills/pku-yuangroup-openai4s into .agents/skills/<skill-name>/662GitHub stars
8stars in 7 days
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248commits in 30 days
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Individual skills in this repository
- admet_genetic
skills/admet_genetic - alphafold2
skills/alphafold2 - audit-dataset
skills/audit-dataset - bioprobench
skills/bioprobench - bio-alignment-io
skills/bioskills/bio-alignment-alignment-io - bio-alignment-trimming
skills/bioskills/bio-alignment-alignment-trimming - bio-alignment-amplicon-clipping
skills/bioskills/bio-alignment-files-alignment-amplicon-clipping - bio-alignment-filtering
skills/bioskills/bio-alignment-files-alignment-filtering - bio-alignment-indexing
skills/bioskills/bio-alignment-files-alignment-indexing - bio-alignment-sorting
skills/bioskills/bio-alignment-files-alignment-sorting - bio-alignment-validation
skills/bioskills/bio-alignment-files-alignment-validation - bio-bam-statistics
skills/bioskills/bio-alignment-files-bam-statistics - bio-duplicate-handling
skills/bioskills/bio-alignment-files-duplicate-handling - bio-pileup-generation
skills/bioskills/bio-alignment-files-pileup-generation - bio-reference-operations
skills/bioskills/bio-alignment-files-reference-operations - bio-sam-bam-basics
skills/bioskills/bio-alignment-files-sam-bam-basics - bio-alignment-msa-parsing
skills/bioskills/bio-alignment-msa-parsing - bio-alignment-msa-statistics
skills/bioskills/bio-alignment-msa-statistics - bio-alignment-multiple
skills/bioskills/bio-alignment-multiple-alignment - bio-alignment-pairwise
skills/bioskills/bio-alignment-pairwise-alignment - bio-alignment-structural
skills/bioskills/bio-alignment-structural-alignment - bio-differential-splicing
skills/bioskills/bio-alternative-splicing-differential-splicing - bio-isoform-switching
skills/bioskills/bio-alternative-splicing-isoform-switching - bio-long-read-splicing
skills/bioskills/bio-alternative-splicing-long-read-splicing - bio-outlier-splicing-detection
skills/bioskills/bio-alternative-splicing-outlier-splicing-detection - bio-sashimi-plots
skills/bioskills/bio-alternative-splicing-sashimi-plots - bio-single-cell-splicing
skills/bioskills/bio-alternative-splicing-single-cell-splicing - bio-splice-variant-prediction
skills/bioskills/bio-alternative-splicing-splice-variant-prediction - bio-splicing-qc
skills/bioskills/bio-alternative-splicing-splicing-qc - bio-splicing-quantification
skills/bioskills/bio-alternative-splicing-splicing-quantification - bio-atac-seq-allele-specific-accessibility
skills/bioskills/bio-atac-seq-allele-specific-accessibility - bio-atac-seq-atac-peak-calling
skills/bioskills/bio-atac-seq-atac-peak-calling - bio-atac-seq-atac-qc
skills/bioskills/bio-atac-seq-atac-qc - bio-atac-seq-co-accessibility
skills/bioskills/bio-atac-seq-co-accessibility - bio-atac-seq-consensus-peakset
skills/bioskills/bio-atac-seq-consensus-peakset - bio-atac-seq-deep-learning-atac
skills/bioskills/bio-atac-seq-deep-learning-atac - bio-atac-seq-differential-accessibility
skills/bioskills/bio-atac-seq-differential-accessibility - bio-atac-seq-enhancer-gene-linking
skills/bioskills/bio-atac-seq-enhancer-gene-linking - bio-atac-seq-footprinting
skills/bioskills/bio-atac-seq-footprinting - bio-atac-seq-motif-deviation
skills/bioskills/bio-atac-seq-motif-deviation - bio-atac-seq-nucleosome-positioning
skills/bioskills/bio-atac-seq-nucleosome-positioning - bio-atac-seq-single-cell-atac
skills/bioskills/bio-atac-seq-single-cell-atac - bio-causal-genomics-colocalization-analysis
skills/bioskills/bio-causal-genomics-colocalization-analysis - bio-causal-genomics-effector-gene-prioritization
skills/bioskills/bio-causal-genomics-effector-gene-prioritization - bio-causal-genomics-fine-mapping
skills/bioskills/bio-causal-genomics-fine-mapping - bio-causal-genomics-genetic-correlation
skills/bioskills/bio-causal-genomics-genetic-correlation - bio-causal-genomics-genomic-sem
skills/bioskills/bio-causal-genomics-genomic-sem - bio-causal-genomics-heritability-partitioning
skills/bioskills/bio-causal-genomics-heritability-partitioning - bio-causal-genomics-mediation-analysis
skills/bioskills/bio-causal-genomics-mediation-analysis - bio-causal-genomics-mendelian-randomization
skills/bioskills/bio-causal-genomics-mendelian-randomization - bio-causal-genomics-pleiotropy-detection
skills/bioskills/bio-causal-genomics-pleiotropy-detection - bio-causal-genomics-proteome-mr-drug-target
skills/bioskills/bio-causal-genomics-proteome-mr-drug-target - bio-causal-genomics-transcriptome-wide-association
skills/bioskills/bio-causal-genomics-transcriptome-wide-association - bio-admet-prediction
skills/bioskills/bio-chemoinformatics-admet-prediction - bio-conformer-generation
skills/bioskills/bio-chemoinformatics-conformer-generation - bio-covalent-design
skills/bioskills/bio-chemoinformatics-covalent-design - bio-free-energy-calculations
skills/bioskills/bio-chemoinformatics-free-energy-calculations - bio-generative-design
skills/bioskills/bio-chemoinformatics-generative-design - bio-ml-docking-rescoring
skills/bioskills/bio-chemoinformatics-ml-docking-rescoring - bio-molecular-descriptors
skills/bioskills/bio-chemoinformatics-molecular-descriptors - bio-molecular-io
skills/bioskills/bio-chemoinformatics-molecular-io - bio-molecular-standardization
skills/bioskills/bio-chemoinformatics-molecular-standardization - bio-pharmacophore-modeling
skills/bioskills/bio-chemoinformatics-pharmacophore-modeling - bio-pose-validation
skills/bioskills/bio-chemoinformatics-pose-validation - bio-protac-degraders
skills/bioskills/bio-chemoinformatics-protac-degraders - bio-qsar-modeling
skills/bioskills/bio-chemoinformatics-qsar-modeling - bio-reaction-enumeration
skills/bioskills/bio-chemoinformatics-reaction-enumeration - bio-retrosynthesis
skills/bioskills/bio-chemoinformatics-retrosynthesis - bio-scaffold-analysis
skills/bioskills/bio-chemoinformatics-scaffold-analysis - bio-shape-similarity
skills/bioskills/bio-chemoinformatics-shape-similarity - bio-similarity-searching
skills/bioskills/bio-chemoinformatics-similarity-searching - bio-substructure-search
skills/bioskills/bio-chemoinformatics-substructure-search - bio-virtual-screening
skills/bioskills/bio-chemoinformatics-virtual-screening - bio-chipseq-allele-specific-binding
skills/bioskills/bio-chip-seq-allele-specific-binding - bio-chipseq-chip-deep-learning
skills/bioskills/bio-chip-seq-chip-deep-learning - bio-chipseq-qc
skills/bioskills/bio-chip-seq-chipseq-qc - bio-chipseq-visualization
skills/bioskills/bio-chip-seq-chipseq-visualization - bio-chipseq-chromatin-state-segmentation
skills/bioskills/bio-chip-seq-chromatin-state-segmentation - bio-chipseq-cut-and-run-tag
skills/bioskills/bio-chip-seq-cut-and-run-tag - bio-chipseq-differential-binding
skills/bioskills/bio-chip-seq-differential-binding - bio-chipseq-motif-analysis
skills/bioskills/bio-chip-seq-motif-analysis - bio-chipseq-peak-annotation
skills/bioskills/bio-chip-seq-peak-annotation - bio-chipseq-peak-calling
skills/bioskills/bio-chip-seq-peak-calling - bio-chipseq-spike-in-normalization
skills/bioskills/bio-chip-seq-spike-in-normalization - bio-chipseq-super-enhancers
skills/bioskills/bio-chip-seq-super-enhancers - bio-clinical-biostatistics-adaptive-designs
skills/bioskills/bio-clinical-biostatistics-adaptive-designs - bio-clinical-biostatistics-bayesian-trials
skills/bioskills/bio-clinical-biostatistics-bayesian-trials - bio-clinical-biostatistics-categorical-tests
skills/bioskills/bio-clinical-biostatistics-categorical-tests - bio-clinical-biostatistics-cdisc-data
skills/bioskills/bio-clinical-biostatistics-cdisc-data-handling - bio-clinical-biostatistics-effect-measures
skills/bioskills/bio-clinical-biostatistics-effect-measures - bio-clinical-biostatistics-logistic-regression
skills/bioskills/bio-clinical-biostatistics-logistic-regression - bio-clinical-biostatistics-missing-data
skills/bioskills/bio-clinical-biostatistics-missing-data-sensitivity - bio-clinical-biostatistics-multiplicity-graphical
skills/bioskills/bio-clinical-biostatistics-multiplicity-graphical - bio-clinical-biostatistics-power-sample-size
skills/bioskills/bio-clinical-biostatistics-power-and-sample-size - bio-clinical-biostatistics-subgroup-analysis
skills/bioskills/bio-clinical-biostatistics-subgroup-analysis - bio-clinical-biostatistics-survival-analysis
skills/bioskills/bio-clinical-biostatistics-survival-analysis - bio-clinical-biostatistics-trial-reporting
skills/bioskills/bio-clinical-biostatistics-trial-reporting - bio-clinical-databases-acmg-classification
skills/bioskills/bio-clinical-databases-acmg-classification - bio-clinical-databases-clinvar-lookup
skills/bioskills/bio-clinical-databases-clinvar-lookup - bio-clinical-databases-dbsnp-queries
skills/bioskills/bio-clinical-databases-dbsnp-queries - bio-clinical-databases-gnomad-frequencies
skills/bioskills/bio-clinical-databases-gnomad-frequencies - bio-clinical-databases-hla-typing
skills/bioskills/bio-clinical-databases-hla-typing - bio-clinical-databases-msi-detection
skills/bioskills/bio-clinical-databases-msi-detection - bio-clinical-databases-myvariant-queries
skills/bioskills/bio-clinical-databases-myvariant-queries - bio-clinical-databases-pharmacogenomics
skills/bioskills/bio-clinical-databases-pharmacogenomics - bio-clinical-databases-polygenic-risk
skills/bioskills/bio-clinical-databases-polygenic-risk - bio-clinical-databases-somatic-signatures
skills/bioskills/bio-clinical-databases-somatic-signatures - bio-clinical-databases-tumor-mutational-burden
skills/bioskills/bio-clinical-databases-tumor-mutational-burden - bio-clinical-databases-variant-prioritization
skills/bioskills/bio-clinical-databases-variant-prioritization - bio-clip-seq-ago-clip-mirna-targets
skills/bioskills/bio-clip-seq-ago-clip-mirna-targets - bio-clip-seq-binding-site-annotation
skills/bioskills/bio-clip-seq-binding-site-annotation - bio-clip-seq-clip-alignment
skills/bioskills/bio-clip-seq-clip-alignment - bio-clip-seq-clip-deep-learning
skills/bioskills/bio-clip-seq-clip-deep-learning - bio-clip-seq-clip-motif-analysis
skills/bioskills/bio-clip-seq-clip-motif-analysis - bio-clip-seq-clip-peak-calling
skills/bioskills/bio-clip-seq-clip-peak-calling - bio-clip-seq-clip-preprocessing
skills/bioskills/bio-clip-seq-clip-preprocessing - bio-clip-seq-clip-qc
skills/bioskills/bio-clip-seq-clip-qc - bio-clip-seq-crosslink-site-detection
skills/bioskills/bio-clip-seq-crosslink-site-detection - bio-clip-seq-differential-clip
skills/bioskills/bio-clip-seq-differential-clip - bio-clip-seq-m6a-clip
skills/bioskills/bio-clip-seq-m6a-clip - bio-clip-seq-stamp-antibody-free
skills/bioskills/bio-clip-seq-stamp-antibody-free - bio-comparative-genomics-ancestral-reconstruction
skills/bioskills/bio-comparative-genomics-ancestral-reconstruction - bio-comparative-genomics-comparative-annotation-projection
skills/bioskills/bio-comparative-genomics-comparative-annotation-projection - bio-comparative-genomics-gene-family-evolution
skills/bioskills/bio-comparative-genomics-gene-family-evolution - bio-comparative-genomics-gene-tree-species-tree-reconciliation
skills/bioskills/bio-comparative-genomics-gene-tree-species-tree-reconciliation - bio-comparative-genomics-genome-distance-and-species-delineation
skills/bioskills/bio-comparative-genomics-genome-distance-and-species-delineation - bio-comparative-genomics-hgt-detection
skills/bioskills/bio-comparative-genomics-hgt-detection - bio-comparative-genomics-introgression-detection
skills/bioskills/bio-comparative-genomics-introgression-detection - bio-comparative-genomics-ortholog-inference
skills/bioskills/bio-comparative-genomics-ortholog-inference - bio-comparative-genomics-pangenome-analysis
skills/bioskills/bio-comparative-genomics-pangenome-analysis - bio-comparative-genomics-positive-selection
skills/bioskills/bio-comparative-genomics-positive-selection - bio-comparative-genomics-synteny-analysis
skills/bioskills/bio-comparative-genomics-synteny-analysis - bio-comparative-genomics-whole-genome-alignment
skills/bioskills/bio-comparative-genomics-whole-genome-alignment - bio-comparative-genomics-whole-genome-duplication
skills/bioskills/bio-comparative-genomics-whole-genome-duplication - bio-copy-number-allele-specific-copy-number
skills/bioskills/bio-copy-number-allele-specific-copy-number - bio-copy-number-cnv-annotation
skills/bioskills/bio-copy-number-cnv-annotation - bio-copy-number-cnv-visualization
skills/bioskills/bio-copy-number-cnv-visualization - bio-copy-number-cnvkit-analysis
skills/bioskills/bio-copy-number-cnvkit-analysis - bio-copy-number-copy-ratio-segmentation
skills/bioskills/bio-copy-number-copy-ratio-segmentation - bio-copy-number-focal-amplification-ecdna
skills/bioskills/bio-copy-number-focal-amplification-ecdna - bio-copy-number-gatk-cnv
skills/bioskills/bio-copy-number-gatk-cnv - bio-copy-number-germline-cnv-interpretation
skills/bioskills/bio-copy-number-germline-cnv-interpretation - bio-copy-number-hrd-scoring
skills/bioskills/bio-copy-number-hrd-scoring - bio-copy-number-recurrent-cnv
skills/bioskills/bio-copy-number-recurrent-cnv - bio-copy-number-subclonal-copy-number
skills/bioskills/bio-copy-number-subclonal-copy-number - bio-crispr-screens-bagel-essentiality
skills/bioskills/bio-crispr-screens-bagel-essentiality - bio-crispr-screens-base-editing-analysis
skills/bioskills/bio-crispr-screens-base-editing-analysis - bio-crispr-screens-batch-correction
skills/bioskills/bio-crispr-screens-batch-correction - bio-crispr-screens-combinatorial-screens
skills/bioskills/bio-crispr-screens-combinatorial-screens - bio-crispr-screens-copy-number-correction
skills/bioskills/bio-crispr-screens-copy-number-correction - bio-crispr-screens-crispresso-editing
skills/bioskills/bio-crispr-screens-crispresso-editing - bio-crispr-screens-drugz-chemogenomic
skills/bioskills/bio-crispr-screens-drugz-chemogenomic - bio-crispr-screens-hit-calling
skills/bioskills/bio-crispr-screens-hit-calling - bio-crispr-screens-in-vivo-screens
skills/bioskills/bio-crispr-screens-in-vivo-screens - bio-crispr-screens-jacks-analysis
skills/bioskills/bio-crispr-screens-jacks-analysis - bio-crispr-screens-library-design
skills/bioskills/bio-crispr-screens-library-design - bio-crispr-screens-mageck-analysis
skills/bioskills/bio-crispr-screens-mageck-analysis - bio-crispr-screens-perturb-seq-analysis
skills/bioskills/bio-crispr-screens-perturb-seq-analysis - bio-crispr-screens-prime-editing-screens
skills/bioskills/bio-crispr-screens-prime-editing-screens - bio-crispr-screens-screen-qc
skills/bioskills/bio-crispr-screens-screen-qc - bio-data-visualization-circos-plots
skills/bioskills/bio-data-visualization-circos-plots - bio-data-visualization-color-palettes
skills/bioskills/bio-data-visualization-color-palettes - bio-data-visualization-dimensionality-reduction-plots
skills/bioskills/bio-data-visualization-dimensionality-reduction-plots - bio-data-visualization-distribution-plots
skills/bioskills/bio-data-visualization-distribution-plots - bio-data-visualization-flow-and-transition-plots
skills/bioskills/bio-data-visualization-flow-and-transition-plots - bio-data-visualization-forest-funnel-plots
skills/bioskills/bio-data-visualization-forest-funnel-plots - bio-data-visualization-genome-tracks
skills/bioskills/bio-data-visualization-genome-tracks - bio-data-visualization-ggplot2-fundamentals
skills/bioskills/bio-data-visualization-ggplot2-fundamentals - bio-data-visualization-heatmaps-clustering
skills/bioskills/bio-data-visualization-heatmaps-clustering - bio-data-visualization-interactive-visualization
skills/bioskills/bio-data-visualization-interactive-visualization - bio-data-visualization-lollipop-protein-maps
skills/bioskills/bio-data-visualization-lollipop-protein-maps - bio-data-visualization-manhattan-qq-locuszoom
skills/bioskills/bio-data-visualization-manhattan-qq-locuszoom - bio-data-visualization-matplotlib-fundamentals
skills/bioskills/bio-data-visualization-matplotlib-fundamentals - bio-data-visualization-multipanel-figures
skills/bioskills/bio-data-visualization-multipanel-figures - bio-data-visualization-network-visualization
skills/bioskills/bio-data-visualization-network-visualization - bio-data-visualization-oncoprint-mutation-matrices
skills/bioskills/bio-data-visualization-oncoprint-mutation-matrices - bio-data-visualization-sequence-logos
skills/bioskills/bio-data-visualization-sequence-logos - bio-data-visualization-statistical-annotation
skills/bioskills/bio-data-visualization-statistical-annotation - bio-data-visualization-upset-plots
skills/bioskills/bio-data-visualization-upset-plots - bio-data-visualization-volcano-and-ma-plots
skills/bioskills/bio-data-visualization-volcano-and-ma-plots - bio-batch-downloads
skills/bioskills/bio-database-access-batch-downloads - bio-biomart-queries
skills/bioskills/bio-database-access-biomart-queries - bio-blast-searches
skills/bioskills/bio-database-access-blast-searches - bio-ensembl-rest
skills/bioskills/bio-database-access-ensembl-rest - bio-entrez-fetch
skills/bioskills/bio-database-access-entrez-fetch - bio-entrez-link
skills/bioskills/bio-database-access-entrez-link - bio-entrez-search
skills/bioskills/bio-database-access-entrez-search - bio-geo-data
skills/bioskills/bio-database-access-geo-data - bio-interaction-databases
skills/bioskills/bio-database-access-interaction-databases - bio-local-blast
skills/bioskills/bio-database-access-local-blast - bio-ncbi-datasets-cli
skills/bioskills/bio-database-access-ncbi-datasets-cli - bio-ortholog-inference
skills/bioskills/bio-database-access-ortholog-inference - bio-remote-homology
skills/bioskills/bio-database-access-remote-homology - bio-sra-data
skills/bioskills/bio-database-access-sra-data - bio-uniprot-access
skills/bioskills/bio-database-access-uniprot-access - bio-differential-expression-batch-correction
skills/bioskills/bio-differential-expression-batch-correction - bio-differential-expression-de-results
skills/bioskills/bio-differential-expression-de-results - bio-differential-expression-de-visualization
skills/bioskills/bio-differential-expression-de-visualization - bio-differential-expression-deseq2-basics
skills/bioskills/bio-differential-expression-deseq2-basics - bio-differential-expression-edger-basics
skills/bioskills/bio-differential-expression-edger-basics - bio-differential-expression-timeseries-de
skills/bioskills/bio-differential-expression-timeseries-de - bio-ecological-genomics-biodiversity-metrics
skills/bioskills/bio-ecological-genomics-biodiversity-metrics - bio-ecological-genomics-community-ecology
skills/bioskills/bio-ecological-genomics-community-ecology - bio-ecological-genomics-conservation-genetics
skills/bioskills/bio-ecological-genomics-conservation-genetics - bio-ecological-genomics-edna-metabarcoding
skills/bioskills/bio-ecological-genomics-edna-metabarcoding - bio-ecological-genomics-landscape-genomics
skills/bioskills/bio-ecological-genomics-landscape-genomics - bio-ecological-genomics-species-delimitation
skills/bioskills/bio-ecological-genomics-species-delimitation - bio-epidemiological-genomics-amr-surveillance
skills/bioskills/bio-epidemiological-genomics-amr-surveillance - bio-epidemiological-genomics-pathogen-typing
skills/bioskills/bio-epidemiological-genomics-pathogen-typing - bio-epidemiological-genomics-phylodynamics
skills/bioskills/bio-epidemiological-genomics-phylodynamics - bio-epidemiological-genomics-transmission-inference
skills/bioskills/bio-epidemiological-genomics-transmission-inference - bio-epidemiological-genomics-variant-surveillance
skills/bioskills/bio-epidemiological-genomics-variant-surveillance - bio-epitranscriptomics-m6a-differential
skills/bioskills/bio-epitranscriptomics-m6a-differential - bio-epitranscriptomics-m6a-peak-calling
skills/bioskills/bio-epitranscriptomics-m6a-peak-calling - bio-epitranscriptomics-m6anet-analysis
skills/bioskills/bio-epitranscriptomics-m6anet-analysis - bio-epitranscriptomics-merip-preprocessing
skills/bioskills/bio-epitranscriptomics-merip-preprocessing - bio-epitranscriptomics-modification-visualization
skills/bioskills/bio-epitranscriptomics-modification-visualization - bio-experimental-design-batch-design
skills/bioskills/bio-experimental-design-batch-design - bio-experimental-design-multiple-testing
skills/bioskills/bio-experimental-design-multiple-testing - bio-experimental-design-power-analysis
skills/bioskills/bio-experimental-design-power-analysis - bio-experimental-design-randomization-blocking
skills/bioskills/bio-experimental-design-randomization-blocking - bio-experimental-design-sample-size
skills/bioskills/bio-experimental-design-sample-size - bio-expression-matrix-counts-ingest
skills/bioskills/bio-expression-matrix-counts-ingest - bio-expression-matrix-gene-id-mapping
skills/bioskills/bio-expression-matrix-gene-id-mapping - bio-expression-matrix-metadata-joins
skills/bioskills/bio-expression-matrix-metadata-joins - bio-expression-matrix-normalization
skills/bioskills/bio-expression-matrix-normalization - bio-expression-matrix-sparse-handling
skills/bioskills/bio-expression-matrix-sparse-handling - bio-flow-cytometry-bead-normalization
skills/bioskills/bio-flow-cytometry-bead-normalization - bio-flow-cytometry-clustering-phenotyping
skills/bioskills/bio-flow-cytometry-clustering-phenotyping - bio-flow-cytometry-compensation-transformation
skills/bioskills/bio-flow-cytometry-compensation-transformation - bio-flow-cytometry-cytometry-qc
skills/bioskills/bio-flow-cytometry-cytometry-qc - bio-flow-cytometry-differential-analysis
skills/bioskills/bio-flow-cytometry-differential-analysis - bio-flow-cytometry-doublet-detection
skills/bioskills/bio-flow-cytometry-doublet-detection - bio-flow-cytometry-fcs-handling
skills/bioskills/bio-flow-cytometry-fcs-handling - bio-flow-cytometry-gating-analysis
skills/bioskills/bio-flow-cytometry-gating-analysis - bio-gene-regulatory-networks-coexpression-networks
skills/bioskills/bio-gene-regulatory-networks-coexpression-networks - bio-gene-regulatory-networks-differential-networks
skills/bioskills/bio-gene-regulatory-networks-differential-networks - bio-gene-regulatory-networks-grn-inference
skills/bioskills/bio-gene-regulatory-networks-grn-inference - bio-gene-regulatory-networks-multiomics-grn
skills/bioskills/bio-gene-regulatory-networks-multiomics-grn - bio-gene-regulatory-networks-perturbation-simulation
skills/bioskills/bio-gene-regulatory-networks-perturbation-simulation - bio-gene-regulatory-networks-scenic-regulons
skills/bioskills/bio-gene-regulatory-networks-scenic-regulons - bio-genome-annotation-annotation-qc
skills/bioskills/bio-genome-annotation-annotation-qc - bio-genome-annotation-annotation-transfer
skills/bioskills/bio-genome-annotation-annotation-transfer - bio-genome-annotation-eukaryotic-gene-prediction
skills/bioskills/bio-genome-annotation-eukaryotic-gene-prediction - bio-genome-annotation-functional-annotation
skills/bioskills/bio-genome-annotation-functional-annotation - bio-genome-annotation-ncrna-annotation
skills/bioskills/bio-genome-annotation-ncrna-annotation - bio-genome-annotation-prokaryotic-annotation
skills/bioskills/bio-genome-annotation-prokaryotic-annotation - bio-genome-annotation-repeat-annotation
skills/bioskills/bio-genome-annotation-repeat-annotation - bio-genome-assembly-assembly-polishing
skills/bioskills/bio-genome-assembly-assembly-polishing - bio-genome-assembly-assembly-qc
skills/bioskills/bio-genome-assembly-assembly-qc - bio-genome-assembly-contamination-detection
skills/bioskills/bio-genome-assembly-contamination-detection - bio-genome-assembly-genome-profiling
skills/bioskills/bio-genome-assembly-genome-profiling - bio-genome-assembly-hifi-assembly
skills/bioskills/bio-genome-assembly-hifi-assembly - bio-genome-assembly-long-read-assembly
skills/bioskills/bio-genome-assembly-long-read-assembly - bio-genome-assembly-metagenome-assembly
skills/bioskills/bio-genome-assembly-metagenome-assembly - bio-genome-assembly-scaffolding
skills/bioskills/bio-genome-assembly-scaffolding - bio-genome-assembly-short-read-assembly
skills/bioskills/bio-genome-assembly-short-read-assembly - bio-genome-engineering-base-editing-design
skills/bioskills/bio-genome-engineering-base-editing-design - bio-genome-engineering-grna-design
skills/bioskills/bio-genome-engineering-grna-design - bio-genome-engineering-hdr-template-design
skills/bioskills/bio-genome-engineering-hdr-template-design - bio-genome-engineering-off-target-prediction
skills/bioskills/bio-genome-engineering-off-target-prediction - bio-genome-engineering-prime-editing-design
skills/bioskills/bio-genome-engineering-prime-editing-design - bio-genome-intervals-bed-file-basics
skills/bioskills/bio-genome-intervals-bed-file-basics - bio-genome-intervals-bedgraph-handling
skills/bioskills/bio-genome-intervals-bedgraph-handling - bio-genome-intervals-bigwig-tracks
skills/bioskills/bio-genome-intervals-bigwig-tracks - bio-genome-intervals-coverage-analysis
skills/bioskills/bio-genome-intervals-coverage-analysis - bio-genome-intervals-gtf-gff-handling
skills/bioskills/bio-genome-intervals-gtf-gff-handling - bio-genome-intervals-interval-arithmetic
skills/bioskills/bio-genome-intervals-interval-arithmetic - bio-genome-intervals-overlap-significance
skills/bioskills/bio-genome-intervals-overlap-significance - bio-genome-intervals-proximity-operations
skills/bioskills/bio-genome-intervals-proximity-operations - bio-hi-c-analysis-compartment-analysis
skills/bioskills/bio-hi-c-analysis-compartment-analysis - bio-hi-c-analysis-contact-pairs
skills/bioskills/bio-hi-c-analysis-contact-pairs - bio-hi-c-analysis-hic-data-io
skills/bioskills/bio-hi-c-analysis-hic-data-io - bio-hi-c-analysis-hic-differential
skills/bioskills/bio-hi-c-analysis-hic-differential - bio-hi-c-analysis-hic-visualization
skills/bioskills/bio-hi-c-analysis-hic-visualization - bio-hi-c-analysis-hichip-plac-loops
skills/bioskills/bio-hi-c-analysis-hichip-plac-loops - bio-hi-c-analysis-loop-calling
skills/bioskills/bio-hi-c-analysis-loop-calling - bio-hi-c-analysis-matrix-operations
skills/bioskills/bio-hi-c-analysis-matrix-operations - bio-hi-c-analysis-tad-detection
skills/bioskills/bio-hi-c-analysis-tad-detection - bio-imaging-mass-cytometry-cell-segmentation
skills/bioskills/bio-imaging-mass-cytometry-cell-segmentation - bio-imaging-mass-cytometry-data-preprocessing
skills/bioskills/bio-imaging-mass-cytometry-data-preprocessing - bio-imaging-mass-cytometry-differential-analysis
skills/bioskills/bio-imaging-mass-cytometry-differential-analysis - bio-imaging-mass-cytometry-interactive-annotation
skills/bioskills/bio-imaging-mass-cytometry-interactive-annotation - bio-imaging-mass-cytometry-phenotyping
skills/bioskills/bio-imaging-mass-cytometry-phenotyping - bio-imaging-mass-cytometry-quality-metrics
skills/bioskills/bio-imaging-mass-cytometry-quality-metrics - bio-imaging-mass-cytometry-spatial-analysis
skills/bioskills/bio-imaging-mass-cytometry-spatial-analysis - bio-immunoinformatics-epitope-prediction
skills/bioskills/bio-immunoinformatics-epitope-prediction - bio-immunoinformatics-immunogenicity-scoring
skills/bioskills/bio-immunoinformatics-immunogenicity-scoring - bio-immunoinformatics-mhc-binding-prediction
skills/bioskills/bio-immunoinformatics-mhc-binding-prediction - bio-immunoinformatics-mhc-class-ii-prediction
skills/bioskills/bio-immunoinformatics-mhc-class-ii-prediction - bio-immunoinformatics-neoantigen-prediction
skills/bioskills/bio-immunoinformatics-neoantigen-prediction - bio-immunoinformatics-tcr-epitope-binding
skills/bioskills/bio-immunoinformatics-tcr-epitope-binding - bio-analytical-validation
skills/bioskills/bio-liquid-biopsy-analytical-validation - bio-cfdna-preprocessing
skills/bioskills/bio-liquid-biopsy-cfdna-preprocessing - bio-ctdna-mutation-detection
skills/bioskills/bio-liquid-biopsy-ctdna-mutation-detection - bio-fragment-analysis
skills/bioskills/bio-liquid-biopsy-fragment-analysis - bio-longitudinal-monitoring
skills/bioskills/bio-liquid-biopsy-longitudinal-monitoring - bio-methylation-based-detection
skills/bioskills/bio-liquid-biopsy-methylation-based-detection - bio-tumor-fraction-estimation
skills/bioskills/bio-liquid-biopsy-tumor-fraction-estimation - bio-long-read-sequencing-basecalling
skills/bioskills/bio-long-read-sequencing-basecalling - bio-long-read-sequencing-clair3-variants
skills/bioskills/bio-long-read-sequencing-clair3-variants - bio-long-read-sequencing-haplotype-phasing
skills/bioskills/bio-long-read-sequencing-haplotype-phasing - bio-long-read-sequencing-isoseq-analysis
skills/bioskills/bio-long-read-sequencing-isoseq-analysis - bio-long-read-sequencing-long-read-alignment
skills/bioskills/bio-long-read-sequencing-long-read-alignment - bio-long-read-sequencing-long-read-qc
skills/bioskills/bio-long-read-sequencing-long-read-qc - bio-long-read-sequencing-medaka-polishing
skills/bioskills/bio-long-read-sequencing-medaka-polishing - bio-long-read-sequencing-nanopore-methylation
skills/bioskills/bio-long-read-sequencing-nanopore-methylation - bio-long-read-sequencing-structural-variants
skills/bioskills/bio-long-read-sequencing-structural-variants - bio-machine-learning-atlas-mapping
skills/bioskills/bio-machine-learning-atlas-mapping - bio-machine-learning-biomarker-discovery
skills/bioskills/bio-machine-learning-biomarker-discovery - bio-machine-learning-model-validation
skills/bioskills/bio-machine-learning-model-validation - bio-machine-learning-omics-classifiers
skills/bioskills/bio-machine-learning-omics-classifiers - bio-machine-learning-prediction-explanation
skills/bioskills/bio-machine-learning-prediction-explanation - bio-machine-learning-survival-analysis
skills/bioskills/bio-machine-learning-survival-analysis - bio-metabolomics-isotope-tracing
skills/bioskills/bio-metabolomics-isotope-tracing - bio-metabolomics-lipidomics
skills/bioskills/bio-metabolomics-lipidomics - bio-metabolomics-metabolite-annotation
skills/bioskills/bio-metabolomics-metabolite-annotation - bio-metabolomics-msdial-preprocessing
skills/bioskills/bio-metabolomics-msdial-preprocessing - bio-metabolomics-normalization-qc
skills/bioskills/bio-metabolomics-normalization-qc - bio-metabolomics-pathway-mapping
skills/bioskills/bio-metabolomics-pathway-mapping - bio-metabolomics-statistical-analysis
skills/bioskills/bio-metabolomics-statistical-analysis - bio-metabolomics-targeted-analysis
skills/bioskills/bio-metabolomics-targeted-analysis - bio-metabolomics-xcms-preprocessing
skills/bioskills/bio-metabolomics-xcms-preprocessing - bio-metagenomics-abundance
skills/bioskills/bio-metagenomics-abundance-estimation - bio-metagenomics-amr-detection
skills/bioskills/bio-metagenomics-amr-detection - bio-metagenomics-contamination-controls
skills/bioskills/bio-metagenomics-contamination-controls - bio-metagenomics-functional-profiling
skills/bioskills/bio-metagenomics-functional-profiling - bio-metagenomics-kraken
skills/bioskills/bio-metagenomics-kraken-classification - bio-metagenomics-visualization
skills/bioskills/bio-metagenomics-metagenome-visualization - bio-metagenomics-metaphlan
skills/bioskills/bio-metagenomics-metaphlan-profiling - bio-metagenomics-strain-tracking
skills/bioskills/bio-metagenomics-strain-tracking - bio-methylation-array-preprocessing
skills/bioskills/bio-methylation-analysis-array-preprocessing - bio-methylation-array-qc-filtering
skills/bioskills/bio-methylation-analysis-array-qc-filtering - bio-methylation-bismark-alignment
skills/bioskills/bio-methylation-analysis-bismark-alignment - bio-methylation-cell-type-deconvolution
skills/bioskills/bio-methylation-analysis-cell-type-deconvolution - bio-methylation-differential-cpg
skills/bioskills/bio-methylation-analysis-differential-cpg-testing - bio-methylation-dmr-detection
skills/bioskills/bio-methylation-analysis-dmr-detection - bio-methylation-epigenetic-clocks
skills/bioskills/bio-methylation-analysis-epigenetic-clocks - bio-methylation-ewas-design
skills/bioskills/bio-methylation-analysis-ewas-design - bio-methylation-calling
skills/bioskills/bio-methylation-analysis-methylation-calling - bio-methylation-methylkit
skills/bioskills/bio-methylation-analysis-methylkit-analysis - bio-microbiome-amplicon-processing
skills/bioskills/bio-microbiome-amplicon-processing - bio-microbiome-differential-abundance
skills/bioskills/bio-microbiome-differential-abundance - bio-microbiome-diversity-analysis
skills/bioskills/bio-microbiome-diversity-analysis - bio-microbiome-functional-prediction
skills/bioskills/bio-microbiome-functional-prediction - bio-microbiome-qiime2-workflow
skills/bioskills/bio-microbiome-qiime2-workflow - bio-microbiome-taxonomy-assignment
skills/bioskills/bio-microbiome-taxonomy-assignment - bio-multi-omics-data-harmonization
skills/bioskills/bio-multi-omics-integration-data-harmonization - bio-multi-omics-integration-design
skills/bioskills/bio-multi-omics-integration-integration-design - bio-multi-omics-mixomics-analysis
skills/bioskills/bio-multi-omics-integration-mixomics-analysis - bio-multi-omics-mofa-integration
skills/bioskills/bio-multi-omics-integration-mofa-integration - bio-multi-omics-similarity-network
skills/bioskills/bio-multi-omics-integration-similarity-network - bio-pathway-enrichment-visualization
skills/bioskills/bio-pathway-analysis-enrichment-visualization - bio-pathway-go-enrichment
skills/bioskills/bio-pathway-analysis-go-enrichment - bio-pathway-gsea
skills/bioskills/bio-pathway-analysis-gsea - bio-pathway-kegg-pathways
skills/bioskills/bio-pathway-analysis-kegg-pathways - bio-pathway-reactome
skills/bioskills/bio-pathway-analysis-reactome-pathways - bio-pathway-wikipathways
skills/bioskills/bio-pathway-analysis-wikipathways - bio-phasing-imputation-genotype-imputation
skills/bioskills/bio-phasing-imputation-genotype-imputation - bio-phasing-imputation-haplotype-phasing
skills/bioskills/bio-phasing-imputation-haplotype-phasing - bio-phasing-imputation-imputation-qc
skills/bioskills/bio-phasing-imputation-imputation-qc - bio-phasing-imputation-reference-panels
skills/bioskills/bio-phasing-imputation-reference-panels - bio-phylo-bayesian-inference
skills/bioskills/bio-phylogenetics-bayesian-inference - bio-phylo-distance-calculations
skills/bioskills/bio-phylogenetics-distance-calculations - bio-phylo-divergence-dating
skills/bioskills/bio-phylogenetics-divergence-dating - bio-phylo-modern-tree-inference
skills/bioskills/bio-phylogenetics-modern-tree-inference - bio-phylo-species-trees
skills/bioskills/bio-phylogenetics-species-trees - bio-phylo-tree-io
skills/bioskills/bio-phylogenetics-tree-io - bio-phylo-tree-manipulation
skills/bioskills/bio-phylogenetics-tree-manipulation - bio-phylo-tree-visualization
skills/bioskills/bio-phylogenetics-tree-visualization - bio-population-genetics-association-testing
skills/bioskills/bio-population-genetics-association-testing - bio-population-genetics-linkage-disequilibrium
skills/bioskills/bio-population-genetics-linkage-disequilibrium - bio-population-genetics-plink-basics
skills/bioskills/bio-population-genetics-plink-basics - bio-population-genetics-population-structure
skills/bioskills/bio-population-genetics-population-structure - bio-population-genetics-rare-variant-association
skills/bioskills/bio-population-genetics-rare-variant-association - bio-population-genetics-scikit-allel-analysis
skills/bioskills/bio-population-genetics-scikit-allel-analysis - bio-population-genetics-selection-statistics
skills/bioskills/bio-population-genetics-selection-statistics - bio-primer-design-primer-basics
skills/bioskills/bio-primer-design-primer-basics - bio-primer-design-primer-specificity
skills/bioskills/bio-primer-design-primer-specificity - bio-primer-design-primer-validation
skills/bioskills/bio-primer-design-primer-validation - bio-primer-design-qpcr-primers
skills/bioskills/bio-primer-design-qpcr-primers - bio-proteomics-data-import
skills/bioskills/bio-proteomics-data-import - bio-proteomics-dia-analysis
skills/bioskills/bio-proteomics-dia-analysis - bio-proteomics-differential-abundance
skills/bioskills/bio-proteomics-differential-abundance - bio-proteomics-peptide-identification
skills/bioskills/bio-proteomics-peptide-identification - bio-proteomics-protein-inference
skills/bioskills/bio-proteomics-protein-inference - bio-proteomics-proteomics-qc
skills/bioskills/bio-proteomics-proteomics-qc - bio-proteomics-ptm-analysis
skills/bioskills/bio-proteomics-ptm-analysis - bio-proteomics-quantification
skills/bioskills/bio-proteomics-quantification - bio-proteomics-spectral-libraries
skills/bioskills/bio-proteomics-spectral-libraries - bio-read-alignment-bowtie2-alignment
skills/bioskills/bio-read-alignment-bowtie2-alignment - bio-read-alignment-bwa-alignment
skills/bioskills/bio-read-alignment-bwa-alignment - bio-read-alignment-hisat2-alignment
skills/bioskills/bio-read-alignment-hisat2-alignment - bio-read-alignment-star-alignment
skills/bioskills/bio-read-alignment-star-alignment - bio-read-qc-adapter-trimming
skills/bioskills/bio-read-qc-adapter-trimming - bio-read-qc-contamination-screening
skills/bioskills/bio-read-qc-contamination-screening - bio-read-qc-fastp-workflow
skills/bioskills/bio-read-qc-fastp-workflow - bio-read-qc-quality-filtering
skills/bioskills/bio-read-qc-quality-filtering - bio-read-qc-quality-reports
skills/bioskills/bio-read-qc-quality-reports - bio-read-qc-rnaseq-qc
skills/bioskills/bio-read-qc-rnaseq-qc - bio-read-qc-umi-processing
skills/bioskills/bio-read-qc-umi-processing - bio-reporting-automated-qc-reports
skills/bioskills/bio-reporting-automated-qc-reports - bio-reporting-figure-export
skills/bioskills/bio-reporting-figure-export - bio-reporting-jupyter-reports
skills/bioskills/bio-reporting-jupyter-reports - bio-reporting-publication-tables
skills/bioskills/bio-reporting-publication-tables - bio-reporting-quarto-reports
skills/bioskills/bio-reporting-quarto-reports - bio-reporting-rmarkdown-reports
skills/bioskills/bio-reporting-rmarkdown-reports - bio-restriction-enzyme-selection
skills/bioskills/bio-restriction-analysis-enzyme-selection - bio-restriction-fragment-analysis
skills/bioskills/bio-restriction-analysis-fragment-analysis - bio-restriction-golden-gate-assembly
skills/bioskills/bio-restriction-analysis-golden-gate-assembly - bio-restriction-mapping
skills/bioskills/bio-restriction-analysis-restriction-mapping - bio-restriction-sites
skills/bioskills/bio-restriction-analysis-restriction-sites - bio-ribo-seq-initiation-site-mapping
skills/bioskills/bio-ribo-seq-initiation-site-mapping - bio-ribo-seq-orf-detection
skills/bioskills/bio-ribo-seq-orf-detection - bio-ribo-seq-riboseq-preprocessing
skills/bioskills/bio-ribo-seq-riboseq-preprocessing - bio-ribo-seq-ribosome-periodicity
skills/bioskills/bio-ribo-seq-ribosome-periodicity - bio-ribo-seq-ribosome-stalling
skills/bioskills/bio-ribo-seq-ribosome-stalling - bio-ribo-seq-translation-efficiency
skills/bioskills/bio-ribo-seq-translation-efficiency - bio-rna-quantification-alignment-free-quant
skills/bioskills/bio-rna-quantification-alignment-free-quant - bio-rna-quantification-count-matrix-qc
skills/bioskills/bio-rna-quantification-count-matrix-qc - bio-rna-quantification-featurecounts-counting
skills/bioskills/bio-rna-quantification-featurecounts-counting - bio-rna-quantification-tximport-workflow
skills/bioskills/bio-rna-quantification-tximport-workflow - bio-rna-structure-covariation-analysis
skills/bioskills/bio-rna-structure-covariation-analysis - bio-rna-structure-ncrna-search
skills/bioskills/bio-rna-structure-ncrna-search - bio-rna-structure-secondary-structure-prediction
skills/bioskills/bio-rna-structure-secondary-structure-prediction - bio-rna-structure-structure-probing
skills/bioskills/bio-rna-structure-structure-probing - bio-batch-processing
skills/bioskills/bio-sequence-io-batch-processing - bio-compressed-files
skills/bioskills/bio-sequence-io-compressed-files - bio-fastq-quality
skills/bioskills/bio-sequence-io-fastq-quality - bio-filter-sequences
skills/bioskills/bio-sequence-io-filter-sequences - bio-format-conversion
skills/bioskills/bio-sequence-io-format-conversion - bio-paired-end-fastq
skills/bioskills/bio-sequence-io-paired-end-fastq - bio-read-sequences
skills/bioskills/bio-sequence-io-read-sequences - bio-sequence-statistics
skills/bioskills/bio-sequence-io-sequence-statistics - bio-write-sequences
skills/bioskills/bio-sequence-io-write-sequences - bio-codon-usage
skills/bioskills/bio-sequence-manipulation-codon-usage - bio-motif-search
skills/bioskills/bio-sequence-manipulation-motif-search - bio-reverse-complement
skills/bioskills/bio-sequence-manipulation-reverse-complement - bio-seq-objects
skills/bioskills/bio-sequence-manipulation-seq-objects - bio-sequence-properties
skills/bioskills/bio-sequence-manipulation-sequence-properties - bio-sequence-slicing
skills/bioskills/bio-sequence-manipulation-sequence-slicing - bio-transcription-translation
skills/bioskills/bio-sequence-manipulation-transcription-translation - bio-single-cell-batch-integration
skills/bioskills/bio-single-cell-batch-integration - bio-single-cell-cell-annotation
skills/bioskills/bio-single-cell-cell-annotation - bio-single-cell-cell-communication
skills/bioskills/bio-single-cell-cell-communication - bio-single-cell-clustering
skills/bioskills/bio-single-cell-clustering - bio-single-cell-cnv-inference
skills/bioskills/bio-single-cell-cnv-inference - bio-single-cell-data-io
skills/bioskills/bio-single-cell-data-io - bio-single-cell-differential-abundance
skills/bioskills/bio-single-cell-differential-abundance - bio-single-cell-doublet-detection
skills/bioskills/bio-single-cell-doublet-detection - bio-single-cell-hashing-demultiplexing
skills/bioskills/bio-single-cell-hashing-demultiplexing - bio-single-cell-lineage-tracing
skills/bioskills/bio-single-cell-lineage-tracing - bio-single-cell-markers-annotation
skills/bioskills/bio-single-cell-markers-annotation - bio-single-cell-metabolite-communication
skills/bioskills/bio-single-cell-metabolite-communication - bio-single-cell-multimodal-integration
skills/bioskills/bio-single-cell-multimodal-integration - bio-single-cell-perturb-seq
skills/bioskills/bio-single-cell-perturb-seq - bio-single-cell-preprocessing
skills/bioskills/bio-single-cell-preprocessing - bio-single-cell-scatac-analysis
skills/bioskills/bio-single-cell-scatac-analysis - bio-single-cell-trajectory-inference
skills/bioskills/bio-single-cell-trajectory-inference - bio-small-rna-seq-differential-mirna
skills/bioskills/bio-small-rna-seq-differential-mirna - bio-small-rna-seq-mirdeep2-analysis
skills/bioskills/bio-small-rna-seq-mirdeep2-analysis - bio-small-rna-seq-mirge3-analysis
skills/bioskills/bio-small-rna-seq-mirge3-analysis - bio-small-rna-seq-smrna-preprocessing
skills/bioskills/bio-small-rna-seq-smrna-preprocessing - bio-small-rna-seq-target-prediction
skills/bioskills/bio-small-rna-seq-target-prediction - bio-small-rna-seq-trf-pirna-profiling
skills/bioskills/bio-small-rna-seq-trf-pirna-profiling - bio-spatial-transcriptomics-high-resolution-binning
skills/bioskills/bio-spatial-transcriptomics-high-resolution-binning - bio-spatial-transcriptomics-image-analysis
skills/bioskills/bio-spatial-transcriptomics-image-analysis - bio-spatial-transcriptomics-spatial-communication
skills/bioskills/bio-spatial-transcriptomics-spatial-communication - bio-spatial-transcriptomics-spatial-data-io
skills/bioskills/bio-spatial-transcriptomics-spatial-data-io - bio-spatial-transcriptomics-spatial-deconvolution
skills/bioskills/bio-spatial-transcriptomics-spatial-deconvolution - bio-spatial-transcriptomics-spatial-domains
skills/bioskills/bio-spatial-transcriptomics-spatial-domains - bio-spatial-transcriptomics-spatial-multiomics
skills/bioskills/bio-spatial-transcriptomics-spatial-multiomics - bio-spatial-transcriptomics-spatial-neighbors
skills/bioskills/bio-spatial-transcriptomics-spatial-neighbors - bio-spatial-transcriptomics-spatial-preprocessing
skills/bioskills/bio-spatial-transcriptomics-spatial-preprocessing - bio-spatial-transcriptomics-spatial-proteomics
skills/bioskills/bio-spatial-transcriptomics-spatial-proteomics - bio-spatial-transcriptomics-spatial-statistics
skills/bioskills/bio-spatial-transcriptomics-spatial-statistics - bio-spatial-transcriptomics-spatial-visualization
skills/bioskills/bio-spatial-transcriptomics-spatial-visualization - bio-structural-biology-alphafold-predictions
skills/bioskills/bio-structural-biology-alphafold-predictions - bio-structural-biology-binding-site-detection
skills/bioskills/bio-structural-biology-binding-site-detection - bio-structural-biology-geometric-analysis
skills/bioskills/bio-structural-biology-geometric-analysis - bio-structural-biology-interface-analysis
skills/bioskills/bio-structural-biology-interface-analysis - bio-structural-biology-modern-structure-prediction
skills/bioskills/bio-structural-biology-modern-structure-prediction - bio-structural-biology-structure-io
skills/bioskills/bio-structural-biology-structure-io - bio-structural-biology-structure-modification
skills/bioskills/bio-structural-biology-structure-modification - bio-structural-biology-structure-navigation
skills/bioskills/bio-structural-biology-structure-navigation - bio-structural-biology-structure-preparation
skills/bioskills/bio-structural-biology-structure-preparation - bio-structural-biology-structure-validation
skills/bioskills/bio-structural-biology-structure-validation - bio-systems-biology-community-metabolic-modeling
skills/bioskills/bio-systems-biology-community-metabolic-modeling - bio-systems-biology-context-specific-models
skills/bioskills/bio-systems-biology-context-specific-models - bio-systems-biology-flux-balance-analysis
skills/bioskills/bio-systems-biology-flux-balance-analysis - bio-systems-biology-gene-essentiality
skills/bioskills/bio-systems-biology-gene-essentiality - bio-systems-biology-metabolic-reconstruction
skills/bioskills/bio-systems-biology-metabolic-reconstruction - bio-systems-biology-model-curation
skills/bioskills/bio-systems-biology-model-curation - bio-systems-biology-strain-design
skills/bioskills/bio-systems-biology-strain-design - bio-tcr-bcr-analysis-immcantation-analysis
skills/bioskills/bio-tcr-bcr-analysis-immcantation-analysis - bio-tcr-bcr-analysis-mixcr-analysis
skills/bioskills/bio-tcr-bcr-analysis-mixcr-analysis - bio-tcr-bcr-analysis-repertoire-visualization
skills/bioskills/bio-tcr-bcr-analysis-repertoire-visualization - bio-tcr-bcr-analysis-scirpy-analysis
skills/bioskills/bio-tcr-bcr-analysis-scirpy-analysis - bio-tcr-bcr-analysis-specificity-annotation
skills/bioskills/bio-tcr-bcr-analysis-specificity-annotation - bio-tcr-bcr-analysis-vdjtools-analysis
skills/bioskills/bio-tcr-bcr-analysis-vdjtools-analysis - bio-temporal-genomics-circadian-rhythms
skills/bioskills/bio-temporal-genomics-circadian-rhythms - bio-temporal-genomics-differential-rhythmicity
skills/bioskills/bio-temporal-genomics-differential-rhythmicity - bio-temporal-genomics-periodicity-detection
skills/bioskills/bio-temporal-genomics-periodicity-detection - bio-temporal-genomics-temporal-clustering
skills/bioskills/bio-temporal-genomics-temporal-clustering - bio-temporal-genomics-temporal-grn
skills/bioskills/bio-temporal-genomics-temporal-grn - bio-temporal-genomics-trajectory-modeling
skills/bioskills/bio-temporal-genomics-trajectory-modeling - bio-variant-calling-clinical-interpretation
skills/bioskills/bio-variant-calling-clinical-interpretation - bio-consensus-sequences
skills/bioskills/bio-variant-calling-consensus-sequences - bio-variant-calling-deepvariant
skills/bioskills/bio-variant-calling-deepvariant - bio-variant-calling-filtering-best-practices
skills/bioskills/bio-variant-calling-filtering-best-practices - bio-gatk-variant-calling
skills/bioskills/bio-variant-calling-gatk-variant-calling - bio-variant-calling-joint-calling
skills/bioskills/bio-variant-calling-joint-calling - bio-variant-calling-structural-variant-calling
skills/bioskills/bio-variant-calling-structural-variant-calling - bio-variant-annotation
skills/bioskills/bio-variant-calling-variant-annotation - bio-variant-calling
skills/bioskills/bio-variant-calling-variant-calling - bio-variant-normalization
skills/bioskills/bio-variant-calling-variant-normalization - bio-vcf-basics
skills/bioskills/bio-variant-calling-vcf-basics - bio-vcf-manipulation
skills/bioskills/bio-variant-calling-vcf-manipulation - bio-vcf-statistics
skills/bioskills/bio-variant-calling-vcf-statistics - bio-workflow-management-cwl-workflows
skills/bioskills/bio-workflow-management-cwl-workflows - bio-workflow-management-nextflow-pipelines
skills/bioskills/bio-workflow-management-nextflow-pipelines - bio-workflow-management-nf-core-pipelines
skills/bioskills/bio-workflow-management-nf-core-pipelines - bio-workflow-management-snakemake-workflows
skills/bioskills/bio-workflow-management-snakemake-workflows - bio-workflow-management-wdl-workflows
skills/bioskills/bio-workflow-management-wdl-workflows - bio-workflows-atacseq-pipeline
skills/bioskills/bio-workflows-atacseq-pipeline - bio-workflows-biomarker-pipeline
skills/bioskills/bio-workflows-biomarker-pipeline - bio-workflows-causal-genomics-pipeline
skills/bioskills/bio-workflows-causal-genomics-pipeline - bio-workflows-chipseq-pipeline
skills/bioskills/bio-workflows-chipseq-pipeline - bio-workflows-clinical-trial-pipeline
skills/bioskills/bio-workflows-clinical-trial-pipeline - bio-workflows-clip-pipeline
skills/bioskills/bio-workflows-clip-pipeline - bio-workflows-cnv-pipeline
skills/bioskills/bio-workflows-cnv-pipeline - bio-workflows-crispr-editing-pipeline
skills/bioskills/bio-workflows-crispr-editing-pipeline - bio-workflows-crispr-screen-pipeline
skills/bioskills/bio-workflows-crispr-screen-pipeline - bio-workflows-cytometry-pipeline
skills/bioskills/bio-workflows-cytometry-pipeline - bio-workflows-edna-pipeline
skills/bioskills/bio-workflows-edna-pipeline - bio-workflows-expression-to-pathways
skills/bioskills/bio-workflows-expression-to-pathways - bio-workflows-fastq-to-variants
skills/bioskills/bio-workflows-fastq-to-variants - bio-workflows-genome-annotation-pipeline
skills/bioskills/bio-workflows-genome-annotation-pipeline - bio-workflows-genome-assembly-pipeline
skills/bioskills/bio-workflows-genome-assembly-pipeline - bio-workflows-grn-pipeline
skills/bioskills/bio-workflows-grn-pipeline - bio-workflows-gwas-pipeline
skills/bioskills/bio-workflows-gwas-pipeline - bio-workflows-hic-pipeline
skills/bioskills/bio-workflows-hic-pipeline - bio-workflows-imc-pipeline
skills/bioskills/bio-workflows-imc-pipeline - bio-workflows-liquid-biopsy-pipeline
skills/bioskills/bio-workflows-liquid-biopsy-pipeline - bio-workflows-longread-sv-pipeline
skills/bioskills/bio-workflows-longread-sv-pipeline - bio-workflows-merip-pipeline
skills/bioskills/bio-workflows-merip-pipeline - bio-workflows-metabolic-modeling-pipeline
skills/bioskills/bio-workflows-metabolic-modeling-pipeline - bio-workflows-metabolomics-pipeline
skills/bioskills/bio-workflows-metabolomics-pipeline - bio-workflows-metagenomics-pipeline
skills/bioskills/bio-workflows-metagenomics-pipeline - bio-workflows-methylation-pipeline
skills/bioskills/bio-workflows-methylation-pipeline - bio-workflows-microbiome-pipeline
skills/bioskills/bio-workflows-microbiome-pipeline - bio-workflows-multi-omics-pipeline
skills/bioskills/bio-workflows-multi-omics-pipeline - bio-workflows-multiome-pipeline
skills/bioskills/bio-workflows-multiome-pipeline - bio-workflows-neoantigen-pipeline
skills/bioskills/bio-workflows-neoantigen-pipeline - bio-workflows-outbreak-pipeline
skills/bioskills/bio-workflows-outbreak-pipeline - bio-workflows-proteomics-pipeline
skills/bioskills/bio-workflows-proteomics-pipeline - bio-workflows-riboseq-pipeline
skills/bioskills/bio-workflows-riboseq-pipeline - bio-workflows-rnaseq-to-de
skills/bioskills/bio-workflows-rnaseq-to-de - bio-workflows-scrnaseq-pipeline
skills/bioskills/bio-workflows-scrnaseq-pipeline - bio-workflows-smrna-pipeline
skills/bioskills/bio-workflows-smrna-pipeline - bio-workflows-somatic-variant-pipeline
skills/bioskills/bio-workflows-somatic-variant-pipeline - bio-workflows-spatial-pipeline
skills/bioskills/bio-workflows-spatial-pipeline - bio-workflows-splicing-pipeline
skills/bioskills/bio-workflows-splicing-pipeline - bio-workflows-tcr-pipeline
skills/bioskills/bio-workflows-tcr-pipeline - bio-workflows-timecourse-pipeline
skills/bioskills/bio-workflows-timecourse-pipeline - boltz
skills/boltz - borzoi
skills/borzoi - catalyst_sar_screening
skills/catalyst_sar_screening - chai1
skills/chai1 - did-analysis
skills/did-analysis - diffdock
skills/diffdock - esmfold2
skills/esmfold2 - evaluate-model
skills/evaluate-model - evidence-walkthrough
skills/evidence-walkthrough - evo2
skills/evo2 - example_stats
skills/example_stats - fair-esm2
skills/fair-esm2 - figure-composer
skills/figure-composer - figure-style
skills/figure-style - indication-dossier
skills/indication-dossier - is-qualitative-analysis
skills/is-qualitative-analysis - is-research-design
skills/is-research-design - is-survey-measurement
skills/is-survey-measurement - ligandmpnn
skills/ligandmpnn - literature-review
skills/literature-review - mineral_spectra_analysis
skills/mineral_spectra_analysis - molecular-dynamics
skills/molecular-dynamics - openfold3
skills/openfold3 - panel-data-preprocessing
skills/panel-data-preprocessing - paper-narrative
skills/paper-narrative - pdf-explore
skills/pdf-explore - plan-ml-experiment
skills/plan-ml-experiment - protein-design-mcp
skills/protein-design-mcp - protein-mutation-enhancement
skills/protein-mutation-enhancement - proteinmpnn
skills/proteinmpnn - reaction-atom-mapping
skills/reaction-atom-mapping - reaction-condition-recommendation
skills/reaction-condition-recommendation - reaction-forward-prediction
skills/reaction-forward-prediction - reaction-yield-estimation
skills/reaction-yield-estimation - remote-compute-nvidia
skills/remote-compute-nvidia - remote-compute-ssh
skills/remote-compute-ssh - retrosynthesis_planning
skills/retrosynthesis_planning - rfdiffusion
skills/rfdiffusion - scgpt
skills/scgpt - scvi-tools
skills/scvi-tools - single-cell-rna-analysis
skills/single-cell-rna-analysis - single-step-retrosynthesis
skills/single-step-retrosynthesis - solublempnn
skills/solublempnn - target_druggability_screening
skills/target_druggability_screening - text-features
skills/text-features - using-model-endpoint
skills/using-model-endpoint - volcengine-datapro
skills/volcengine-datapro
How it works
admet_genetic
When your agent uses it
ADMET-guided genetic molecule optimization workflow from seed SMILES; use when the agent needs to build or run an RDKit/SA-Score/ADMET-AI GA pipeline for molecule optimization, enforce molecule lineage logs, render optimization-history HTML dashboards, and write candidate triage reports.
What it loads into context
Frontmatter
216 tokensAlways available to the agent
SKILL.md
2,958 tokensWhen the skill is activated
Reference documents
7,470 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Special Reminder
- Prerequisites
- Data Contracts
- Core Workflow
- Import
- Formal runs
- Artifacts
- Shaping a formal run's source tree
- Reproducible Example
- Dashboard QA
Scripts executed separately
examples/build_example.pykernel.py
Files
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Read SKILL.md
# ADMET Genetic Optimization
Use this skill to build and run a molecular optimization loop from seed SMILES. The target artifact is a ranked set of optimized candidate molecules with auditable lineage, scores, and report artifacts.
The sidecar deliberately does not provide a fixed GA engine. The agent must
assemble and tune mutation, crossover, evaluation, filtering, and selection for
the user's objective. `kernel.py` provides reusable molecule normalization,
ADMET aggregation, lineage validation, and result visualization.
## Special Reminder
In this skill, when see `references/<file_name>.md` is suggested, use host call to retrieval the complementary material.
```python
host.skills.read("admet_genetic", "references/<file_name>.md")
```alphafold2
When your agent uses it
Predict protein structure for monomers and multimers with AlphaFold2 via the ColabFold runner (Mirdita et al. 2022, github.com/sokrypton/ColabFold; AlphaFold2 Jumper et al. 2021). Reach for this skill to fold a sequence or complex with the AF2/AF2-Multimer evoformer, to validate designed sequences by self-consistency pLDDT, ipTM, and RMSD, or to run a quick MSA-backed prediction using the public MMseqs2 server.
What it loads into context
Frontmatter
408 tokensAlways available to the agent
SKILL.md
1,405 tokensWhen the skill is activated
Reference documents
2,327 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Running it
- Unified-memory defaults loop forever in the GPU sandbox — patch them out
- The MSA server is the wall-clock bottleneck, and it is shared
- Errors worth recognizing
Files
README.mddoc4,255 bytesREADME_zh.mddoc4,280 bytesSKILL.mdcore5,062 bytesSKILL.mdcore5,062 bytes
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# AlphaFold2 (ColabFold runner)
This skill wraps AlphaFold2 and AlphaFold2-Multimer through `colabfold_batch`,
which replaces DeepMind's local-database MSA pipeline with a call to the public
MMseqs2 server — so a prediction is one command and one FASTA, not a 2 TB
database mount. AF2 remains the reference monomer predictor and the multimer
model is still a strong protein–protein validator, but it does not handle
ligands or nucleic acids; for those, route to `boltz`, `chai1`, or `openfold3`.
The ColabFold code is MIT (github.com/sokrypton/ColabFold) and the AlphaFold2
code is Apache-2.0 (github.com/google-deepmind/alphafold); the AF2 model
parameters are CC-BY-4.0 with DeepMind's terms of use.
## Running it
audit-dataset
When your agent uses it
Audit tabular datasets before analysis or training for schema drift, missing values, duplicate rows or IDs, target imbalance, and entity or group leakage across splits using pure-stdlib helpers.
What it loads into context
Frontmatter
71 tokensAlways available to the agent
SKILL.md
518 tokensWhen the skill is activated
Reference documents
1,981 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflow
- Import and run
- Interpretation
- Required output
Scripts executed separately
kernel.py
Files
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Read SKILL.md
# Audit a dataset
Use this skill before statistics, model training, or external publication. The
goal is a compact, machine-readable audit plus explicit decisions about every
issue that could invalidate downstream results.
## Workflow
1. Load records without silently coercing values. Preserve source row IDs.
2. Call `audit_rows` on a representative or complete list of row mappings.
3. Inspect missingness and observed type mixtures column by column.
4. Resolve duplicate records and non-unique identifiers deliberately.
5. If a split column exists, check both stable IDs and grouping entities for
train/validation/test overlap.bioprobench
When your agent uses it
Score an LLM's biological-protocol reasoning on the BioProBench benchmark: protocol QA, step ordering, error detection, protocol generation, and LLM-judged error reasoning; or generate the responses.
What it loads into context
Frontmatter
110 tokensAlways available to the agent
SKILL.md
2,444 tokensWhen the skill is activated
Reference documents
3,769 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- The input contract is the thing that bites
- Getting the data
- Scoring an existing response file
- Generating responses first
- Dependencies and network access
- Citation
Scripts executed separately
Scripts/LLM-as-a-judge_for_REA-ERR.pyScripts/generate_response.pyScripts/generate_response_local.pyScripts/prompt_format.pykernel.py
Files
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Read SKILL.md
# BioProBench — protocol understanding and reasoning
Biological protocols are where a plausible-sounding answer becomes a failed
experiment: a wrong dosage, a swapped step, an unflagged hazard. BioProBench
scores a model on five tasks over real wet-lab protocols, roughly 5,000
instances in the full release.
| Task | What it measures | Metrics returned |
| --- | --- | --- |
| `PQA` | Protocol question answering — reagents, dosages, parameters | `Accuracy`, `Brier_Score`, `Failed_Rate` |
| `ORD` | Step ordering — reconstructing procedural sequence | `Exact_Match`, `Kendall_Tau`, `Failed_Rate` |
| `ERR` | Error correction — is this modified step valid | `accuracy`, `precision`, `recall`, `f1`, `failed_rate` |
| `GEN` | Protocol generation — synthesising steps | `BLEU`, `METEOR`, `ROUGE-L`, `KW_F1`, `Step_Recall`, `Redundancy_Penalty`, `Failed_Rate` |
| `REA-ERR` | Error reasoning, graded by an LLM judge | `Consistency`, `Failure_Rate`, `Total`, `Failed`, `Total_Items`, `Judged`, `Unjudged`, `Coverage` |bio-alignment-io
When your agent uses it
Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO. Supports Clustal, PHYLIP, Stockholm, FASTA, Nexus, and other alignment formats for phylogenetics and conservation analysis. Use when reading, writing, or converting alignment file formats.
What it loads into context
Frontmatter
152 tokensAlways available to the agent
SKILL.md
5,207 tokensWhen the skill is activated
Reference documents
685 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Required Import
- Format Coverage Map
- Reading Alignments
- Writing Alignments
- Format Conversion
- Accessing Alignment Data
- Working with Alignment Objects
- Creating Alignments Programmatically
- Format Selection for Downstream Tools
- Format-Specific Notes
- Batch Processing Multiple Files
- Alternative: Bio.Align Module I/O
- Quick Reference: Common Operations
- Common Errors
- Related Skills
- References
Scripts executed separately
scripts/batch_convert.pyscripts/convert_formats.pyscripts/read_alignment.pyscripts/slice_alignment.py
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: BioPython 1.83+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Alignment File I/O
Read, write, and convert multiple sequence alignment files in various formats.
bio-alignment-trimming
When your agent uses it
Trim multiple sequence alignments using ClipKIT, trimAl, BMGE, Divvier, or HMMcleaner with mode selection guidance per downstream goal. Use when removing unreliable columns or contaminating residues before phylogenetic inference, HMM building, or selection analysis.
What it loads into context
Frontmatter
147 tokensAlways available to the agent
SKILL.md
6,053 tokensWhen the skill is activated
Reference documents
1,329 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Goal-Driven Tool Selection
- ClipKIT Modes
- trimAl Modes
- BMGE: Block Mapping and Gathering with Entropy
- Divvier: Column Splitting Instead of Removal
- HMMcleaner: Per-Residue Cleaning
- Gblocks: The Legacy Default
- PhyIN: Phylogenetic Incompatibility Trimming
- Decision Tree by Downstream Goal
- TCS Column Masking for Selection Analysis
- MACSE Frameshift Markers Need Post-Processing
- Aggressiveness Cap
- Column Mapping for Reproducibility
- Common Errors
- Related Skills
- References
Scripts executed separately
scripts/bmge_trim.pyscripts/clipkit_trim.pyscripts/divvier_split.pyscripts/trimal_modes.py
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: ClipKIT 2.1+, trimAl 1.4+, BMGE 1.12+, Divvier 1.01+, HMMcleaner (current CPAN release of `Bio::MUST::Apps::HmmCleaner`), BioPython 1.83+
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `clipkit --version`, `trimal --version`, `BMGE --help`, `Divvier --help`
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Alignment Trimming
**"Remove unreliable columns from this MSA"** -> Filter or split columns based on gap fraction, conservation, entropy, or per-residue quality.
- CLI: `clipkit`, `trimal`, `BMGE`, `Divvier`, `HMMcleaner`bio-alignment-amplicon-clipping
When your agent uses it
Trim PCR primers from aligned reads in amplicon-panel BAMs using samtools ampliconclip. Use when processing SARS-CoV-2 ARTIC, hereditary cancer panels, ctDNA hot-spot panels, or any amplicon assay where primer-derived bases would falsely confirm reference at primer footprints.
What it loads into context
Frontmatter
159 tokensAlways available to the agent
SKILL.md
2,443 tokensWhen the skill is activated
Reference documents
1,199 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Why Primer Trimming After Alignment
- Tool Selection
- Soft-Clip vs Hard-Clip
- Basic ampliconclip Workflow
- Primer BED Format
- SARS-CoV-2 ARTIC Comparison
- After Clipping: Required Re-Processing
- Why Not Markdup
- Common Errors
- Quick Reference
- Related Skills
Scripts executed separately
scripts/ampliconclip_workflow.sh
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: samtools 1.19+, pysam 0.22+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Alignment Amplicon Clipping
**"Trim primer-derived bases from amplicon BAM"** -> Soft- or hard-clip the 5' primer footprint after alignment using a primer BED, then repair fixmate/MD/NM tags.bio-alignment-filtering
When your agent uses it
Filter alignments by flags, mapping quality, and regions using samtools view and pysam. Use when extracting specific reads, removing low-quality alignments, or subsetting to target regions.
What it loads into context
Frontmatter
132 tokensAlways available to the agent
SKILL.md
4,369 tokensWhen the skill is activated
Reference documents
2,072 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Filter Flags
- Common FLAG Values
- Filter by FLAG
- Filter by Mapping Quality
- Filter by Region
- Combined Filters
- Subsample Reads (Deterministic, Pair-Consistent)
- Expression Filtering
- Filter by Read Group
- pysam Python Alternative
- Quick Reference
- Common Filter Combinations
- Related Skills
Scripts executed separately
scripts/filter_bam.py
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: pysam 0.22+, samtools 1.19+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Alignment Filtering
**"Filter my BAM file to keep only high-quality reads"** -> Select reads by FLAG bits, mapping quality, and genomic regions using samtools view or pysam.bio-alignment-indexing
When your agent uses it
Create and use BAI/CSI indices for BAM/CRAM files using samtools and pysam. Use when enabling random access to alignment files or fetching specific genomic regions.
What it loads into context
Frontmatter
129 tokensAlways available to the agent
SKILL.md
2,878 tokensWhen the skill is activated
Reference documents
1,355 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Index Types
- samtools index
- Index Requirements
- Using Indices for Region Access
- pysam Python Alternative
- Index File Locations
- idxstats - Index Statistics
- FASTA Index (faidx)
- Quick Reference
- Index Staleness
- Contig-Naming Sanity Check
- Common Errors
- Related Skills
Scripts executed separately
scripts/fetch_regions.py
Files
SKILL.mdcore9,961 bytesSKILL.mdcore9,961 bytesSKILL.mdcore9,961 bytesSKILL.mdcore9,961 bytesSKILL.mdcore9,961 bytesSKILL.mdcore9,961 bytesSKILL.mdcore9,961 bytesSKILL.mdcore9,961 bytesSKILL.mdcore9,961 bytesSKILL.mdcore9,961 bytesSKILL.mdcore9,961 bytesSKILL.mdcore9,961 bytesSKILL.mdcore9,961 bytesSKILL.mdcore9,961 bytesSKILL.mdcore9,961 bytesSKILL.mdcore9,961 bytesSKILL.mdcore9,961 bytesSKILL.mdcore9,961 bytesSKILL.mdcore9,961 bytesSKILL.mdcore9,961 bytesSKILL.mdcore9,961 bytesSKILL.mdcore9,961 bytesSKILL.mdcore9,961 bytesSKILL.mdcore9,961 bytesSKILL.mdcore9,961 bytesSKILL.mdcore9,961 bytesSKILL.mdcore9,961 bytesreferences/usage-guide.mddoc4,955 bytesscripts/fetch_regions.pyruns1,205 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: pysam 0.22+, samtools 1.19+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Alignment Indexing
Create indices for random access to alignment files using samtools and pysam.bio-alignment-sorting
When your agent uses it
Sort alignment files by coordinate or read name using samtools and pysam. Use when preparing BAM files for indexing, variant calling, or paired-end analysis.
What it loads into context
Frontmatter
125 tokensAlways available to the agent
SKILL.md
3,045 tokensWhen the skill is activated
Reference documents
1,241 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Sort Orders
- samtools sort
- samtools collate vs sort -n
- Check Sort Order
- pysam Python Alternative
- samtools merge
- Common Workflows
- Performance Tips
- Quick Reference
- Common Errors
- Related Skills
Scripts executed separately
scripts/sort_pipeline.sh
Files
SKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesSKILL.mdcore10,572 bytesreferences/usage-guide.mddoc4,521 bytesscripts/sort_pipeline.shruns855 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: pysam 0.22+, samtools 1.19+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Alignment Sorting
Sort alignment files by coordinate or read name using samtools and pysam.bio-alignment-validation
When your agent uses it
Validate alignment quality with insert size distribution, proper pairing rates, GC bias, strand balance, and other post-alignment metrics. Use when verifying alignment data quality before variant calling or quantification.
What it loads into context
Frontmatter
131 tokensAlways available to the agent
SKILL.md
4,287 tokensWhen the skill is activated
Reference documents
2,009 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Two Different Validations
- Insert Size Distribution
- Proper Pairing Rate
- GC Bias
- Strand Balance
- Mapping Quality Distribution
- Chromosome Coverage Balance
- Mismatch Rate
- Comprehensive Validation Script
- Python Validation Module
- Quality Thresholds Summary
- Related Skills
Scripts executed separately
scripts/validate_alignment.pyscripts/validate_alignment.sh
Files
SKILL.mdcore15,095 bytesSKILL.mdcore15,095 bytesSKILL.mdcore15,095 bytesSKILL.mdcore15,095 bytesSKILL.mdcore15,095 bytesSKILL.mdcore15,095 bytesSKILL.mdcore15,095 bytesSKILL.mdcore15,095 bytesSKILL.mdcore15,095 bytesSKILL.mdcore15,095 bytesSKILL.mdcore15,095 bytesSKILL.mdcore15,095 bytesSKILL.mdcore15,095 bytesSKILL.mdcore15,095 bytesSKILL.mdcore15,095 bytesSKILL.mdcore15,095 bytesSKILL.mdcore15,095 bytesSKILL.mdcore15,095 bytesSKILL.mdcore15,095 bytesSKILL.mdcore15,095 bytesSKILL.mdcore15,095 bytesreferences/usage-guide.mddoc8,378 bytesscripts/validate_alignment.pyruns3,541 bytesscripts/validate_alignment.shruns1,244 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: matplotlib 3.8+, numpy 1.26+, picard 3.1+, pysam 0.22+, samtools 1.19+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Alignment Validation
Post-alignment quality control to verify alignment quality and identify issues.bio-bam-statistics
When your agent uses it
Generate alignment statistics using samtools flagstat, stats, depth, coverage, and mosdepth. Use when assessing alignment quality, calculating coverage, or generating QC reports.
What it loads into context
Frontmatter
127 tokensAlways available to the agent
SKILL.md
4,412 tokensWhen the skill is activated
Reference documents
1,814 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Quick Summary Commands
- samtools flagstat
- samtools idxstats
- samtools stats
- samtools depth
- samtools coverage
- pysam Python Alternative
- Quick Reference
- QC Thresholds Are Assay-Specific
- What Flagstat Does Not Reveal
- Insert Size Caveats
- Related Skills
Scripts executed separately
scripts/qc_report.py
Files
SKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesreferences/usage-guide.mddoc6,762 bytesscripts/qc_report.pyruns2,057 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: pysam 0.22+, samtools 1.19+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# BAM Statistics
**"Get alignment statistics and coverage from my BAM file"** -> Generate read counts, mapping rates, per-chromosome statistics, depth profiles, and coverage summaries.bio-duplicate-handling
When your agent uses it
Mark and remove PCR/optical duplicates using samtools fixmate and markdup. Use when preparing alignments for variant calling or when duplicate reads would bias analysis.
What it loads into context
Frontmatter
127 tokensAlways available to the agent
SKILL.md
4,189 tokensWhen the skill is activated
Reference documents
1,596 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Why Remove Duplicates?
- When to Mark Duplicates -- and When NOT To
- Tool Selection: markdup vs Picard vs UMI-aware
- Optical Distance Is Platform-Specific
- Multi-Library Pooled Marking
- Duplicate Marking Workflow
- samtools fixmate
- samtools markdup
- Duplicate Statistics
- pysam Python Alternative
- Alternative: From Aligner
- UMI-Aware Deduplication
- Quick Reference
- Duplicate FLAG
- Common Errors
- Lossy Operations
- Related Skills
Scripts executed separately
scripts/markdup_pipeline.sh
Files
SKILL.mdcore15,042 bytesSKILL.mdcore15,042 bytesSKILL.mdcore15,042 bytesSKILL.mdcore15,042 bytesSKILL.mdcore15,042 bytesSKILL.mdcore15,042 bytesSKILL.mdcore15,042 bytesSKILL.mdcore15,042 bytesSKILL.mdcore15,042 bytesSKILL.mdcore15,042 bytesSKILL.mdcore15,042 bytesSKILL.mdcore15,042 bytesSKILL.mdcore15,042 bytesSKILL.mdcore15,042 bytesSKILL.mdcore15,042 bytesSKILL.mdcore15,042 bytesSKILL.mdcore15,042 bytesSKILL.mdcore15,042 bytesSKILL.mdcore15,042 bytesSKILL.mdcore15,042 bytesSKILL.mdcore15,042 bytesSKILL.mdcore15,042 bytesSKILL.mdcore15,042 bytesSKILL.mdcore15,042 bytesSKILL.mdcore15,042 bytesSKILL.mdcore15,042 bytesreferences/usage-guide.mddoc5,968 bytesscripts/markdup_pipeline.shruns758 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: picard 3.1+, pysam 0.22+, samtools 1.19+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Duplicate Handling
**"Remove PCR duplicates from my BAM file"** -> Mark or remove duplicate reads using the fixmate-sort-markdup pipeline to prevent duplicate bias in variant calling.bio-pileup-generation
When your agent uses it
Generate pileup data for variant calling using samtools mpileup and pysam. Use when preparing data for variant calling, analyzing per-position read data, or calculating allele frequencies.
What it loads into context
Frontmatter
130 tokensAlways available to the agent
SKILL.md
4,101 tokensWhen the skill is activated
Reference documents
2,263 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- What is Pileup?
- samtools mpileup vs bcftools mpileup (Deprecation)
- Output Format
- Quality Filtering Options
- BAQ: Base Alignment Quality (Critical Default)
- Variant Calling Pipeline (Modern: bcftools mpileup)
- pysam Python Alternative
- Pileup Options Summary
- Quick Reference
- Common Errors
- Related Skills
Scripts executed separately
scripts/allele_counts.py
Files
SKILL.mdcore14,715 bytesSKILL.mdcore14,715 bytesSKILL.mdcore14,715 bytesSKILL.mdcore14,715 bytesSKILL.mdcore14,715 bytesSKILL.mdcore14,715 bytesSKILL.mdcore14,715 bytesSKILL.mdcore14,715 bytesSKILL.mdcore14,715 bytesSKILL.mdcore14,715 bytesSKILL.mdcore14,715 bytesSKILL.mdcore14,715 bytesSKILL.mdcore14,715 bytesSKILL.mdcore14,715 bytesSKILL.mdcore14,715 bytesSKILL.mdcore14,715 bytesSKILL.mdcore14,715 bytesreferences/usage-guide.mddoc8,351 bytesscripts/allele_counts.pyruns1,846 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: bcftools 1.19+, pysam 0.22+, samtools 1.19+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Pileup Generation
Generate pileup data for variant calling and position-level analysis.bio-reference-operations
When your agent uses it
Generate consensus sequences and manage reference files using samtools. Use when creating consensus from alignments, indexing references, or creating sequence dictionaries.
What it loads into context
Frontmatter
120 tokensAlways available to the agent
SKILL.md
3,395 tokensWhen the skill is activated
Reference documents
1,894 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- samtools faidx - Index Reference FASTA
- samtools dict - Create Sequence Dictionary
- samtools consensus - Generate Consensus
- pysam Python Alternative
- Reference Preparation Workflow
- Common Operations
- Quick Reference
- Related Skills
Scripts executed separately
scripts/prepare_reference.sh
Files
SKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesSKILL.mdcore12,614 bytesreferences/usage-guide.mddoc7,264 bytesscripts/prepare_reference.shruns837 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: GATK 4.5+, bcftools 1.19+, pysam 0.22+, samtools 1.19+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Reference Operations
Generate consensus sequences and manage reference files using samtools.bio-sam-bam-basics
When your agent uses it
View, convert, and understand SAM/BAM/CRAM alignment files using samtools and pysam. Use when inspecting alignments, converting between formats, or understanding alignment file structure.
What it loads into context
Frontmatter
133 tokensAlways available to the agent
SKILL.md
4,286 tokensWhen the skill is activated
Reference documents
1,560 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Format Overview
- SAM Format Structure
- samtools view
- Format Conversion
- Common Flags
- MAPQ Is Not Portable Across Aligners
- 0-Based vs 1-Based Coordinates (Footgun)
- CIGAR Operations
- Context-Specific Tags
- Provenance: @PG Chain
- CRAM Reference Resolution (Critical)
- pysam Python Alternative
- Quick Reference
- Related Skills
Scripts executed separately
scripts/convert_formats.shscripts/view_bam.py
Files
SKILL.mdcore14,978 bytesSKILL.mdcore14,978 bytesSKILL.mdcore14,978 bytesSKILL.mdcore14,978 bytesSKILL.mdcore14,978 bytesSKILL.mdcore14,978 bytesSKILL.mdcore14,978 bytesSKILL.mdcore14,978 bytesSKILL.mdcore14,978 bytesSKILL.mdcore14,978 bytesSKILL.mdcore14,978 bytesSKILL.mdcore14,978 bytesSKILL.mdcore14,978 bytesSKILL.mdcore14,978 bytesSKILL.mdcore14,978 bytesSKILL.mdcore14,978 bytesSKILL.mdcore14,978 bytesSKILL.mdcore14,978 bytesSKILL.mdcore14,978 bytesSKILL.mdcore14,978 bytesSKILL.mdcore14,978 bytesSKILL.mdcore14,978 bytesreferences/usage-guide.mddoc5,757 bytesscripts/convert_formats.shruns938 bytesscripts/view_bam.pyruns900 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: pysam 0.22+, samtools 1.19+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# SAM/BAM/CRAM Basics
**"Read a BAM file"** -> Open a binary alignment file and iterate over aligned reads with their mapping coordinates, flags, and quality scores.bio-alignment-msa-parsing
When your agent uses it
Parse and analyze multiple sequence alignments using Biopython. Extract sequences, identify conserved regions, analyze gaps, work with annotations, and manipulate alignment data for downstream analysis. Use when parsing or manipulating multiple sequence alignments.
What it loads into context
Frontmatter
140 tokensAlways available to the agent
SKILL.md
5,279 tokensWhen the skill is activated
Reference documents
794 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Required Import
- Loading Alignments
- Extracting Sequence Information
- Column-wise Analysis
- Gap Analysis
- Alignment Trimming
- Gap Handling for Phylogenetics
- Identifying Unreliable Alignment Regions
- Consensus Sequence
- Extracting Regions
- Sequence Filtering
- Working with Annotations
- Position Mapping
- Sequence Weighting and Neff
- Coevolution: Mutual Information with APC
- A2M / A3M Conventions
- Streaming Large Alignments
- Quick Reference: Common Operations
- Common Errors
- Related Skills
- References
Scripts executed separately
scripts/a2m_a3m_io.pyscripts/analyze_alignment.pyscripts/clean_alignment.pyscripts/consensus_sequence.pyscripts/find_conserved.pyscripts/gap_analysis.pyscripts/henikoff_weights.pyscripts/mi_apc.pyscripts/neff.py
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: BioPython 1.83+, numpy 1.26+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# MSA Parsing and Analysis
Parse multiple sequence alignments to extract information, analyze content, and prepare for downstream analysis.
bio-alignment-msa-statistics
When your agent uses it
Calculate alignment statistics including sequence identity, conservation scores, substitution matrices, and similarity metrics. Use when comparing alignment quality, measuring sequence divergence, and analyzing evolutionary patterns.
What it loads into context
Frontmatter
127 tokensAlways available to the agent
SKILL.md
5,686 tokensWhen the skill is activated
Reference documents
646 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Required Import
- Pairwise Identity
- Conservation Scoring Methods
- Conservation Score
- Substitution Counts
- Information Content
- Gap Statistics
- Alignment Quality Metrics
- Position-Specific Score Matrix (PSSM)
- Effective Sequence Number (Neff)
- Mutual Information with APC
- Distance Correction Models
- Alignment Quality Assessment
- Note on Bio.Align.AlignInfo
- Quick Reference: Metrics
- Common Errors
- Related Skills
- References
Scripts executed separately
scripts/capra_singh_jsd.pyscripts/conservation_profile.pyscripts/entropy_analysis.pyscripts/gap_statistics.pyscripts/identity_matrix.pyscripts/kimura_protein_distance.pyscripts/pssm.pyscripts/substitution_counts.py
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: BioPython 1.83+, numpy 1.26+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# MSA Statistics
Calculate sequence identity, conservation scores, substitution counts, and other alignment metrics.
bio-alignment-multiple
When your agent uses it
Perform multiple sequence alignment using MAFFT, MUSCLE5, ClustalOmega, or T-Coffee. Guides tool and algorithm selection based on dataset size, sequence divergence, and downstream application. Use when aligning three or more homologous sequences for phylogenetics, conservation analysis, or evolutionary studies.
What it loads into context
Frontmatter
153 tokensAlways available to the agent
SKILL.md
8,597 tokensWhen the skill is activated
Reference documents
1,027 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- MSA Algorithm Taxonomy
- Tool Selection
- Critical Concepts
- Running MAFFT
- Running MUSCLE5
- Running ClustalOmega
- Running T-Coffee
- Codon-Aware Alignment
- Post-Alignment Validation Checklist
- When NOT to Run MSA
- Quick Reference
- Common Errors
- Related Skills
- References
Scripts executed separately
scripts/codon_alignment.pyscripts/run_msa.py
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: MAFFT 7.520+, MUSCLE 5.1+, ClustalOmega 1.2.4+, T-Coffee 13+, PAL2NAL 14+, BioPython 1.83+
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `mafft --version`, `muscle -version`, `clustalo --version`
- Python: `pip show biopython` then `help(module.function)` to check signatures
If code throws errors, introspect the installed tool and adapt the example to match the actual CLI flags rather than retrying.
# Multiple Sequence Alignment
**"Align multiple sequences"** -> Compute an optimal alignment of three or more homologous sequences using progressive, iterative, or consistency-based methods.
- CLI: `mafft` (most versatile), `muscle` (highest accuracy), `clustalo` (scales well), `t_coffee` (consistency-based)bio-alignment-pairwise
When your agent uses it
Perform pairwise sequence alignment using Biopython Bio.Align.PairwiseAligner. Use when comparing two sequences, finding optimal alignments, scoring similarity, and identifying local or global matches between DNA, RNA, or protein sequences.
What it loads into context
Frontmatter
139 tokensAlways available to the agent
SKILL.md
5,529 tokensWhen the skill is activated
Reference documents
680 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Required Import
- Pairwise Library Selection
- Core Concepts
- Creating an Aligner
- Performing Alignments
- Alignment Output Format
- Accessing Alignment Data
- Alignment Counts (Identities, Mismatches, Gaps)
- Common Scoring Configurations
- Substitution Matrix Selection
- Working with SeqRecord Objects
- Iterating Over Multiple Alignments
- Substitution Matrix from Alignment
- Export Alignment to Different Formats
- Quick Reference: Scoring Parameters
- Common Errors
- Percent Identity: Definitions Matter
- Statistical Significance: Karlin-Altschul
- When Alignment Is NOT Appropriate
- Related Skills
- References
Scripts executed separately
scripts/alignment_from_file.pyscripts/empirical_pvalue.pyscripts/global_alignment.pyscripts/local_alignment.pyscripts/protein_alignment.py
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: BioPython 1.83+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Pairwise Sequence Alignment
**"Align two sequences"** -> Compute an optimal alignment between a pair of sequences using dynamic programming.
- Python: `PairwiseAligner()` (BioPython Bio.Align)bio-alignment-structural
When your agent uses it
Align protein structures using Foldseek 3Di, TM-align, US-align, DALI, or Foldmason for structural MSA. Predict, score, and superpose backbone coordinates when sequence identity is below the twilight zone or remote-homology detection is required. Use when sequence MSA fails (<25% identity), when the dark proteome is the target, when AlphaFoldDB / ESM Atlas search is needed, or when structural superposition is the goal.
What it loads into context
Frontmatter
189 tokensAlways available to the agent
SKILL.md
6,154 tokensWhen the skill is activated
Reference documents
1,389 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- When to Use Structural Alignment
- Pairwise Structural Alignment Tool Selection
- Structural Search at Scale: Foldseek
- Structural Multiple Sequence Alignment
- Hybrid Sequence-Structure Approaches
- AlphaFold Integration
- pLM-Based Sequence Aligners
- Visualisation and Inspection
- Decision Tree by Goal
- Common Errors
- Related Skills
- References
Scripts executed separately
scripts/biopython_superimposer.pyscripts/foldmason_msa.pyscripts/foldseek_search.pyscripts/tm_align_pairwise.py
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: Foldseek 8+, TM-align 20220412+, US-align 20231222+, Foldmason 1+, BioPython 1.83+, pymol-open-source 3.0+
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `foldseek --version`, `TMalign`, `USalign`, `foldmason --version`
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Structural Alignment
**"Align two protein structures"** -> Compute backbone-aware superposition and a fold-similarity score (TM-score, RMSD, or LDDT).
- CLI pairwise: `TMalign A.pdb B.pdb`, `USalign A.pdb B.pdb`bio-differential-splicing
When your agent uses it
Detects differential alternative splicing between conditions using rMATS-turbo (binomial LRT on junction counts), leafcutter (Dirichlet-multinomial GLM on intron clusters), MAJIQ V3 deltapsi/HET (Bayesian posterior on LSVs), SUPPA2 (empirical-null on TPM-derived PSI), or Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage). Reports FDR-corrected significance and delta PSI effect sizes. Tools differ in statistical model, annotation dependence, calibration regime, and replicate-count requirements. Use when comparing splicing patterns between treatment groups, tissues, or disease states.
What it loads into context
Frontmatter
238 tokensAlways available to the agent
SKILL.md
6,823 tokensWhen the skill is activated
Reference documents
982 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Statistical Model Taxonomy
- Decision Tree by Experimental Design
- rMATS-turbo Differential Analysis
- leafcutter Differential Intron Usage
- MAJIQ V3 Differential Analysis
- SUPPA2 Differential Analysis
- Shiba for Low-Coverage / Few-Replicate Designs
- Per-Tool Failure Modes
- Reconciliation: When Tools Disagree
- rMATS Output Columns Reference
- Replicate Count and Power
- Significance and Effect-Size Thresholds
- Confounder Handling
- Multi-Group / Multi-Factor Designs
- Common Errors
- Result Prioritization
- Common Pitfalls
- Related Skills
- References
Scripts executed separately
scripts/diff_splicing_leafcutter.Rscripts/diff_splicing_rmats.sh
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: rMATS-turbo 4.3+, SUPPA2 2.4+, leafcutter 0.2.9+, MAJIQ 3.0+, Shiba 0.5+, STAR 2.7.11+, regtools 1.0+, pandas 2.2+, R 4.4+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Differential Splicing
bio-isoform-switching
When your agent uses it
Analyzes differential transcript usage (DTU) and isoform switches with functional consequence prediction (NMD via 50nt rule, ORF disruption, protein domain loss/gain, signal peptide changes, IDR alterations, coding-potential shifts). Tools include IsoformSwitchAnalyzeR v2 (auto-selects satuRn for >5 reps else DEXSeq), the manual DRIMSeq -> DEXSeq/satuRn -> stageR DTU pipeline, and fishpond/swish for inferential-uncertainty-aware DTE. Distinguishes DTU from DGE and DTE; integrates external annotators (CPC2, Pfam, SignalP, IUPred2A or DeepTMHMM). Use when investigating how splicing differences alter protein function or trigger NMD-mediated degradation.
What it loads into context
Frontmatter
263 tokensAlways available to the agent
SKILL.md
6,250 tokensWhen the skill is activated
Reference documents
993 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- DGE vs DTE vs DTU: Which Question Is Being Asked?
- Tool Selection for DTU
- Decision Tree by Research Question
- IsoformSwitchAnalyzeR v2 Workflow
- Functional Consequence Annotation
- NMD Prediction (The 50-nt Rule)
- AS-NMD as a Regulatory Layer
- Manual DTU Pipeline (DRIMSeq + DEXSeq + stageR)
- fishpond/swish for Inferential-Uncertainty-Aware Testing
- Per-Tool Failure Modes
- Reconciliation: When DTU and Event-Level Disagree
- Single-Cell DTU
- Visualization
- Significance Thresholds
- Common Errors
- Common Pitfalls
- Related Skills
- References
Scripts executed separately
scripts/isoform_switch_analysis.R
Files
SKILL.mdcore23,913 bytesreferences/usage-guide.mddoc4,008 bytesscripts/isoform_switch_analysis.Rruns5,269 bytes
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## Version Compatibility
Reference examples tested with: IsoformSwitchAnalyzeR 2.11+, DRIMSeq 1.34+, DEXSeq 1.52+, satuRn 1.14+, stageR 1.28+, fishpond 2.14+, tximport 1.34+, tximeta 1.24+, Salmon 1.10+
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Isoform Switching and Differential Transcript Usage
Identify shifts in *which* transcript a gene predominantly uses between conditions, and predict functional consequences. Statistically distinct from DGE and DTE; biologically distinct because the same gene-level expression can hide a complete isoform switch with major protein-level consequences.bio-long-read-splicing
When your agent uses it
Analyzes alternative splicing from PacBio Iso-Seq (HiFi, Kinnex/MAS-Iso-seq) and Oxford Nanopore (direct cDNA, direct RNA, R10.4.1+) long-read RNA-seq with full-isoform resolution. Tools include FLAIR (correct/collapse/quantify/diffSplice for PacBio + ONT), IsoQuant (de-novo or annotation-guided isoform discovery 2024 SOTA), Bambu (annotation-aware Bayesian discovery + quantification with Novel Discovery Rate), SQANTI3 (isoform classification: FSM/ISM/NIC/NNC + artifact flags), rMATS-long (event calling on long-read isoforms), and minimap2 (-ax splice:hq for HiFi; -ax splice -k14 for ONT cDNA; add -uf only for direct RNA or stranded cDNA preps). Solves microexon detection, recursive splicing, complex multi-exon isoforms, and DTU without transcript-quantification uncertainty. Use when short-read AS limitations (anchor length, complex isoforms, microexons, recursive splicing, transcript ambiguity) demand full-isoform resolution.
What it loads into context
Frontmatter
342 tokensAlways available to the agent
SKILL.md
6,593 tokensWhen the skill is activated
Reference documents
1,277 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- When Long-Read Wins
- Platform Selection Matrix
- Decision Tree by Use Case
- Splice-Aware Alignment
- FLAIR Workflow (correct -> collapse -> quantify -> diffSplice)
- IsoQuant for Discovery + Quantification
- Bambu for Annotation-Aware Discovery + Quantification
- SQANTI3 Classification
- rMATS-long for Differential Isoform Analysis on Long-Read Data
- DTU on Long-Read Counts
- Single-Cell Long-Read for Splicing
- Per-Tool Failure Modes
- Reconciliation: When Long-Read Tools Disagree
- Quality Control for Long-Read Splicing
- Common Errors
- Quality Thresholds
- Common Pitfalls
- Related Skills
- References
Scripts executed separately
scripts/longread_splicing_pipeline.sh
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: FLAIR 2.0+, IsoQuant 3.5+, Bambu 3.4+, SQANTI3 5.4+, minimap2 2.26+, samtools 1.19+, rMATS-long 0.2+, IsoSeq3 4.0+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Long-Read Splicing Analysis
bio-outlier-splicing-detection
When your agent uses it
Detects aberrant splicing in single rare-disease patients vs a control panel using FRASER 2.0 (Bioconductor; Beta-binomial autoencoder on Intron Jaccard Index, default delta cutoff 0.1, q hyperparameter), OUTRIDER (gene-level outlier expression via autoencoder denoising), LeafcutterMD (Dirichlet-multinomial outlier mode of LeafCutter for annotation-free junctions), and DROP (Snakemake pipeline integrating FRASER2 + OUTRIDER + monoallelic expression for clinical diagnostics). The statistical model is fundamentally different from differential splicing — single-sample-vs-cohort outlier detection rather than two-group comparison. Standard tool in EU rare-disease (Solve-RD) and NIH UDN programs. Use when applying RNA-seq to undiagnosed Mendelian disease, validating predicted splice variants in clinical samples, or detecting cryptic splicing in disease tissue.
What it loads into context
Frontmatter
295 tokensAlways available to the agent
SKILL.md
5,423 tokensWhen the skill is activated
Reference documents
1,020 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Tool Taxonomy
- Decision Tree by Diagnostic Scenario
- When to Use Outlier vs Differential
- FRASER 2.0 Workflow
- OUTRIDER for Gene-Level Outlier Expression
- LeafcutterMD for Annotation-Free Outlier Intron Usage
- DROP Pipeline (Integrated Workflow)
- Variant + Outlier Integration
- Cohort Size and Power
- Tissue Choice for Mendelian RNA-seq
- Hyperparameter Tuning
- Per-Tool Failure Modes
- Reconciliation: When Outlier Tools Disagree
- Disease-Specific Expectations
- Common Errors
- Common Pitfalls
- Quality Thresholds
- Related Skills
- References
Scripts executed separately
scripts/fraser2_rare_disease.R
Files
SKILL.mdcore20,727 bytesSKILL.mdcore20,727 bytesSKILL.mdcore20,727 bytesSKILL.mdcore20,727 bytesreferences/usage-guide.mddoc3,911 bytesscripts/fraser2_rare_disease.Rruns2,091 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: FRASER 2.0 (>=1.99.0), OUTRIDER 1.20+, LeafcutterMD via leafcutter 0.2.9+, DROP 1.4+, R 4.4+, BiocManager 1.30+
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Outlier Splicing Detection
For clinical RNA-seq diagnostics in rare disease, the question is not "what differs between groups?" but "what is aberrant in this single patient relative to a panel of unaffected samples?". The statistical framework is **single-sample-vs-cohort outlier detection**, fundamentally different from two-group differential splicing. Tools in this space are designed for clinical Mendelian diagnostic settings.bio-sashimi-plots
When your agent uses it
Creates sashimi-style plots showing RNA-seq read coverage and splice junction counts using ggsashimi (general-purpose, condition-grouped overlays), rmats2sashimiplot (rMATS-output-aware), MAJIQ-VOILA (LSV posteriors interactive HTML), leafviz (leafcutter clusters Shiny), Jutils (tool-agnostic heatmaps and sashimi for rMATS/leafcutter/MntJULiP/MAJIQ output), or pyGenomeTracks (multi-track publication figures). Tool choice depends on the upstream differential-splicing tool's output format and the publication vs interactive use case. Use when visualizing specific splicing events, validating differential splicing calls, or producing publication-quality figures.
What it loads into context
Frontmatter
251 tokensAlways available to the agent
SKILL.md
5,084 tokensWhen the skill is activated
Reference documents
879 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Tool Selection Matrix
- Decision Tree by Goal
- ggsashimi for Publication Overlays
- Batch Plotting from rMATS Hits
- rmats2sashimiplot
- MAJIQ-VOILA Interactive HTML
- leafviz Shiny App
- Jutils for Tool-Agnostic Output
- pyGenomeTracks for Multi-Track Figures
- Reading Sashimi Plots (Interpretation Guide)
- Per-Tool Failure Modes
- Customization Reference
- Best Practices
- Common Errors
- Troubleshooting
- Related Skills
- References
Scripts executed separately
scripts/plot_sashimi.py
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: ggsashimi 1.1+, rmats2sashimiplot 3.0+, MAJIQ 3.0+, leafcutter 0.2.9+, pyGenomeTracks 3.8+, ggplot2 3.5+, pandas 2.2+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Sashimi Plot Visualization
Visualize RNA-seq coverage tracks with splice junction arcs labeled by read count. Sashimi plots originated with MISO (Katz 2010 *Nat Methods*); modern tools differ in input handling, group aggregation logic, and customization. Tool choice is not interchangeable — some tools work only with specific upstream output formats.bio-single-cell-splicing
When your agent uses it
Analyzes alternative splicing at single-cell resolution. The first decision is library chemistry — 10X 3' is fundamentally limited (RT primes from poly-A, R2 falls in 3' UTR, <0.1 junction read per cell per AS event). Plate-based full-length methods (Smart-seq3, FLASH-seq, VASA-seq, STORM-seq) and single-cell long-read (MAS-Iso-seq, scISOr-Seq2) are the chemistries that give per-cell isoform structure. Tools include MARVEL (R, Smart-seq integrated), BRIE2 (Bayesian PSI with regulatory features and ELBO_gain test), scQuint (junction-cluster, plate-based; not for 10X), SpliZ (annotation-free Z-score), Psix (graph-smoothness regulated AS), and Sierra (alternative polyadenylation, often confused with AS). Use when analyzing isoform usage in scRNA-seq, identifying cell-type-specific splicing, or determining whether scRNA-seq chemistry supports splicing analysis at all.
What it loads into context
Frontmatter
320 tokensAlways available to the agent
SKILL.md
7,117 tokensWhen the skill is activated
Reference documents
1,220 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The 10X 3' Problem (Quantified)
- Decision: Does the Chemistry Support Splicing Analysis?
- Tool Selection Matrix
- Decision Tree by Goal
- MARVEL Plate-Based Workflow
- BRIE2 Bayesian PSI
- SpliZ for Annotation-Free Discovery
- Psix for Regulated AS Along Trajectories
- Sierra for APA (Not Splicing)
- Pseudobulk for Statistical Power
- Single-Cell Long-Read = Future of Single-Cell Splicing
- Per-Tool Failure Modes
- Reconciliation: When Single-Cell Tools Disagree
- Quantitative Concepts Unique to Single-Cell
- Cell-Type-Specific Splicing Biology
- Quality Thresholds
- Common Errors
- Common Pitfalls
- Related Skills
- References
Scripts executed separately
scripts/sc_splicing_brie2.py
Files
SKILL.mdcore26,408 bytesSKILL.mdcore26,408 bytesSKILL.mdcore26,408 bytesSKILL.mdcore26,408 bytesSKILL.mdcore26,408 bytesSKILL.mdcore26,408 bytesSKILL.mdcore26,408 bytesreferences/usage-guide.mddoc4,566 bytesscripts/sc_splicing_brie2.pyruns6,239 bytes
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## Version Compatibility
Reference examples tested with: MARVEL 2.0+, BRIE2 0.2.4+, scQuint 0.1+, SpliZ 0.0.1+, Sierra 1.0+, Psix 0.1+, anndata 0.10+, scanpy 1.10+, pandas 2.2+, scipy 1.13+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Single-Cell Splicing Analysis
bio-splice-variant-prediction
When your agent uses it
Predicts whether a DNA variant alters mRNA splicing using sequence-based deep-learning tools — SpliceAI (10kb context dilated CNN, clinical default), Pangolin (multi-tissue), MMSplice (modular per-region CNN with calibrated ΔPSI), SpliceTransformer/TrASPr (tissue-aware transformers), SpliceVault (empirical 300K-RNA lookup of likely mis-splicing outcomes), CADD-Splice (composite score). Applies the ClinGen SVI 2023 framework for ACMG/AMP variant interpretation (PVS1, PP3, BP4 evidence codes), HGVS splicing nomenclature (c.123+1G>A, c.123-3T>G, r.spl?), extended-window scoring for deep-intronic pseudoexons, tissue-specific predictions, branchpoint variant detection (BPHunter, LaBranchoR), and splice-switching ASO design. Use when interpreting splice impact of clinical variants, prioritizing VUS, identifying deep-intronic pathogenic variants, or designing ASOs.
What it loads into context
Frontmatter
318 tokensAlways available to the agent
SKILL.md
6,882 tokensWhen the skill is activated
Reference documents
1,171 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Predictor Taxonomy
- Tool Selection Matrix
- Decision Tree by Use Case
- ClinGen SVI 2023 Framework
- SpliceAI Workflow
- Pangolin for Tissue-Specific Prediction
- SpliceVault for Empirical Mis-Splicing Outcomes
- MMSplice for Calibrated ΔPSI
- HGVS Splicing Nomenclature
- Extended-Window Scoring for Deep-Intronic Variants
- Concordance Across Predictors
- Branchpoint Variant Detection
- Splice-Switching ASO Design
- Per-Tool Failure Modes
- Population Database Lookup
- Common Errors
- Common Pitfalls
- Quality Thresholds
- Related Skills
- References
Scripts executed separately
scripts/spliceai_clingen_classify.py
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: SpliceAI 1.3+, Pangolin 1.0+, MMSplice 2.4+, pyensembl 2.3+, pysam 0.22+, pandas 2.2+, gffutils 0.13+, tensorflow 2.15+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Splice Variant Prediction
Predict whether a DNA variant alters mRNA splicing. **Distinct from "variant pathogenicity" generally**: a variant can be a strong splice disruptor without being pathogenic for the gene's standard mechanism, or pathogenic for reasons orthogonal to splicing. Splice prediction asks specifically: does this variant change splice-site usage?bio-splicing-qc
When your agent uses it
Assesses RNA-seq data quality specifically for alternative splicing analysis. QC layers include experimental design audit (library prep, read length, depth, replicates), STAR 2-pass cohort-style alignment, junction saturation curves and discovery plateau detection, novel-vs-known junction ratio diagnostics, junction-overhang distribution, splice-site strength scoring (MaxEntScan intrinsic + SpliceAI context-aware), strandedness verification, GENCODE basic vs comprehensive choice, and rRNA contamination screening. Splicing analysis is more demanding than DGE on read length, depth, library prep, alignment strategy, and annotation choice — failures silently bias PSI estimates and inflate novel-junction false positives. Use when evaluating data suitability for splicing analysis, troubleshooting low event detection, or designing sequencing experiments where AS is a primary endpoint.
What it loads into context
Frontmatter
255 tokensAlways available to the agent
SKILL.md
6,646 tokensWhen the skill is activated
Reference documents
875 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- QC Layer Taxonomy
- Decision Tree by Question
- Experimental Design Audit (Before Sequencing)
- STAR 2-Pass Alignment
- Junction Saturation
- Novel-vs-Known Junction Ratio
- Junction Read Overhang and Coverage
- Splice Site Strength (MaxEntScan and SpliceAI)
- Picard CollectRnaSeqMetrics and Gene-Body Coverage
- Strandedness Verification
- Annotation Choice
- rRNA Contamination Check
- Per-Tool Failure Modes
- Common Errors
- Quality Thresholds
- Troubleshooting Low Event Detection
- Common Pitfalls
- Related Skills
- References
Scripts executed separately
scripts/splicing_qc.py
Files
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## Version Compatibility
Reference examples tested with: RSeQC 5.0+, STAR 2.7.11+, samtools 1.19+, pysam 0.22+, regtools 1.0+, maxentpy 0.0.1+, spliceai 1.3+, matplotlib 3.8+, pandas 2.2+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Splicing-Specific Quality Control
Splicing analysis is more demanding than DGE on read length, depth, library prep, alignment strategy, and annotation choice. Failures in any of these silently bias PSI estimates and inflate novel-junction false positives. The decision sequence is: experimental design -> library prep -> alignment strategy -> annotation -> diagnostic metrics. Each layer's failure mode is distinct.bio-splicing-quantification
When your agent uses it
Quantifies alternative splicing as PSI (percent spliced in) from RNA-seq using rMATS-turbo (BAM-based event), SUPPA2 (TPM-based event), MAJIQ V3 (LSV-based Bayesian), leafcutter (annotation-free intron clusters), VAST-TOOLS (cross-species with microexon support), Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage), or IRFinder-S (intron retention coverage-aware). Distinguishes the five canonical event classes (SE, A5SS, A3SS, MXE, RI), special classes (microexons, exitrons, AFE/ALE), intron retention subtypes (canonical RI vs detained introns), and applies effective-length normalization. Use when measuring splice-site usage or isoform inclusion ratios from short-read RNA-seq.
What it loads into context
Frontmatter
279 tokensAlways available to the agent
SKILL.md
7,243 tokensWhen the skill is activated
Reference documents
990 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Event Taxonomy (Beyond Standard SE/A5SS/A3SS/MXE/RI)
- Tool Selection Matrix
- PSI Definition and Effective Length Normalization
- Sign Conventions for Alternative Splice Sites
- rMATS-turbo Workflow
- SUPPA2 Workflow
- MAJIQ V3 Workflow
- leafcutter Junction Quantification
- Per-Tool Failure Modes
- Reconciliation: When rMATS and leafcutter Disagree
- Intron Retention: Canonical vs Detained vs Co-Transcriptional Unspliced
- Microexon Detection
- Quality Thresholds
- Decision Tree by Scenario
- Common Errors
- Output Interpretation
- Common Pitfalls
- Related Skills
- References
Scripts executed separately
scripts/quantify_splicing.py
Files
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## Version Compatibility
Reference examples tested with: rMATS-turbo 4.3+, SUPPA2 2.4+, leafcutter 0.2.9+, MAJIQ 3.0+, IRFinder-S 2.0+, kallisto 0.50+, Salmon 1.10+, pandas 2.2+, STAR 2.7.11+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Splicing Quantification
bio-atac-seq-allele-specific-accessibility
When your agent uses it
Detect allele-specific chromatin accessibility from ATAC-seq using WASP, GATK ASEReadCounter, or RASQUAL. Use when mapping cis-regulatory genetic variants from heterozygous SNPs, separating cis from trans regulation, building chromatin QTL (caQTL) maps, validating GWAS variant function with allelic imbalance, or detecting reference allele mapping bias before downstream analysis.
What it loads into context
Frontmatter
183 tokensAlways available to the agent
SKILL.md
4,658 tokensWhen the skill is activated
Reference documents
1,856 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Reference Allele Mapping Bias (The Single Most Important Issue)
- Per-Tool Failure Modes
- Decision Tree by Setting
- Cohort caQTL Pipeline
- Within-Peak ASE Aggregation
- RASQUAL Joint Modeling
- Reconciliation
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/wasp_ase_pipeline.sh
Files
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## Version Compatibility
Reference examples tested with: WASP 0.3.4+, GATK 4.4+, RASQUAL 1.1+, samtools 1.19+, bcftools 1.19+, vcftools 0.1.16+, plink 2.00+, MatrixEQTL 2.3+, QuASAR 0.1+, bowtie2 2.5+, bwa-mem2 2.2.1+, scipy 1.11+ (false_discovery_control), pandas 2+, pybedtools 0.10+.
Verify before use:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws unexpected errors, introspect the installed package and adapt rather than retrying.
# Allele-Specific Accessibility
**"Does this heterozygous SNP affect chromatin accessibility on its allele?"** -> Count ATAC reads supporting reference vs alternative allele at heterozygous sites in the same individual; significant deviation from 50:50 indicates cis-regulatory effect. Requires careful handling of reference-allele mapping bias (WASP filtering) and within-individual binomial testing.bio-atac-seq-atac-peak-calling
When your agent uses it
Call accessible chromatin regions from ATAC-seq BAM files using MACS3, MACS2, Genrich, or HMMRATAC. Use when identifying open chromatin from aligned ATAC-seq, choosing between point-source vs HMM peak callers, applying ENCODE-style pseudoreplicate IDR, removing blacklist regions, or fixing 501bp consensus peaks for downstream differential analysis.
What it loads into context
Frontmatter
184 tokensAlways available to the agent
SKILL.md
6,025 tokensWhen the skill is activated
Reference documents
1,892 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Shift-Extend vs BAMPE: The Critical Choice
- Effective Genome Size
- Effective Genome Size: When It Matters
- Per-Tool Failure Modes
- ENCODE 3 vs ENCODE 4 Differences
- Super-Enhancer Detection
- ENCODE 4 ATAC-seq Pipeline (Reference Implementation)
- IDR for Reproducible Peaks
- Decision Tree by Experimental Scenario
- Reconciliation: When Callers Disagree
- Blacklist and Greylist
- NFR-Only Peak Calling
- Output Files (narrowPeak)
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/call_atac_peaks.sh
Files
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## Version Compatibility
Reference examples tested with: MACS3 3.0.2+, MACS2 2.2.9+, Genrich 0.6.1+, HMMRATAC 1.2+ (now bundled in MACS3 as `macs3 hmmratac`), samtools 1.19+, bedtools 2.31+, IDR 2.0.4+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws unexpected errors, introspect the installed binary (`<tool> -h`) and adapt the example to match the actual CLI rather than retrying.
# ATAC-seq Peak Calling
**"Call accessible regions from my ATAC-seq BAM"** -> Identify Tn5-hypersensitive open chromatin, treating fragments as point insertion events (not protein-bound regions as in ChIP-seq) and accounting for the lack of input control.
- CLI (canonical, ENCODE 4): `macs2 callpeak -t atac.bam -f BAM -g hs -n sample --nomodel --shift -75 --extsize 150 --keep-dup all -B --SPMR -p 0.01` (use `-f BAM`, not `-f BAMPE` -- BAMPE reads true fragment ends and ignores `--shift`/`--extsize`)bio-atac-seq-atac-qc
When your agent uses it
ATAC-seq library quality control -- TSS enrichment, FRiP, fragment-size periodicity, library complexity (NRF/PBC1/PBC2), mitochondrial fraction, and ENCODE 4 thresholds. Use when assessing whether an ATAC-seq library passes ENCODE acceptance criteria, diagnosing transposition artefacts, comparing Omni-ATAC vs standard prep quality, or selecting which replicates to drop before peak calling.
What it loads into context
Frontmatter
187 tokensAlways available to the agent
SKILL.md
5,416 tokensWhen the skill is activated
Reference documents
1,635 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- ENCODE 4 ATAC-seq Acceptance Thresholds
- TSS Enrichment: ENCODE Method vs ATACseqQC Method
- Fragment-Size Periodicity Patterns
- Per-Metric Failure Modes
- Library Complexity (NRF, PBC1, PBC2)
- Cross-Replicate QC
- Library Complexity Extrapolation (preseq)
- Sex-Chromosome QC
- Cell-Cycle Effect on Accessibility
- Spike-in QC (Drosophila or E. coli Chromatin)
- Comprehensive QC Aggregation
- MultiQC Aggregation
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/atac_qc_metrics.R
Files
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## Version Compatibility
Reference examples tested with: deepTools 3.5+, Picard 3.1+, samtools 1.19+, bedtools 2.31+, ATACseqQC 1.26+, pysam 0.22+, pyBigWig 0.3+, numpy 1.26+, pandas 2.2+, MultiQC 1.21+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt.
# ATAC-seq Quality Control
**"Does my ATAC library pass ENCODE quality criteria?"** -> Compute the seven canonical metrics (depth, alignment rate, mitochondrial fraction, library complexity, fragment-size periodicity, TSS enrichment, FRiP) and compare against ENCODE 4 thresholds, then diagnose failures.bio-atac-seq-co-accessibility
When your agent uses it
Infer cis-regulatory connections (peak-to-peak co-accessibility) from scATAC-seq using Cicero, ArchR getCoAccessibility, or SCENIC+. Use when linking enhancer accessibility to promoter accessibility, identifying enhancer-gene pairs from chromatin alone (without paired RNA), running gene-regulatory inference combining ATAC + RNA, or comparing predicted regulatory contacts against Hi-C/Micro-C ground truth.
What it loads into context
Frontmatter
182 tokensAlways available to the agent
SKILL.md
4,658 tokensWhen the skill is activated
Reference documents
1,573 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- What Co-accessibility Captures vs What It Doesn't
- Algorithmic Taxonomy
- How Cicero Works (Conceptually)
- Per-Tool Failure Modes
- Decision Tree by Goal
- Cicero Standard Workflow
- ArchR getCoAccessibility
- Visualizing Connections
- SCENIC+ TF-Driven Networks
- Cicero Alpha Mathematics
- ABC Model Cross-Reference
- HiChIP H3K27ac as Orthogonal Anchor
- Hi-C / Micro-C Concordance
- Reconciliation
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/cicero_workflow.R
Files
SKILL.mdcore18,617 bytesSKILL.mdcore18,617 bytesreferences/usage-guide.mddoc6,100 bytesscripts/cicero_workflow.Rruns4,195 bytes
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## Version Compatibility
Reference examples tested with: Cicero 1.20+, monocle3 1.3+, ArchR 1.0.2+, SCENIC+ 1.0+, pycisTopic 1.0+, Signac 1.13+, GenomicRanges 1.54+, GenomicInteractions 1.36+, BSgenome.Hsapiens.UCSC.hg38 1.4+.
Verify before use:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws unexpected errors, introspect the installed package and adapt rather than retrying.
# Co-accessibility (cis-Regulatory Linkage)
**"Which enhancers connect to which promoters in my scATAC data?"** -> Use cell-to-cell variability in joint accessibility of nearby peaks to infer cis-regulatory connections without explicit RNA expression. Output is a peak-pair graph with co-accessibility scores; thresholding produces enhancer-gene candidate pairs.
bio-atac-seq-consensus-peakset
When your agent uses it
Build a differential-ready consensus peakset from per-replicate ATAC-seq peaks using iterative overlap removal, fixed-width re-centering, and majority-rule overlap. Use when generating a stable peak coordinate system for downstream differential accessibility, ML feature engineering, cross-sample comparison, or fixed-width peak counts; covers Corces 2018 iterative overlap (501 bp), DiffBind summit re-centering, and ENCODE consistency rules.
What it loads into context
Frontmatter
187 tokensAlways available to the agent
SKILL.md
5,039 tokensWhen the skill is activated
Reference documents
1,394 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Why a Consensus Peakset Matters
- Strategy Taxonomy
- Iterative Overlap Removal (Corces 2018)
- Per-Strategy Failure Modes
- Decision Tree by Goal
- Reconciliation Across Methods
- Width-Re-centering Patterns
- Per-Condition Consensus Then Union
- R-native Fixed-Width Re-centering (GenomicRanges)
- Cross-Organism / Cross-Build Consensus
- Peak-Width Adaptive (When ATAC + Broad Histone Marks Co-analyzed)
- ENCODE-rE2G Candidate Element Lists
- Counting Reads in Consensus Peaks
- Blacklist Filtering
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/iterative_overlap.sh
Files
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## Version Compatibility
Reference examples tested with: bedtools 2.31+, samtools 1.19+, BEDOPS 2.4.41+, GenomicRanges 1.54+, DiffBind 3.12+, Subread 2.0.2+ (featureCounts; `--countReadPairs` requires >= 2.0.2), pybedtools 0.10+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws unexpected errors, introspect the installed package and adapt rather than retrying.
# Consensus Peakset Construction
**"Build a single peakset to count reads against for all my samples"** -> Combine per-replicate or per-condition peak calls into a non-redundant, fixed-width set of regions. The strategy chosen drives FDR calibration, peak-width fairness, and reproducibility downstream.bio-atac-seq-deep-learning-atac
When your agent uses it
Sequence-based deep learning for ATAC-seq using chromBPNet, BPNet, scBasset, or Enformer. Use when correcting Tn5 bias with neural networks beyond k-mer models, predicting per-base accessibility profiles, scoring in silico variant effects at GWAS or rare-variant SNPs, discovering motifs via DeepLIFT/TF-MoDISco from a trained model, or generating cell-type-specific accessibility predictions for unobserved cell states.
What it loads into context
Frontmatter
195 tokensAlways available to the agent
SKILL.md
5,015 tokensWhen the skill is activated
Reference documents
1,751 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- When Deep Learning Helps vs When Classical Pipelines Suffice
- Per-Tool Failure Modes
- Decision Tree by Goal
- chromBPNet Standard Pipeline
- DeepLIFT + TF-MoDISco for Motif Discovery
- In Silico Variant Effect Prediction
- Reconciliation
- GPU and Compute Considerations
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/chrombpnet_pipeline.sh
Files
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## Version Compatibility
Reference examples tested with: chrombpnet 0.1.7+, bpnet-lite 0.6+ (github.com/jmschrei/bpnet-lite), scBasset 0.1.0+ (basenji2 fork), tangermeme 0.1+, tfmodisco-lite 2.2+, DeepLIFT 0.6+, captum 0.7+, tensorflow 2.13+, pytorch 2.1+, kipoi 0.8+.
Verify before use:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws unexpected errors, introspect the installed package and adapt rather than retrying. Deep-learning tooling evolves rapidly; method papers post 2023 may have superseded reference implementations.
# Sequence-Based Deep Learning for ATAC-seq
**"Score the effect of a GWAS SNP on chromatin accessibility"** -> Train (or use pre-trained) sequence-to-accessibility CNNs that take 1-5 kb DNA windows and predict per-base Tn5 cleavage profiles. Outputs include: bias-corrected accessibility, single-base mutation effect predictions, and DeepLIFT contribution scores convertible to motifs via TF-MoDISco.
bio-atac-seq-differential-accessibility
When your agent uses it
Identify differentially accessible chromatin regions across conditions using DiffBind, csaw, DESeq2, or edgeR. Use when comparing ATAC-seq accessibility between treatment groups, choosing between consensus-peak vs sliding-window approaches, picking the correct normalization (full library vs reads-in-peaks), correcting batch with SVA/RUVseq, or interpreting log2FC and FDR thresholds in a chromatin context.
What it loads into context
Frontmatter
184 tokensAlways available to the agent
SKILL.md
5,900 tokensWhen the skill is activated
Reference documents
1,962 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Decision Tree by Experimental Scenario
- Consensus Peak Set Strategy
- Normalization: The ATAC-Specific Choice
- Per-Tool Failure Modes
- Reconciliation: When Tools Disagree
- Effect Size and Threshold Selection
- Hidden Batch with SVA / RUVseq
- Spike-in Normalization
- Permutation Testing for Low Replicate Designs
- DESeq2 Likelihood Ratio Test for Time-Courses
- Hi-C-Loop-Anchored Differential
- Annotate Differential Peaks
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/diff_accessibility.R
Files
SKILL.mdcore24,282 bytesreferences/usage-guide.mddoc7,523 bytesscripts/diff_accessibility.Rruns4,270 bytes
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## Version Compatibility
Reference examples tested with: DiffBind 3.12+, DESeq2 1.42+, edgeR 4.0+, csaw 1.36+, limma 3.58+, GenomicRanges 1.54+, ChIPseeker 1.38+, Subread 2.0+ (featureCounts), sva 3.50+, RUVSeq 1.36+.
Before using code patterns, verify installed versions match:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws unexpected errors, introspect the installed package and adapt rather than retrying.
# Differential Accessibility
**"Find chromatin regions that change accessibility between my conditions"** -> Build a sample-by-region count matrix, normalize for library size and chromatin compaction, fit a generalized linear model (negative-binomial), and extract regions with significant accessibility change.
- R (consensus-peak workflow): `DiffBind` -> count -> normalize -> contrast -> analyzebio-atac-seq-enhancer-gene-linking
When your agent uses it
Predict enhancer-gene regulatory connections from ATAC-seq using ABC, ENCODE-rE2G, HiChIP, or Cicero. Use when linking distal enhancers to target genes, choosing between contact-aware (ABC, ENCODE-rE2G), accessibility-only (Cicero), and orthogonal (HiChIP H3K27ac, EpiMap) approaches, validating predictions against CRISPRi-FlowFISH gold-standard, or building cell-type-specific regulatory maps for fine-mapping or therapeutic target discovery.
What it loads into context
Frontmatter
218 tokensAlways available to the agent
SKILL.md
4,990 tokensWhen the skill is activated
Reference documents
1,892 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- ABC Mathematics
- ENCODE-rE2G Differences from ABC
- Per-Tool Failure Modes
- Decision Tree by Available Data
- ABC Standard Pipeline
- ENCODE-rE2G
- CRISPRi-FlowFISH Validation Framework
- Reconciling Methods
- Combining Multiple Predictions
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/run_abc.sh
Files
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## Version Compatibility
Reference examples tested with: ABC-Enhancer-Gene-Prediction 0.2.2+ (Engreitz lab), ENCODE-rE2G v1.0+ (EngreitzLab), Cicero 1.20+, GenomicInteractions 1.36+, FitHiChIP 9.1+, HiC-Pro 3.1+, FAN-C 0.9+, MACS3 3.0+, samtools 1.19+, bedtools 2.31+.
Verify before use:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws unexpected errors, introspect the installed package and adapt rather than retrying.
# Enhancer-Gene Linking
**"Which gene does this distal accessible region regulate?"** -> Predict the enhancer's target gene using a model that combines accessibility activity, 3D contact frequency, and (optionally) sequence-based chromatin predictions. Output is a per-(enhancer, gene) score that can be thresholded for high-confidence calls.bio-atac-seq-footprinting
When your agent uses it
Detect transcription factor binding footprints in ATAC-seq using TOBIAS, HINT-ATAC, Wellington, or scprinter. Use when identifying bound TF sites within accessible regions, correcting Tn5 insertion bias before footprinting, choosing between cleavage-based and aggregate-based footprinters, or comparing differential TF activity between conditions.
What it loads into context
Frontmatter
164 tokensAlways available to the agent
SKILL.md
4,894 tokensWhen the skill is activated
Reference documents
1,800 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Tn5 Bias and Why Correction Matters
- Tn5 Cut Geometry: +4 / -5 Dual-Cut
- Bias Correction Alternatives
- In Silico Variant Effect at Footprinted TF Motifs
- Per-TF Footprinting Failure Modes
- Decision Tree by Goal
- TOBIAS Three-Step Pipeline
- Differential Reading
- Reconciling TOBIAS vs HINT-ATAC
- NFR-Filtering Before Footprinting
- Motif Database Choice
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/run_tobias.sh
Files
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## Version Compatibility
Reference examples tested with: TOBIAS 0.16+, RGT HINT-ATAC 1.0.2+, Wellington (pyDNase) 0.3+, scprinter 0.1+, samtools 1.19+, bedtools 2.31+, pyBigWig 0.3+, MEME suite 5.5+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws unexpected errors, introspect the installed package and adapt rather than retrying.
# TF Footprinting
**"Identify TF binding footprints in my ATAC-seq data"** -> Detect short DNA stretches (typically 6-20 bp) of reduced Tn5 cleavage within accessible regions, where a bound TF physically protects DNA. Requires (1) Tn5 sequence-bias correction, (2) per-base footprint scoring, (3) motif-anchored detection.
bio-atac-seq-motif-deviation
When your agent uses it
Analyze TF motif accessibility variability across samples or single cells using chromVAR. Use when identifying TF motifs whose accessibility correlates with conditions, computing per-sample motif z-scores after matched background correction, comparing to ArchR / Signac equivalents, or distinguishing motif-accessibility signal from per-site footprinting.
What it loads into context
Frontmatter
160 tokensAlways available to the agent
SKILL.md
5,155 tokensWhen the skill is activated
Reference documents
1,560 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- What chromVAR Computes
- Algorithmic Taxonomy
- chromVAR vs Footprinting -- Different Questions
- Per-Tool Failure Modes
- Decision Tree by Setting
- chromVAR Workflow (Bulk)
- Differential Motif Activity (limma on z-scores)
- chromVAR for Single-Cell ATAC (Signac)
- chromVAR for Single-Cell ATAC (ArchR)
- Reconciling chromVAR vs ArchR vs Signac
- Variability Score Interpretation
- Background Peak Matching Mathematics
- chromVAR vs scBasset for Single-Cell
- DecoupleR Multi-Method TF Activity
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/chromvar_analysis.R
Files
SKILL.mdcore21,004 bytesSKILL.mdcore21,004 bytesreferences/usage-guide.mddoc6,400 bytesscripts/chromvar_analysis.Rruns3,517 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: chromVAR 1.24+, motifmatchr 1.24+, JASPAR2024 0.99+, TFBSTools 1.40+, BSgenome.Hsapiens.UCSC.hg38 1.4+, SummarizedExperiment 1.32+, limma 3.58+, ggplot2 3.5+, Matrix 1.6+, ArchR 1.0.2+, Signac 1.13+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws unexpected errors, introspect the installed package and adapt rather than retrying.
# Motif Deviation (chromVAR)
**"Which TF motifs explain accessibility variation across my samples or cells?"** -> Compute per-sample (or per-cell) deviation z-scores: how many standard deviations above expectation each TF motif's accessibility falls, controlling for GC content and overall accessibility via matched background peak sets.
- R: `chromVAR::computeDeviations(counts, motifs)` -> per-sample z-scoresbio-atac-seq-nucleosome-positioning
When your agent uses it
Map nucleosome center positions, occupancy, and fuzziness from ATAC-seq fragment-size patterns using NucleoATAC, ATACseqQC, DANPOS3, or scprinter. Use when characterizing nucleosome organization at promoters and enhancers, calling +1/-1 nucleosomes flanking NFRs, generating V-plots for chromatin structure visualization, or comparing nucleosome positioning between conditions.
What it loads into context
Frontmatter
187 tokensAlways available to the agent
SKILL.md
5,204 tokensWhen the skill is activated
Reference documents
1,754 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Nucleosome Physics for ATAC
- Fragment-Size Classes (Buenrostro 2013, refined)
- V-Plot Interpretation
- Algorithmic Taxonomy
- +1 Nucleosome Calling
- Per-Tool Failure Modes
- Decision Tree by Goal
- Estimating NRL from Fragment-Size Distribution
- V-Plot in Python
- Differential Nucleosome Positioning (DANPOS3 dpos)
- Histone Variant Detection from Fragment Size
- Long-Read Single-Molecule Chromatin (Fiber-seq, NanoNOMe)
- Nucleosome Fuzziness
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/nucleosome_analysis.R
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: NucleoATAC 0.3.4+, ATACseqQC 1.26+, DANPOS 3.1+, samtools 1.19+, pysam 0.22+, pyBigWig 0.3+, BSgenome.Hsapiens.UCSC.hg38 1.4+, TxDb.Hsapiens.UCSC.hg38.knownGene 3.18+.
NucleoATAC is unmaintained since 2018 but remains the canonical ATAC-specific nucleosome caller; ATACseqQC, DANPOS3, and scprinter are actively developed alternatives. Verify versions before use:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws unexpected errors, introspect the installed package and adapt rather than retrying.
# Nucleosome Positioning
**"Where are the nucleosomes in my ATAC-seq data?"** -> Use fragment-size classes (Tn5 cuts twice through naked DNA generating short fragments; once on each side of a single nucleosome generating ~147+linker fragments) to call nucleosome centers, occupancy scores, and the spacing pattern around regulatory elements.bio-atac-seq-single-cell-atac
When your agent uses it
Process and analyze single-cell ATAC-seq data with Signac, ArchR, SnapATAC2, or Cell Ranger ATAC. Use when handling 10X scATAC or 10X Multiome (paired RNA+ATAC) data, performing per-cell QC, choosing between ArchR/Signac/SnapATAC2 ecosystems, building per-cluster consensus peaksets, integrating with paired scRNA-seq, doublet detection (AMULET vs ArchR vs scDblFinder), or running pseudobulk differential accessibility per cluster.
What it loads into context
Frontmatter
213 tokensAlways available to the agent
SKILL.md
6,366 tokensWhen the skill is activated
Reference documents
2,119 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Ecosystem Choice (The Most Important Decision)
- 10X Multiome Caveat (Paired RNA + ATAC)
- Per-Cell QC Thresholds
- Doublet Detection: Three Approaches
- Per-Tool Failure Modes
- Decision Tree by Goal
- Standard Signac Workflow
- Per-Cluster Pseudobulk Peak Calling
- ArchR Workflow
- SnapATAC2 Workflow (Python)
- Reconciliation Across Tools
- cellranger-atac vs cellranger-arc
- Cell-Cycle Correction for scATAC
- Sex-Chromosome QC for scATAC
- scArches Reference Mapping
- chromBPNet Per-Cluster Pseudobulk
- AMULET Depth Threshold
- Multiome WNN Integration (Signac)
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/signac_workflow.R
Files
SKILL.mdcore24,123 bytesSKILL.mdcore24,123 bytesSKILL.mdcore24,123 bytesreferences/usage-guide.mddoc7,808 bytesscripts/signac_workflow.Rruns3,782 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: Cell Ranger ATAC 2.1+, Signac 1.13+, Seurat 5.0+, ArchR 1.0.2+, SnapATAC2 2.8+, AMULET 1.1+, scDblFinder 1.16+, scater 1.30+, scvi-tools 1.1+, GenomicRanges 1.54+, JASPAR2024 0.99+, BSgenome.Hsapiens.UCSC.hg38 1.4+, EnsDb.Hsapiens.v86 2.99+, MACS3 3.0+. SnapATAC2 2.8+ uses `pp.import_fragments`; older 2.5-2.7 used `pp.import_data` (renamed/removed in 2.9).
Verify before use:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws unexpected errors, introspect the installed package and adapt rather than retrying.
# Single-Cell ATAC-seq
**"Process my 10X scATAC data from cellranger output"** -> Build a per-cell fragment matrix, compute per-cell QC, dimensionality reduction (TF-IDF + LSI / spectral / autoencoder), cluster, call cluster-level pseudobulk peaks, annotate cell types via gene-activity scores, and integrate with paired scRNA-seq if Multiome.bio-causal-genomics-colocalization-analysis
When your agent uses it
Test whether two or more traits share a causal variant at a locus using Bayesian colocalization (coloc.abf, coloc.susie, HyPrColoc, moloc, eCAVIAR, SMR/HEIDI, PWCoCo, SharePro). Use when integrating GWAS with eQTL/sQTL/pQTL/mQTL, distinguishing shared causal variants from LD-driven coincidence, handling allelic heterogeneity, choosing between single-causal vs multi-causal methods, picking PP.H4 thresholds, running sensitivity over p12, or harmonising summary statistics for colocalization.
What it loads into context
Frontmatter
221 tokensAlways available to the agent
SKILL.md
10,228 tokensWhen the skill is activated
Reference documents
3,449 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Per-Method Failure Modes
- PP.H4 Threshold Framework
- Default Priors and the p12 Sensitivity Question
- eCAVIAR CLPP Threshold Framework
- LD Matrix Construction for coloc.susie
- SMR vs coloc Reconciliation
- moloc Multi-Omic Framework (3-5 Traits)
- HyPrColoc Cluster-Based Coloc (Many Traits)
- PWCoCo (Conditional Pairwise Coloc)
- Standard coloc.abf Pipeline
- coloc.susie Multi-Causal Pipeline
- SMR + HEIDI Pipeline
- Allele Harmonisation (Critical Pre-Step)
- Anticipated Reviewer Pushback
- Common Errors
- Tool Install Notes
- Reviewer-Grade Reporting Template
- References
- Related Skills
Files
SKILL.mdcore36,359 bytesSKILL.mdcore36,359 bytesSKILL.mdcore36,359 bytesSKILL.mdcore36,359 bytesreferences/usage-guide.mddoc12,240 bytesscripts/coloc_abf_pipeline.Rasset3,557 bytesscripts/coloc_analysis.Rasset2,251 bytesscripts/coloc_susie.Rasset2,513 bytesscripts/coloc_susie_multicausal.Rasset3,349 bytesscripts/regional_plots.Rasset2,570 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: coloc 5.2.3+, susieR 0.12.35+, hyprcoloc 1.0+ (GitHub jrs95/hyprcoloc), SMR 1.3.1+ (CLI, cnsgenomics.com), eCAVIAR 2.2+ (compiled from caviar/eCAVIAR repo), PWCoCo 1.0+ (jwr-git/pwcoco), moloc 0.1+ (clagiamba/moloc), SharePro_coloc 7.0+ (zhwm/SharePro_coloc), R >= 4.1.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('coloc')`; check `?coloc.abf`, `?coloc.susie`, `?runsusie`
- CLI: `smr --version`, `pwcoco --help`, `sharepro_coloc.py --help`
If code throws AttributeError, NULL list elements, or `Error in coloc.abf: dataset must have...`, introspect the installed package signature and adapt the example rather than retrying.
# Colocalization Analysis
**"Test whether my GWAS signal and an eQTL share the same causal variant"** -> Compute Bayesian posterior probabilities over five hypotheses (H0 neither, H1 trait-1-only, H2 trait-2-only, H3 distinct causal variants, H4 shared causal variant) to discriminate true causal overlap from LD-driven coincidence, then run sensitivity analysis over the p12 prior.
bio-causal-genomics-effector-gene-prioritization
When your agent uses it
Maps GWAS-implicated loci to candidate effector (causal) genes by integrating variant-to-gene (V2G) features via Open Targets L2G (Mountjoy 2021), MAGMA gene-based association (de Leeuw 2015), FUMA SNP2GENE, cS2G combined SNP-to-gene scores (Gazal 2022), Polygenic Priority Scores (PoPS, Weeks 2023), FLAMES, INQUISIT, DEPICT, and enhancer-gene predictors (ABC, ENCODE-rE2G). Use when narrowing a GWAS lead locus to a candidate causal gene, picking between proximity, eQTL-based, and similarity-based prioritizers, integrating multi-evidence streams (fine-mapping, colocalization, ABC enhancer-gene, distance, chromatin), reconciling discordant L2G vs PoPS calls, prioritizing tissue-specific eQTL evidence, or triangulating across at least three independent lines of evidence for a publication-grade effector-gene nomination.
What it loads into context
Frontmatter
318 tokensAlways available to the agent
SKILL.md
10,942 tokensWhen the skill is activated
Reference documents
3,053 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Per-Method Failure Modes
- Per-Credible-Set Gene-Assignment Hierarchy
- Multi-Evidence Integration Framework
- Quantitative Thresholds
- MAGMA Gene-Based and Gene-Set Pipeline
- Open Targets L2G via GraphQL
- PoPS Polygenic Priority Score
- Multi-Evidence Integration: Concordance Scoring
- Reconciliation: When Methods Disagree
- Common Errors
- Tool Install Notes
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/magma_genebased.sh
Files
SKILL.mdcore41,867 bytesSKILL.mdcore41,867 bytesSKILL.mdcore41,867 bytesSKILL.mdcore41,867 bytesSKILL.mdcore41,867 bytesSKILL.mdcore41,867 bytesreferences/usage-guide.mddoc11,588 bytesscripts/magma_genebased.shruns2,136 bytesscripts/multi_evidence_integration.Rasset3,696 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: MAGMA 1.10+ (cncr.nl/research/magma), FUMA web platform v1.6+ (fuma.ctglab.nl), Open Targets Genetics API (REST + GraphQL, June 2024 release), PoPS (head of `FinucaneLab/pops`, 2024), cS2G pre-computed scores (Zenodo record 7754032, Gazal 2022), ABC-Enhancer-Gene-Prediction 0.2.2+, ENCODE-rE2G v1.0+ (Gschwind 2023 preprint), DEPICT v1 rel194, INQUISIT (Fachal 2020 supplementary), Python 3.9-3.11, R 4.3+, PLINK 1.9 + PLINK 2.0.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `magma --help` to confirm gene-window, gene-annot, and gene-set flag names
- Python: `pip show gentropy`; introspect endpoints at `api.platform.opentargets.org/api/v4/graphql`
- R: `packageVersion('coloc')` etc. for upstream evidence integration
If a script throws an error about an argument that has moved (e.g. an Open Targets endpoint renamed during a release) or a model file schema change, introspect the installed tool and adapt rather than retrying. Open Targets Genetics deprecated the standalone Genetics Portal in 2024 in favour of the integrated platform; verify endpoint URLs at the time of use.
# Effector Gene Prioritization
**"Which gene at this GWAS locus is actually the causal mediator?"** -> Integrate fine-mapping, colocalization, chromatin-based enhancer-gene predictions, distance, and gene-similarity priors into a per-locus per-gene confidence score, then require concordance across multiple orthogonal evidence streams before nominating a causal effector. Effector gene prioritization is the bridge between statistical fine-mapping (variant level) and biological hypothesis (gene level); it is the most failure-prone step in GWAS-to-target pipelines because the nearest-gene assumption is wrong roughly 30-50% of the time at well-studied loci.bio-causal-genomics-fine-mapping
When your agent uses it
Resolves GWAS associations to candidate causal variants and credible sets via SuSiE, susie_rss, FINEMAP, CAVIAR, DAP-G, PAINTOR, PolyFun, SuSiEx, MultiSuSiE, and FOCUS. Use when narrowing a GWAS lead SNP to a 95 percent credible set, choosing between in-sample and reference LD, calibrating non-sparse loci with SuSiE-inf or FINEMAP-inf, integrating functional priors via PolyFun, fine-mapping across ancestries with SuSiEx, diagnosing LD mismatch via estimate_s_rss and kriging_rss, handling HLA or long-range LD, or feeding credible sets into coloc.susie for colocalization.
What it loads into context
Frontmatter
252 tokensAlways available to the agent
SKILL.md
9,641 tokensWhen the skill is activated
Reference documents
1,852 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Decision Tree by Experimental Scenario
- Critical LD Diagnostic Block (susie_rss)
- Per-Tool Failure Modes
- Reconciliation: When Methods Disagree
- Quantitative Thresholds
- Functional Priors with PolyFun
- Cross-Ancestry Fine-Mapping with SuSiEx
- FINEMAP CLI Pattern
- Coloc.susie Integration
- HLA and Long-Range LD: When to Stop
- TWAS Fine-Mapping (FOCUS) -- delegated
- Required Reporting Schema for Fine-Mapping
- Anticipated Reviewer Pushback
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/finemap_pipeline.shscripts/susiex_multiancestry.sh
Files
SKILL.mdcore34,687 bytesSKILL.mdcore34,687 bytesSKILL.mdcore34,687 bytesSKILL.mdcore34,687 bytesreferences/usage-guide.mddoc6,721 bytesscripts/finemap_pipeline.shruns1,976 bytesscripts/pip_visualization.Rasset3,187 bytesscripts/susie_finemapping.Rasset2,330 bytesscripts/susie_rss_finemap.Rasset6,281 bytesscripts/susiex_multiancestry.shruns2,478 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: susieR 0.12.27+, coloc 5.2.3+, FINEMAP 1.4.2+, PolyFun (head of `omerwe/polyfun` 2024), PAINTOR V3.0, SuSiEx (head of `getian107/SuSiEx`), DAP-G (head of `xqwen/dap`), pyfocus 0.8+, R 4.3+, PLINK 1.9 / 2.0.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('susieR')` then `?susie_rss` to confirm argument names (e.g., `prior_weights` vs `prior_variance` semantics)
- CLI: `finemap --help`, `SuSiEx --help`, `PAINTOR --help`, `dap-g --help` to confirm flags
- Python: `polyfun.py --help`
If a call throws an error about an argument that no longer exists, introspect the installed function and adapt rather than retrying.
# Fine-Mapping
**"Narrow my GWAS locus to the variants likely to be causal"** -> Fit a sparse Bayesian regression that propagates LD into posterior inclusion probabilities (PIPs) and credible sets, then validate that credible sets correspond to physically reasonable haplotypes given the LD reference.bio-causal-genomics-genetic-correlation
When your agent uses it
Estimates bivariate genetic correlation (rg) between traits from GWAS summary statistics or individual-level genotypes using cross-trait LDSC, HDL, LAVA, rho-HESS, GREML-bivariate, Popcorn, and HDL-L. Use when quantifying shared genetic architecture between two traits, screening MR validity before causal inference, distinguishing global from locus-level rg, estimating trans-ancestry rg, separating partial from full causation via LCV gcp, or producing a STROBE-MR-compliant cross-trait sensitivity battery. Cross-trait LDSC intercept absorbs sample overlap and is NOT a bias; HDL is biased under sample overlap above ~5%. High rg between exposure and outcome motivates CHP-aware MR sensitivity (CAUSE, LHC-MR).
What it loads into context
Frontmatter
252 tokensAlways available to the agent
SKILL.md
9,511 tokensWhen the skill is activated
Reference documents
2,511 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Cross-Trait LDSC Intercept: Sample Overlap is Absorbed, Not a Bias
- HDL Bias Under Sample Overlap
- Relationship to MR Causal Inference
- Decision Tree by Scenario
- Per-Method Failure Modes
- Quantitative Thresholds
- Cross-Trait LDSC: Standard Workflow
- HDL: Lower-Variance rg for Independent Samples
- LAVA: Local Genetic Correlation Per Locus
- rho-HESS: Alternative Local rg
- Popcorn: Trans-Ancestry rg
- Reconciliation Across Methods
- Common Errors
- Required Reporting for rg Analyses
- Anticipated Reviewer Pushback
- Tool Installation Notes
- References
- Related Skills
Scripts executed separately
scripts/ldsc_crosstrait_rg.sh
Files
SKILL.mdcore36,639 bytesSKILL.mdcore36,639 bytesSKILL.mdcore36,639 bytesSKILL.mdcore36,639 bytesSKILL.mdcore36,639 bytesSKILL.mdcore36,639 bytesreferences/usage-guide.mddoc10,155 bytesscripts/lava_local_rg.Rasset2,583 bytesscripts/ldsc_crosstrait_rg.shruns3,358 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: LDSC v1.0.1+ (Python 3; prefer `abdenlab/ldsc-python3` v2.0.0 -- `belowlab/ldsc` v3.0.1 README states the `--h2 / --rg / --h2-cts` CLI is broken; use Docker `jtb114/ldsc:latest` for the belowlab fallback; original `bulik/ldsc` is Python 2.7 unmaintained since 2019), HDL 1.4.0+ (R; GitHub `zhenin/HDL`), LAVA 0.1.0+ (R; GitHub `josefin-werme/LAVA`), HESS 0.5.4+ (Python; huwenboshi/hess), Popcorn 1.0+ (Python; brielin/Popcorn), GCTA 1.94+ (GREML-bivariate), baselineLD_v2.2 / eur_w_ld_chr LD-score panels from alkesgroup.broadinstitute.org/LDSCORE, UKB-array SVD eigen reference for HDL.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `python3 -c 'import <module>; help(<module>)'`
- R: `packageVersion('<pkg>')` then `?function_name`
- CLI: `<tool> --version` then `<tool> --help`
If code throws an LD-score "category not found" error, an HDL reference-panel mismatch, or a LAVA locus-ID lookup failure, introspect the installed LD-score column headers and the supplied partitioning file rather than retrying with default flags.
# Genetic Correlation
**"Estimate the genetic correlation between two traits from GWAS summary statistics"** -> Decompose the bivariate genetic architecture into a single global rg (cross-trait LDSC, HDL), per-locus local rg (LAVA, rho-HESS, HDL-L), or cross-population rg (Popcorn). Genetic correlation is the central cross-trait statistic in causal genomics: it quantifies shared etiology, motivates CHP-aware MR sensitivity when high, gates LCV's gcp partial-causation parameter, and feeds into multi-trait analysis frameworks (MTAG, GenomicSEM). Tool choice is a decision about the **regime** (sumstats vs individual-level; global vs local; same-ancestry vs trans-ancestry) and the **sample-overlap structure** between input GWAS.bio-causal-genomics-genomic-sem
When your agent uses it
Fits structural equation models to GWAS summary statistics using GenomicSEM (Grotzinger 2019), including common-factor models, confirmatory factor models, ESEM, common-factor GWAS with Q_SNP heterogeneity, multivariate Wald tests, and stratified GenomicSEM partitioned heritability. Reconciles results against MTAG multi-trait analysis. Handles sample overlap via the LDSC sampling-covariance matrix, identifies and resolves Heywood cases, and verifies model fit with CFI / RMSEA. Use when modeling latent genetic architecture across correlated traits, running multivariate GWAS on a shared factor, distinguishing factor-mediated from trait-specific SNP effects, or comparing GenomicSEM common-factor results against MTAG when both depend on accurate sampling covariance.
What it loads into context
Frontmatter
253 tokensAlways available to the agent
SKILL.md
8,747 tokensWhen the skill is activated
Reference documents
2,248 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Statistical Model Taxonomy
- MTAG vs GenomicSEM Common-Factor GWAS
- Decision Tree by Scenario
- Per-Method Failure Modes
- Model Fit Diagnostics
- Quantitative Thresholds
- Standard Workflow
- Common-Factor GWAS with Q_SNP
- MTAG Comparison
- Stratified GenomicSEM (Partitioned Heritability of Factor)
- Computational Footprint
- Reconciliation: When GenomicSEM and MTAG Disagree
- Common Errors
- Anticipated Reviewer Pushback
- Tool Installation
- References
- Related Skills
Scripts executed separately
scripts/mtag_pipeline.sh
Files
SKILL.mdcore33,114 bytesSKILL.mdcore33,114 bytesSKILL.mdcore33,114 bytesSKILL.mdcore33,114 bytesSKILL.mdcore33,114 bytesSKILL.mdcore33,114 bytesreferences/usage-guide.mddoc8,944 bytesscripts/genomic_sem_commonfactor.Rasset4,770 bytesscripts/mtag_pipeline.shruns2,624 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: GenomicSEM 0.0.5+ (GitHub `GenomicSEM/GenomicSEM`), lavaan 0.6-17+, LDSC v1.0.1+ (Python 3; prefer `abdenlab/ldsc-python3` v2.0.0 -- `belowlab/ldsc` v3.0.1 README states the CLI is broken; Docker `jtb114/ldsc:latest` is the belowlab fallback), baselineLD_v2.2 annotations (alkesgroup.broadinstitute.org/LDSCORE), MTAG 1.0.8+ (Python; `JonJala/mtag`), R 4.4+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('GenomicSEM')` then `?ldsc`, `?commonfactor`, `?usermodel`, `?commonfactorGWAS`
- Python (LDSC, MTAG): `<tool>.py -h` and inspect the source under `ldsc/` or `mtag/`
GenomicSEM is GitHub-only (never on CRAN). If `ldsc()` or `usermodel()` throws an error about lavaan syntax or non-positive-definite covariance, introspect the installed API (`getMethod('ldsc')`) and adapt rather than retrying.
# Genomic SEM
**"Model the latent genetic architecture across several correlated GWAS"** -> Treat each GWAS as a measured indicator of one or more latent genetic factors and fit a structural equation model to the LDSC-derived genetic covariance matrix S and its sampling covariance V (Grotzinger 2019 Nat Hum Behav 3:513). The framework extends naturally to a multivariate GWAS in which a SNP is regressed on a latent factor (common-factor GWAS), with Q_SNP testing whether the SNP effect is homogeneous across factor loadings. Sample overlap between input GWAS is absorbed by the off-diagonals of V; ignoring V inflates Type-I.
bio-causal-genomics-heritability-partitioning
When your agent uses it
Estimates SNP heritability and partitions it across functional annotations, cell types, and loci from GWAS summary statistics or individual-level genotypes. Implements LDSC, stratified LDSC with the baseline-LD model, Finucane 2018 cell-type prioritization, LDAK SumHer, HDL, HESS local heritability, BOLT-REML, GCTA-GREML, graphREML, and Popcorn cross-population genetic correlation. Use when computing total h2_SNP from summary stats, partitioning heritability across functional categories, prioritizing trait-relevant tissues or cell types from ENCODE/Roadmap chromatin marks, reconciling LDSC vs LDAK enrichment estimates, computing local heritability with HESS, estimating genetic correlation between traits, or producing publication-grade enrichment with calibrated sensitivity to model assumptions.
What it loads into context
Frontmatter
275 tokensAlways available to the agent
SKILL.md
9,780 tokensWhen the skill is activated
Reference documents
3,807 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Statistical Model Taxonomy
- Decision Tree by Scenario
- LDSC Intercept Interpretation (Postdoc Nuance)
- LDSC vs LDAK Reconciliation
- Cell-Type Prioritization (Finucane 2018)
- Quantitative Thresholds
- LDSC Standard Workflow
- Cross-Trait LDSC for Genetic Correlation
- Per-Method Failure Modes
- LDAK SumHer Pipeline
- HESS Local h2 Pipeline
- HDL Genetic Correlation (R)
- Common Errors
- Tool Install Notes
- References
- Related Skills
Scripts executed separately
scripts/ldak_sumher.shscripts/ldsc_partitioned_h2.sh
Files
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## Version Compatibility
Reference examples tested with: LDSC v1.0.1+ (Python 3 fork; prefer `abdenlab/ldsc-python3` v2.0.0 which retains the working `--h2 / --rg / --h2-cts` CLI -- `belowlab/ldsc` v3.0.1 explicitly broke that CLI per its README and is best run via Docker `jtb114/ldsc:latest`), LDAK 6.0+, BOLT-LMM 2.4.1+, GCTA 1.94+, HESS 0.5.4+, HDL 1.4.0+ (R; GitHub `zhenin/HDL`), Popcorn 1.0+ (Python; brielin/Popcorn), baselineLD_v2.2 annotations (alkesgroup.broadinstitute.org/LDSCORE).
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `python3 -c 'import <module>; help(<module>)'`
- R: `packageVersion('<pkg>')` then `?function_name`
- CLI: `<tool> --version` then `<tool> --help`
LDSC's official repository (bulik/ldsc) is Python 2.7 only and unmaintained since 2019; use the Python 3 community forks. If code throws ImportError, AttributeError, or a "category not found" error in the LD score file, introspect the installed binary and the actual LD-score column headers rather than retrying.
# Heritability Partitioning
**"Estimate SNP heritability and partition it across functional categories, cell types, and loci"** -> Decompose `h2_SNP` from GWAS summary statistics (or individual-level genotypes) into contributions from baseline annotations (coding, conserved, regulatory), tissue-specific chromatin marks, and per-locus components, then reconcile model-dependent enrichment estimates across LDSC and LDAK. Tool choice is a decision about the **regime** (summary-stat vs individual-level; one-trait vs two-trait genetic correlation; total vs partitioned vs local) and the **model assumption** about how per-SNP heritability scales with LD, MAF, and functional annotation (GCTA model vs LDAK-Thin vs baseline-LD).bio-causal-genomics-mediation-analysis
When your agent uses it
Decompose total effects into direct and indirect paths through mediators using mediation, CMAverse 4-way, HIMA/HIMA2 high-dimensional, BAMA, two-step / MVMR mediation, or double-ML medDML. Use when testing whether a molecular phenotype (expression, methylation, protein) mediates a treatment-outcome relationship, decomposing exposure-mediator interaction via VanderWeele 4-way, screening high-dimensional EWAS mediators, or running MR-based mediation when sequential ignorability is implausible.
What it loads into context
Frontmatter
205 tokensAlways available to the agent
SKILL.md
9,023 tokensWhen the skill is activated
Reference documents
2,411 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- 4-Way Decomposition Framework
- Decision Tree by Scenario
- Sequential Ignorability and Why It Always Needs Sensitivity
- Reconciliation: Observational vs MR Mediation
- Anticipated Reviewer Pushback
- Quantitative Thresholds
- Working Code Patterns
- Tool Install Notes
- Common Errors
- References
- Related Skills
Files
SKILL.mdcore34,579 bytesreferences/usage-guide.mddoc9,961 bytesscripts/cmaverse_4way.Rasset4,989 bytesscripts/eqtl_mediation.Rasset3,304 bytesscripts/mvmr_mediation.Rasset4,792 bytesscripts/sensitivity_analysis.Rasset2,802 bytes
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## Version Compatibility
Reference examples tested with: R 4.3+, mediation 4.5.0+, CMAverse 0.1.0+ (GitHub `BS1125/CMAverse`), HIMA >= 2.3.0 (GitHub `YinanZheng/HIMA`; archived from CRAN 2026-07), bama 1.3+, causalweight 1.0.5+ (medDML), MVMR 0.4+, TwoSampleMR 0.6+, EValue 4.1+, gesttools 1.3+, ipw 1.0.11+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- HIMA must be pinned at `>= 2.3.0` for the code patterns below; the formula interface `hima(formula, data.pheno, data.M, mediator.type, penalty, ...)` was introduced in 2.3.0. On HIMA 2.2.x the classic engine is the top-level `hima(X, Y, M, COV.XM=, COV.MY=, Y.family=, penalty=)` (positional order X, Y, M) with no formula interface; HIMA 2.2.x is NOT API-compatible with the examples here.
- In 2.3+ the classic engine is `hima_classic(X, M, Y, COV.XM=, COV.MY=, Y.type=)` (positional order X, M, Y; outcome type via `Y.type`, not `Y.family`); use it only to reproduce the original 2016-2021 SIS+penalty pipelines.
If code throws an error, introspect the installed package (`?hima`, `args(cmest)`) and adapt the example to match the actual API rather than retrying.
# Mediation Analysis
**"Does expression of GENE_X mediate the SNP-to-disease effect?"** -> Decompose the total effect of a treatment (genotype, exposure) on an outcome into direct and indirect paths through one or more mediators, with explicit handling of exposure-mediator interaction, sensitivity to unmeasured confounding, and high-dimensional mediator screening.bio-causal-genomics-mendelian-randomization
When your agent uses it
Estimate causal effects of an exposure on an outcome from GWAS summary statistics using genetic instruments. Implements IVW (fixed/random), MR-Egger, weighted median/mode, MR-RAPS, CAUSE, GSMR-HEIDI, MR-PRESSO, MVMR, MR-Clust, LCV, and LHC-MR via TwoSampleMR, MendelianRandomization, MR-PRESSO, cause, and lhcMR. Use when testing causal direction between traits, evaluating drug-target effects via cis-pQTL/cis-eQTL, performing multivariable mediation MR, distinguishing causation from correlated horizontal pleiotropy, or producing STROBE-MR-compliant sensitivity batteries.
What it loads into context
Frontmatter
245 tokensAlways available to the agent
SKILL.md
9,450 tokensWhen the skill is activated
Reference documents
2,559 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Statistical Model Taxonomy
- Decision Tree by Experimental Scenario
- One-Sample vs Two-Sample Bias Direction
- Drug-Target / cis-MR Framework
- Per-Method Failure Modes
- Quantitative Thresholds
- TwoSampleMR Standard Workflow
- MR-PRESSO Outlier Detection
- CAUSE for Correlated Horizontal Pleiotropy
- MVMR with Conditional F
- Bidirectional and Steiger
- Reconciliation: When Methods Disagree
- Cohort Gotchas
- Anticipated Reviewer Pushback
- Common Errors
- Tool Installation Notes
- STROBE-MR Reporting
- References
- Related Skills
Files
SKILL.mdcore34,947 bytesSKILL.mdcore34,947 bytesSKILL.mdcore34,947 bytesreferences/usage-guide.mddoc9,661 bytesscripts/cis_mr_drug_target.Rasset5,609 bytesscripts/mr_visualization.Rasset2,376 bytesscripts/two_sample_mr.Rasset6,190 bytesscripts/twosamplemr_analysis.Rasset3,567 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: TwoSampleMR 0.6.0+, MendelianRandomization 0.10+, MR-PRESSO 1.0+, cause 1.2+, MVMR 0.4+, ieugwasr 1.0+, MRlap 0.0.3.2+, coloc 5.2+, mrclust 0.1+, lhcMR 0.0.1+, R 4.4+. Both TwoSampleMR 0.6.0 and ieugwasr 1.0 are the JWT-transition versions; older versions still expect deprecated OAuth.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI (plink, GCTA-GSMR): `<tool> --version` then `<tool> --help`
If code throws an error referencing a function that has moved (e.g. `ieugwasr::ld_clump` vs `TwoSampleMR::clump_data`) or an OAuth token failure, introspect the installed API and adapt the example rather than retrying.
# Mendelian Randomization
**"Test whether trait X causally affects trait Y from GWAS summary statistics"** -> Use genetic variants as instrumental variables (IVs) that satisfy three assumptions (relevance, independence, exclusion restriction) to estimate `beta_causal = beta_outcome / beta_exposure` under the IV framework (Davey Smith & Ebrahim 2003 IJE 32:1; Burgess & Thompson 2021 Chapman & Hall/CRC, 2nd ed.). Tool choice is a decision about the **regime** (one-sample vs two-sample, sparse vs polygenic, drug-target vs polygenic exposure) and the **pleiotropy model** (balanced, directional InSIDE, correlated horizontal). Wrong tool inflates Type-I error or attenuates true effects in a direction predictable from the bias structure.
bio-causal-genomics-pleiotropy-detection
When your agent uses it
Detect and adjust for horizontal pleiotropy in two-sample Mendelian randomization by distinguishing uncorrelated (UHP) from correlated (CHP) pleiotropy and choosing among Egger, MR-PRESSO, MR-RAPS, CAUSE, LHC-MR, LCV, MR-Clust, MR-Mix, and contamination-mixture methods. Use when validating an MR causal claim, running the STROBE-MR sensitivity battery, suspecting a shared heritable confounder, working under weak-instrument or polygenic-exposure regimes, or reconciling discordant estimates across robust methods.
What it loads into context
Frontmatter
228 tokensAlways available to the agent
SKILL.md
9,262 tokensWhen the skill is activated
Reference documents
2,552 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- UHP vs CHP: The Central Postdoc-Grade Distinction
- Operational Decision Flow (4 Steps)
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Per-Method Failure Modes
- Quantitative Thresholds
- Standard Sensitivity Battery (Working Reference)
- CAUSE for CHP-Aware Estimation
- MR-RAPS Loss Function and Overdispersion
- MR-Clust for Mechanism Heterogeneity
- LHC-MR Workflow
- Bidirectional MR Procedure
- LCV (Latent Causal Variable)
- Required Supplementary Tables
- Reconciliation Across Methods
- Anticipated Reviewer Pushback
- STROBE-MR Reporting (Skrivankova 2021)
- Common Errors
- References
- Related Skills
Files
SKILL.mdcore34,696 bytesreferences/usage-guide.mddoc9,842 bytesscripts/bidirectional_mr.Rasset2,507 bytesscripts/cause_analysis.Rasset5,742 bytesscripts/mr_presso_analysis.Rasset2,765 bytesscripts/sensitivity_battery.Rasset6,841 bytesscripts/simex_egger_correction.Rasset1,645 bytes
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## Version Compatibility
Reference examples tested with: TwoSampleMR 0.5.11+, MendelianRandomization 0.9.0+, MR-PRESSO 1.0+, CAUSE 1.2.0+, MR-Clust 0.1.0+, MRMix 0.1+, mr.raps 0.4.1+ (GitHub), LHC-MR 0.0.0.9000+ (GitHub), LCV (script-based, no version tag), simex 1.8+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- For GitHub-only packages, check the repo HEAD vs the local install date
If code throws errors, introspect the installed package and adapt the example rather than retrying.
# Pleiotropy Detection in Mendelian Randomization
**"Validate my MR result against pleiotropic bias"** -> Decompose violations of the exclusion-restriction assumption into uncorrelated horizontal pleiotropy (UHP, addressable by Egger / median / mode / MR-PRESSO) and correlated horizontal pleiotropy (CHP, addressable only by CAUSE / LHC-MR / LCV), then run a method battery whose assumptions span both regimes.
bio-causal-genomics-proteome-mr-drug-target
When your agent uses it
Runs cis-pQTL Mendelian randomization for drug-target validation using UKB-PPP (Olink), deCODE (SomaScan), Fenland, INTERVAL, ARIC, and FinnGen-PPP proteomes plus colocalization triangulation, phenome-wide on-target adverse-effect scans, cross-platform Olink/SomaScan replication, and PAV (protein-altering variant) sensitivity. Use when nominating or de-risking a drug target from plasma-proteome GWAS, mimicking pharmacological inhibition via cis-pQTL instruments, separating shared-causal from LD-confounded signal under the Schmidt 2020 cis-MR framework, screening on-target adverse phenotypes pheWAS-style, or producing publication-grade STROBE-MR plus PP.H4 evidence for a target gene.
What it loads into context
Frontmatter
267 tokensAlways available to the agent
SKILL.md
10,845 tokensWhen the skill is activated
Reference documents
2,619 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Data Source Taxonomy
- Cis-MR Methodological Taxonomy
- Decision Tree by Scenario
- Per-Method Failure Modes
- Triangulation Requirement (Operational Postdoc Rule)
- Phenome-Wide Drug-Target MR
- Cis-MR Standard Workflow
- Cis-IVW with Correlated Instruments
- PAV Annotation via VEP
- Cis-Window Width: 500 kb vs 1 Mb Decision
- Quantitative Thresholds
- Reconciliation: When Evidence Streams Disagree
- Drug Repurposing and Target Nomination
- Common Errors
- Anticipated Reviewer Pushback
- Tool Installation Notes
- References
- Related Skills
Files
SKILL.mdcore40,294 bytesSKILL.mdcore40,294 bytesSKILL.mdcore40,294 bytesreferences/usage-guide.mddoc10,041 bytesscripts/cis_pqtl_mr.Rasset5,691 bytesscripts/phewas_drug_target_mr.Rasset3,717 bytes
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## Version Compatibility
Reference examples tested with: TwoSampleMR 0.5.11+, MendelianRandomization 0.10+, MR-PRESSO 1.0+, coloc 5.2.3+, susieR 0.12.35+, ieugwasr 1.0+, plink2 2.00a5+, R 4.4+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI: `plink2 --version`; VEP `vep --help`
If code throws OAuth or rate-limit errors from OpenGWAS, or a missing `dataset$N` from coloc, introspect the installed API and adapt the example rather than retrying. UKB-PPP, deCODE, and Fenland summary statistics changed file layouts between 2023 and 2025; verify column headers before passing into `format_data()`.
# Proteome-Wide Drug-Target Mendelian Randomization
**"Does genetically lowering plasma protein X cause a change in disease Y, mimicking a drug?"** -> Use cis-pQTLs in the gene window for protein X as instruments under the Schmidt 2020 framework (Nat Commun 11:3255), restrict the exclusion-restriction violation to the geometric neighbourhood of the encoding gene, triangulate with colocalization (3-tier PP.H4 ladder below) and cross-platform replication (Olink vs SomaScan), and flag protein-altering-variant (PAV) confounding. A single significant cis-MR estimate is necessary but not sufficient for a drug-target claim; the operational bar is MR + coloc + cross-platform agreement + PAV-excluded sensitivity.
bio-causal-genomics-transcriptome-wide-association
When your agent uses it
Performs gene-level association from GWAS summary statistics via genetically predicted tissue expression using FUSION, PrediXcan, S-PrediXcan, S-MultiXcan, UTMOST, MOSTWAS, kTWAS, EpiXcan, TIGAR-V2, and probabilistic fine-mapping with FOCUS and MA-FOCUS. Use when running TWAS from GWAS sumstats, prioritising candidate causal genes from a GWAS lead locus, picking single-tissue vs cross-tissue models, identifying LD-induced TWAS false positives, choosing ancestry-matched prediction weights, fine-mapping co-regulated TWAS hits, or triangulating TWAS with cis-eQTL Mendelian randomization and colocalization to nominate a causal gene.
What it loads into context
Frontmatter
258 tokensAlways available to the agent
SKILL.md
11,505 tokensWhen the skill is activated
Reference documents
2,910 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Decision Tree by Experimental Scenario
- Per-Tool Failure Modes
- TWAS - MR - Coloc Triangulation
- Quantitative Thresholds
- FUSION Pipeline
- S-PrediXcan + S-MultiXcan Pipeline
- FOCUS Probabilistic Fine-Mapping
- When Standard Pipeline is Insufficient
- Reconciliation: When Methods Disagree
- Common Errors
- Required Reporting for Publication
- Anticipated Reviewer Pushback
- Tool Install Notes
- References
- Related Skills
Scripts executed separately
scripts/focus_finemap.shscripts/s_predixcan_pipeline.sh
Files
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## Version Compatibility
Reference examples tested with: FUSION (head of `gusevlab/fusion_twas`, scripts dated 2023+), MetaXcan / S-PrediXcan / S-MultiXcan 0.7.5+ (`hakyimlab/MetaXcan`), PrediXcan model files from PredictDB (GTEx v8 elastic-net + MASHR), UTMOST (head of `Joker-Jerome/UTMOST`), pyfocus 0.8+ (`bogdanlab/focus`), MA-FOCUS (head of `mancusolab/ma-focus`), TIGAR-V2 (head of `yanglab-emory/TIGAR`), PLINK 1.9 + PLINK 2.0, R 4.3+, Python 3.9-3.11.
Before using code patterns, verify installed versions match. If versions differ:
- R: `Rscript --version`; for FUSION scripts inspect `--help` flags directly in the source
- Python: `pip show pyfocus` (MetaXcan is git-cloned, not on PyPI) then `SPrediXcan.py --help`, `SMulTiXcan.py --help`, `focus finemap --help`
- CLI: `plink2 --version`; FUSION ships as R scripts not a binary
If a script throws an error about an argument that has moved (e.g. `--gwas_file` vs `--gwas-file`) or a model database schema change, introspect the installed script with `--help` and adapt rather than retrying. PredictDB model file paths change with GTEx version; pin the version explicitly in scripts.
# Transcriptome-Wide Association
**"Find genes whose predicted tissue expression is associated with my GWAS trait"** -> Train SNP -> expression prediction models on a reference eQTL panel, apply the per-gene SNP weights to GWAS summary statistics or genotypes, and produce a gene-level Z-score equivalent to a weighted sum of SNP Z-scores. The output is a gene-by-tissue association, but TWAS is NOT direct evidence of causal mediation: an LD-tagged eQTL signal produces the same statistical association as a truly causal one, and the dominant failure modes are LD-induced false positives at gene-dense loci, tissue mis-specification, and ancestry mismatch between GWAS and prediction weights.bio-admet-prediction
When your agent uses it
Predicts ADMET properties using ADMETlab 3.0 (119 platform features, including 77 prediction models with modeled-endpoint uncertainty), ADMET-AI, DeepChem MolNet, and chemprop D-MPNN with explicit handling of OECD QSAR principles, applicability domain assessment, calibration, hERG/CYP/AMES endpoints, and PAINS / Lipinski / Ro5 / Veber / BBB druglikeness filters. Use when filtering compounds for drug-likeness, prioritizing leads by predicted safety, or building an in-house ADMET QSAR model.
What it loads into context
Frontmatter
214 tokensAlways available to the agent
SKILL.md
4,944 tokensWhen the skill is activated
Reference documents
491 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- ADMET Model Taxonomy
- Decision Tree by Scenario
- OECD QSAR Principles (5 Pillars)
- Applicability Domain Methods
- ADMETlab 3.0 API
- chemprop D-MPNN for Custom Endpoints
- hERG Cardiotoxicity Endpoint
- CYP Inhibition (DDI Risk)
- PAINS, BRENK, REOS Filters
- Lipinski / Veber / Drug-Likeness
- Per-Tool Failure Modes
- Reconciliation Across Models
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/predict_admet.py
Files
SKILL.mdcore20,204 bytesSKILL.mdcore20,204 bytesSKILL.mdcore20,204 bytesSKILL.mdcore20,204 bytesSKILL.mdcore20,204 bytesSKILL.mdcore20,204 bytesSKILL.mdcore20,204 bytesSKILL.mdcore20,204 bytesreferences/usage-guide.mddoc2,275 bytesscripts/predict_admet.pyruns4,024 bytes
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## Version Compatibility
Reference examples tested with: RDKit 2024.09+, requests 2.31+, DeepChem 2.8+, chemprop 2.0+ (note major API change from 1.x), admet-ai 1.3+, pandas 2.2+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# ADMET Prediction
Predict absorption, distribution, metabolism, excretion, and toxicity properties of drug candidates. ADMET prediction underpins lead selection and de-risking; calibrated, applicability-domain-aware predictions distinguish a working filter from a costly false-confidence rejection. Modern best practice combines online services (ADMETlab 3.0 with uncertainty estimates), open-source models (chemprop D-MPNN), and rule-based filters (Lipinski / Veber / BBB heuristics) -- each with known failure modes.
bio-conformer-generation
When your agent uses it
Generates 3D conformer ensembles using RDKit ETKDGv3 with knowledge-enhanced distance geometry, MMFF94/UFF force-field optimization, CREST + GFN2-xTB semi-empirical refinement, and macrocycle-aware torsion preferences. Provides explicit decision rules for single vs ensemble conformer use, RMSD pruning, energy windows, conformer count, and force-field choice. Use when preparing 3D ligands for docking, generating descriptor input for 3D QSAR, or sampling macrocycle/peptide conformational ensembles.
What it loads into context
Frontmatter
208 tokensAlways available to the agent
SKILL.md
5,319 tokensWhen the skill is activated
Reference documents
839 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Conformer Method Taxonomy
- Decision Tree by Scenario
- ETKDGv3 (Modern Default)
- Force-Field Optimization
- RMSD Pruning
- Energy Window Filtering
- Macrocycle Handling
- CREST + GFN2-xTB for High-Quality Sampling
- Boltzmann Averaging of Properties
- ML-Based Conformer Generation and Search
- Per-Tool Failure Modes
- Reconciliation: ETKDGv3 vs CREST
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/gen_conformers.py
Files
SKILL.mdcore21,668 bytesSKILL.mdcore21,668 bytesSKILL.mdcore21,668 bytesSKILL.mdcore21,668 bytesSKILL.mdcore21,668 bytesSKILL.mdcore21,668 bytesSKILL.mdcore21,668 bytesSKILL.mdcore21,668 bytesSKILL.mdcore21,668 bytesSKILL.mdcore21,668 bytesreferences/usage-guide.mddoc3,272 bytesscripts/gen_conformers.pyruns3,792 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: RDKit 2024.09+, xtb 6.7+, CREST 3.0+, OpenMM 8.1+ for follow-up MD.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `xtb --version`; `crest --version`
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Conformer Generation
Generate 3D conformer ensembles for molecules from 2D structures. The choice of method depends on molecule size, flexibility, and downstream use: ETKDG (Riniker & Landrum 2015) and its ETKDGv3 macrocycle update (Wang et al. 2020) are modern defaults for drug-like molecules, MMFF94/UFF provide fast energy minimization, and CREST + GFN2-xTB provide higher-cost semi-empirical sampling. A single conformer may be insufficient when the downstream result is conformation-sensitive; determine ensemble size by convergence of the downstream descriptor, alignment, or docking result.bio-covalent-design
When your agent uses it
Designs covalent inhibitors and warheads targeting cysteine, lysine, serine, threonine, tyrosine, and aspartate residues, with explicit handling of warhead reactivity (acrylamide, chloroacetamide, vinyl sulfone, sulfonyl fluoride, fluorosulfate, aldehyde, boronate, nitrile), reversibility (kinact/Ki, t_residence), glutathione (GSH) stability, intrinsic reactivity assays, and covalent docking (DOCKovalent, GOLD, HCovDock). Use when designing covalent inhibitors for targeted covalent inhibition (TCI), KRAS G12C-style approaches, or rationalizing covalent SAR.
What it loads into context
Frontmatter
241 tokensAlways available to the agent
SKILL.md
4,344 tokensWhen the skill is activated
Reference documents
709 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Reactive Residue Taxonomy
- Warhead Chemistry
- Decision Tree by Scenario
- Kinetics: kinact / Ki
- Intrinsic Reactivity Assays
- Covalent Docking Tools
- Example: KRAS G12C Inhibitor Design Workflow
- Reactivity Surrogates (computed without experiment)
- Per-Tool Failure Modes
- Reconciliation: Irreversible vs Reversible Covalent
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/warhead_classifier.py
Files
SKILL.mdcore17,324 bytesSKILL.mdcore17,324 bytesSKILL.mdcore17,324 bytesSKILL.mdcore17,324 bytesSKILL.mdcore17,324 bytesSKILL.mdcore17,324 bytesreferences/usage-guide.mddoc3,124 bytesscripts/warhead_classifier.pyruns3,345 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: RDKit 2024.09+, OpenEye / AutoDock Vina 1.2+ (for covalent extensions), GOLD (commercial), DOCKovalent (web service), HCovDock 1.0+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show rdkit` then `help(rdkit.Chem)` to check signatures
- CLI: check version output of each docking tool
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Covalent Inhibitor Design
Design molecules that form covalent bonds with target protein residues. Clinically validated targeted covalent inhibitors include KRAS G12C inhibitors (sotorasib, adagrasib), BTK inhibitors (ibrutinib), and EGFR inhibitors (osimertinib). Covalent design requires balancing **intrinsic reactivity** (must form bond) vs **selectivity** (only the intended residue), **reversibility** (irreversible vs reversible covalent), and **drug-likeness** (warheads can hurt PK).bio-free-energy-calculations
When your agent uses it
Performs alchemical free-energy calculations including relative binding free energy (RBFE / FEP+) and absolute binding free energy (ABFE) via OpenFE, FEP+, GROMACS, AMBER pmemd, and OpenMM with explicit lambda scheduling, soft-core potentials, MBAR/BAR analysis, cycle-closure validation, and protocol-appropriate enhanced sampling. Compares ML alternatives (Boltz-2 affinity, DeepDock). Use when ranking analogs by binding affinity beyond docking accuracy, performing prospective lead optimization, or validating SAR predictions.
What it loads into context
Frontmatter
206 tokensAlways available to the agent
SKILL.md
4,375 tokensWhen the skill is activated
Reference documents
822 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- FEP Method Taxonomy
- Decision Tree by Scenario
- Relative Binding Free Energy (RBFE) Setup
- Lambda Window Scheduling
- Enhanced Sampling
- MBAR/BAR Analysis
- Cycle Closure Analysis
- Absolute Binding Free Energy (ABFE)
- MM/PBSA, MM/GBSA Endpoint Methods
- Force Field Selection
- Per-Tool Failure Modes
- Reconciliation: FEP+ vs OpenFE
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/openfe_rbfe.py
Files
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## Version Compatibility
Reference examples tested with: OpenFE 1.7+, OpenMM 8.1+, GROMACS 2024+, AMBER pmemd 22+, alchemlyb 2.1+, pymbar 4.0+, RDKit 2024.09+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `openfe --version`; `gmx --version`; `pmemd.cuda --version`
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Free Energy Calculations
Predict binding free-energy differences (RBFE) or standard binding free energies (ABFE) using alchemical methods. FEP+ is a commercial workflow and OpenFE is an open-source framework. Accuracy and cost vary substantially with system, perturbation, force field, setup, sampling, and evaluation design; report the protocol and benchmark relevant to the intended decision. The Boltz-2 report includes benchmark-specific comparisons with FEP methods but does not replace prospective validation on the project chemistry.bio-generative-design
When your agent uses it
Designs novel molecules using REINVENT 4 (de novo, scaffold decoration, linker design, R-group, molecular optimization), MolMIM, Diffusion-based generators (DiGress, DiffSMol), and JT-VAE with explicit handling of multi-parameter optimization (MPO), goal-directed scoring functions, transfer/reinforcement/curriculum learning, synthetic accessibility scoring, and chemical space exploration vs exploitation. Use when designing new chemical matter against a target, decorating a scaffold, linking fragments, or optimizing a hit for multiple ADMET / activity properties simultaneously.
What it loads into context
Frontmatter
209 tokensAlways available to the agent
SKILL.md
4,215 tokensWhen the skill is activated
Reference documents
827 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Generator Mode Taxonomy
- Learning Algorithm Taxonomy
- Decision Tree by Scenario
- REINVENT 4 Setup
- Scoring Function Design (Most Important Part)
- Multi-Parameter Optimization (MPO)
- Reward Hacking (Production Pitfall)
- Synthetic Accessibility Scoring
- Diffusion-Based Generation (Modern Alternatives)
- Constrained / Goal-Directed Generation
- MolMIM (NVIDIA BioNeMo)
- Per-Tool Failure Modes
- Reconciliation: REINVENT vs Diffusion
- Common Errors
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: REINVENT 4.0+, RDKit 2024.09+, PyTorch 2.1+, MolMIM (NVIDIA BioNeMo), chemprop 2.0+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Generative Molecular Design
Generate novel molecules biased toward desired properties using deep generative models. REINVENT 4 (Loeffler et al. 2024, AstraZeneca) provides four generator families: Reinvent (de novo), Libinvent (scaffold decoration and library design), Linkinvent (linker design), and Mol2Mol (similarity-constrained molecular optimization). These support design tasks including R-group replacement and scaffold hopping and can be used with transfer learning, reinforcement learning, and curriculum learning. For specific niches: MolMIM (NVIDIA BioNeMo) for latent-space property optimization, DiffSMol / DiGress for diffusion-based generation, and JT-VAE for latent-space optimization. The art of generative design is in the **scoring function**: poorly-designed scoring rewards uninteresting molecules, while well-designed scoring captures both activity and developability.
bio-ml-docking-rescoring
When your agent uses it
Performs ML-based protein-ligand pose prediction and scoring using DiffDock-L (diffusion-based), Boltz-1 / Boltz-2 (foundation model with affinity), Chai-1, AlphaFold3 ligand, EquiBind, TANKBind, NeuralPLexer, and hybrid workflows (DiffDock pose + GNINA rescore + PoseBusters QC). Explicit handling of when ML beats classical docking, when classical beats ML, the PB-invalid pose problem, and rescoring as the standard production hybrid. Use when modern docking is needed: foundation-model ligand-pose prediction, AI rescoring of classical poses, or scaffold-hopping in cross-docking scenarios.
What it loads into context
Frontmatter
235 tokensAlways available to the agent
SKILL.md
4,228 tokensWhen the skill is activated
Reference documents
799 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- ML Docking Method Taxonomy
- Candidate Workflows to Benchmark by Scenario
- PoseBusters Problem (Critical)
- DiffDock-L + GNINA Hybrid Workflow
- Boltz-2 for Affinity (Modern Alternative to FEP First-Pass)
- AlphaFold3 Ligand Prediction
- Chai-1 (Open Alternative to AlphaFold3)
- ML Docking Failure Modes by Tool
- Reconciliation: ML vs Classical
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/ml_hybrid_dock.sh
Files
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## Version Compatibility
Reference examples tested with: DiffDock-L (Corso et al. 2024), Boltz-1 1.0+, Boltz-2 (Passaro et al. 2025), Chai-1 0.4+, AlphaFold 3 (DeepMind), EquiBind, TANKBind, GNINA 1.1+, and PoseBusters 0.6+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `diffdock --version`; `boltz --version`
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# ML Docking and Rescoring
Use machine-learning models for protein-ligand pose prediction and affinity scoring. Foundation models such as AlphaFold 3, Boltz, and Chai-1 handle protein-ligand complex prediction, while DiffDock-L extends the original DiffDock method for ligand-pose sampling (Corso et al. 2023, 2024). Boltz-2 reports affinity prediction approaching physics-based free-energy methods on its evaluated benchmarks at substantially lower computational cost. Physical plausibility remains a separate requirement: on the PoseBusters Benchmark, the original DiffDock produced a correct and physically valid pose for 12% of complexes, compared with 58% for Vina and 55% for GOLD (Buttenschoen et al. 2024). Use ML sampling with independent scoring and physical validation rather than treating model confidence as sufficient.bio-molecular-descriptors
When your agent uses it
Calculates molecular fingerprints (ECFP/Morgan, FCFP, MACCS, RDKit, AtomPair, TopologicalTorsion, Avalon, MAP4, MHFP6) and physicochemical descriptors (Lipinski, QED, TPSA, Crippen LogP, 3D shape) with explicit choice tables, bit vs count semantics, and partial-charge model selection. Use when featurizing molecules for similarity, QSAR, virtual screening, or ML, or selecting the correct fingerprint for a chemotype-aware task.
What it loads into context
Frontmatter
203 tokensAlways available to the agent
SKILL.md
5,173 tokensWhen the skill is activated
Reference documents
498 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Fingerprint Taxonomy
- Bit vs Count Vectors
- Morgan / ECFP Radius Math
- FCFP vs ECFP
- 3D Descriptors and Conformer Dependence
- Partial Charge Methods
- MAP4 and MHFP6 for Diverse Libraries
- Physicochemical Descriptors
- Drug-Likeness Rule Sets
- QED (Weighted Drug-Likeness)
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/calculate_descriptors.py
Files
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## Version Compatibility
Reference examples tested with: RDKit 2024.09+, numpy 1.26+, pandas 2.2+, map4 1.1+ (MAP4), mhfp 1.9+. Use `mapchiral` separately when the stereochemistry-aware MAP4C fingerprint is intended.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Molecular Descriptors
Featurize molecules for similarity search, QSAR, virtual screening, or ML. Fingerprint performance is **dataset- and objective-dependent**: ECFP4 is a strong drug-like baseline, atom-pair and topological-torsion fingerprints expose longer-range topology, MAP4/MHFP6 target broader chemical-space searches, and 3D conformer-based descriptors are needed when shape and stereochemistry matter.
bio-molecular-io
When your agent uses it
Reads, writes, and converts molecular file formats (SMILES, InChI, SDF V2000/V3000, MOL2, PDB, and BinaryCIF) using RDKit and Open Babel with rigorous handling of aromaticity perception, stereochemistry, implicit/explicit hydrogens, kekulization, and salt/fragment separation. Use when loading chemical libraries, debugging parse failures, or preparing molecules for downstream standardization, descriptor calculation, or docking.
What it loads into context
Frontmatter
191 tokensAlways available to the agent
SKILL.md
3,970 tokensWhen the skill is activated
Reference documents
532 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Format Taxonomy
- Aromaticity Perception (most common silent error)
- Stereochemistry Layers
- Reading SMILES with Stereo Preservation
- Reading SDF with Property Carryover
- Open Babel for MOL2 / PDBQT
- InChI for Canonical Identity
- Per-Format Failure Modes
- Charge Models on I/O
- Drawing for QC
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/molecule_io.py
Files
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## Version Compatibility
Reference examples tested with: RDKit 2024.09+, Open Babel 3.1.1+, ChEMBL structure_pipeline 1.2+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `obabel -V`; `obabel -L formats`
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Molecular I/O
Parse, write, and convert molecular file formats. Most downstream errors trace back to silent I/O issues: incorrect aromaticity perception, lost stereochemistry, mishandled charges, dropped stereo bonds, or non-canonical tautomers. This skill enumerates each format's failure modes and prescribes the correct toolchain for each scenario.bio-molecular-standardization
When your agent uses it
Standardizes molecular structures using the ChEMBL structure pipeline for normalization and parent selection plus RDKit rdMolStandardize for explicit custom steps such as tautomer canonicalization, salt/solvent stripping, charge handling, stereochemistry handling, mixture selection, and isotope normalization. Explicitly compares ChEMBL, canSARchem, RDKit, and PubChem standardization choices. Use when preparing libraries for QSAR training, joining datasets across sources, deduplicating compound collections, or building canonical compound registries.
What it loads into context
Frontmatter
198 tokensAlways available to the agent
SKILL.md
4,253 tokensWhen the skill is activated
Reference documents
889 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Standardization Pipeline Stages
- Pipeline Reconciliation
- ChEMBL Structure Pipeline (Reference Implementation)
- Full Standardization with rdMolStandardize
- Salt Stripping Edge Cases
- Tautomer Canonicalization (debated)
- Stereochemistry Standardization
- Standardization for ML Training (avoiding data leakage)
- Per-Tool Failure Modes
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/standardize_library.py
Files
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## Version Compatibility
Reference examples tested with: RDKit 2024.09+ and chembl_structure_pipeline 1.2+. MolVS 0.1.1 is a legacy package; use RDKit's maintained `rdMolStandardize` module for custom pipelines.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Molecular Standardization
Convert raw molecular structures into a consistent form for ML training data, deduplication, registry, and cross-database joining. Skipping standardization can create data leakage when alternate representations of one compound enter different splits, distort QSAR inputs, and cause database join misses. The ChEMBL structure pipeline (Bento et al. 2020) is built on RDKit and applies ChEMBL-specific normalization and parent-selection rules. canSARchem (Dolciami et al. 2022) adds canonical-tautomer selection before parent extraction. RDKit's maintained `rdMolStandardize` module provides primitives for building an explicit custom pipeline.
bio-pharmacophore-modeling
When your agent uses it
Builds and applies 3D pharmacophore models using RDKit Pharm3D, the apo2ph4 receptor-based workflow (Heider et al. 2023), Pharmer / Pharmit for search, and PharmacoForge for protein-pocket-conditioned pharmacophore generation (Flynn et al. 2025), covering ligand-based pharmacophores from active-set alignment and receptor-based pharmacophores from binding-pocket geometry. Explicitly handles feature types, geometric tolerances, partial matching, and pharmacophore-based virtual screening. Use when identifying scaffold-hopping candidates, building shape-and-feature search queries, or transferring SAR across chemotypes.
What it loads into context
Frontmatter
227 tokensAlways available to the agent
SKILL.md
4,486 tokensWhen the skill is activated
Reference documents
841 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Pharmacophore Feature Types
- Method Taxonomy
- Decision Tree by Scenario
- Ligand-Based Pharmacophore (RDKit Pharm3D)
- Receptor-Based Pharmacophore (apo2ph4 workflow)
- Pharmacophore Search (Pharmit / Pharmer)
- Pharmacophore Quality Validation
- Pocket-Conditioned Pharmacophore Generation (PharmacoForge)
- Pharmacophore vs Shape vs 2D Fingerprint
- Per-Tool Failure Modes
- Reconciliation: Ligand-Based vs Receptor-Based
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/pharmacophore.py
Files
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## Version Compatibility
Reference examples tested with: RDKit 2024.09+, Pharmit web service, and PLIP 2.4+ (interaction analysis). Verify the deployed Pharmit/Pharmer interface and query format before automation.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show rdkit` then `help(rdkit.Chem.Pharm3D)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Pharmacophore Modeling
Build 3D pharmacophore queries that capture the essential interaction features of a ligand-target binding event. A pharmacophore is the *spatial arrangement of pharmacophore features* (donor, acceptor, hydrophobe, aromatic, charged) sufficient for activity, abstracted from any specific chemotype. Use pharmacophores for scaffold hopping, virtual-screening prefilters, and cross-target SAR transfer. Derive interaction features directly from a co-crystal when available, use apo2ph4 to derive models from an apo pocket (Heider et al. 2023), or align known actives for a ligand-based model. PharmacoForge generates candidate 3D pharmacophores conditioned on a protein pocket; those pharmacophores can then retrieve matching molecules from a library (Flynn et al. 2025).
bio-pose-validation
When your agent uses it
Validates docked / generated protein-ligand poses using PoseBusters physical-validity tests, strain energy quantification, geometric checks (planarity, vdW overlap, bond/angle distortion), and pose-energy reasonableness. Use when QC-ing docking results, comparing classical vs ML docking outputs, or filtering pose lists before SAR analysis.
What it loads into context
Frontmatter
165 tokensAlways available to the agent
SKILL.md
3,852 tokensWhen the skill is activated
Reference documents
696 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- PoseBusters Test Suite
- When to Apply PoseBusters
- PoseBusters Usage
- Python Library API
- Strain Energy Quantification
- vdW Overlap with Protein
- Aromatic Ring Planarity
- Model-Specific Failure Diagnosis
- Reconciliation: PoseBusters vs RMSD
- Integration into VS Pipeline
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/validate_poses.py
Files
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## Version Compatibility
Reference examples tested with: PoseBusters 0.6+, RDKit 2024.09+, pandas 2.2+, posecheck 0.5+ (optional).
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Pose Validation
Test docked or AI-generated protein-ligand poses for physical plausibility. PoseBusters (Buttenschoen et al. 2024) provides geometric, chemical, and energetic checks that flag implausible poses, including non-planar aromatic rings, van der Waals clashes, broken bonds, altered stereochemistry, and unfavorable internal energies. On the Astex Diverse Set, DiffDock achieved 72% RMSD success but only 47% combined RMSD-and-PB-valid success; the size of this gap is dataset- and method-dependent. PB-valid status complements RMSD for downstream SAR, FEP setup, or generative-model training.
bio-protac-degraders
When your agent uses it
Designs PROTACs, molecular glues, and bivalent degraders with explicit handling of E3 ligase choice (VHL, CRBN, IAP, MDM2, KEAP1), linker design (length, composition, rigidity), ternary complex prediction (PRosettaC, DeepTernary, AlphaFold3), cooperativity (alpha), DC50 / Dmax characterization, hook effect, and prediction-experiment reconciliation. Use when designing targeted protein degraders, planning linker SAR, predicting ternary complex stability, or building generative degrader workflows.
What it loads into context
Frontmatter
217 tokensAlways available to the agent
SKILL.md
4,657 tokensWhen the skill is activated
Reference documents
842 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- E3 Ligase Choice
- Linker Design Principles
- Decision Tree by Scenario
- Ternary Complex Prediction Tools
- Cooperativity (Alpha)
- DC50 / Dmax Characterization
- Ternary Complex Modeling Workflow
- Linker Geometry Assessment
- Generative Linker Design
- Per-Tool Failure Modes
- Reconciliation: PRosettaC vs AlphaFold3
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/protac_enumerate.py
Files
SKILL.mdcore20,327 bytesSKILL.mdcore20,327 bytesSKILL.mdcore20,327 bytesSKILL.mdcore20,327 bytesreferences/usage-guide.mddoc3,752 bytesscripts/protac_enumerate.pyruns4,842 bytes
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## Version Compatibility
Reference examples tested with: PRosettaC (web service), DeepTernary research code, AlphaFold3, Boltz-1 / Boltz-2, RDKit 2024.09+, OpenMM 8.1+ (for ternary MD).
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# PROTAC and Bivalent Degrader Design
Design bifunctional molecules (PROTACs) that recruit an E3 ubiquitin ligase to a target protein, inducing target ubiquitination and proteasomal degradation. PROTACs differ from traditional drugs: a productive **ternary complex** (target + PROTAC + E3) is required, not just target binding. The modality has produced clinical programs, but their development and regulatory status changes rapidly and must be checked from current sources. PROTAC design balances **target ligand binding**, **E3 ligand binding**, **linker geometry** (length, rigidity, chemistry), **cooperativity**, **dose-dependent ternary-complex formation**, and **cell permeability**. Negative cooperativity and the high-concentration hook effect are distinct phenomena, although cooperativity can influence the dose-response profile.
bio-qsar-modeling
When your agent uses it
Builds QSAR / QSPR models using chemprop D-MPNN, MolFormer, Uni-Mol, ChemBERTa, random forest baselines, and Gaussian processes with explicit handling of OECD 5 principles, applicability domain (kNN, leverage, conformal prediction, Mahalanobis), scaffold-balanced splits, ensemble uncertainty, calibration (Platt, isotonic), feature importance (SHAP, atomic attribution), and prospective validation. Use when building target-specific predictive models from in-house bioassay data, ADMET endpoints, or selectivity profiles.
What it loads into context
Frontmatter
209 tokensAlways available to the agent
SKILL.md
5,045 tokensWhen the skill is activated
Reference documents
818 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Model Taxonomy
- Decision Tree by Scenario
- OECD 5 Principles
- Applicability Domain Methods
- chemprop 2.x Training (CLI)
- Scaffold-Balanced Split
- Conformal Prediction for Calibrated Uncertainty
- SHAP / Atomic Attribution
- Bayesian Optimization for Active Learning
- Calibration (Platt / Isotonic)
- Multi-Task QSAR
- Per-Tool Failure Modes
- Reconciliation: Classical RF vs chemprop vs Transformer
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/chemprop_pipeline.sh
Files
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## Version Compatibility
Reference examples target: chemprop 2.2.x (major API change from 1.x), RDKit 2024.09+, scikit-learn >=1.4,<1.6, MAPIE >=0.8,<1.0 for the `MapieRegressor` example, shap 0.44+, and pytorch 2.1+. Recheck examples before widening these bounds because Chemprop, scikit-learn calibration, and MAPIE interfaces evolve independently.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `chemprop train --help` (chemprop 2.x); `chemprop_train --help` (1.x legacy)
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# QSAR Modeling
Build quantitative structure-activity relationship models from molecular structure inputs. The choice of model, featurization, and split strategy determines whether the model captures transferable chemical signal or memorizes the training data. chemprop D-MPNN with optional Morgan / RDKit descriptors is a useful open-source approach; transformer-based methods (MolFormer, Uni-Mol, ChemBERTa) should be compared on the same split and endpoint. The OECD validation principles support transparent documentation and evaluation of (Q)SAR models, but following them does not by itself confer regulatory acceptance.bio-reaction-enumeration
When your agent uses it
Enumerates virtual chemical libraries via reaction SMARTS transformations using RDKit and reaction templates, with explicit handling of atom mapping, RDChiral template extraction, product validation, RECAP/BRICS fragmentation, R-group decomposition, matched molecular pair analysis (MMPA), and Free-Wilson analysis. Use when generating combinatorial libraries from building blocks, enumerating analog series, deriving structure-activity rules, or extracting transformations from reaction data.
What it loads into context
Frontmatter
185 tokensAlways available to the agent
SKILL.md
5,032 tokensWhen the skill is activated
Reference documents
468 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Operation Taxonomy
- Reaction SMARTS Basics
- Common Reaction Templates
- Combinatorial Library Enumeration
- RECAP Fragmentation
- BRICS Fragmentation
- R-Group Decomposition
- Matched Molecular Pairs Analysis (MMPA)
- Free-Wilson Analysis
- Template Extraction from Reaction Data
- Per-Tool Failure Modes
- Reconciliation: Free-Wilson vs MMPA
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/enumerate_reactions.py
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: RDKit 2024.09+, RDChiral 1.1+, mmpdb 3.1+, scikit-learn 1.4+, numpy 1.26+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Reaction Enumeration
Generate virtual libraries by applying reaction SMARTS to building blocks, enumerate analog series via matched molecular pairs, decompose into R-groups for SAR modeling, or extract transformations from reaction data. Reaction enumeration sits at the intersection of medicinal chemistry, lead optimization, and de novo design; DOGS is one published example of reaction-driven de novo design (Hartenfeller et al. 2012). The two key operations are **transform** (apply known reactions to make new compounds) and **mine** (extract rules from observed analog series). RDKit's reaction SMARTS handles the former; mmpdb / Free-Wilson handle analog-series analysis, while mapped-reaction template extraction requires separate tooling such as RDChiral.
bio-retrosynthesis
When your agent uses it
Performs retrosynthetic planning using AiZynthFinder (template-based MCTS), maintained or version-pinned template-free models, ASKCOS, and emerging RetroSynFormer with explicit handling of route scoring, configurable MCTS rewards, building-block availability, and forward-prediction checks. Use when assessing synthetic feasibility of generated or selected molecules, planning multi-step syntheses, building synthesis-aware design pipelines, or screening libraries for retro-route feasibility.
What it loads into context
Frontmatter
184 tokensAlways available to the agent
SKILL.md
3,622 tokensWhen the skill is activated
Reference documents
755 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Retrosynthesis Method Taxonomy
- Decision Tree by Scenario
- AiZynthFinder Setup
- Route Output Analysis
- Search Rewards and Route Scoring
- Building Block Stocks
- Forward Validation with Molecular Transformer
- Template-Free with Chemformer
- Disconnection-Aware Design (DAD)
- Cost-Aware Synthesis
- Per-Tool Failure Modes
- Reconciliation: AiZynthFinder vs Chemformer
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/aizynth_batch.py
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: AiZynthFinder 4.4+, RDKit 2024.09+, RDChiral 1.1+, and ASKCOS Lite 0.5+. Chemformer is archived legacy research code; if reproducing it, pin an exact repository commit, checkpoint, and configuration rather than assuming a current pip package or stable Python API.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `aizynthcli --version`
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Retrosynthesis
Plan synthetic routes from a target molecule back to commercially available building blocks. AiZynthFinder combines Monte Carlo Tree Search (MCTS), template-based expansion, configurable search rewards, and route scorers (Saigiridharan et al. 2024). Chemformer is a published template-free transformer baseline whose official repository is now archived; use its exact historical environment for reproduction or select a maintained model with a documented interface. ASKCOS is another open-source synthesis-planning platform. A useful workflow combines retrosynthesis with building-block availability and an independently configured forward-prediction check, while recognizing that a round-trip model match is not experimental validation.bio-scaffold-analysis
When your agent uses it
Analyzes chemical libraries by scaffold using Bemis-Murcko scaffolds, generic frameworks, cyclic skeletons, matched molecular pair (MMP) analysis via mmpdb, R-group decomposition, Free-Wilson analysis, scaffold hopping, and chemotype-aware ML train/test splits. Use when identifying chemotype clusters in a library, deriving SAR transformation rules, decomposing series into R-groups, performing scaffold-balanced QSAR splits, or planning analog campaigns.
What it loads into context
Frontmatter
188 tokensAlways available to the agent
SKILL.md
3,960 tokensWhen the skill is activated
Reference documents
689 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Scaffold Representation Taxonomy
- Library Chemotype Clustering
- Bemis-Murcko Scaffold Split (ML)
- R-Group Decomposition
- Matched Molecular Pair Analysis (MMPA) via mmpdb
- Context-Based MMPA
- Scaffold Hopping
- Series Detection
- Per-Tool Failure Modes
- Reconciliation: Scaffold Definition Disagreements
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/scaffold_split.py
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: RDKit 2024.09+, mmpdb 3.1+, scikit-learn 1.4+, datamol 0.12+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Scaffold Analysis
Analyze chemical libraries by their underlying scaffolds. Bemis-Murcko (1996) is the canonical scaffold decomposition: ring systems + linkers, with all R-groups stripped. Generic framework + cyclic skeleton are progressively-more-abstract views. Scaffold analysis underpins QSAR train/test splits (preventing data leakage), library diversity assessment, chemotype clustering, R-group decomposition for SAR modeling, and matched molecular pair analysis (MMPA). The choice of scaffold representation determines whether two compounds are "the same series" -- a critical decision for medicinal chemistry workflows.
bio-shape-similarity
When your agent uses it
Performs 3D shape-based similarity searching using ROCS (OpenEye), USRCAT (ultra-fast), Open3DAlign (RDKit), ESPSim (electrostatic), and ShaEP with explicit handling of Tanimoto-Combo (shape + color), shape vs ECFP4 complementarity, conformer-ensemble searching, alignment optimization, and scaffold hopping. Use when searching for shape-mimicking compounds with different scaffolds, identifying bioisosteric replacements, prospective scaffold hopping, or expanding hit series beyond 2D similarity.
What it loads into context
Frontmatter
210 tokensAlways available to the agent
SKILL.md
3,851 tokensWhen the skill is activated
Reference documents
760 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Shape Method Taxonomy
- Decision Tree by Scenario
- Tanimoto-Combo Scoring (ROCS Standard)
- USRCAT (Ultra-Fast Shape Recognition + Atom Types)
- Open3DAlign (RDKit)
- Conformer-Ensemble Shape Searching
- ESP Similarity (Electrostatic)
- Shape vs ECFP4 Complementarity
- Per-Tool Failure Modes
- Reconciliation: Shape vs Pharmacophore
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/shape_search.py
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: RDKit 2024.09+ (Open3DAlign and USRCAT); official ShaEP syntax checked against ShaEP 1.4.2; ROCS/FastROCS/ROCS X are commercial OpenEye products.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Shape Similarity
Search for compounds with similar 3D shape (and optionally chemical features) to a query molecule. Shape-based screening complements 2D fingerprint search: it can find scaffold-hopped compounds that ECFP4 misses (different scaffolds with similar shape). ROCS (OpenEye) is the industry-standard commercial tool; Open3DAlign (RDKit), USRCAT (Schreyer & Blundell 2012), and ShaEP are open-source alternatives. Modern best practice combines shape with color (chemical-feature similarity) via Tanimoto-Combo: matches share both shape and pharmacophore feature distribution.
bio-similarity-searching
When your agent uses it
Performs molecular similarity searching using Tanimoto, Tversky, Dice, and cosine coefficients on bit/count fingerprints with explicit choice rules for symmetric vs asymmetric measures, scaffold-hopping vs lead-optimization regimes, activity-cliff diagnosis, and large-library nearest-neighbor methods (BulkTanimoto, MHFP6 LSH forest, USRCAT). Use when ranking compounds by structural resemblance to a query, clustering libraries, finding analogs, or diagnosing activity cliffs.
What it loads into context
Frontmatter
190 tokensAlways available to the agent
SKILL.md
4,609 tokensWhen the skill is activated
Reference documents
452 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Similarity Coefficient Taxonomy
- When to Use Which Coefficient
- Tanimoto Thresholds (Repository Starting Heuristics)
- Decision Tree by Scenario
- Tanimoto Similarity (single query, large library)
- Tversky for Asymmetric Substructure-Like Search
- Butina Clustering
- Diversity Selection (MaxMin)
- Maximum Common Substructure
- Activity Cliff Diagnosis
- Large-Library Nearest Neighbor (MHFP6 + LSH Forest)
- Per-Tool Failure Modes
- Reconciliation: Cliffs Across Methods
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/similarity_search.py
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: RDKit 2024.09+, scikit-learn 1.4+, mhfp 1.9+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Similarity Searching
Find structurally similar compounds and cluster libraries by similarity. The choice of similarity coefficient and fingerprint is **task-aware**: Tanimoto for symmetric similarity in lead optimization, Tversky for asymmetric "substructure-like" queries, Dice for higher sensitivity in low-similarity regimes, and MaxCommon Substructure (MCS) for scaffold-hopping. Tanimoto similarity above 0.7 is not a guarantee of activity preservation; activity cliffs (similar molecules with dissimilar activities) are common (Maggiora 2014).
bio-substructure-search
When your agent uses it
Searches molecular libraries for substructure matches using SMARTS patterns with explicit handling of recursive SMARTS, ring membership, aromaticity dialect, vector binding, atom map indices, and reactive/PAINS/REOS/Brenk filter catalogs. Use when filtering compounds by pharmacophore features, functional groups, scaffold matches, or screening for assay-interference / structural alerts.
What it loads into context
Frontmatter
168 tokensAlways available to the agent
SKILL.md
4,827 tokensWhen the skill is activated
Reference documents
455 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- SMARTS Grammar Essentials
- Common SMARTS Patterns
- Basic Substructure Match
- Recursive SMARTS for Context-Aware Patterns
- Structural-Alert Filter Catalogs
- When to Apply Each Filter
- PAINS Filter
- Reaction-Reactive Group Filter (custom)
- Library Filtering with Multiple Patterns
- Atom Map Indices in SMARTS
- Per-Tool Failure Modes
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/substructure_search.py
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: RDKit 2024.09+. SMARTS dialect follows Daylight specification with RDKit extensions.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show rdkit` then `help(rdkit.Chem.MolFromSmarts)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Substructure Search
Search molecular collections for structural patterns using SMARTS. The choice of SMARTS dialect, atom/bond matching mode, and structural-alert catalog determines whether the search is correctly capturing the intended chemistry. PAINS (Baell & Holloway 2010) is the most-cited but most-misunderstood filter -- it identifies patterns of assay interference, not "bad molecules". Knowing when to apply each catalog and how to interpret hits is essential.
bio-virtual-screening
When your agent uses it
Performs structure-based virtual screening using AutoDock Vina, SMINA, GNINA (CNN scoring), and DiffDock-L hybrid workflows with explicit choice rules across rigid vs flexible docking, cross-docking vs self-docking, binding-site detection (P2Rank, fpocket), receptor preparation (PDB2PQR, PROPKA), ligand preparation (meeko, OpenBabel), and ultralarge-library screening (ZINC22, Enamine REAL). Use when screening chemical libraries against a protein target to find candidate binders, ranking docking poses, or selecting a docking workflow for a specific scenario.
What it loads into context
Frontmatter
220 tokensAlways available to the agent
SKILL.md
6,184 tokensWhen the skill is activated
Reference documents
567 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Docking Tool Taxonomy
- Decision Tree by Scenario
- Receptor Preparation
- Ligand Preparation
- Binding Site Detection
- Vina Docking (Single Ligand)
- GNINA with CNN Scoring (modern default)
- Virtual Screening Pipeline (Hierarchical)
- Ultralarge Library Screening (ZINC22, Enamine REAL)
- Per-Tool Failure Modes
- Reconciliation: Vina vs GNINA Disagreement
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/virtual_screen.py
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: AutoDock Vina 1.2.5+, SMINA 2020-12+, GNINA 1.1+ for `rescore` (GNINA 1.3+ for the six-mode interface documented below), RDKit 2024.09+, meeko 0.5+, P2Rank 2.4+, ProDy 2.4+, pdb2pqr 3.6+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `vina --version`; `gnina --version`; `smina --version`
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Virtual Screening
Screen chemical libraries against protein targets via molecular docking. Vina is the de-facto default, SMINA adds flexibility (Vinardo scoring, custom scoring), and GNINA adds CNN-based pose scoring (Top-1 redock 58%->73% over Vina, cross-dock 27%->37%). Deep-learning docking (DiffDock-L, EquiBind, NeuralPLexer) competes in pose accuracy, but physical validity is method- and dataset-dependent; the workflow therefore combines ML pose sampling with classical scoring and explicit geometry checks. For ultralarge libraries (>1M), library preparation, hierarchical filtering, and HPC orchestration become the limiting steps.bio-chipseq-allele-specific-binding
When your agent uses it
Detects allele-specific transcription factor or histone modification binding from heterozygous-variant ChIP-seq using WASP (reference-bias filter; mandatory upstream), RASQUAL (joint QTL + bias-corrected testing), BaalChIP (Bayesian beta-binomial with copy-number-aware overdispersion), and AlleleSeq (personalized diploid genome). Handles imprinted-locus awareness, X-inactivation artifacts, cancer copy-number imbalance, and integration with downstream caQTL / bQTL mapping. Use when identifying variants with allelic effects on TF binding, fine-mapping causal regulatory variants, validating deep-learning variant predictions, or characterizing cis-acting regulatory effects.
What it loads into context
Frontmatter
239 tokensAlways available to the agent
SKILL.md
4,211 tokensWhen the skill is activated
Reference documents
1,820 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Method Taxonomy
- Universal First Step: WASP Reference-Bias Filter
- Workflow: BaalChIP (Recommended for Cancer / Copy-Number-Imbalanced Samples)
- Workflow: RASQUAL (Joint cis-QTL + ASB)
- Workflow: AlleleSeq (Personalized Diploid Genome)
- Three Universal Pitfalls
- Per-Tool Failure Modes
- Reconciliation
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/baalchip_workflow.R
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: WASP 0.3.4+, RASQUAL 1.1+, BaalChIP 1.30+ (Bioconductor), AlleleSeq 2.0+, samtools 1.19+, bcftools 1.19+, GATK 4.5+, pysam 0.22+.
# Allele-Specific Binding (ASB)
**"Identify variants that affect transcription factor or histone modification binding in cis"** -> Compare ChIP-seq read counts at the reference and alternate alleles of heterozygous variants in a single sample. Differential read counts (ALT vs REF at hetSNPs in peaks) reveal allele-specific binding.
- CLI (mandatory bias filter): WASP `mapping pipeline` to remove reference-allele mapping bias
- CLI (joint association): RASQUAL for cis-QTL + ASB (genotype VCF piped in via tabix)
- R (Bayesian beta-binomial): BaalChIP with copy-number-aware overdispersion
- CLI (personalized genome): AlleleSeq with phased diploid genome
- Statistical test: beta-binomial likelihood ratio or chi-squared on count tables
bio-chipseq-chip-deep-learning
When your agent uses it
Trains and applies base-resolution deep learning models on ChIP-seq / ChIP-nexus / CUT&RUN data. Uses BPNet (Avsec 2021 Nat Genet 53:354; soft motif syntax from ChIP-nexus), chromBPNet (Pampari A et al 2024 bioRxiv; bias-factorized base-resolution profiles), EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input, ~100 kb effective receptive field), DeepSEA (Zhou 2015; multi-task CNN), and JASPAR 2026 deep-learning collection (1259 BPNet ChIP models). Performs in silico mutagenesis for variant-effect prediction, DeepLIFT/Grad attribution, and TF-MoDISco motif discovery from attribution scores. Use when predicting variant effects on TF binding, discovering soft motif syntax / cooperativity, integrating ChIP-seq with sequence-only predictions, or applying precomputed JASPAR Deep Learning models to new variants.
What it loads into context
Frontmatter
304 tokensAlways available to the agent
SKILL.md
3,905 tokensWhen the skill is activated
Reference documents
1,898 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Model Taxonomy
- Decision Tree: Which Model
- In Silico Mutagenesis Workflow
- TF-MoDISco for Soft Motif Syntax
- Training chromBPNet from Scratch
- EnFormer Application
- Using JASPAR 2026 Deep Learning Models (Precomputed)
- Per-Tool Failure Modes
- Reconciliation with PWM-Based Analysis
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/chrombpnet_variant_effect.py
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: chrombpnet 0.1.7+, BPNet 0.0.23+, TF-MoDISco-lite 2.0+, EnFormer (Avsec lab Colab + DeepMind release), tensorflow 2.13+, pytorch 2.0+, JASPAR 2026 deep-learning collection (released 2025).
# Deep Learning for ChIP-seq
**"Predict TF binding from sequence and quantify variant effects on binding"** -> Train base-resolution convolutional / transformer models on ChIP-seq / ChIP-nexus / CUT&RUN profiles; predict reference and alternate-allele binding profiles for variants; extract motif syntax via TF-MoDISco from sequence-attribution scores.
- Python (modern): chrombpnet (bias-factorized; ATAC/DNase/ChIP)
- Python (canonical TF ChIP): BPNet (originally for ChIP-nexus; soft motif syntax)
- Python (long-range): EnFormer (Avsec 2021 Nat Methods 18:1196; 196 kb input window, ~100 kb effective receptive field; tissue-aggregated training)
- Python (multi-task): DeepSEA (Zhou 2015; older but still used)
- Precomputed: JASPAR 2026 Deep Learning collection (1259 BPNet ChIP models from ENCODE; 240 TFs)
bio-chipseq-qc
When your agent uses it
Assesses ChIP-seq quality across antibody specificity, fragmentation, enrichment, replicate concordance, and library complexity. Computes FRiP, NSC/RSC (phantompeakqualtools), library complexity (NRF/PBC1/PBC2), deepTools plotFingerprint (JS distance, AUC, synthetic JS), ChIPQC, IDR with ENCODE Nself/Nt rules, and detects hyper-ChIPable artifacts. Use when validating an antibody, diagnosing failed peak calls, deciding whether to proceed with downstream analysis, grading against ENCODE thresholds, or auditing replicate concordance.
What it loads into context
Frontmatter
223 tokensAlways available to the agent
SKILL.md
4,949 tokensWhen the skill is activated
Reference documents
2,372 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Antibody Problem is the Real Problem
- Fragment-Size Distribution is a Free Diagnostic
- QC Metric Battery with ENCODE Thresholds
- Hyper-ChIPable Region Detection
- Computing the Battery
- IDR and Replicate Consistency Rules
- ENCODE 3 vs ENCODE 4 Thresholds (unchanged for most QC)
- Per-Tool Failure Modes
- Reconciliation: When Metrics Disagree
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/calculate_frip.shscripts/chipseq_qc.pyscripts/run_idr.sh
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: deepTools 3.5+, phantompeakqualtools 1.2.2+, ChIPQC 1.42+, IDR 2.0.4+, samtools 1.19+, bedtools 2.31+, pysam 0.22+, pybedtools 0.9+, MACS2 2.2.9+, MACS3 3.0.4+.
Verify versions before relying on numerical thresholds — phantompeakqualtools has known R-version compatibility issues with R ≥ 4.0 (use kundajelab fork or pin to R 3.6).
# ChIP-seq Quality Control
**"Should I trust this ChIP-seq experiment?"** -> Validate antibody, fragmentation, enrichment, replicate concordance, library complexity, and absence of hyper-ChIPable artifacts before committing to downstream peak calling and differential analysis.
- CLI: `Rscript run_spp.R -c=chip.bam -out=cc.txt` (NSC/RSC), `plotFingerprint -b chip.bam input.bam` (enrichment shape), `idr --samples rep1.np rep2.np` (replicate IDR)
- R: ChIPQC package (Carroll & Stark; computes the full ENCODE metric battery)
- Python: pysam + pybedtools for custom FRiP and library-complexity metrics
bio-chipseq-visualization
When your agent uses it
Visualizes ChIP-seq data using deepTools (computeMatrix, plotHeatmap, plotProfile, bamCoverage, bamCompare), pyGenomeTracks (modern INI-driven track plots), Gviz (R browser-style), EnrichedHeatmap (ComplexHeatmap-based), ChIPseeker tag heatmaps, and IGV batch screenshots. Handles bigWig normalization choices (CPM, BPM, RPGC, spike-in scaled), bamCompare operations (log2 ratio, subtract) with SES scaling, k-means clustering of heatmaps for biological subgrouping, and spike-in-scaled tracks for global-shift experiments. Use when generating publication-quality ChIP-seq signal heatmaps, profile plots, genome-browser tracks, or comparing samples visually.
What it loads into context
Frontmatter
256 tokensAlways available to the agent
SKILL.md
3,866 tokensWhen the skill is activated
Reference documents
1,951 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- bigWig Normalization Decision Tree
- deepTools Workflow
- pyGenomeTracks (Modern Browser-Style Plotting)
- R: Gviz and EnrichedHeatmap
- ChIPseeker Tag Heatmap (R)
- IGV Batch Scripts
- Per-Tool Failure Modes
- Reconciliation: When Visualizations Disagree
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/deeptools_heatmap.sh
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: deepTools 3.5+, pyGenomeTracks 3.9+, Gviz 1.46+, EnrichedHeatmap 1.32+, ChIPseeker 1.38+, IGV 2.17+, samtools 1.19+, bedtools 2.31+.
# ChIP-seq Visualization
**"Visualize ChIP-seq signal around features of interest"** -> Generate normalized signal tracks (bigWig), heatmaps centered on TSS/peaks, average profile plots, and genome-browser views — with normalization that supports the biological claim (within-sample vs cross-sample vs spike-in scaled).
- CLI (production): deepTools `bamCoverage` -> `computeMatrix` -> `plotHeatmap` / `plotProfile`
- CLI (config-driven tracks): pyGenomeTracks (replaces Gviz for many use cases)
- R (publication): Gviz, EnrichedHeatmap, ChIPseeker tag heatmaps
- GUI: IGV with batch scripts for reproducible screenshots
The single most consequential choice is **bigWig normalization** — it determines whether visual comparison reflects biology. Get this right before generating any heatmap or browser view.bio-chipseq-chromatin-state-segmentation
When your agent uses it
Segments the genome into chromatin states from combinatorial histone modification and chromatin factor ChIP-seq data. Uses ChromHMM (multivariate HMM on binarized signal, v1.27), Segway (Dynamic Bayesian Network on continuous signal), EpiSegMix (flexible-distribution HMM with duration modeling, 2024), EpiLogos (multi-biosample visualization), IDEAS (cell-type-aware joint), and full-stack ChromHMM (Vu Ernst 2022) for cross-cell-type segmentations. Handles state-count selection (15 vs 18 vs 25 states), binarization choice, OverlapEnrichment / NeighborhoodEnrichment downstream analysis, and cross-biosample integration. Use when learning chromatin states from a histone mark panel, characterizing learned states by genomic feature enrichment, or comparing chromatin landscapes across cell types.
What it loads into context
Frontmatter
281 tokensAlways available to the agent
SKILL.md
4,276 tokensWhen the skill is activated
Reference documents
1,942 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Tool Taxonomy
- ChromHMM Workflow
- Choosing State Count
- Segway Workflow
- EpiLogos Visualization
- Full-Stack ChromHMM (Vu Ernst 2022)
- Per-Tool Failure Modes
- Reconciliation
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/chromhmm_pipeline.sh
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: ChromHMM 1.27+, Segway 3.0+, EpiSegMix 1.0+, EpiLogos (Meuleman lab), IDEAS 1.20+, samtools 1.19+, bedtools 2.31+. ChromHMM requires Java 8+; runs as `java -mx<MEMORY> -jar ChromHMM.jar <command>`.
# Chromatin State Segmentation
**"Integrate multiple histone modification ChIP-seq tracks into chromatin states"** -> Learn a small set of recurring combinatorial patterns of histone marks (active promoter, active enhancer, poised enhancer, polycomb-repressed, heterochromatic, transcribed, etc.) and segment the genome by which state each region belongs to. Output: per-state genomic intervals, state-by-mark emission matrix, and state-state transition matrix.
- CLI (canonical): ChromHMM `BinarizeBam` -> `LearnModel` -> `OverlapEnrichment` / `NeighborhoodEnrichment`
- CLI (continuous signal): Segway `train` -> `posterior` -> `annotate`
- CLI (flexible distributions): EpiSegMix (2024)
- Visualization across biosamples: EpiLogos (Meuleman lab)
- Cell-type-aware joint: IDEAS
bio-chipseq-cut-and-run-tag
When your agent uses it
Analyzes CUT&RUN (Skene Henikoff 2017) and CUT&Tag (Kaya-Okur 2019) chromatin profiling data. Handles SEACR vs MACS2 peak calling (with the btaf375 2025 benchmark guidance), pA-MNase vs pA-Tn5 vs pAG-Tn5 chimera differences, E. coli spike-in carryover normalization, IgG-only control logic (no input), characteristic fragment-size signatures (25-75 bp for CUT&Tag), and lower depth requirements (5M reads typical vs 25M for ChIP). Use when calling peaks from CUT&RUN/CUT&Tag, scaling by E. coli spike-in carryover, choosing SEACR norm mode, or comparing CUT&RUN/Tag results to traditional ChIP.
What it loads into context
Frontmatter
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SKILL.md
4,596 tokensWhen the skill is activated
Reference documents
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Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Protocol Variant Taxonomy
- Algorithmic Taxonomy
- SEACR Workflow (Canonical CUT&RUN/Tag Caller)
- E. coli Spike-In Carryover (Automatic Spike-In)
- QC Differences from Traditional ChIP
- Fragment Size as Diagnostic (Critical for CUT&Tag)
- Per-Tool Failure Modes
- Reconciliation: When CUT&RUN/Tag Disagrees with ChIP
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/cutandrun_pipeline.sh
Files
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## Version Compatibility
Reference examples tested with: SEACR 1.3+, MACS2 2.2.9+, MACS3 3.0.4+, samtools 1.19+, bowtie2 2.5+, bedtools 2.31+, deepTools 3.5+, GoPeaks 1.0+, LanceOtron (pip).
# CUT&RUN / CUT&Tag
**"Analyze CUT&RUN or CUT&Tag chromatin profiling data"** -> Use the lower-background, lower-input alternatives to traditional ChIP. CUT&RUN tethers MNase to an antibody via Protein A; CUT&Tag tethers Tn5 via Protein A/G. Both bypass cross-linking, fragmentation, and IP washes — producing 10-100× lower background, allowing 100-1000× lower cell input, and shifting the peak-calling problem from "find signal in noise" to "find signal in near-zero background."
- Aligner: bowtie2 (CUT&RUN/Tag standard) or bwa-mem; chromap optional
- Peak calling (CUT&RUN/Tag): SEACR (Meers 2019), MACS2 with `-f BAMPE --keep-dup all`, or both for consensus
- Spike-in: E. coli carryover from bacterially-produced pA-MNase/Tn5 (automatic, variable)
- Control: IgG-only (no input control; native chromatin has no meaningful "input")
CUT&RUN/CUT&Tag has different QC thresholds, different peak calling defaults, different spike-in protocols, and different antibody requirements than traditional ChIP. Treating it as ChIP fails silently.bio-chipseq-differential-binding
When your agent uses it
Identifies differentially bound ChIP-seq regions between conditions using DiffBind, csaw (sliding windows), DESeq2/edgeR/PyDESeq2 on count matrices, NormR (control-aware), or MAnorm2. Distinguishes three distinct normalization problems (composition bias, trended bias, global shifts) and matches each to its appropriate fix including spike-in scaling. Use when comparing ChIP-seq binding between experimental conditions, choosing normalization for global vs local changes, integrating spike-in data, or reconciling DiffBind/DESeq2 disagreement.
What it loads into context
Frontmatter
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SKILL.md
5,314 tokensWhen the skill is activated
Reference documents
1,861 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Three Distinct Normalization Problems
- Algorithmic Taxonomy
- Decision Tree: Choosing Normalization
- Spike-In Scaling Factor Calculation
- DiffBind Workflow (BAMs + Peaks)
- csaw Workflow (Windows-Based)
- DESeq2 from Count Matrix
- Per-Tool Failure Modes
- Reconciliation: When Methods Disagree
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/csaw_windows_diff.Rscripts/pydeseq2_from_counts.py
Files
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## Version Compatibility
Reference examples tested with: DiffBind 3.20+, DESeq2 1.42+, edgeR 4.0+, csaw 1.36+, PyDESeq2 0.5+, NormR 1.28+, MAnorm2 1.2+, ChIPseqSpikeInFree 1.6+.
DiffBind 3.0+ changed defaults: `summits=200` (was FALSE), `dba.normalize()` now required, blacklist filtering on by default, full library size normalization replaces reads-in-peaks. Always run `packageVersion('DiffBind')` and inspect `dba.normalize(obj, bRetrieve=TRUE)` to confirm what was applied.
# Differential ChIP-seq Binding
**"Compare protein-DNA binding between experimental conditions"** -> Identify regions where IP signal changes significantly, accounting for sequencing depth, composition bias, trended biases, and global shifts that confound naive normalization.
- R (BAM + peaks): `DiffBind::dba()` -> `dba.count()` -> `dba.normalize()` -> `dba.analyze()`
- R (count matrix): `DESeq2::DESeq()` or `edgeR::glmQLFTest()` on a peaks-by-samples matrix
- R (windows-based, global-shift-robust): `csaw::windowCounts()` -> `csaw::normFactors()` -> `edgeR::glmQLFTest()`
- R (control-aware): `normr::diffR(chip1.bam, chip2.bam, genome)` joint binomial mixturebio-chipseq-motif-analysis
When your agent uses it
Discovers de novo motifs and tests known motif enrichment in ChIP-seq, ATAC-seq, or other peak sequences using HOMER, MEME-ChIP (STREME, CentriMo, TOMTOM, FIMO), monaLisa, and AME. Handles background selection (GC-matched, dinucleotide-shuffled, Markov order-2, peak-flanks), motif databases (JASPAR 2024 CORE PWMs, JASPAR 2026 deep-learning collection, HOCOMOCO v12, HOMER built-in), centrally-enriched motif testing, and differential motif analysis. Use when identifying TF binding motifs in peaks, testing for known TF enrichment, scanning for motif instances, comparing motif content between conditions, or interpreting motifs from deep learning models.
What it loads into context
Frontmatter
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SKILL.md
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Reference documents
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The playbook
- Version Compatibility
- Tool Taxonomy
- Background Selection — The Biggest Source of Error
- Window Around Summit Matters
- HOMER Workflow
- MEME-ChIP Workflow
- monaLisa Workflow (R, Regression-Based)
- Per-Tool Failure Modes
- Reconciliation: When Motif Methods Disagree
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/motif_analysis.sh
Files
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## Version Compatibility
Reference examples tested with: HOMER 4.11+, MEME suite 5.5+ (STREME replaces DREME from 5.4+), monaLisa 1.10+, JASPAR 2024 CORE, HOCOMOCO v12, BioPython 1.83+, bedtools 2.31+.
DREME was removed from MEME suite 5.4+; use STREME instead. Some tutorials still reference DREME — verify the installed version via `meme --version`. JASPAR 2026 (released late 2025) integrates 1259 BPNet ChIP models in a Deep Learning collection; the CORE collection remains the standard PWM source.
# Motif Analysis on ChIP-seq Peaks
**"Find enriched DNA binding motifs in my ChIP-seq peaks"** -> Discover de novo motif patterns and test for known TF motif enrichment in peak sequences, with appropriate background to control for compositional and positional biases.
- CLI (HOMER, fast): `findMotifsGenome.pl peaks.bed hg38 outdir/ -size 200 -p 8`
- CLI (MEME-ChIP, comprehensive): `meme-chip -db JASPAR.meme peaks.fa`
- R (regression-based, selective enrichment): `monaLisa::calcBinnedMotifEnrR(seqs, bins, pwms)`
- CLI (deep-learning-derived motifs): TF-MoDISco on BPNet attribution scores (see chip-deep-learning)bio-chipseq-peak-annotation
When your agent uses it
Annotates ChIP-seq peaks to genomic features, nearest genes, ENCODE candidate cis-regulatory elements (cCREs), and regulatory domains. Uses ChIPseeker (R), HOMER annotatePeaks.pl (CLI), pyranges (Python), GREAT/rGREAT (regulatory domain gene-set enrichment), ChIP-Enrich (locus-length-adjusted), ENCODE SCREEN cCRE classification (PLS/pELS/dELS/CA-CTCF/CA-H3K4me3), and ENCODE-rE2G for cell-type-specific enhancer-gene linking. Handles nearest-TSS vs host-gene ambiguity, promoter window definition, and feature priority. Use when assigning genomic context to peaks, linking enhancer peaks to target genes, classifying peaks against ENCODE cCRE registry, or running gene-set enrichment on peak-associated genes.
What it loads into context
Frontmatter
270 tokensAlways available to the agent
SKILL.md
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Reference documents
1,985 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Choosing an Annotation Approach
- Nearest-TSS vs Host-Gene Convention
- Coordinate Systems and TSS
- ChIPseeker (R)
- HOMER annotatePeaks.pl (CLI)
- ENCODE cCRE Classification
- GREAT / rGREAT (Regulatory Domain Gene-Set Enrichment)
- ChIP-Enrich (Locus-Length-Adjusted Gene-Set Enrichment)
- ENCODE-rE2G (Modern Enhancer-Gene Linking)
- Per-Tool Failure Modes
- Reconciliation: When Methods Disagree
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/annotate_peaks.py
Files
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## Version Compatibility
Reference examples tested with: ChIPseeker 1.38+, GenomicFeatures 1.54+, rtracklayer 1.62+, HOMER 4.11+, rGREAT 2.4+, chipenrich 2.26+, pyranges 0.0.129+, pandas 2.2+.
ENCODE cCRE registry expanded to 2.37M human and 967k mouse elements (Moore JE et al 2026 Nature). SCREEN web app at screen.encodeproject.org provides browser access; ENCODE provides bed files for batch annotation.
# Peak Annotation
**"What genes and regulatory elements do my peaks correspond to?"** -> Assign each peak to a genomic feature (promoter, exon, intron, intergenic), its target gene (via nearest-TSS or host-gene), and where applicable an ENCODE cCRE class (PLS/pELS/dELS/CA-CTCF/CA-H3K4me3).
- R (gene-feature): `ChIPseeker::annotatePeak(peaks, TxDb=txdb)`
- CLI (gene-feature): `annotatePeaks.pl peaks.bed hg38 -gtf annotation.gtf`
- Python (custom): pyranges + pandas
- R (cCRE classification): intersect peaks with ENCODE cCRE BED from SCREENbio-chipseq-peak-calling
When your agent uses it
Calls ChIP-seq peaks with MACS3, MACS2, HOMER, or SPP across narrow (TF) and broad (histone) modes. Handles input control matching, fragment-size modeling vs --nomodel, effective genome size, ENCODE-style IDR vs naive overlap, hyper-ChIPable artifacts, and aligner-specific shifts. Use when calling peaks from ChIP-seq alignments, choosing between narrow vs broad mode for a histone mark, deciding model vs nomodel for low-depth data, applying ENCODE pseudoreplicate IDR, or reconciling MACS vs HOMER vs SPP results.
What it loads into context
Frontmatter
228 tokensAlways available to the agent
SKILL.md
5,633 tokensWhen the skill is activated
Reference documents
2,873 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Critical Pre-Call Validation
- Algorithmic Taxonomy
- Decision: Narrow vs Broad
- Decision: Model vs --nomodel
- Effective Genome Size — Often Wrong, Always Matters
- Hyper-ChIPable Regions Are a Persistent Artifact
- Pipeline Reference: ENCODE TF vs Histone
- Replicate Handling: IDR vs Naive Overlap
- ENCODE 3 vs ENCODE 4 Differences
- Per-Tool Failure Modes
- Reconciliation: When Callers Disagree
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/macs3_peak_calling.sh
Files
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## Version Compatibility
Reference examples tested with: MACS3 3.0.4+, MACS2 2.2.9+, HOMER 4.11+, SPP 1.16+, samtools 1.19+, bedtools 2.31+, IDR 2.0.4+.
Before running, verify versions: `<tool> --version` and `<tool> --help` to confirm flags. If a flag is missing, check the changelog — MACS2->MACS3 is API-compatible for `callpeak` but `predictd`, `bdgpeakcall`, and `hmmratac` differ.
# ChIP-seq Peak Calling
**"Identify protein-DNA binding sites from ChIP-seq alignments"** -> Detect statistically enriched genomic regions by comparing IP signal to input control (or genomic background), with peak shape (narrow/broad) determined by target biology (TF vs histone mark).
- CLI (ENCODE TF default): `macs2 callpeak -t chip.bam -c input.bam -f BAM -g hs -n sample --keep-dup all -p 1e-2`
- CLI (ENCODE histone default): same with `--broad --broad-cutoff 0.1` for H3K27me3, H3K9me3, H3K36me3
- CLI (alternative): `macs3 callpeak ...` (API-identical, active development), HOMER `findPeaks tags/ -style histone -i input_tags/`, SPP via phantompeakqualtools wrapper
bio-chipseq-spike-in-normalization
When your agent uses it
Normalizes ChIP-seq data using exogenous spike-in (ChIP-Rx with Drosophila chromatin per Orlando 2014 / Egan 2016; E. coli carryover for CUT&RUN/CUT&Tag). Distinguishes RRPM from Rx-Input scaling, integrates with DiffBind / DESeq2 / edgeR / csaw via sizeFactors and DiffBind library-size vectors, applies the Patel et al 2024 *Nat Biotechnol* failure-mode framework, and validates that normalization is applied at the read level (not peak counts). Use when global signal shifts are expected (HDACi, BETi, EZH2i, dosage, target knockdown), when ChIPseqSpikeInFree detects post-hoc shifts, or when validating internal-control regions before publication.
What it loads into context
Frontmatter
264 tokensAlways available to the agent
SKILL.md
4,747 tokensWhen the skill is activated
Reference documents
2,114 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- When Spike-In Is Required
- Spike-In Protocol Taxonomy
- Scaling Factor Calculation
- Workflow: Drosophila ChIP-Rx Spike-In
- Workflow: E. coli Spike-In (CUT&RUN/CUT&Tag Automatic)
- ChIPseqSpikeInFree: Post-Hoc Detection
- Internal-Control Sanity Check (Mandatory)
- Per-Tool Failure Modes
- Reconciliation
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/spikein_chiprx_diffbind.R
Files
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## Version Compatibility
Reference examples tested with: DiffBind 3.20+, DESeq2 1.42+, edgeR 4.0+, csaw 1.36+, ChIPseqSpikeInFree 1.6+, SpikChIP 1.0+, SpikeFlow (NAR Genom Bioinform 2024), samtools 1.19+, bowtie2 2.5+.
# ChIP-seq Spike-In Normalization
**"Account for global signal changes that defeat standard normalization"** -> Add exogenous reference chromatin (Drosophila for human/mouse ChIP-Rx; E. coli carryover for CUT&RUN/CUT&Tag) at fixed concentration BEFORE IP, derive scaling factors from spike-in read counts, and apply at the read or size-factor level (never to peak counts) to enable quantitative cross-condition comparison.
- CLI: align reads to combined target + spike genome; count spike reads via `samtools view -c`
- R (DiffBind integration): `dba.normalize(obj, spikein = TRUE)`
- R (DESeq2 / edgeR): `sizeFactors(dds) <- 1 / scale_factors` (note inverse)
- CLI (deepTools tracks): `bamCoverage --scaleFactor <derived>` (use alone; `--normalizeUsing` compounds with it)
- Wrapper: SpikeFlow (Snakemake; 2024) automates end-to-end
- Post-hoc detection: ChIPseqSpikeInFree (when no spike-in was added)bio-chipseq-super-enhancers
When your agent uses it
Identifies super-enhancers from H3K27ac, MED1, or BRD4 ChIP-seq using ROSE, ROSE2, LILY, HOMER -style super, and ENCODE dELS cross-referencing. Handles peak stitching parameters, ranking choices, hockey-stick inflection, marker choice (H3K27ac vs MED1/BRD4), and cross-condition comparison with spike-in normalization. Constructs core regulatory circuitry (Saint-Andre 2016) from SE-encoded TFs. Use when identifying cell-identity / cancer-associated regulatory domains, comparing super-enhancers between conditions, identifying master transcription factor networks, or predicting BET-inhibitor responsiveness.
What it loads into context
Frontmatter
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SKILL.md
4,435 tokensWhen the skill is activated
Reference documents
1,945 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Marker Choice: H3K27ac vs MED1 vs BRD4
- Algorithmic Taxonomy
- Decision Tree: SE Calling Workflow
- ROSE / ROSE2 Workflow
- Cross-Condition SE Comparison
- Core Regulatory Circuitry (Saint-André 2016)
- ENCODE dELS Cross-Reference
- Per-Tool Failure Modes
- Reconciliation: When SE Calls Disagree
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/analyze_super_enhancers.pyscripts/run_rose.sh
Files
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## Version Compatibility
Reference examples tested with: ROSE (stjude/ROSE, 2018+), ROSE2 (linlabbcm/rose2, 2021+), LILY (BoevaLab/LILY, 2020+), HOMER 4.11+, samtools 1.19+, bedtools 2.31+, GenomicRanges 1.54+.
The original Young-lab ROSE is Python 2; ROSE2 (linlabbcm/rose2) and the stjude/ROSE fork are the Python-3 implementations with the same algorithm. For hg38 data use stjude/ROSE (`python ROSE_main.py`, whose genomeDict includes HG38); rose2's released genomeDict covers only HG18/HG19/MM8/MM9/MM10/RN4/RN6, so `rose2 -g HG38` fails. LILY (Boeva 2017) is a refactored implementation with input-control background subtraction for low-quality H3K27ac data.
# Super-Enhancer Calling
**"Identify super-enhancers driving cell identity / cancer biology"** -> Stitch nearby active enhancer peaks (H3K27ac, MED1, or BRD4) within a stitching window, exclude proximal-promoter signal, rank by total signal, find the hockey-stick inflection point where signal sharply increases, and classify all stitched regions above the inflection as super-enhancers.
- CLI (stjude/ROSE, Python 3): `python ROSE_main.py -g HG38 -i peaks.gff -r h3k27ac.bam -c input.bam -s 12500 -t 2500 -o rose_out/`
- CLI (HOMER): `findPeaks tag_dir/ -style super -i input_tag_dir/`
- CLI (LILY): variant with input-control background subtraction
- R (custom hockey-stick): rank enhancers by signal, find tangent-line inflectionbio-clinical-biostatistics-adaptive-designs
When your agent uses it
Designs adaptive clinical trials including group-sequential (O'Brien-Fleming, Pocock, Lan-DeMets spending), sample-size re-estimation (blinded Friede-Kieser, unblinded Cui-Hung-Wang, Mehta-Pocock promising zone), seamless Phase 2/3 with treatment-arm selection, population enrichment, and response-adaptive randomisation. Covers FDA 2019 Final Adaptive Designs Guidance, FDA 2022 Master Protocols, and ICH E20 Step 2b/3 draft (June 2025, NOT final). Use when planning interim analyses, sample-size re-estimation, or master/platform-trial designs.
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Frontmatter
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SKILL.md
7,781 tokensWhen the skill is activated
Reference documents
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Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Regulatory Status -- The 2024-2026 Landscape
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Group-Sequential Designs
- Sample-Size Re-Estimation
- Combination Tests and CRP Principle
- Adaptive Enrichment
- Response-Adaptive Randomisation -- The Ethics Fight
- Bayesian Platform Trials
- Phase I Dose-Finding -- BOIN, mTPI, CRM
- Reconciliation: When Methods Disagree
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: R `rpact` 4.2+ (Wassmer/Brannath), `gsDesign` 3.6+ and `gsDesign2` 1.1+ (Anderson/Merck), `adaptr`, `simtrial`. Commercial: East/EastHorizon (Cytel), ADDPLAN (ICON), FACTS (Berry Consultants).
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name`
- Python adaptive packages are limited; R is the regulatory de facto standard
If code throws an error, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Adaptive Clinical Trial Designs
**"Design an adaptive trial"** -> Pre-specify a design with one or more interim adaptations (early stopping, sample-size re-estimation, treatment selection, population enrichment, randomisation ratio changes) that strongly controls Type-I error at the trial-wide level via combination tests or the Conditional Rejection Probability principle.
bio-clinical-biostatistics-bayesian-trials
When your agent uses it
Designs Bayesian clinical trials including Phase I dose-finding (BOIN, CRM, EWOC, mTPI-2), meta-analytic-predictive (MAP) priors with robust mixtures for external data borrowing, EXNEX for basket trials, hierarchical models for safety AE (Berry-Berry), Bayesian platform trials (I-SPY 2, GBM AGILE, REMAP-CAP), and posterior probability stopping rules. Covers FDA Bayesian Devices Guidance (2010), FDA Bayesian Methodology in Drugs Draft (January 2026), BOIN Fit-for-Purpose qualification (December 2021), and Project Optimus dose-optimisation. Use when designing dose-finding studies, platform trials, or sensitivity analyses with informative priors.
What it loads into context
Frontmatter
263 tokensAlways available to the agent
SKILL.md
8,298 tokensWhen the skill is activated
Reference documents
1,588 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Regulatory Status -- The 2024-2026 Bayesian Pivot
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Phase I Dose-Finding -- BOIN, CRM, mTPI-2
- MAP Priors and RBesT
- EXNEX for Basket Trials
- Bayesian Platform Trials
- Hierarchical Models for Safety Multiplicity (Berry-Berry 2004)
- Power Priors for Borrowing
- External Control Arms and Real-World Evidence (RWE)
- Spiegelhalter Skeptical/Enthusiastic Priors
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: R `RBesT` 1.7+ (Roche), `OncoBayes2` 0.8+ (Novartis), `BOIN` 2.7+, `dfcrm` 0.2-2+, `escalation` 0.1+, `trialr` 0.1.6+, `bayesDP`, `psborrow2` (FDA-supported), `rstan` / `cmdstanr`, `brms`. Legacy: `JAGS`, `WinBUGS`.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name`
- Confirmatory regulatory work: validate against pinned package versions in submission
If code throws an error, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Bayesian Clinical Trials
**"Design a Bayesian clinical trial"** -> Specify a prior, likelihood, and decision rule with frequentist operating characteristics demonstrated via simulation; for dose-finding use FDA-endorsed BOIN; for borrowing use robust MAP priors; for adaptive platforms use posterior probability of efficacy stopping with simulation-calibrated thresholds.
bio-clinical-biostatistics-categorical-tests
When your agent uses it
Tests associations between categorical variables in clinical data using chi-square, Fisher's exact, Boschloo, Cochran-Mantel-Haenszel, and modern McNemar variants with calibrated confidence intervals (Wilson, Newcombe, Miettinen-Nurminen). Use when analyzing categorical outcomes, paired binary endpoints, or testing treatment-outcome independence in confirmatory or exploratory clinical trials.
What it loads into context
Frontmatter
175 tokensAlways available to the agent
SKILL.md
6,428 tokensWhen the skill is activated
Reference documents
1,485 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Decision Tree by Experimental Scenario
- Chi-Square Test (Pearson, no continuity correction)
- Fisher's Exact -- and why Boschloo is usually better
- Cochran-Mantel-Haenszel (Stratified)
- McNemar's Test for Paired Binary Data
- Effect Sizes for Categorical Data
- Reconciliation: When Methods Disagree
- Confidence Intervals for Proportions and Differences
- Post-Hoc Pairwise Comparisons
- Quantitative Thresholds
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/categorical_tests_clinical.py
Files
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## Version Compatibility
Reference examples tested with: scipy 1.12+ (Boschloo and Barnard added in 1.7), statsmodels 0.14+, pingouin 0.5+, exact2x2 (R) 1.6+, pandas 2.1+, numpy 1.26+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R packages cited for reference (exact2x2, Exact, ratesci): use `packageVersion()` then `?function_name` to verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Categorical Association Tests for Clinical Data
**"Test association between categorical variables"** -> Determine whether treatment and a categorical clinical outcome are statistically independent (or that marginal proportions agree, for paired data) using a test calibrated to the design, the sample size, and the regulatory question.
bio-clinical-biostatistics-cdisc-data
When your agent uses it
Reads, validates, and prepares CDISC SDTM and ADaM clinical trial data for analysis. Covers SDTM domain joins (DM, AE, EX, VS, LB, DS), ADaM architecture (ADSL, BDS, OCCDS, ADTTE) with traceability, treatment-emergent AE conventions, baseline derivation, SUPPQUAL/NSV handling, Define-XML 2.1, and Pinnacle 21 / CORE validation. Use when working with clinical trial datasets in CDISC SDTM/ADaM format, preparing analysis-ready data, or validating for regulatory submission.
What it loads into context
Frontmatter
236 tokensAlways available to the agent
SKILL.md
7,958 tokensWhen the skill is activated
Reference documents
1,910 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Aggregation Strategy Taxonomy -- Choose the Right Question
- Decision Tree by Scenario
- SDTM vs ADaM -- The Regulatory Layer Cake
- SDTM Domain Overview
- Reading .xpt Files
- SAS XPT v5 vs Dataset-JSON -- The 2025-2026 Transition
- Joining Domains -- The Right Way
- ADaM Architecture -- The Postdoc Deep Dive
- Treatment-Emergent AE -- The Convention Variation
- Baseline Derivation
- SUPPQUAL and the NSV Transition
- Date Handling -- The Partial-Date Reality
- Validation -- Pinnacle 21 and CORE
- Population Flags
- Missing Data Considerations -- The Clinical Reasoning Layer
- Common Pitfalls
- Quantitative Thresholds and Conventions
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/cdisc_data_preparation.py
Files
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## Version Compatibility
Reference examples tested with: pyreadstat 1.2+, pandas 2.1+, numpy 1.26+. CDISC standards referenced: SDTM 2.0 / SDTMIG 3.4 (SDTM 3.0 / SDTMIG 4.0 in public review through April 2026); ADaMIG v1.3 (2021); OCCDS v1.1 (Nov 2021); BDS-for-TTE v1.0; Define-XML 2.1 (FDA-recommended for studies starting on/after March 15, 2023); Dataset-JSON v1.1 (Dec 2024; FDA Federal Register notice April 2025); Pinnacle 21 Community 4.0+; CORE (CDISC Open Rules Engine, 2021). Define-XML 2.1 FDA support began March 15, 2021 and is required for studies starting on/after March 15, 2023.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R packages cited (essential for ADaM derivation): admiral (Roche/openpharma), metacore, metatools, xportr
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# CDISC SDTM and ADaM Data Handling
**"Load clinical trial data"** -> Parse CDISC SDTM domain files; build or consume ADaM analysis-ready datasets; preserve subject-level and event-level structure; respect traceability and validation expectations for regulatory submission.
- Python: `pyreadstat.read_xport()`, `pd.read_sas()`, `pd.merge()`bio-clinical-biostatistics-effect-measures
When your agent uses it
Computes and interprets treatment effect measures (OR, RR, RD, HR, NNT) with calibrated confidence intervals (Wilson, Newcombe, Miettinen-Nurminen, MOVER, profile likelihood, Bender NNT) and reports marginal vs conditional estimands per FDA 2023 covariate adjustment guidance. Use when reporting treatment effects in confirmatory trials, comparing effect sizes across studies, or constructing forest plots.
What it loads into context
Frontmatter
187 tokensAlways available to the agent
SKILL.md
7,709 tokensWhen the skill is activated
Reference documents
1,448 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Crude Effect Measures from 2x2 Tables
- Modern Confidence Intervals -- the Postdoc Toolkit
- Number Needed to Treat (NNT) -- the Bender 2001 *CCT* 22:102 Way
- Marginal RD via G-Computation -- The FDA 2023 Recipe
- Marginal vs Conditional Effects -- The Core ICH E9(R1) Question
- Reconciliation: When Methods Disagree
- Modified Poisson Regression for Common Outcomes
- Forest Plots
- Quantitative Thresholds
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/effect_measures_clinical.py
Files
SKILL.mdcore28,352 bytesSKILL.mdcore28,352 bytesSKILL.mdcore28,352 bytesSKILL.mdcore28,352 bytesSKILL.mdcore28,352 bytesSKILL.mdcore28,352 bytesSKILL.mdcore28,352 bytesSKILL.mdcore28,352 bytesSKILL.mdcore28,352 bytesSKILL.mdcore28,352 bytesreferences/usage-guide.mddoc5,637 bytesscripts/effect_measures_clinical.pyruns3,809 bytes
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## Version Compatibility
Reference examples tested with: statsmodels 0.14+, numpy 1.26+, pandas 2.1+, matplotlib 3.8+, marginaleffects (Python) 0.0.13+ / (R) 0.20+. R packages cited: ratesci, exact2x2, marginaleffects, riskCommunicator, RobinCar.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Treatment Effect Measures for Clinical Trials
**"Compute treatment effect sizes"** -> Estimate the population-level treatment contrast (OR, RR, RD, HR, NNT) with a confidence interval calibrated to sample size and a clear declaration of whether the estimand is marginal or conditional under ICH E9(R1).
bio-clinical-biostatistics-logistic-regression
When your agent uses it
Performs logistic regression for clinical trial outcomes (binary, ordinal, multinomial) with marginal-vs-conditional estimand reporting per FDA 2023 covariate adjustment guidance, g-computation/standardisation for marginal effects, modified Poisson for RR, Brant test for proportional odds, Firth penalty for separation, and Hauck-Donner detection. Use when modeling binary or ordinal endpoints in confirmatory or exploratory clinical trials.
What it loads into context
Frontmatter
189 tokensAlways available to the agent
SKILL.md
7,410 tokensWhen the skill is activated
Reference documents
1,568 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Standard Workflow
- Marginal vs Conditional Estimand -- The FDA 2023 Pivot
- Modified Poisson for Common Outcomes -- Direct RR
- Proportional Odds and Brant Test
- Separation and Firth Penalty
- Hauck-Donner Effect Detection
- Reconciliation: When Methods Disagree
- Per-Method Failure Modes
- Model Diagnostics
- Quantitative Thresholds
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/logistic_regression_clinical.py
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: statsmodels 0.14+, scipy 1.12+, numpy 1.26+, pandas 2.1+, firthmodels 0.3+, marginaleffects 0.0.13+ (Python) / 0.20+ (R). R packages cited: RobinCar, marginaleffects, brant, MASS, VGAM.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Logistic Regression for Clinical Outcomes
**"Model clinical outcomes with logistic regression"** -> Estimate the marginal or conditional treatment effect on a binary or ordinal endpoint using a model that respects randomisation stratification, declares its estimand, and survives covariate misspecification.
bio-clinical-biostatistics-missing-data
When your agent uses it
Implements missing-data sensitivity analyses for confirmatory clinical trials including MMRM under MAR (with Kenward-Roger correction), reference-based multiple imputation (J2R, CR, CIR, LMCF per Carpenter-Roger 2013), Permutt delta-adjustment / tipping-point analysis, pattern-mixture identifying restrictions (CCMV, NCMV, ACMV), and the Cro vs Bartlett variance debate. Use when handling missing primary or secondary endpoint data in regulatory submissions following NRC 2010 and ICH E9(R1).
What it loads into context
Frontmatter
209 tokensAlways available to the agent
SKILL.md
8,248 tokensWhen the skill is activated
Reference documents
1,369 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Foundation -- NRC 2010 and ICH E9(R1)
- Missing-Data Mechanisms
- Algorithmic Taxonomy
- Decision Tree by Scenario
- MMRM Under MAR -- The FDA-Preferred Continuous Analysis
- Reference-Based Multiple Imputation -- The rbmi Framework
- The Variance Debate -- Cro vs Bartlett
- Permutt Tipping-Point Analysis
- Pattern-Mixture and Selection Models
- Multiple Imputation in Python -- The sklearn Caveats
- Decisive Regulatory Cases
- Per-Method Failure Modes
- Reconciliation: When Methods Disagree
- Quantitative Thresholds
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/missing_data_sensitivity.py
Files
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## Version Compatibility
Reference examples tested with: R `mmrm` 0.3+ (Roche/openpharma), R `rbmi` 1.5+ (Roche/Bayer via insightsengineering), R `mice` 3.16+, R `mitools` 2.4+, Python `sklearn` 1.4+, `statsmodels` 0.14+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name`
- Python: `pip show <package>` then `help(module.function)`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Missing Data Sensitivity for Confirmatory Trials
**"Handle missing data in a confirmatory clinical trial"** -> Pre-specify the missing-data assumption per ICH E9(R1); execute the primary analysis under the chosen assumption (typically MAR via MMRM or MI); run clinically-articulable MNAR sensitivity analyses (reference-based MI per Carpenter-Roger 2013); report the tipping delta that would overturn the conclusion (Permutt 2016).
bio-clinical-biostatistics-multiplicity-graphical
When your agent uses it
Implements multiplicity control for confirmatory clinical trials using graphical procedures (Bretz-Maurer-Hommel), gatekeeping (parallel, serial, mixed), Hochberg/Hommel/Holm with PRDS, and the closed-testing principle (Marcus-Peritz-Gabriel; Goeman 2021 admissibility). Covers FDA Multiple Endpoints Final Guidance (October 2022), graphical procedures via R gMCP, primary + key-secondary + subgroup hierarchies, and FWER vs FDR distinction. Use when designing the multiplicity strategy for confirmatory trials with multiple primary or key secondary endpoints.
What it loads into context
Frontmatter
237 tokensAlways available to the agent
SKILL.md
6,280 tokensWhen the skill is activated
Reference documents
1,225 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Foundational Theorem -- Closed Testing Is Necessary
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Bretz-Maurer Graphical Procedures -- The Modern Standard
- Gatekeeping Procedures
- Hochberg vs Hommel vs Holm
- FDA Multiple Endpoints Final Guidance (October 2022)
- The "Almighty Primary Endpoint" Critique
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: R `gMCP` 0.8.16+, `graphicalMCP` 0.2+, `gatekeeping`, `multcomp`, `multxpert`; Python `statsmodels` 0.14+ for basic FDR/FWER methods.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name`
- Python: `pip show <package>` then `help(module.function)`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Multiplicity Control for Confirmatory Trials
**"Design the multiplicity strategy for my trial"** -> Specify a closed-testing procedure (graphical, gatekeeping, hierarchical, or step-down Bonferroni-Holm) that controls family-wise error rate at the trial-wide level across primary endpoints, key secondary endpoints, and subgroup analyses, with provable strong FWER control.
bio-clinical-biostatistics-power-sample-size
When your agent uses it
Computes sample size and power for clinical trials including continuous, binary, and time-to-event endpoints; superiority, non-inferiority, and equivalence designs; FDA 2016 non-inferiority margin selection with M1/M2 framework; Schoenfeld 1981 and Lakatos 1988 for survival; Schuirmann TOST and 80-125% bioequivalence; minimum clinically important difference (MCID) vs δ distinction. Use when justifying trial size in protocol or SAP per CONSORT 2025 item 16a.
What it loads into context
Frontmatter
223 tokensAlways available to the agent
SKILL.md
8,560 tokensWhen the skill is activated
Reference documents
1,674 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Foundational Distinction -- δ vs MCID
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Continuous Outcomes -- Two-Sample t-Test
- Binary Outcomes -- The Continuity Correction Debate
- Survival (Time-to-Event)
- Non-Inferiority Designs -- The FDA 2016 Framework
- Equivalence Designs and Bioequivalence
- Crossover Designs -- Bioequivalence and Repeated-Measures
- MCID -- Anchor-Based vs Distribution-Based
- Multiple Endpoints -- See multiplicity-graphical
- Reconciliation: When Methods Disagree
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/power_sample_size.py
Files
SKILL.mdcore31,998 bytesSKILL.mdcore31,998 bytesSKILL.mdcore31,998 bytesSKILL.mdcore31,998 bytesSKILL.mdcore31,998 bytesSKILL.mdcore31,998 bytesSKILL.mdcore31,998 bytesSKILL.mdcore31,998 bytesreferences/usage-guide.mddoc6,465 bytesscripts/power_sample_size.pyruns9,347 bytes
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## Version Compatibility
Reference examples tested with: statsmodels 0.14+, scipy 1.12+, numpy 1.26+, pandas 2.1+. R packages cited: pwr, gsDesign (Anderson/Merck), gsDesign2, rpact (Wassmer/Brannath), presize, npsurvSS, nph, simtrial.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Power and Sample Size for Clinical Trials
**"Justify the trial's sample size"** -> Compute the n needed to detect a pre-specified alternative δ with power 1-β at significance α, accounting for endpoint distribution, design (superiority/NI/equivalence), expected dropout, multiplicity, and stratification — and distinguish δ (the effect the trial is powered to detect) from MCID (the clinically meaningful difference).
bio-clinical-biostatistics-subgroup-analysis
When your agent uses it
Performs subgroup and heterogeneous treatment effect (HTE) analyses for clinical trials. Covers Mantel-Haenszel pooling, Breslow-Day, interaction tests in regression, RERI for additive interaction, modern data-adaptive HTE methods (STEPP, SIDES, causal forests, X/R-learners), Bayesian shrinkage (Dixon-Simon, MAP, EXNEX), graphical multiplicity (Bretz-Maurer), and credibility frameworks (Sun BMJ, EMA 2019). Use when analyzing treatment effects across patient subgroups for regulatory submissions or precision-medicine claims.
What it loads into context
Frontmatter
220 tokensAlways available to the agent
SKILL.md
8,811 tokensWhen the skill is activated
Reference documents
1,663 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Senn Foundation -- Why Most Subgroup Claims Are Wrong
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Mantel-Haenszel and Stratified Analysis
- Interaction Terms in Regression -- The Correct Way
- RERI for Additive Interaction
- Modern Data-Adaptive HTE Methods
- Bayesian Shrinkage -- The Postdoc Argument
- Multiplicity for Subgroup Analyses
- Pre-Specified vs Post-Hoc and EMA 2019
- Quantitative vs Qualitative Interaction
- The Winner's Curse and Sun et al 2012
- Reconciliation: When Methods Disagree
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/subgroup_analysis_clinical.py
Files
SKILL.mdcore35,552 bytesSKILL.mdcore35,552 bytesSKILL.mdcore35,552 bytesSKILL.mdcore35,552 bytesSKILL.mdcore35,552 bytesSKILL.mdcore35,552 bytesSKILL.mdcore35,552 bytesSKILL.mdcore35,552 bytesSKILL.mdcore35,552 bytesSKILL.mdcore35,552 bytesreferences/usage-guide.mddoc7,022 bytesscripts/subgroup_analysis_clinical.pyruns3,706 bytes
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## Version Compatibility
Reference examples tested with: statsmodels 0.14+, scipy 1.12+, numpy 1.26+, pandas 2.1+, matplotlib 3.8+, scikit-learn 1.4+. R packages cited: grf, policytree, causalToolbox, personalized, SIDES, stepp, gMCP, partykit, RBesT, brms.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Subgroup Analysis and Heterogeneous Treatment Effects
**"Analyze treatment effects across subgroups"** -> Test whether treatment effects differ across pre-specified or data-discovered subgroups using interaction tests, stratified estimators, modern data-adaptive HTE methods, or Bayesian shrinkage -- with explicit declaration of confirmatory vs exploratory intent and credibility assessment.
bio-clinical-biostatistics-survival-analysis
When your agent uses it
Performs time-to-event analysis for clinical trials including Cox proportional hazards regression with PH diagnostics, restricted mean survival time (RMST) under non-PH, competing risks via Fine-Gray vs cause-specific Cox, weighted log-rank and MaxCombo for non-proportional hazards, recurrent events (Andersen-Gill, PWP, WLW), and interval-censored data. Use when analyzing time-to-event endpoints (OS, PFS, DOR, TTR, TTNT) in oncology or other clinical trials.
What it loads into context
Frontmatter
205 tokensAlways available to the agent
SKILL.md
9,585 tokensWhen the skill is activated
Reference documents
1,579 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- PH Almost Never Holds
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Cox PH Diagnostics -- The Therneau-Grambsch Test (and Its Pitfalls)
- Restricted Mean Survival Time (RMST) -- The Modern Alternative
- Competing Risks -- The Andersen-Keiding Framework
- Log-Rank Variants and MaxCombo
- Interval Censoring -- When Standard Right-Censoring Is Wrong
- Recurrent Events -- AG vs PWP vs WLW
- Oncology PFS/OS Estimands -- The 2024 Reality
- ADaM ADTTE -- The CNSR Convention Trap
- Per-Method Failure Modes
- Reconciliation: When Methods Disagree
- Quantitative Thresholds
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/survival_clinical.py
Files
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## Version Compatibility
Reference examples tested with: lifelines 0.27+, scikit-survival 0.21+, statsmodels 0.14+, pandas 2.1+, numpy 1.26+. R packages cited (still the SOTA for survival): survival 3.8+, survRM2, cmprsk, riskRegression, mstate, flexsurv, icenReg, rpsftm.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Time-to-Event Analysis for Clinical Trials
**"Analyze time-to-event endpoint"** -> Estimate a hazard, survival probability, cumulative incidence, or restricted mean time using a method calibrated to (a) whether proportional hazards holds, (b) whether competing events exist, (c) whether censoring is informative, and (d) which estimand the trial targets under ICH E9(R1).
bio-clinical-biostatistics-trial-reporting
When your agent uses it
Prepares statistical reports for clinical trials following CONSORT 2025, SPIRIT 2025, ICH E9(R1) estimands, and FDA 2023 covariate adjustment guidance. Covers Table 1 generation, analysis populations (ITT/FAS/PP/Safety), the 5 ICH E9(R1) intercurrent-event strategies, MMRM under MAR (mmrm), reference-based MI (rbmi J2R/CR/CIR), Permutt tipping-point sensitivity, and Rubin's-rules vs frequentist variance debate. Use when preparing regulatory submissions, defining estimands, or implementing missing-data sensitivity analyses.
What it loads into context
Frontmatter
232 tokensAlways available to the agent
SKILL.md
9,464 tokensWhen the skill is activated
Reference documents
2,032 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Methodological Shift -- The Estimand Comes First
- The Five Intercurrent-Event Strategies
- Decision Tree for Estimand Selection
- MMRM -- The FDA-Favoured MAR Analysis
- Reference-Based Multiple Imputation -- The rbmi Framework
- The Variance Debate -- Cro/Carpenter vs Bartlett/Wolbers
- Permutt Tipping-Point Analysis -- The Analyst as Adversary
- Decisive Regulatory Cases -- The 2020-2025 Casebook
- Table 1 -- Baseline Characteristics
- Analysis Populations -- ITT vs FAS vs PP vs Safety
- Missing Data Mechanisms -- The Practical Framework
- Standard MMRM-MAR Primary Analysis Code
- Multiple Imputation in Python (sklearn)
- Co-Primary Endpoints and Multiplicity
- Reconciliation: When Methods Disagree
- CONSORT 2025 (April 2025) -- What Changed
- CONSORT Flow Diagram
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/trial_reporting_clinical.py
Files
SKILL.mdcore38,011 bytesSKILL.mdcore38,011 bytesSKILL.mdcore38,011 bytesSKILL.mdcore38,011 bytesSKILL.mdcore38,011 bytesSKILL.mdcore38,011 bytesSKILL.mdcore38,011 bytesreferences/usage-guide.mddoc7,938 bytesscripts/trial_reporting_clinical.pyruns4,365 bytes
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## Version Compatibility
Reference examples tested with: tableone 0.9+, statsmodels 0.14+, scikit-learn 1.4+, pandas 2.1+, numpy 1.26+. R packages cited (essential for current regulatory work): mmrm 0.3+ (Roche/openpharma), rbmi 1.5+ (Roche/Bayer via insightsengineering), gMCP, RBesT.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Trial Reporting Under CONSORT 2025 + ICH E9(R1)
**"Prepare a clinical trial statistical report"** -> Define the estimand explicitly per ICH E9(R1); execute a covariate-adjusted primary analysis targeting the right summary measure; pre-specify the missing-data strategy and run regulatory-grade sensitivity analyses; structure the output per CONSORT 2025 and the new SPIRIT 2025 alignment.
bio-clinical-databases-acmg-classification
When your agent uses it
Applies ACMG/AMP 2015 framework with ClinGen SVI specifications, Tavtigian 2018/2020 Bayesian point system, Abou Tayoun 2018 PVS1 decision tree, Pejaver 2022 and Bergquist 2025 calibrated PP3/BP4 thresholds for REVEL/BayesDel/AlphaMissense, Brnich 2020 PS3/BS3 OddsPath, Walker 2023 SpliceAI splicing framework, and AMP/ASCO/CAP 2017 tumor tiers. Use when classifying germline variants P / LP / VUS / LB / B, applying VCEP-specific CSpec rules, computing Whiffin BS1, or assigning cancer Tier I-IV per Li 2017.
What it loads into context
Frontmatter
255 tokensAlways available to the agent
SKILL.md
8,064 tokensWhen the skill is activated
Reference documents
1,791 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Tavtigian Bayesian Point System: The Engine Inside All Modern Classifiers
- PVS1 Decision Tree (Abou Tayoun 2018 *Hum Mutat* 39:1517)
- Pejaver 2022 PP3/BP4 Calibrated Thresholds (the load-bearing 2024+ calibration)
- PM2_Supporting (ClinGen SVI 2020)
- PS3/BS3 Functional Evidence (Brnich 2020 *Genome Med* 12:3)
- ClinGen SVI Splicing Subgroup 2023 (Walker *AJHG* 110:1046)
- BS1 / BA1 (Whiffin Max-Credible-AF)
- ClinGen VCEP CSpec Hierarchy
- Cancer Somatic Framework (Li 2017 *J Mol Diagn* 19:4)
- Decision Tree by Variant Type
- Standard Workflow: ACMG Classification
- Per-Operation Failure Modes
- Reconciliation: When Tools Disagree
- Quantitative Thresholds and Conventions
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/acmg_classify.py
Files
SKILL.mdcore28,135 bytesSKILL.mdcore28,135 bytesSKILL.mdcore28,135 bytesSKILL.mdcore28,135 bytesreferences/usage-guide.mddoc6,303 bytesscripts/acmg_classify.pyruns11,240 bytes
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## Version Compatibility
Reference examples tested with: requests 2.31+, pandas 2.2+, AutoPVS1 (Xiang 2020), InterVar 2.2+, GeneBe 1.0+ (Stawiński 2024 *Clin Genet*). ACMG/AMP Bayesian point system is Tavtigian 2018 *Genet Med* / 2020 *Hum Mutat*. Pejaver 2022 *AJHG* PP3/BP4 calibrated thresholds. ClinGen Splicing Subgroup 2023 (Walker *AJHG*). v3.2 ACMG SF list (Miller 2023). The ACMG 2.0 framework is in development as of May 2026; not yet published.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. VCEP-specific CSpec rules override default ACMG application; the authoritative directory is `https://cspec.genome.network/cspec/ui/svi/all`.
# ACMG/AMP Variant Classification Framework
**'Classify this variant per ACMG/AMP'** -> Apply 28-criterion framework using Tavtigian point system; gate on ClinGen SVI specifications and VCEP-specific overrides; assign P / LP / VUS / LB / B classification with evidence trail.
bio-clinical-databases-clinvar-lookup
When your agent uses it
Queries ClinVar for variant pathogenicity classifications, ClinGen VCEP curations, and somatic-vs-germline interpretations via REST API, weekly VCF, or bulk XML. Use when determining clinical significance, triangulating conflicting interpretations, or aggregating evidence against the ACMG/AMP framework with ClinGen SVI specifications.
What it loads into context
Frontmatter
168 tokensAlways available to the agent
SKILL.md
5,815 tokensWhen the skill is activated
Reference documents
1,438 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Identifier Hierarchy (VCV / SCV / RCV): Get This Wrong and Everything Downstream Breaks
- Star Ratings and the Override Hierarchy
- ClinGen Variant Curation Expert Panels (VCEPs)
- ACMG/AMP, ClinGen SVI Specifications, and the Bayesian Point System
- Conflicting Interpretations and Conflict Resolution
- Decision Tree by Query Scenario
- REST API Query (E-utilities)
- Local VCF Query (Weekly Snapshot)
- ClinGen Allele Registry (CA IDs): The Real Cross-Database Anchor
- Per-Operation Failure Modes
- Reconciliation: When Sources Disagree
- Quantitative Thresholds and Operational Conventions
- ClinVar Somatic vs Germline: 2024 Tripartite
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/clinvar_query.py
Files
SKILL.mdcore22,841 bytesSKILL.mdcore22,841 bytesSKILL.mdcore22,841 bytesSKILL.mdcore22,841 bytesSKILL.mdcore22,841 bytesSKILL.mdcore22,841 bytesSKILL.mdcore22,841 bytesSKILL.mdcore22,841 bytesreferences/usage-guide.mddoc5,724 bytesscripts/clinvar_query.pyruns6,825 bytes
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## Version Compatibility
Reference examples tested with: requests 2.31+, cyvcf2 0.30+, pandas 2.2+, bcftools 1.19+, Entrez Direct 21.0+, lxml 5.0+ (for v2 XML schema).
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. ClinVar XML schema v2 (rolled out in 2024) replaces `<ClinVarSet>` with `<VariationArchive>` as the top-level anchor; XSLT or parsers targeting the legacy element silently emit zero records.
# ClinVar Lookup and Clinical-Significance Triangulation
**'Look up the clinical significance of this variant'** -> Retrieve ClinVar VCV-level aggregate, SCV-level submissions, ClinGen Variant Curation Expert Panel (VCEP) overrides, and conflict-resolution status.
bio-clinical-databases-dbsnp-queries
When your agent uses it
Resolves rsIDs, navigates RsMergeArch/SNPHistory merge chains, and converts between rsID, SPDI, HGVS, and VCF representations using the dbSNP Build 156 JSON architecture. Use when normalizing variant identifiers, joining variant databases by cluster ID, or tracking deprecated rsIDs through historical merges.
What it loads into context
Frontmatter
169 tokensAlways available to the agent
SKILL.md
5,467 tokensWhen the skill is activated
Reference documents
1,318 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- rsID Is a Cluster Identifier, Not a Variant Identifier
- Build 156 Schema Overhaul: What Changed
- RsMergeArch: The Multi-Hop Merge Footgun
- SPDI: The Canonical Variant Representation
- Decision Tree by Query Scenario
- Single rsID Resolution
- Multi-Hop Merge Resolution
- SPDI <-> HGVS <-> VCF Conversion
- ALFA Frequencies vs gnomAD
- Per-Operation Failure Modes
- Reconciliation: When Sources Disagree
- Quantitative Thresholds and Conventions
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/dbsnp_lookup.py
Files
SKILL.mdcore20,885 bytesSKILL.mdcore20,885 bytesSKILL.mdcore20,885 bytesSKILL.mdcore20,885 bytesSKILL.mdcore20,885 bytesSKILL.mdcore20,885 bytesreferences/usage-guide.mddoc4,969 bytesscripts/dbsnp_lookup.pyruns7,894 bytes
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## Version Compatibility
Reference examples tested with: myvariant 1.0+, requests 2.31+, biopython 1.83+, Entrez Direct 21.0+. dbSNP Build 156 (September 2022) is the current schema; Build 151 (2017) was the last with relational SQL dumps. Builds 152-155 dual-released JSON+SQL; 156+ is JSON-only.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. The Variation Services REST API uses path-based versioning (`/variation/v0/`); E-utilities `db=snp` returns thin legacy summaries missing build-156 schema fields.
# dbSNP Queries and rsID Normalization
**'Look up this rsID / normalize variant representations'** -> Resolve rsIDs through merge chains, compute canonical SPDI, and convert between rsID, HGVS-g, HGVS-c, and VCF allele representations.
bio-clinical-databases-gnomad-frequencies
When your agent uses it
Queries gnomAD v4 (807k samples), v3, v2.1.1, and constraint metrics with grpmax FAF95, bottleneck-group exclusion, LOEUF interpretation, SV/CNV/mtDNA catalogs, and Whiffin max-credible-AF framework. Use when filtering rare variants, applying ACMG BS1/BA1, ranking genes by LoF intolerance, or selecting between v2 (GRCh37 + chrX/Y constraint) and v4 (GRCh38 + 807k samples).
What it loads into context
Frontmatter
212 tokensAlways available to the agent
SKILL.md
7,014 tokensWhen the skill is activated
Reference documents
1,551 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- v2.1.1 / v3.1.2 / v4.x: When to Use Which
- v4 Ancestry Groups: popmax -> grpmax Terminology
- Filtering Allele Frequency (FAF95): The ACMG-Grade AF
- Constraint Metrics: pLI, LOEUF, missense Z
- Subsets: non_cancer, non_neuro, controls
- SV Catalog and CNV
- mtDNA (Laricchia 2022 *Genome Res* 32:569)
- VEP Version Pinning
- Decision Tree by Query Scenario
- Single Variant Query (GraphQL)
- ACMG BS1/BA1 Application
- Gene-Level Constraint (LOEUF)
- Bulk Query via Hail (cohort-scale)
- Per-Operation Failure Modes
- Reconciliation: When Sources Disagree
- Quantitative Thresholds and Conventions
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/gnomad_query.py
Files
SKILL.mdcore25,050 bytesSKILL.mdcore25,050 bytesSKILL.mdcore25,050 bytesSKILL.mdcore25,050 bytesSKILL.mdcore25,050 bytesSKILL.mdcore25,050 bytesreferences/usage-guide.mddoc5,390 bytesscripts/gnomad_query.pyruns7,753 bytes
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## Version Compatibility
Reference examples tested with: requests 2.31+, hail 0.2.130+, pandas 2.2+, myvariant 1.0+. Current gnomAD release is **v4.1 (May 2024)**; v4.1 fixed the v4.0 AN under-counting issue that inflated rare-variant AF estimates by 5-10%.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- Hail: `hl.version()`; pin to >=0.2.130 for v4 schema
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. The gnomAD browser GraphQL API at `https://gnomad.broadinstitute.org/api` is the supported public endpoint; Hail Tables on Google Cloud Storage at `gs://gcp-public-data--gnomad/` are the supported bulk access.
# gnomAD Frequency Queries and Constraint
**'How rare is this variant in the general population?'** -> Pull allele frequency, grpmax FAF95 (the ACMG-grade frequency), LOEUF gene-level constraint, structural variant catalog, mtDNA frequencies, and the appropriate dataset version per use case.
bio-clinical-databases-hla-typing
When your agent uses it
Calls HLA class I and class II alleles at 2/4/6/8-field resolution from WGS/WES/RNA-seq/long-read data using OptiType, HLA-LA, T1K, Polysolver, HLA-HD, arcasHLA, StarPhase, or HIBAG imputation. Use when typing for HSCT, solid-organ transplant, neoantigen prediction, PGx screening (B*57:01, B*15:02, etc.), or disease-association studies, with reconciliation across tools and IPD-IMGT/HLA version mismatch handling.
What it loads into context
Frontmatter
230 tokensAlways available to the agent
SKILL.md
7,685 tokensWhen the skill is activated
Reference documents
1,995 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Resolution Levels and What Each Application Requires
- G-Groups vs P-Groups: Routinely Confused
- DRB1 + DRB3/4/5 Linkage: The Mandatory Sanity Check
- Algorithmic Taxonomy: Short-Read Tools
- Long-Read and Ultra-High-Resolution
- SNP-Based HLA Imputation: The Ancestry Footgun
- Decision Tree by Scenario
- HLA and Pharmacogenomics
- Standard Workflow: T1K on WGS/WES
- OptiType for Class I (TCGA-Compatible)
- HLA-LA for Class II (PRG-Based)
- arcasHLA for RNA-seq
- SNP-Array Imputation (HIBAG): For GWAS Cohorts
- Per-Operation Failure Modes
- Reconciliation: When Tools Disagree
- Quantitative Thresholds and Conventions
- CIWD v3.0.0 Ambiguity Catalogue
- TCE3 Core vs Non-Core (Arrieta-Bolaños 2022/2024 *Blood*)
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/optitype_workflow.shscripts/t1k_workflow.sh
Files
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## Version Compatibility
Reference examples tested with: OptiType 1.3.5, HLA-LA 1.0.4, T1K 1.0.6 (Song 2023), Polysolver 4.0, HLA-HD 1.7.1, arcasHLA 0.6.0, StarPhase 1.0+ (PacBio), HIBAG 1.40+, samtools 1.19+, bwa-mem 0.7.17+. IPD-IMGT/HLA database release frequency is quarterly; tools must be re-bundled with the current release to capture new alleles (~38,000 alleles at Jan 2024; ~43,000+ by Jul 2025).
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. Tool reference-bundle vintage matters more than algorithm choice for non-European cohorts; a 2022-bundled HLA-LA will silently miss thousands of post-2022 alleles dominant in African and South Asian ancestry.
# HLA Typing for Clinical Applications
**'Determine HLA genotype for HSCT / neoantigen prediction / PGx screening'** -> Call HLA class I (A, B, C) and class II (DRB1, DRB3/4/5, DQA1, DQB1, DPA1, DPB1) alleles at the resolution required by the downstream application.
bio-clinical-databases-msi-detection
When your agent uses it
Calls microsatellite instability from WES/WGS/targeted-panel with MSIsensor, MSIsensor-pro, MSIsensor-ct (panel-aware), mSINGS, and MANTIS for FDA pembrolizumab MSI-H pan-tumor / Lynch syndrome / dMMR ICI biomarker. Use when stratifying ICI eligibility (Le 2015), pairing MSI with TMB-H (Sha 2020 / Salem 2018), screening Lynch syndrome (universal IHC + MSI), or distinguishing MSI-H tumors from POLE-exo hypermutator with overlapping signatures.
What it loads into context
Frontmatter
226 tokensAlways available to the agent
SKILL.md
5,633 tokensWhen the skill is activated
Reference documents
1,631 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Regulatory and Trial Landscape
- MSI vs dMMR vs TMB-H: The Conceptual Hierarchy
- Tool Taxonomy
- Decision Tree by Scenario
- Bethesda Panel and Modern NGS-Derived Loci
- Standard Workflow: MSIsensor-pro Tumor-Only
- Paired Tumor-Normal MSIsensor
- MANTIS Step-wise Difference
- MSI-H Classification Logic
- Per-Operation Failure Modes
- Reconciliation: When Sources Disagree
- Quantitative Thresholds and Conventions
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/msi_detection.sh
Files
SKILL.mdcore20,070 bytesSKILL.mdcore20,070 bytesSKILL.mdcore20,070 bytesSKILL.mdcore20,070 bytesSKILL.mdcore20,070 bytesreferences/usage-guide.mddoc5,623 bytesscripts/msi_detection.shruns4,032 bytes
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## Version Compatibility
Reference examples tested with: MSIsensor-pro 1.2+, MSIsensor 0.6+, MANTIS 1.0.5+, samtools 1.19+, mSINGS 5.6+, pandas 2.2+, cyvcf2 0.30+. FDA pembrolizumab MSI-H / dMMR pan-tumor approval is from 2017 (Le 2015 *NEJM*; KEYNOTE-016/164/158); approval extended to colorectal first-line in 2020.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. MSIsensor-pro replaces MSIsensor for tumor-only assays; MSIsensor-ct is the bTMB-equivalent for ctDNA panels.
# MSI Detection; The Companion ICI Biomarker to TMB
**'Detect MSI status from this somatic sequencing data'** -> Profile microsatellite instability across canonical loci (Bethesda 5 panel + extended NGS-derived sites); classify MSI-H / MSS / MSI-L per Bethesda / FDA / KEYNOTE convention.
bio-clinical-databases-myvariant-queries
When your agent uses it
Queries myvariant.info BioThings aggregator for ClinVar, gnomAD, dbSNP, dbNSFP, COSMIC, CADD, and CIViC annotations in batched, version-tracked requests. Use when annotating variant lists from multiple databases simultaneously without managing per-source APIs, and when reproducibility-grade analyses require recording source data versions via _meta.
What it loads into context
Frontmatter
176 tokensAlways available to the agent
SKILL.md
4,982 tokensWhen the skill is activated
Reference documents
1,271 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- BioThings Architecture (Lelong 2022 *Bioinformatics*)
- Aggregated Sources: ~21 and Counting
- Scopes and Query Forms
- Reproducibility: The `_meta` Field
- Comparison to Alternatives
- Decision Tree by Query Scenario
- Standard Annotation Workflow
- Elasticsearch Query Patterns
- Per-Operation Failure Modes
- Reconciliation: When Sources Inside myvariant Disagree
- Quantitative Thresholds and Conventions
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/query_myvariant.py
Files
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## Version Compatibility
Reference examples tested with: myvariant 1.0.0+, requests 2.31+, pandas 2.2+. myvariant.info aggregates >=21 sources; the operative version of each source is queryable via the `_meta` field and the `/v1/metadata` endpoint.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. dbNSFP version drift is the dominant staleness vector: AlphaMissense was added to dbNSFP v4.4 (~2024); querying `dbnsfp.alphamissense.score` returns whatever version of dbNSFP is currently loaded; check `_meta.src.dbnsfp.version`.
# MyVariant.info Queries; Aggregated Annotation
**'Annotate my variants with ClinVar + gnomAD + CADD + AlphaMissense in one batch'** -> Query the BioThings myvariant.info aggregator with field selection and version tracking, then parse nested responses.
- Python: `myvariant.MyVariantInfo().getvariant(hgvs_or_rsid, fields=['clinvar', 'gnomad_exome', 'dbnsfp'])`bio-clinical-databases-pharmacogenomics
When your agent uses it
Queries PharmGKB / CPIC / DPWG for drug-gene interactions; calls CYP2D6/CYP2C9/CYP2C19/DPYD/TPMT/NUDT15/UGT1A1/SLCO1B1 star alleles and phenotype with PharmCAT, Cyrius (CYP2D6 structural variants), Aldy, Stargazer; applies Caudle 2020 activity-score translation. Use when implementing pharmacogenomic-guided prescribing, applying CPIC vs DPWG guidance, screening HLA risk alleles for ICI / antiepileptics / abacavir, or interpreting compound TPMT+NUDT15 thiopurine risk.
What it loads into context
Frontmatter
246 tokensAlways available to the agent
SKILL.md
9,786 tokensWhen the skill is activated
Reference documents
1,952 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Governance: CPIC vs DPWG vs PharmGKB vs FDA
- PharmGKB Clinical Annotation Levels: What 1A Actually Means
- Star Allele Nomenclature (PharmVar)
- Algorithmic Taxonomy: Star Allele Callers
- HLA-Drug Associations: Mechanistically Distinct from CYP
- Non-CYP Pharmacogenes: Variant-Level Detail
- Decision Tree by Scenario
- PharmCAT Workflow (Recommended Multi-Gene Pipeline)
- CYP2D6 Activity Score Calculation
- DPYD Activity Score (CPIC)
- PharmGKB API for Drug-Gene Pair Lookup
- Per-Operation Failure Modes
- Reconciliation: When Tools Disagree
- Quantitative Thresholds and Conventions
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/pharmgkb_query.py
Files
SKILL.mdcore30,879 bytesSKILL.mdcore30,879 bytesSKILL.mdcore30,879 bytesSKILL.mdcore30,879 bytesSKILL.mdcore30,879 bytesSKILL.mdcore30,879 bytesreferences/usage-guide.mddoc6,577 bytesscripts/pharmgkb_query.pyruns8,191 bytes
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## Version Compatibility
Reference examples tested with: PharmCAT 2.13+, Cyrius 1.1+ (Chen 2021), Aldy 4.0+, Stargazer 2.0+, StarPhase 1.0+ (PacBio HiFi), HIBAG 1.40+, requests 2.31+, pandas 2.2+. CPIC guideline versions are gene-specific; PharmVar releases are quarterly. DPYD dosing uses the CPIC gene activity-score system (Amstutz 2018 *Clin Pharmacol Ther* 103:210, the 2017-update guideline); the 2025 TPMT/NUDT15 update (Maillard 2026) refines compound-IM dosing.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. PharmVar is the authoritative star-allele source (`https://www.pharmvar.org`); the older Human CYP Allele Nomenclature Database was deprecated in 2017.
# Pharmacogenomics; Star Alleles, Activity Scores, and CPIC/DPWG Guidance
**'What is my patient's CYP2D6 metabolizer status and should I adjust their tamoxifen dose?'** -> Call star alleles (haplotype-level), translate diplotype -> activity score -> phenotype, apply CPIC + DPWG dosing.
bio-clinical-databases-polygenic-risk
When your agent uses it
Constructs and validates polygenic risk scores using LDpred2-auto, SBayesRC, MegaPRS, PRS-CS, PROSPER, MUSSEL, BridgePRS, JointPRS, PRSmix, or PGS Catalog Calculator with ancestry-aware reference panels (HapMap3, UKB-LD), ancestry-conditional calibration, and PRS-RS reporting standards. Use when computing PRS for cohorts, applying absolute-risk transformation, assessing cross-ancestry portability (Martin 2017 / Ding 2023 continuous ancestry), or auditing PRS manuscripts against the 22-item PRS-RS reviewer checklist.
What it loads into context
Frontmatter
228 tokensAlways available to the agent
SKILL.md
8,232 tokensWhen the skill is activated
Reference documents
2,082 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Method Landscape: 2026 Operational Ranking
- Multi-Ancestry: The Big Problem
- Calibration: The Hingorani Reframing
- PRS-RS Reporting Standards (Wand 2021 *Nature*)
- Decision Tree by Scenario
- Standard Workflow: LDpred2-auto
- SBayesRC Workflow (Functional Annotations)
- PGS Catalog Calculator (Production Pipeline)
- Multi-Ancestry PRS-CSx
- Score Normalization and Ancestry Recalibration
- Sample Overlap Detection (EraSOR / Bivariate LDSC Intercept)
- Per-Operation Failure Modes
- Reconciliation: When Methods Disagree
- Quantitative Thresholds and Conventions
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/prsice2_workflow.sh
Files
SKILL.mdcore29,873 bytesSKILL.mdcore29,873 bytesSKILL.mdcore29,873 bytesSKILL.mdcore29,873 bytesSKILL.mdcore29,873 bytesSKILL.mdcore29,873 bytesreferences/usage-guide.mddoc7,403 bytesscripts/ldpred2_analysis.Rasset5,030 bytesscripts/prsice2_workflow.shruns3,290 bytes
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## Version Compatibility
Reference examples tested with: bigsnpr 1.12+ (LDpred2; Privé 2020), PRSice-2 2.3.5+, PRS-CS 1.0.0+ (Ge 2019), gctb 2.5+ (SBayesR/SBayesS/SBayesRC; Zheng 2024), LDAK 6.0+ (MegaPRS; Zhang 2021), pgsc_calc 2.0+ (nf-core; Lambert 2024), Hail 0.2.130+, numpy 1.26+, pandas 2.2+. No general FDA PRS guidance document exists as of May 2026; the operative regulatory text is the August 2025 Federal Register notice on Cancer Predisposition Risk Assessment Systems (Class II device with special controls).
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name`
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. The LDpred2-auto `snp_ldpred2_auto()` signature changed in bigsnpr 1.11+; pin `allow_jump_sign = FALSE` and `shrink_corr = 0.95` explicitly.
# Polygenic Risk Scores; Construction, Calibration, Reporting
**'Compute a PRS for my cohort using these GWAS summary statistics'** -> Match variants to target genotypes, choose method by data availability + trait architecture, derive LD-aware effect estimates, score, normalize by ancestry, transform to absolute risk.bio-clinical-databases-somatic-signatures
When your agent uses it
Extracts and assigns COSMIC v3.4 mutational signatures (86 SBS / 11 DBS / 18 ID / 21 CN / 16 SV) from somatic VCFs using SigProfilerSuite, MutationalPatterns, MuSiCal mvNMF, SigNet, or HRDetect. Use when characterizing DNA-damage etiology (BRCA1/2 HRD, MMR-D, POLE, APOBEC3A, UV, tobacco, aflatoxin, 5-FU/SBS17b, platinum, colibactin SBS88), routing PARP inhibitor decisions, or auditing de novo extraction vs refit choice for cohort size.
What it loads into context
Frontmatter
240 tokensAlways available to the agent
SKILL.md
7,540 tokensWhen the skill is activated
Reference documents
1,849 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- COSMIC v3.4 Catalog: Evolution and Composition
- Etiology Table (Postdoc-grade)
- Tool Taxonomy
- De Novo vs Refit: The Field's Most-Contested Choice
- Decision Tree by Scenario
- Standard Workflow: SigProfilerSuite
- MutationalPatterns Strict Refit (R Alternative)
- HRDetect for BRCA1/2 Deficiency
- Per-Operation Failure Modes
- Reconciliation: When Methods Disagree
- Quantitative Thresholds and Conventions
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/sigprofiler_analysis.py
Files
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## Version Compatibility
Reference examples tested with: SigProfilerMatrixGenerator 1.2+ (Bergstrom 2019), SigProfilerExtractor 1.1.24+ (Islam 2022), SigProfilerAssignment 0.1+ (Diaz-Gay 2023), MutationalPatterns 3.12+ (Manders 2022), MuSiCal 0.7+ (Jin 2024), SigNet (Serrano 2023, bioRxiv), HRDetect (Davies 2017 / Degasperi 2022 implementations), pandas 2.2+, R 4.3+. COSMIC v3.4 (2023, COSMIC v98): 86 SBS, 11 DBS, 18 ID, 21 CN, 16 SV signatures (v3.6 is the current catalog as of 2026).
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. COSMIC signature naming evolves: SBS40 was split to SBS40a/b/c in v3.4 (Senkin 2024); SBS17 split to SBS17a/b (5-FU); SBS10 split to SBS10a-d (POLE/POLD1).
# Somatic Mutational Signatures; Etiology, Extraction, Clinical Use
**'Extract mutational signatures from this tumor cohort and identify HRD/MMR/APOBEC processes'** -> Generate 96-context (or DBS/ID/CN/SV) matrix from VCF; choose de novo extraction (NMF) vs refit-to-COSMIC by cohort size; map dominant signatures to etiology; flag clinical actionability.
bio-clinical-databases-tumor-mutational-burden
When your agent uses it
Calculates tumor mutational burden from WES/WGS/panel data with Friends of Cancer Research harmonization equations, per-assay calibration (FDA 10/Mb = 7.8 TSO500 = 8.4 OncomineTML), synonymous/indel/germline filtering, hypermutator tiering, blood TMB, and integration with HLA-LOH and neoantigen quality (Luksza 2017 fitness). Use when assessing ICI eligibility under tumor-specific cutoffs (McGrail 2021), comparing tissue vs bTMB, or auditing TMB-H reporting against ESMO 2024 and FDA pembrolizumab pan-tumor 2020.
What it loads into context
Frontmatter
253 tokensAlways available to the agent
SKILL.md
7,853 tokensWhen the skill is activated
Reference documents
1,756 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Regulatory and Trial Landscape
- Friends of Cancer Research Harmonization: Cross-Panel Calibration
- Variant-Counting Subtleties
- Hypermutator Tiering
- The Tumor-Type-Specific Cutoff Debate
- Blood TMB (bTMB): The Negative-Trial Story
- Neoantigen Quality: Beyond Raw TMB
- Decision Tree by Scenario
- Standard Workflow
- TMB-MSI Concordance and Reconciliation
- Per-Operation Failure Modes
- Reconciliation: When Sources Disagree
- Quantitative Thresholds and Conventions
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/tmb_calculation.py
Files
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## Version Compatibility
Reference examples tested with: cyvcf2 0.30+, VEP 111+ (or snpEff 5.2+), pandas 2.2+, numpy 1.26+, LOHHLA 1.0+ (McGranahan 2017), DASH 1.0+ (Pyke 2022). v4.1 (May 2024) gnomAD is current for germline subtraction. Friends of Cancer Research TMB harmonization framework (Vega 2021 *Ann Oncol*) and ESMO 2024 (Mosele *Ann Oncol*) define the operational thresholds.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. TMB calculation requires VCF with VEP / snpEff / Funcotator consequence annotations; the panel size used as denominator MUST match the assay's actual scored region, NOT the panel's total content.
# Tumor Mutational Burden; Calculation, Harmonization, ICI Eligibility
**'Calculate TMB from this somatic VCF and apply ICI eligibility cutoff'** -> Count nonsynonymous coding variants passing VAF/depth/germline filters; divide by assay scored region in Mb; apply assay-calibrated TMB-H cutoff; integrate with MSI / HLA-LOH / neoantigen quality.
bio-clinical-databases-variant-prioritization
When your agent uses it
Prioritizes rare-disease variants from trio/quad WES/WGS with de novo (DeNovoGear, Triodenovo), compound-heterozygous phasing (WhatsHap), mosaic VAF tiering, phenotype-driven ranking (Exomiser, Phen2Gene, AMELIE), ClinGen gene-disease validity gating, and ACMG SF v3.2 secondary findings reporting. Use when running diagnostic exome / genome pipelines, identifying candidate Mendelian disease genes, screening for incidental findings, or auditing VUS reclassification cycles. The ACMG/AMP classification framework (PVS1 decision tree, Pejaver PP3/BP4 calibration, Tavtigian point system) is in clinical-databases/acmg-classification.
What it loads into context
Frontmatter
256 tokensAlways available to the agent
SKILL.md
6,898 tokensWhen the skill is activated
Reference documents
1,781 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Pipeline Architecture: The Standard Rare-Disease Funnel
- Inheritance-Based Filtering
- De Novo Calling: Trio Analysis
- Phenotype-Driven Prioritization
- ClinGen Gene-Disease Validity: Mandatory Gating
- ACMG Secondary Findings v3.2 (Miller 2023 *Genet Med* 25:100866)
- Decision Tree by Scenario
- Standard Pipeline Workflow
- Per-Operation Failure Modes
- Reconciliation: When Sources Disagree
- Quantitative Thresholds and Conventions
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/prioritize_variants.py
Files
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## Version Compatibility
Reference examples tested with: pandas 2.2+, cyvcf2 0.30+, pyhgvs 0.12+, Exomiser 14.0+ (Smedley 2015), Phen2Gene 1.2+ (Zhao 2020), DeNovoGear 1.1.1+ (Ramu 2013), WhatsHap 2.0+ (Patterson 2015), HPO 2024+ (Human Phenotype Ontology). ACMG Secondary Findings list is v3.2 (Miller 2023): 81 genes.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. Phenotype-driven prioritization REQUIRES high-quality HPO terms; without rich phenotypic input Exomiser/AMELIE degrade significantly.
# Rare-Disease Variant Prioritization Pipeline
**'Prioritize candidate disease-causing variants from this trio exome'** -> Filter to rare + functional + inheritance-consistent variants; rank by phenotype concordance; flag ACMG SF v3.2 incidental findings; report tiers with classification logic deferred to `acmg-classification`.
bio-clip-seq-ago-clip-mirna-targets
When your agent uses it
Identify direct miRNA-target interactions from AGO HITS-CLIP, AGO-CLEAR-CLIP (chimeric reads), HEAP (Halo-Ago2 mouse), chimeric eCLIP / miR-eCLIP (deep miRNA-target profiling), or CLASH using chimeric-read processing pipelines, seed-pairing analysis, and 3' auxiliary pairing rules. Use when distinguishing direct miRNA targets from indirect, integrating CLIP-derived target maps with TargetScan / miRDB / DIANA predictions, applying canonical 7mer-8mer seed matching with 3' UTR context, or recovering miRNA-mRNA chimeras at scale.
What it loads into context
Frontmatter
245 tokensAlways available to the agent
SKILL.md
5,473 tokensWhen the skill is activated
Reference documents
1,142 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Methods Taxonomy
- Critical Choice: Chimeric vs Computational miRNA-Target Pairing
- miRNA-Target Pairing Rules
- Chimeric eCLIP / miR-eCLIP Workflow
- CLEAR-CLIP (Moore 2015) Analysis
- miR-eCLIP Probe Enrichment
- Per-Method Failure Modes
- Decision Tree by Use Case
- Reconciliation: AGO-CLIP vs TargetScan vs Chimeric
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/run_chimeric_eclip.sh
Files
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## Version Compatibility
Reference examples tested with: eCLIP pipeline (Yeo lab), chimeric eCLIP analysis scripts (Yeo lab), HEAP pipeline (Li 2020), Hyb pipeline (Travis 2014), TargetScanHuman 8.0, miRDB 6.0, samtools 1.19+, bedtools 2.31+, pyHyb 0.4+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws unexpected errors, introspect the installed package and adapt the example to match the actual API rather than retrying.
# AGO-CLIP and miRNA Target Identification
**"Identify direct miRNA-target interactions experimentally"** -> Use Argonaute (AGO1-4) CLIP-seq variants to map miRNA-binding sites on mRNAs, then resolve which miRNA pairs with each site. Three approaches: (a) standard AGO-CLIP recovers AGO-bound sites but cannot say which miRNA; (b) chimeric methods (CLEAR-CLIP, chimeric eCLIP / miR-eCLIP) ligate the miRNA to its target during library prep, producing miRNA-mRNA chimeric reads that unambiguously assign miRNA-target pairs; (c) HEAP uses HaloTag-Ago2 for in vivo profiling. The chimeric methods are the gold standard for direct miRNA-target identification; standard AGO-CLIP must be combined with computational seed-matching (TargetScan, miRDB) to infer miRNA pairing. Resolution: chimeric reads pinpoint single miRNA-target pairs; AGO-only CLIP identifies "AGO-binding sites" of which a subset are miRNA targets.
bio-clip-seq-binding-site-annotation
When your agent uses it
Annotate CLIP-seq peaks or crosslink sites to RNA features (5'UTR, CDS, 3'UTR, intron, splice junction, snoRNA, tRNA, ncRNA, repeat elements) with ChIPseeker, RCAS, RBP-Maps (Yeo splicing regulatory maps), and bedtools, applying feature-priority hierarchies, transcript-context resolution, and metagene aggregation. Use when characterizing where in transcripts an RBP binds, comparing peak distribution across regions, generating splicing-regulatory maps relative to alternative-splicing events, or distinguishing exonic vs intronic vs UTR binding.
What it loads into context
Frontmatter
233 tokensAlways available to the agent
SKILL.md
6,007 tokensWhen the skill is activated
Reference documents
1,343 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Critical Choice: Annotation Hierarchy
- RBP Class -> Expected Annotation
- RBP-Maps: Splicing Regulatory Maps
- Per-Tool Failure Modes
- Metagene Analysis
- Decision Tree by Scenario
- Reconciliation: When Annotations Disagree
- Common Errors
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: ChIPseeker 1.40+, RCAS 1.30+, GenomicFeatures 1.56+, GenomicRanges 1.56+, rbp-maps (Yeo github), bedtools 2.31+, pybedtools 0.10+, pyranges 0.0.129+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws unexpected errors, introspect the installed package and adapt the example to match the actual API rather than retrying. ChIPseeker 1.40+ changed the priority defaults; verify the priority vector before pipelining.
# Binding Site Annotation
**"Annotate where in transcripts my RBP binds"** -> Map CLIP peaks or single-nucleotide crosslink sites to RNA features and report the per-feature distribution. The interpretation is RBP-class-specific: splicing factors (PTBP1, U2AF2, RBFOX) bind intron-exon junctions; mRNA-stability regulators (HuR, PUM2) bind 3' UTRs; translation factors (EIF3J, RPS19) bind 5' UTRs and CDS; and small-ncRNA-binding RBPs (NSUN2 tRNAs, LARP7 7SK, TROVE2 Y-RNAs) bind specific non-coding transcripts. A correct annotation pipeline (a) resolves overlapping features by priority, (b) preserves transcript-isoform context, (c) generates metagene distributions, and (d) flags repeat-element overlap separately.bio-clip-seq-clip-alignment
When your agent uses it
Align preprocessed CLIP-seq reads (eCLIP, iCLIP, iCLIP2, PAR-CLIP) to genome with STAR or bowtie2 using crosslink-preserving parameters, choosing between unique-mapper-only and multi-mapper-aware alignment for repeat-binding RBPs, deciding STAR vs HISAT2 memory trade-offs, and applying ENCODE-compatible filters. Use when turning preprocessed CLIP FASTQ into a deduplicated, MAPQ-filtered BAM ready for peak calling or crosslink-site detection.
What it loads into context
Frontmatter
205 tokensAlways available to the agent
SKILL.md
5,713 tokensWhen the skill is activated
Reference documents
1,251 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Critical Choice: Unique-Mapper-Only vs Multi-Mapper Rescue
- Per-Aligner Failure Modes
- ENCODE 4 eCLIP STAR Parameters (Reference)
- Post-Alignment Filtering
- Multi-Mapper Rescue with CLAM (Repeat-Binding RBPs)
- HISAT2 Low-Memory Alternative
- Decision Tree by Use Case
- Allele-Specific Alignment with WASP
- Reconciliation: When Alignment Outputs Disagree
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/align_clip.sh
Files
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## Version Compatibility
Reference examples tested with: STAR 2.7.11b+, bowtie2 2.5.3+, HISAT2 2.2.1+, samtools 1.19+, CLAM 1.2+, umi_tools 1.1.5+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws unexpected errors, introspect the installed binary (`<tool> -h`) and adapt the example to match the actual CLI rather than retrying.
# CLIP-seq Alignment
**"Align preprocessed CLIP reads to genome with crosslink-preserving parameters"** -> Map UMI-extracted, adapter-trimmed reads to the genome (NOT transcriptome) with end-to-end alignment, strict mismatch ceiling, and unique-mapper-only filtering by default. The 5' end of the read (R2 5' in paired-end eCLIP; R1 5' in iCLIP) carries the reverse-transcriptase truncation = crosslink site -1; any soft-clipping or 5' trimming during alignment destroys nucleotide resolution.
- CLI (eCLIP / iCLIP / iCLIP2, ENCODE pattern): `STAR --runMode alignReads --genomeDir STAR_index --readFilesIn R1.trim.fq.gz R2.trim.fq.gz --readFilesCommand zcat --outFilterType BySJout --outFilterMultimapNmax 1 --alignEndsType EndToEnd --outFilterMismatchNoverReadLmax 0.04 --outSAMtype BAM SortedByCoordinate --outSAMattributes All --outFileNamePrefix sample_`bio-clip-seq-clip-deep-learning
When your agent uses it
Predict RBP binding from RNA sequence using deep learning models (RBPNet sequence-to-signal, RNAProt RNN, GraphProt2 GCN with structure, DeepCLIP, DeepRiPe multi-modal CNN) for variant-effect prediction, in silico binding-site discovery, model interpretation, and transfer learning from CLIP and RBNS datasets. Use when computational prediction of RBP binding from sequence is needed, evaluating variant effects on binding without further wet-lab experiments, comparing model performance, or training a custom model on ENCODE eCLIP data.
What it loads into context
Frontmatter
213 tokensAlways available to the agent
SKILL.md
4,715 tokensWhen the skill is activated
Reference documents
1,059 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Models Taxonomy
- Critical Choice: Binary Classification vs Sequence-to-Signal
- Variant-Effect Prediction Workflow
- Training a Custom Model (RNAProt Example)
- RBPNet Sequence-to-Signal Workflow
- Per-Tool Failure Modes
- Decision Tree by Use Case
- Reconciliation: Model Predictions vs CLIP / RBNS
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/train_rnaprot.py
Files
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## Version Compatibility
Reference examples tested with: RBPNet (Horlacher et al 2023 github), RNAProt 0.5+, GraphProt2 (Uhl et al 2021 github), DeepCLIP 1.0+ (Gronning 2020), DeepRiPe (Ohler lab), pytorch 2.2+, tensorflow 2.15+, scikit-learn 1.4+, biopython 1.83+, transformers 4.40+ (for RNA foundation models).
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- Frameworks: check pytorch / tensorflow versions; reproducibility depends on framework version
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# CLIP-seq Deep Learning
**"Predict RBP binding from RNA sequence using deep learning"** -> Train or apply neural networks that learn the sequence (and optionally structure) preference of an RBP from CLIP-seq peaks or single-nucleotide crosslink sites. The output is per-base or per-site binding probability for any input sequence, enabling: (a) variant-effect prediction at heterozygous SNPs; (b) in silico binding-site discovery on transcripts not covered by CLIP; (c) systematic comparison across RBPs via shared model architectures; (d) interpretation via attribution / saliency to recover RBP-specific motifs and structural preferences. Modern models (RBPNet 2023) predict per-nucleotide crosslink count distributions rather than binary peak/non-peak, providing single-nt resolution outputs.
bio-clip-seq-clip-motif-analysis
When your agent uses it
Discover RBP binding motifs from CLIP-seq peaks or single-nucleotide crosslink sites using HOMER, MEME/STREME, kpLogo, mCross (CL-position-registered motifs), PEKA (positional k-mer enrichment), RBPamp (affinity), and RNA Bind-n-Seq (RBNS) cross-validation. Use when characterizing RBP sequence specificity, registering motifs to crosslink positions, validating in vivo CLIP motifs against in vitro RBNS Kd, reconciling motif disagreements across tools, or correcting for the uracil crosslinking bias that contaminates raw CLIP motif logos.
What it loads into context
Frontmatter
227 tokensAlways available to the agent
SKILL.md
5,871 tokensWhen the skill is activated
Reference documents
1,120 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Critical Choice: Peak-Based vs Crosslink-Site-Based Motif Discovery
- Uracil Crosslinking Bias
- Per-Tool Failure Modes
- Decision Tree by Scenario
- Reconciliation: When Motif Tools Disagree
- Workflow: De Novo + Known + CL-Registered
- Quality Checks
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/find_motifs.sh
Files
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## Version Compatibility
Reference examples tested with: HOMER 4.11+, MEME Suite 5.5+ (STREME, MEME-ChIP, FIMO), bedtools 2.31+, kpLogo 1.1+, mCross v1+, PEKA v1+, RBPamp 0.9+, ggseqlogo 0.1+, biopython 1.83+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws unexpected errors, introspect the installed tool and adapt the example to match the actual API rather than retrying.
# CLIP-seq Motif Analysis
**"Find enriched RNA motifs at my RBP binding sites"** -> Discover the in vivo sequence preference of an RNA-binding protein from CLIP-seq peaks or single-nucleotide crosslink sites. The fundamental confound is the uracil bias of UV254 crosslinking: U is the most-crosslinked base, so naive motif logos centered on CL positions are U-enriched even for non-U-binding RBPs. Modern tools (mCross, PEKA) register motifs relative to the CL position and correct for this bias; legacy tools (HOMER, MEME) need careful background selection.
bio-clip-seq-clip-peak-calling
When your agent uses it
Call protein-RNA binding sites from CLIP-seq BAM with CLIPper, PureCLIP, Skipper, Piranha, omniCLIP, CTK, CLAM, or Paraclu. Use when choosing between coverage-based, HMM-based, beta-binomial window-based, and crosslink-site-based peak callers; applying ENCODE eCLIP thresholds (log2 IP/SMInput >= 3, -log10 p >= 3); deciding when SMInput is mandatory; or reconciling peak-set discordance between callers for the same RBP.
What it loads into context
Frontmatter
219 tokensAlways available to the agent
SKILL.md
7,525 tokensWhen the skill is activated
Reference documents
1,459 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Critical Choice: Coverage-Based vs Crosslink-Site-Based vs Window-Based
- ENCODE eCLIP Stringency Thresholds
- Per-Caller Failure Modes
- IDR for CLIP-seq
- Decision Tree by Scenario
- Reconciliation: When Callers Disagree
- Strand-Specific Peak Calling
- Multi-Mapper Rescue with CLAM (Repeat Binders)
- Quality Metrics for Peak Sets
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/call_peaks.sh
Files
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## Version Compatibility
Reference examples tested with: CLIPper 2.0+, PureCLIP 1.3.1+, Piranha 1.2.1+, omniCLIP 0.2.0+, CTK 1.1.4+, CLAM 1.2+, Paraclu 9+, Skipper (commit 2023.05+), MACS3 3.0+, bedtools 2.31+, samtools 1.19+, idr 2.0.4+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws unexpected errors, introspect the installed binary and adapt the example to match the actual CLI rather than retrying. PureCLIP 2.x changed several flag names; Skipper is distributed as a Snakemake workflow with frequently-evolving paths.
# CLIP-seq Peak Calling
**"Call protein-RNA binding sites from my deduplicated CLIP BAM"** -> Identify regions where read pile-up (and, for iCLIP/eCLIP, single-nucleotide truncations) exceed background from a size-matched input (SMInput) control. The choice of peak caller depends on (a) the CLIP variant (HITS-CLIP, iCLIP, eCLIP, PAR-CLIP), (b) whether SMInput is available, (c) the RBP binding mode (narrow motif vs broad zones vs repeat-binding), and (d) the goal (publication-comparable ENCODE peaks vs single-nucleotide crosslink sites vs high-recall site lists).
bio-clip-seq-clip-preprocessing
When your agent uses it
Preprocess CLIP-seq reads (eCLIP, iCLIP, iCLIP2, iCLIP3, irCLIP, PAR-CLIP, FLASH) with protocol-specific UMI extraction, adapter trimming, length filtering, and post-alignment PCR-duplicate collapse. Use when raw CLIP FASTQ must be turned into deduplicated, crosslink-preserving BAM input for peak calling; choosing between two-pass and single-pass adapter trimming; deciding minimum read length; or mapping UMI patterns to specific eCLIP/iCLIP/iCLIP2/iCLIP3 library preps.
What it loads into context
Frontmatter
223 tokensAlways available to the agent
SKILL.md
5,436 tokensWhen the skill is activated
Reference documents
1,267 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Read Structure by Protocol
- Critical Choice: One Adapter Pass vs Two
- Per-Protocol Failure Modes
- UMI Deduplication Decision
- Pre-Mapping rRNA Filter (Optional but Standard)
- Library Complexity Assessment
- QC Checkpoints After Preprocessing
- Common Errors
- Reconciliation: Preprocessing Tools
- References
- Related Skills
Scripts executed separately
scripts/preprocess_clip.sh
Files
SKILL.mdcore19,522 bytesSKILL.mdcore19,522 bytesSKILL.mdcore19,522 bytesSKILL.mdcore19,522 bytesSKILL.mdcore19,522 bytesreferences/usage-guide.mddoc4,598 bytesscripts/preprocess_clip.shruns5,044 bytes
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## Version Compatibility
Reference examples tested with: umi_tools 1.1.5+, cutadapt 4.6+, fastp 0.23.4+, samtools 1.19+, pysam 0.22+, picard 3.1+, preseq 3.2+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# CLIP-seq Preprocessing
**"Preprocess raw CLIP reads into UMI-deduplicated, alignable FASTQ"** -> Extract random barcodes, trim adapters without disturbing the 5' truncation site, length-filter to remove unmappable shorts, and (post-alignment) collapse PCR duplicates by UMI + position. The 5' end of the read carries the iCLIP/eCLIP truncation signature one base downstream of the protein-RNA crosslink; preserving this base is the single most important constraint of CLIP preprocessing.
bio-clip-seq-clip-qc
When your agent uses it
Comprehensive quality control for CLIP-seq libraries (eCLIP, iCLIP, iCLIP2, PAR-CLIP) covering library complexity (preseq), FRiP, IDR replicate reproducibility, read-distribution metagene, SMInput vs IgG control rationale, rRNA / snoRNA contamination, fragment-length distribution, and ENCODE-compliance thresholds. Use when assessing whether a CLIP library passed, deciding lenient vs stringent peak thresholds, comparing replicates with IDR rescue and self-consistency ratios, or distinguishing failed IP from over-amplified library.
What it loads into context
Frontmatter
219 tokensAlways available to the agent
SKILL.md
6,032 tokensWhen the skill is activated
Reference documents
1,066 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- QC Stage Hierarchy
- Library Complexity with preseq
- FRiP (Fraction Reads in Peaks)
- IDR for CLIP Reproducibility
- SMInput vs IgG Control: Which?
- Read Distribution Metagene
- Fragment-Length Distribution (Paired-End)
- Pre-Map rRNA / snoRNA Contamination Check
- Antibody Validation Sanity Check
- Per-Stage Failure Modes
- Decision Tree by Failure
- Standard QC Report (MultiQC)
- ENCODE-Style Comprehensive QC Table
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/clip_qc.sh
Files
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## Version Compatibility
Reference examples tested with: preseq 3.2+, picard 3.1+, samtools 1.19+, bedtools 2.31+, deeptools 3.5+, idr 2.0.4+, MultiQC 1.21+, RSeQC 5.0+, pysam 0.22+, fastp 0.23+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws unexpected errors, introspect the installed binary and adapt the example to match the actual CLI rather than retrying.
# CLIP-seq Quality Control
**"Did my CLIP library pass?"** -> Assess preprocessing retention, alignment rate, library complexity, replicate reproducibility (IDR), fraction reads in peaks (FRiP), read-distribution metagene, rRNA/snoRNA contamination, fragment-length distribution, and SMInput vs IP enrichment. ENCODE eCLIP compliance is the canonical bar: >= 1M unique fragments per replicate, IDR rescue and self-consistency ratios both < 2, FRiP >= 0.005 (narrow-binding), library complexity rising linearly with depth on preseq lc_extrap. A library can fail at any of these stages, and the failure mode determines whether the data is salvageable.
bio-clip-seq-crosslink-site-detection
When your agent uses it
Detect single-nucleotide crosslink (CL) sites in CLIP-seq data using truncation patterns (iCLIP/eCLIP CITS), crosslink-induced mutations (HITS-CLIP CIMS deletions, PAR-CLIP T-to-C), or HMM/kernel-density methods (PureCLIP, PARalyzer, CTK). Use when single-nucleotide resolution is required for motif registration (mCross), allele-specific binding (BEAPR), variant-effect prediction, or comparing crosslink chemistry across CLIP variants.
What it loads into context
Frontmatter
207 tokensAlways available to the agent
SKILL.md
5,616 tokensWhen the skill is activated
Reference documents
1,135 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Crosslink Chemistry by CLIP Variant
- Algorithmic Taxonomy
- Critical Choice: Truncation vs Mutation vs HMM
- Per-Tool Failure Modes
- Decision Tree by Use Case
- Reconciliation: When Detection Methods Disagree
- Workflow: PureCLIP -> mCross -> ASB
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/detect_crosslinks.sh
Files
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## Version Compatibility
Reference examples tested with: PureCLIP 1.3.1+, CTK 1.1.4+, PARalyzer 1.5+, wavClusteR 2.34+, pyCRAC 1.5+, samtools 1.19+, bedtools 2.31+, pysam 0.22+, R 4.3+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws unexpected errors, introspect the installed tool and adapt the example to match the actual CLI rather than retrying.
# CLIP-seq Crosslink-Site Detection
**"Detect single-nucleotide crosslink sites in my CLIP data"** -> Identify the exact base where the protein-RNA UV adduct caused the reverse transcriptase to stop (truncation, in iCLIP/eCLIP), to read through with a mutation (deletion in HITS-CLIP, T->C in PAR-CLIP), or to leave a multi-base signature (PARalyzer kernel density for PAR-CLIP). Single-nucleotide resolution is the foundation of motif registration (mCross), allele-specific binding (BEAPR/ASPRIN), variant-effect prediction, and the most rigorous comparisons across CLIP variants. The detection chemistry differs by CLIP type: iCLIP/eCLIP read 5' end is one nucleotide downstream of the crosslink (CITS); PAR-CLIP reads contain T->C transitions at the crosslink (CIMS substitution); HITS-CLIP reads contain deletions at the crosslink (CIMS deletion).bio-clip-seq-differential-clip
When your agent uses it
Identify differentially bound regions across CLIP-seq conditions (knockdown vs control, treatment vs vehicle, disease vs healthy) using DEWSeq (sliding-window DESeq2), Flipper (Skipper-downstream), ASpeak, edgeR, or limma-voom. Use when computing condition-level changes in RBP binding intensity, choosing peak-level vs window-level vs crosslink-level testing, designing replicate experiments, or distinguishing biological binding shifts from technical confounders.
What it loads into context
Frontmatter
199 tokensAlways available to the agent
SKILL.md
5,860 tokensWhen the skill is activated
Reference documents
1,029 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Critical Decision: The Interaction-Term Design
- Critical Choice: Peak-Level vs Window-Level vs Crosslink-Level
- DEWSeq Workflow (Window-Level Differential)
- Flipper Workflow (Skipper-Coupled)
- Peak-Level Differential (CLIPper Upstream)
- RBP Knockdown Experiment Design
- Per-Tool Failure Modes
- Decision Tree by Scenario
- Reconciliation: When Differential Tools Disagree
- Common Errors
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: DEWSeq 1.18+, htseq-clip 2.0+, DESeq2 1.44+, edgeR 4.2+, limma 3.60+, Flipper (commit 2024.04+), Skipper (commit 2023.05+), pybedtools 0.10+, pyranges 0.0.129+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws unexpected errors, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Differential CLIP-seq Analysis
**"Identify regions with changed RBP binding across conditions"** -> Test for condition-level differences in IP enrichment relative to SMInput, accounting for replicate variance and (where available) sequencing depth normalization. Three statistical scales are possible: peak-level (test each peak as a unit), window-level (test fixed transcriptome windows; DEWSeq, Flipper), or crosslink-site level (test single-nt positions). The choice depends on the biology (narrow regulatory shift vs broad binding-mode change) and on which upstream peak caller was used (CLIPper -> peak-level; Skipper -> window-level; PureCLIP -> CL-level).bio-clip-seq-m6a-clip
When your agent uses it
Map N6-methyladenosine (m6A) RNA modifications at single-nucleotide resolution using miCLIP (Linder 2015), miCLIP2 + m6Aboost machine learning (Kortel 2021), GLORI (Liu 2023, antibody-free chemical conversion), DART-seq (Meyer 2019, APOBEC1-YTH fusion), m6Anet (nanopore direct RNA), or MeRIP-seq with calibration. Use when distinguishing antibody-based from antibody-free m6A detection methods, applying the DRACH motif constraint, reconciling cross-method disagreements (DART 44% in DRACH vs GLORI), or detecting m6Am at the cap.
What it loads into context
Frontmatter
252 tokensAlways available to the agent
SKILL.md
6,598 tokensWhen the skill is activated
Reference documents
1,228 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Methods Taxonomy
- Critical Choice: Antibody-Based vs Antibody-Free
- DRACH Motif Constraint
- Cross-Method Discordance
- miCLIP2 Workflow
- GLORI Workflow (Antibody-Free Stoichiometric)
- DART-seq Workflow (Editing-Based)
- m6Anet Workflow (Nanopore)
- Per-Method Failure Modes
- Decision Tree by Use Case
- Reconciliation: When Methods Disagree
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/run_miclip2.sh
Files
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## Version Compatibility
Reference examples tested with: miCLIP2 pipeline (Kortel 2021), m6Aboost 1.0+, GLORI-tools (Liu 2023), Bullseye 1.0+, m6Anet 2.1+, EpiNano 1.2+, MeRIPSeq tools (exomePeak2 1.16+), nanocompore 1.0+, samtools 1.19+, bedtools 2.31+, R 4.3+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws unexpected errors, introspect the installed package and adapt the example to match the actual API rather than retrying.
# m6A CLIP (N6-Methyladenosine Profiling)
**"Map m6A modifications at single-nucleotide resolution"** -> Profile m6A on RNA using one of three orthogonal approaches: antibody-based UV-CL (miCLIP/miCLIP2), antibody-free chemical conversion (GLORI), or enzyme-fusion editing (DART-seq with APOBEC1-YTH). Nanopore direct RNA (m6Anet, nanocompore, EpiNano) provides a fourth modality. The DRACH consensus motif (D=A/G/U, R=A/G, A=m6A, C=C, H=A/C/U) constrains plausible sites but is not exclusive - only a fraction of DRACH instances are methylated; some m6A sites occur outside DRACH. Cross-method discordance is real: only ~44% of DART-seq C->U mutations fall within DRACH motifs (Guo 2025 reanalysis of the DART-seq data), suggesting many DART sites are not consensus m6A. GLORI is the new (2023) gold standard for stoichiometric single-base m6A.bio-clip-seq-stamp-antibody-free
When your agent uses it
Profiles RNA-binding protein targets without antibody or UV crosslinking using STAMP (APOBEC1-RBP fusion, C-to-U editing), scSTAMP (single-cell), TRIBE/HyperTRIBE (ADAR-RBP, A-to-I editing), DART-seq (APOBEC1-YTH for m6A), or Bullseye/SAILOR edit-site detection pipelines. Use when antibody is unavailable or specificity is doubtful, when single-cell RBP profiling is needed (scSTAMP), or when in vivo RBP profiling without UV is preferred.
What it loads into context
Frontmatter
215 tokensAlways available to the agent
SKILL.md
5,124 tokensWhen the skill is activated
Reference documents
1,142 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Methods Taxonomy
- STAMP vs m6A-Specific Methods
- Critical Choice: STAMP (APOBEC1) vs TRIBE (ADAR)
- scSTAMP / scTRIBE Single-Cell Workflow
- Editing-Site Detection Pipelines
- Per-Method Failure Modes
- Decision Tree by Use Case
- Reconciliation: STAMP vs CLIP
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/run_stamp.sh
Files
SKILL.mdcore18,983 bytesSKILL.mdcore18,983 bytesSKILL.mdcore18,983 bytesSKILL.mdcore18,983 bytesSKILL.mdcore18,983 bytesSKILL.mdcore18,983 bytesreferences/usage-guide.mddoc4,142 bytesscripts/run_stamp.shruns3,040 bytes
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## Version Compatibility
Reference examples tested with: STAMP / scSTAMP (Brannan 2021 Yeo lab github), Bullseye 1.0+, SAILOR 1.1+, samtools 1.19+, REDItools2 1.3+, JACUSA2 2.0+, scanpy 1.10+, anndata 0.10+, pysam 0.22+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws unexpected errors, introspect the installed package and adapt the example to match the actual CLI rather than retrying.
# STAMP / Antibody-Free RBP Profiling
**"Profile RBP-RNA targets without UV crosslinking or immunoprecipitation"** -> Express a fusion of the RBP-of-interest with a deaminase (APOBEC1 for STAMP, ADAR for TRIBE) in cells; the deaminase edits RNA nucleotides adjacent to where the RBP binds, producing a C-to-U (STAMP) or A-to-I (TRIBE, read as A-to-G) editing signature in standard RNA-seq. The targets are recovered computationally from the editing pattern. Three properties make this approach valuable: (a) no UV crosslinking required (works in tissue/in vivo); (b) no IP step (no antibody needed - the RBP itself targets the deaminase); (c) compatible with single-cell readout because the editing signal exists in standard scRNA-seq (scSTAMP, scTRIBE). Trade-off: editing is offset from the binding site (typically 0-50 nt away); resolution is approximate; off-target editing from deaminase alone must be subtracted.
bio-comparative-genomics-ancestral-reconstruction
When your agent uses it
Reconstruct ancestral states at internal phylogenetic nodes for sequences (PAML codeml, IQ-TREE --ancestral, GRASP, FastML), discrete traits (corHMM hidden-rate Markov, ape::ace, phytools::make.simmap stochastic mapping, BayesTraits), and continuous traits (phytools::fastAnc, geiger Brownian/OU, RPANDA). Use when designing constructs for ancestral protein resurrection, tracing trait evolution along a tree, performing stochastic character mapping, testing models of trait evolution (BM vs OU vs EB), inferring ancestral genome content via Dollo or DTL reconciliation, or quantifying ancestral-state uncertainty for downstream comparative analyses.
What it loads into context
Frontmatter
239 tokensAlways available to the agent
SKILL.md
9,772 tokensWhen the skill is activated
Reference documents
1,436 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Decision Tree by Experimental Scenario
- Per-Tool Failure Modes
- Quantitative Thresholds
- PAML codeml Ancestral Reconstruction
- IQ-TREE2 Marginal Reconstruction
- Stochastic Mapping (phytools::make.simmap)
- Continuous-Trait ASR with Model Adequacy
- GRASP Indel-Aware Sequence ASR (Protein Resurrection Workflow)
- Reconciliation: When Methods Disagree
- Cohort Gotchas
- Anticipated Reviewer Pushback
- Common Errors
- Tool Installation Notes
- References
- Related Skills
Scripts executed separately
scripts/ancestral_reconstruction.py
Files
SKILL.mdcore37,203 bytesSKILL.mdcore37,203 bytesSKILL.mdcore37,203 bytesSKILL.mdcore37,203 bytesSKILL.mdcore37,203 bytesSKILL.mdcore37,203 bytesSKILL.mdcore37,203 bytesSKILL.mdcore37,203 bytesSKILL.mdcore37,203 bytesSKILL.mdcore37,203 bytesSKILL.mdcore37,203 bytesSKILL.mdcore37,203 bytesSKILL.mdcore37,203 bytesSKILL.mdcore37,203 bytesSKILL.mdcore37,203 bytesSKILL.mdcore37,203 bytesSKILL.mdcore37,203 bytesSKILL.mdcore37,203 bytesreferences/usage-guide.mddoc5,745 bytesscripts/ancestral_reconstruction.pyruns7,463 bytes
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## Version Compatibility
Reference examples tested with: PAML 4.10.7+, IQ-TREE 2.3.6+, GRASP 2024+ (web/CLI), FastML 3.11+, RevBayes 1.2.4+, BayesTraits V4.1+, R 4.4+, ape 5.8+, phytools 2.3+, corHMM 2.9+, geiger 2.0.11+, phangorn 2.12+, RERconverge 0.3.0+, BioPython 1.84+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('corHMM')` then `?ancRECON`, `?make.simmap`, `?ace`
- CLI: `codeml` (no `--version`; check `paml -h` or examine `Phylip.tre` example), `iqtree2 --version`, `revbayes --version`
- Python: `pip show biopython`; check `Bio.Phylo.PAML.codeml` API
If code throws `AttributeError`, `ImportError`, missing slot errors on R S4 objects, or PAML `mlc` parsing failures, introspect the installed package (`?` in R, `help()` in Python) and adapt the example rather than retrying. PAML output formats are stable across 4.9 -> 4.10; IQ-TREE's `--ancestral` flag replaced `-asr` in v2.0+.
# Ancestral State Reconstruction
**"What did this gene / trait / genome look like at an internal node?"** -> Choose the reconstruction framework that matches the data class (sequence / discrete trait / continuous trait / gene content) and the inference question (point estimate vs full posterior; marginal vs joint vs scaled-conditional). The single most common mistake is reconstructing under a site-independent or trait-stationary model when the underlying biology demands a hidden-rate or epistatic model -- the resulting "ancestor" is mathematically optimal under the wrong model and is silently wrong (Beaulieu & O'Meara 2016 Syst Biol 65:583; Boyko & Beaulieu 2021 MEE 12:468).bio-comparative-genomics-comparative-annotation-projection
When your agent uses it
Project gene annotations across genomes using TOGA (Kirilenko 2023 whole-genome-alignment chain-based projection with intactness classification), CESAR 2.0 (Sharma, Schwede & Hiller 2017 codon-aware exon projection), LiftOff (Shumate & Salzberg 2021 reference-based annotation transfer), Liftover (UCSC), GeMoMa (Keilwagen 2019 evidence-based projection), and Comparative Annotation Toolkit (CAT). Use when transferring annotations from a well-annotated reference to query genome(s), classifying gene-loss vs gene-intact across many genomes at scale, building Zoonomia-style comparative annotations across hundreds of mammals or birds (Kirilenko 2023), detecting pseudogenization, projecting alternative isoforms, or selecting between WGA-anchored (TOGA) vs ortholog-based (LiftOff) annotation transfer strategies.
What it loads into context
Frontmatter
286 tokensAlways available to the agent
SKILL.md
6,790 tokensWhen the skill is activated
Reference documents
1,356 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Decision Tree by Experimental Scenario
- Per-Tool Failure Modes
- Quantitative Thresholds
- TOGA Standard Workflow
- LiftOff for Pairwise Annotation Transfer
- Comparative Annotation Toolkit (CAT)
- Reconciliation: When Methods Disagree
- Cohort Gotchas
- Anticipated Reviewer Pushback
- Common Errors
- Tool Installation Notes
- References
- Related Skills
Scripts executed separately
scripts/toga_annotation_projection.sh
Files
SKILL.mdcore28,031 bytesSKILL.mdcore28,031 bytesSKILL.mdcore28,031 bytesSKILL.mdcore28,031 bytesSKILL.mdcore28,031 bytesSKILL.mdcore28,031 bytesSKILL.mdcore28,031 bytesreferences/usage-guide.mddoc5,641 bytesscripts/toga_annotation_projection.shruns2,997 bytes
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## Version Compatibility
Reference examples tested with: TOGA 1.1.7+ (hillerlab/TOGA; Kirilenko 2023 Science 380:eabn3107), CESAR 2.0 (Sharma, Schwede & Hiller 2017 Bioinformatics 33:3985), LiftOff 1.6.3+ (Shumate & Salzberg 2021 Bioinformatics 37(12):1639-1643), Comparative Annotation Toolkit (CAT) 2.4+, GeMoMa 1.9+ (Keilwagen 2019 Methods Mol Biol 1962:161), UCSC liftOver 2024+, Cactus 2.9.1+ (for HAL input), HAL toolkit 2.3+, NextFlow 24+ for TOGA pipeline, BUSCO 5.7+ / Compleasm 0.2.7+ for QC, Luigi + Toil for CAT, R 4.4+. The current TOGA expects HAL from Cactus 2.5+; older HAL formats may fail.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `toga.py --help`; `cesar --help`; `liftoff --version`
- Python: `pip show liftoff`
- Java: `gemoma --help` (Java 11+)
If code throws `TOGA chain file missing`, `CESAR fragment not found`, `LiftOff annotation not parsed`, the toolchain expects specific input formats: TOGA needs HAL or chain/net files from Cactus / LASTZ; CESAR needs exon-level GFF; LiftOff needs reference GFF and aligned FASTA. Pre-process with the appropriate format conversion.
# Comparative Annotation Projection
**"Annotate this new genome using my well-annotated reference"** -> Annotation projection from a reference is the modern alternative to de novo gene prediction; it produces high-quality, comparable annotations across genomes by leveraging evolutionary conservation. The 2023-era standard is **TOGA + CESAR 2.0** (Kirilenko 2023 Science 380:eabn3107), which uses whole-genome alignment chains + ML classification + codon-aware exon projection to scale to hundreds of genomes (Zoonomia: 488 mammals; Bird10000 Genomes: 501 birds). For ortholog-based projection (no WGA needed), **LiftOff** (Shumate & Salzberg 2021 Bioinformatics 37(12):1639) is the standard. The critical decision is **WGA-anchored (TOGA) vs ortholog-anchored (LiftOff)**: TOGA explicitly classifies gene intactness vs loss using the alignment chains, LiftOff relies on reciprocal-best-hit equivalents.bio-comparative-genomics-gene-family-evolution
When your agent uses it
Model gene-family birth-death dynamics across a species tree using CAFE5 (Mendes et al 2020 Bioinformatics 36:5516 gamma-distributed rate categories), CAFE5-error (annotation-error-aware), Count (Csurös 2010 ancestral state reconstruction), BadiRate (Librado 2012 likelihood + parsimony), DupliPHY-Family, and ALE/AleRax (for per-family DTL; see [[gene-tree-species-tree-reconciliation]]). Test lineage-specific gene-family expansions and contractions, distinguish biological dynamics from annotation artifacts, account for assembly fragmentation, identify functional enrichment in expanded / contracted families. Use when correlating gene-family changes with phenotype evolution, ranking lineages by adaptive gene-family-rate shifts, post-WGD dosage-balance analysis, or building Birth-death models from OrthoFinder presence/absence matrices.
What it loads into context
Frontmatter
280 tokensAlways available to the agent
SKILL.md
6,679 tokensWhen the skill is activated
Reference documents
1,310 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Decision Tree by Experimental Scenario
- Per-Tool Failure Modes
- Quantitative Thresholds
- CAFE5 Standard Workflow
- CAFE5-error for Annotation Heterogeneity
- Count for Per-Branch Ancestral State
- Functional Enrichment Downstream
- Reconciliation: When Methods Disagree
- Cohort Gotchas
- Anticipated Reviewer Pushback
- Common Errors
- Tool Installation Notes
- References
- Related Skills
Scripts executed separately
scripts/cafe5_birth_death_analysis.sh
Files
SKILL.mdcore26,836 bytesSKILL.mdcore26,836 bytesSKILL.mdcore26,836 bytesSKILL.mdcore26,836 bytesSKILL.mdcore26,836 bytesSKILL.mdcore26,836 bytesSKILL.mdcore26,836 bytesreferences/usage-guide.mddoc5,400 bytesscripts/cafe5_birth_death_analysis.shruns3,444 bytes
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## Version Compatibility
Reference examples tested with: CAFE5 5.1.0+ (Mendes et al 2020 Bioinformatics 36(22-23):5516-5518), Count 11.0319+ (Csurös 2010 Bioinformatics 26:1910), BadiRate 1.35+ (Librado 2012 Bioinformatics 28:279), DupliPHY-Family (Ames et al 2012), CAFExp (legacy CAFE 4.2 -- DEPRECATED; use CAFE5), OrthoFinder 3.0+ for HOG input, R 4.4+, mclust 6.1+, phytools 2.3+, ETE4 4.1.0+ for tree manipulation. ALE/GeneRax/AleRax in companion skill [[gene-tree-species-tree-reconciliation]].
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `cafe5 --help`; `Count.exe` (Java); `badirate --help`
- R: `packageVersion('phytools')`
- Python: `pip show ete4`
If code throws `CAFE5: lambda did not converge`, `Count negative branch length`, `BadiRate gamma not initialized`, the most common causes are: (1) annotation heterogeneity inflating family sizes, (2) saturated families (CAFE5 needs reasonable rate variation), (3) negative branch lengths in input tree (Count requires ultrametric). Pre-process: filter OG matrix to families present in >= 50% of species; resolve polytomies; ultrametricize tree.
# Gene Family Evolution
**"Which gene families expanded or contracted in which lineages?"** -> Birth-death models on phylogeny (Hahn 2005; Csurös 2010) treat each orthogroup's per-species count as evolving under a stochastic birth-death process; lineage-specific rate shifts are detected as departures from a global rate. **Annotation heterogeneity is the single largest confounder**: different annotation pipelines predict different numbers of genes per family, producing apparent lineage-specific expansions that are artifacts of annotation choice (Tonkin-Hill 2020 demonstrated this for bacterial pangenomes). Consistent annotation + BUSCO/Compleasm completeness filtering are mandatory before any birth-death model interpretation. CAFE5 (Mendes et al 2020 Bioinformatics 36:5516) replaces older CAFE versions with gamma-distributed rate categories for more biologically realistic modeling.bio-comparative-genomics-gene-tree-species-tree-reconciliation
When your agent uses it
Reconcile gene trees against a species tree under probabilistic models of duplication, transfer, and loss (DTL) using ALE (Szöllősi 2013 amalgamated likelihood), GeneRax (Morel 2020 ML reconciliation), AleRax (Morel 2024 co-estimation), Whale.jl (Bayesian DL+WGD), RANGER-DTL 2 parsimony, NOTUNG, ecceTERA, and Treerecs. Use when inferring ancestral gene-family content, distinguishing duplication from horizontal transfer from differential loss, rooting deep species trees from gene-content signals (STRIDE / Williams 2017 ALE-rooting), counting DTL events per branch, refining noisy gene trees against a species tree, modeling WGD events jointly with DTL, or producing publication-grade gene-family histories for phylogenomic / comparative analyses.
What it loads into context
Frontmatter
279 tokensAlways available to the agent
SKILL.md
8,438 tokensWhen the skill is activated
Reference documents
1,425 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Decision Tree by Experimental Scenario
- Per-Tool Failure Modes
- Quantitative Thresholds
- ALE Standard Workflow
- GeneRax for ML Reconciliation with Refinement
- AleRax Co-Estimation
- Whale.jl Bayesian DL + WGD
- Reconciliation: When Methods Disagree
- Cohort Gotchas
- Anticipated Reviewer Pushback
- Common Errors
- Tool Installation Notes
- References
- Related Skills
Scripts executed separately
scripts/ale_dtl_reconciliation.sh
Files
SKILL.mdcore32,665 bytesSKILL.mdcore32,665 bytesSKILL.mdcore32,665 bytesSKILL.mdcore32,665 bytesSKILL.mdcore32,665 bytesSKILL.mdcore32,665 bytesSKILL.mdcore32,665 bytesSKILL.mdcore32,665 bytesSKILL.mdcore32,665 bytesSKILL.mdcore32,665 bytesSKILL.mdcore32,665 bytesSKILL.mdcore32,665 bytesreferences/usage-guide.mddoc5,850 bytesscripts/ale_dtl_reconciliation.shruns2,747 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: ALE 1.0+ (ssolo/ALE github), GeneRax 2.1.3+ (BenoitMorel/GeneRax), AleRax 1.2.0+ (BenoitMorel/AleRax; Morel 2024 Bioinformatics 40:btae162), Whale.jl 2.0+ (arzwa/Whale.jl), RANGER-DTL 2.0+ (Bansal lab; Bansal 2018 Bioinformatics 34:3214), NOTUNG 2.9.1.5+ (Stolzer 2012; Chen 2000), ecceTERA 1.2.5+, Treerecs 1.2+, IQ-TREE 2.3.6+, MrBayes 3.2.7+, BUSCO 5.7+, ete4 4.1.0+, BioPython 1.84+. Open Tree of Life and NCBI Taxonomy reference databases at 2024-Q3 minimum for species-tree-aware inference.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `ALEml_undated --help`, `ALEml --help` (dated), `generax --help`, `alerax --help`
- Julia: `using Whale; Whale.WhaleProblem`; `]status` for package versions
- Python: `pip show ete4`; `ete4 --help`
If code throws `species tree mismatch`, `gene tree taxa not in species tree`, or `MPI process pool failure`, these reconciliation tools share strict label-consistency requirements: species labels must match exactly across the species tree and gene trees (case-sensitive, no whitespace), and gene IDs typically encode species via prefix (`species|gene_id` separator convention). Use `sed` / `awk` normalization scripts before reconciliation.
# Gene Tree Species Tree Reconciliation
**"Where did this gene family come from, and what events shaped its history?"** -> Reconcile gene trees against species trees under explicit probabilistic models of duplication (D), horizontal transfer (T), and loss (L). The reconciliation framework converts gene-tree-species-tree discordance into a quantitative history of evolutionary events. Modern probabilistic methods (ALE, GeneRax, AleRax) **distinguish gene-tree-error-driven discordance from biological discordance** by integrating over gene-tree uncertainty -- a critical advance over parsimony reconciliation (NOTUNG, RANGER) which treats input gene trees as fixed and inflates duplication/loss counts from gene-tree noise (Boussau 2013 Genome Res 23:323; Morel 2020 MBE 37:2763).bio-comparative-genomics-genome-distance-and-species-delineation
When your agent uses it
Compute genome-to-genome distances (ANI, AAI, dDDH, k-mer Mash) and assign taxonomic classifications using skani (Shaw 2023), FastANI (Jain 2018), pyani / pyANI ANIb / ANIm, OrthoANI (Lee 2016), AAI (amino-acid identity), dDDH via TYGS / GGDC, GTDB-Tk (Chaumeil 2020 standard prokaryote taxonomy), and Mash MinHash (Ondov 2016). Use when delineating prokaryote species (95% ANI threshold; Jain 2018 Nat Commun 9:5114), assigning genomes to GTDB taxonomy with ANI radius, computing genome similarity matrices for clustering, classifying archaea, evaluating MAG (metagenome-assembled genome) species assignment, applying skani for fast metagenomic ANI screening, or reconciling 16S rRNA-based taxonomy with whole-genome ANI.
What it loads into context
Frontmatter
308 tokensAlways available to the agent
SKILL.md
7,579 tokensWhen the skill is activated
Reference documents
1,334 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Decision Tree by Experimental Scenario
- Per-Tool Failure Modes
- Quantitative Thresholds
- skani Standard Workflow
- GTDB-Tk Classification Workflow
- TYGS / GGDC for dDDH
- Mash for Fast Clustering
- Reconciliation: When Methods Disagree
- Cohort Gotchas
- Anticipated Reviewer Pushback
- Common Errors
- Tool Installation Notes
- References
- Related Skills
Scripts executed separately
scripts/skani_ani_species_delineation.sh
Files
SKILL.mdcore27,443 bytesSKILL.mdcore27,443 bytesSKILL.mdcore27,443 bytesSKILL.mdcore27,443 bytesSKILL.mdcore27,443 bytesreferences/usage-guide.mddoc4,894 bytesscripts/skani_ani_species_delineation.shruns3,483 bytes
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## Version Compatibility
Reference examples tested with: skani 0.2.5+ (Shaw & Yu 2023 Nat Methods 20:1661; bluenote-1577/skani), FastANI 1.34+ (Jain 2018 Nat Commun 9:5114), pyani 0.3.0+ (Pritchard 2016 Anal Methods 8:12), pyskani 0.1+ (Larralde 2025), OrthoANI 1.40+ (Lee 2016 Int J Syst Evol Microbiol 66:1100), OrthoANIu 1.2+, GTDB-Tk 2.7.1+ (Chaumeil 2022 Bioinformatics 38:5315), GTDB release 220 (2024-Q3+), TYGS web (Meier-Kolthoff & Goker 2019 Nat Commun 10:2182), GGDC v3.0 (web), Mash 2.3+ (Ondov 2016 Genome Biol 17:132), Dashing 2 (Baker & Langmead 2023 Genome Res 33:1218), CompareM 0.1.2+ for AAI (Parks/Cherubini), pyANI 0.3.1+, BLAT 36+, DIAMOND 2.1+. JSpeciesWS web (Richter et al 2016 Bioinformatics 32:929).
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `skani --version`; `fastANI --version`; `gtdbtk --version`; `mash --version`; `pyani --version`
- Python: `pip show gtdbtk pyani`
If code throws `GTDB-Tk database not found`, `skani sketch incompatible`, `Mash sketch version`, these tools have database-version coupling: GTDB-Tk requires the GTDB release matched to the binary version; Mash/skani sketches are forward-compatible but not always backward. Check `gtdbtk check_install` for database completeness.
# Genome Distance and Species Delineation
**"Are these genomes the same species, and what species are they?"** -> Prokaryote species delineation has shifted from 16S rRNA identity (now considered insufficient at < 98.7%) to **whole-genome ANI** at a 95% threshold (Jain 2018 Nat Commun 9:5114; corroborating Goris 2007 and Konstantinidis 2005). The modern operational standard for taxonomy is **GTDB-Tk** (Chaumeil 2020/2022 Bioinformatics 38:5315), which assigns genomes to the Genome Taxonomy Database (GTDB) using ANI radius + marker-gene placement. **skani** (Shaw & Yu 2023 Nat Methods 20:1661) has replaced FastANI as the default ANI tool in GTDB-Tk 2.4+ for being 20-30x faster while maintaining accuracy. The 95% ANI threshold is robust but not absolute -- the species circumscription radius varies by genus (Parks 2018 Nat Biotech 36:996).
bio-comparative-genomics-hgt-detection
When your agent uses it
Detect horizontal gene transfer (HGT / LGT) using compositional methods (GC%, codon usage, tetranucleotide z-scores via SIGI-HMM, AlienHunter, IslandViewer 4, IslandPath-DIMOB), phylogenetic-incongruence methods (AvP, HGTphyloDetect, ALE / GeneRax / AleRax reconciliation, RANGER-DTL), and BLAST-distribution methods (HGTector v2, DarkHorse, Alien Index). Use when screening prokaryote genomes for genomic islands and HGT events, distinguishing HGT from incomplete lineage sorting / differential gene loss / hybridization, mapping donor lineages via phylogenetic placement, separating eukaryotic HGT from contamination, ruling out gBGC as a false signal, or quantifying DTL rates with ALE/GeneRax on bacterial trees.
What it loads into context
Frontmatter
280 tokensAlways available to the agent
SKILL.md
8,946 tokensWhen the skill is activated
Reference documents
1,581 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Decision Tree by Experimental Scenario
- Per-Tool Failure Modes
- Quantitative Thresholds
- HGTector v2 Workflow
- ALE Probabilistic DTL Reconciliation
- AvP Eukaryotic HGT Phylogenetic Workflow
- IslandViewer 4 + mobileOG-db Integration
- Reconciliation: When Methods Disagree
- Cohort Gotchas
- Anticipated Reviewer Pushback
- Common Errors
- Tool Installation Notes
- References
- Related Skills
Scripts executed separately
scripts/hgt_detection.py
Files
SKILL.mdcore34,375 bytesSKILL.mdcore34,375 bytesSKILL.mdcore34,375 bytesSKILL.mdcore34,375 bytesSKILL.mdcore34,375 bytesSKILL.mdcore34,375 bytesSKILL.mdcore34,375 bytesSKILL.mdcore34,375 bytesreferences/usage-guide.mddoc6,120 bytesscripts/hgt_detection.pyruns8,143 bytes
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## Version Compatibility
Reference examples tested with: HGTector 2.0b3+, AvP 1.0.4+, HGTphyloDetect 1.0+, ALE 1.0+ (ssolo/ALE github), GeneRax 2.1.3+, AleRax 1.2.0+ (Morel 2024), RANGER-DTL 2.0+, IslandViewer 4 (web), mobileOG-db 1.0+, MetaCHIP 1.10+, IQ-TREE 2.3.6+, BioPython 1.84+, DIAMOND 2.1.10+. Open Tree of Life and NCBI Taxonomy reference databases updated 2024-Q3 minimum for HGTector/AvP.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show hgtector` then `hgtector search --help`
- CLI: `ALEml_undated --help`, `generax --help`, `alerax --help`
- DB: `hgtector database --check` for taxonomy version
If code throws `Taxonomy ID not found`, `database version mismatch`, or `KeyError` on NCBI taxids, refresh the local taxonomy dump (NCBI updates monthly). ALE/GeneRax expect newick gene trees with bootstraps; AleRax expects gene-tree distributions (uniform bootstrap samples or UFBoot trees).
# Horizontal Gene Transfer Detection
**"Are these genes horizontally acquired, and from where?"** -> HGT signal lives in three orthogonal signal classes: composition (recent transfers carry donor codon usage; erodes by Lawrence-Ochman 1998 amelioration in ~50-200 Myr), phylogeny (gene tree nests within distant clade), and phyletic distribution (patchy taxonomic presence). No single class proves HGT; **claims require concordance across at least two classes** plus mandatory exclusion of contamination and differential gene loss (DGL). The most consequential failure mode in eukaryotic HGT detection is contamination passing all three classes silently (Boothby 2015 tardigrade "17% HGT" refuted by Koutsovoulos 2016; Crisp 2015 human "145 HGTs" refuted by Salzberg 2017 GB 18:85).bio-comparative-genomics-introgression-detection
When your agent uses it
Detect introgression and admixture between species or populations using Dsuite (Malinsky 2021 fast D-statistics), Patterson's D / ABBA-BABA test (Green 2010; Durand 2011), f4-ratio and f-branch statistic (Malinsky 2018), TreeMix (Pickrell & Pritchard 2012), HyDe (Blischak 2018), QuIBL (Edelman 2019), sprime (Browning 2018), Twisst (Martin 2017), PhyloNet (Than 2008) for explicit phylogenetic networks, and qpAdm / qpGraph (Patterson 2012). Distinguish introgression from incomplete lineage sorting (ILS), ancestral structure, ghost-lineage admixture, and rate variation. Use when testing inter-species gene flow, dating admixture events, identifying introgressed segments, building phylogenetic networks for reticulate evolution, or applying the ABBAclustering (Koppetsch-Malinsky-Matschiner 2024) framework for divergent-species gene flow.
What it loads into context
Frontmatter
331 tokensAlways available to the agent
SKILL.md
8,351 tokensWhen the skill is activated
Reference documents
1,347 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Decision Tree by Experimental Scenario
- Per-Method Failure Modes
- Quantitative Thresholds
- Dsuite Standard Workflow
- TreeMix for Population Tree with Migration Edges
- QuIBL for Locus-Level ILS vs Introgression
- qpAdm Rotation Test
- Reconciliation: When Methods Disagree
- Cohort Gotchas
- Anticipated Reviewer Pushback
- Common Errors
- Tool Installation Notes
- References
- Related Skills
Scripts executed separately
scripts/dsuite_abba_baba_workflow.sh
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: Dsuite 0.5+ (millanek/Dsuite; Malinsky 2021 Mol Ecol Res 21:584; ABBAclustering option from Koppetsch-Malinsky-Matschiner 2024 Syst Biol), HyDe 0.4.3+ (Blischak 2018 Syst Biol 67:821), QuIBL (Edelman 2019 Science 366:594), TreeMix 1.13+ (Pickrell & Pritchard 2012 PLoS Genet 8:e1002967), sprime (Browning 2018 Cell 173:53), Twisst (Martin & Van Belleghem 2017 Genetics 206:429), PhyloNet 3.8.2+ (NakhlehLab/PhyloNet; Than-Ruths-Nakhleh 2008 BMC Bioinf 9:322) and PhyloNetworks 0.16+ (JuliaPhylo/PhyloNetworks; Solis-Lemus, Bastide & Ane 2017 MBE 34:3292), qpAdm / qpGraph (AdmixTools v2.0+; Maier 2023), ADMIXTOOLS2 R wrapper (Maier 2023 eLife 12:e85492), MaCS-like simulators (msprime 1.3+ for testing), bcftools 1.21+, samtools 1.21+, vcftools 0.1.16+, R 4.4+. See upstream Dsuite docs for visualization helpers.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `Dsuite --version`; `treemix --help`; `qpDstat --help` (AdmixTools)
- Python: `pip show msprime hyde`
- R: `packageVersion('admixtools')`
If code throws `Dsuite SETS file format error`, `TreeMix matrix singular`, `qpAdm rotation failed`, these tools share strict input requirements: Dsuite needs a SETS file mapping samples to populations + an outgroup; TreeMix needs allele-frequency matrix; qpAdm needs ind / snp / geno trio (EIGENSTRAT format).
# Introgression and Admixture Detection
**"Has there been gene flow between these species / populations?"** -> Tests for inter-population admixture span site-frequency (ABBA-BABA, f4), tree-topology (Dsuite f-branch, QuIBL, Twisst), explicit-network (PhyloNet), and haplotype-tract (sprime) approaches. The fundamental confounder is **incomplete lineage sorting (ILS)**: under a symmetric tree with no gene flow, the ABBA and BABA patterns occur with equal frequency from ancestral polymorphism. **A significant D-statistic indicates EITHER (a) introgression, OR (b) ancestral structure, OR (c) sampling from a ghost lineage** -- additional evidence is required to distinguish (Green 2010 Science 328:710; Durand 2011 MBE 28:2239; Eriksson & Manica 2012 PNAS 109:13956). For high-confidence claims, combine D-statistic with **f-branch** mapping (assigns admixture to specific branches), Twisst / QuIBL topology-weighting, and TreeMix migration edges.bio-comparative-genomics-ortholog-inference
When your agent uses it
Infer orthologous genes and gene families across species using OrthoFinder3 (HOG-based phylogenetic orthology), SonicParanoid2, Broccoli, ProteinOrtho, OMA / FastOMA hierarchical orthologous groups, eggNOG-mapper, JustOrthologs, and TOGA whole-genome-alignment orthology. Use when building single-copy ortholog sets for phylogenomics, classifying co-orthologs and in/out-paralogs after gene duplication, propagating functional annotation via orthology with awareness of the ortholog conjecture, distinguishing speciation from duplication via gene-tree species-tree reconciliation, computing Quest-for-Orthologs benchmark performance, or running synteny-aware ortholog detection in WGD-affected lineages.
What it loads into context
Frontmatter
259 tokensAlways available to the agent
SKILL.md
9,194 tokensWhen the skill is activated
Reference documents
1,530 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Decision Tree by Experimental Scenario
- Per-Method Failure Modes
- Quantitative Thresholds
- OrthoFinder3 Standard Workflow
- SonicParanoid2 + Cross-Validation
- TOGA for Vertebrate-Scale Orthology with Gene-Loss
- Functional Annotation Transfer with eggNOG-mapper
- Reconciliation: When Methods Disagree
- Cohort Gotchas
- Anticipated Reviewer Pushback
- Common Errors
- Tool Installation Notes
- References
- Related Skills
Scripts executed separately
scripts/ortholog_analysis.py
Files
SKILL.mdcore34,503 bytesSKILL.mdcore34,503 bytesSKILL.mdcore34,503 bytesSKILL.mdcore34,503 bytesSKILL.mdcore34,503 bytesSKILL.mdcore34,503 bytesSKILL.mdcore34,503 bytesSKILL.mdcore34,503 bytesSKILL.mdcore34,503 bytesSKILL.mdcore34,503 bytesreferences/usage-guide.mddoc5,950 bytesscripts/ortholog_analysis.pyruns6,620 bytes
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## Version Compatibility
Reference examples tested with: OrthoFinder 3.0+ (Emms et al 2026 Nat Methods 23:1327), SonicParanoid 2.0.8+ (Cosentino 2024), Broccoli 1.2+ (Derelle 2020), ProteinOrtho 6.3.0+ (Lechner 2011 + recent), OMA standalone 2.6.0+, FastOMA 0.3.5+ (Majidian 2025), eggNOG-mapper 2.1.12+, JustOrthologs 2.0+, DIAMOND 2.1.10+, MMseqs2 17-b804f+, IQ-TREE 2.3.6+, BUSCO 5.7+, Compleasm 0.2.7+, BioPython 1.84+, R 4.4+ for downstream tree-based reconciliation.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `orthofinder --help`; `sonicparanoid --help`; `oma --help`
- Python: `pip show eggnog-mapper`; `which fastoma`
If code throws `Diamond requires N more sequences than provided`, `KeyError on species tree taxa`, `STAG branch length 0`, or `HOG file format mismatch`, the OrthoFinder v2 -> v3 file layout changed (Orthogroups/ -> Phylogenetic_Hierarchical_Orthogroups/; rooted gene trees are now per-HOG); update parsing accordingly.
# Ortholog Inference
**"Find the orthologs of my gene(s) across these species"** -> Choose between graph-based (RBH / similarity-clustering: fast, lower recall) and tree-based (gene-tree reconciliation: higher accuracy, slower) frameworks; recognize that "orthology" splits into 1:1, 1:many, many:many, and the practical unit for most pipelines is the **HOG (Hierarchical Orthologous Group)** -- a maximal cluster of genes descended from a single ancestral gene at a defined taxonomic level (Altenhoff 2013 PLoS ONE 8:e53786). The "ortholog conjecture" (orthologs more functionally similar than paralogs) is supported but weakly (Altenhoff 2012 PLoS Comp Biol 8:e1002514); don't treat 1:1 ortholog labeling as automatic functional equivalence.
bio-comparative-genomics-pangenome-analysis
When your agent uses it
Build and analyze pangenomes for prokaryotes (Panaroo, PPanGGOLiN, PEPPAN, GET_HOMOLOGUES, anvi'o pangenomics) and eukaryotes (Minigraph-Cactus, PGGB, vg pangenome graphs). Implement Tettelin core/accessory/cloud genome decomposition (Tettelin 2005), Heap's law open/closed pangenome modeling, gene presence/absence GWAS (Scoary, pyseer), pangenome graph variant calling (vg, PanGenie), and structural-variation graph indexing. Use when assembling species- or genus-level pan-gene catalogs, separating core from accessory/shell/cloud genes, testing gene-content associations with phenotypes, building pangenome graphs from haplotype-resolved assemblies, calling SVs from pangenome graphs, or selecting between bacterial-pangenome and eukaryotic-pangenome workflows.
What it loads into context
Frontmatter
290 tokensAlways available to the agent
SKILL.md
8,963 tokensWhen the skill is activated
Reference documents
1,578 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Decision Tree by Experimental Scenario
- Per-Tool Failure Modes
- Quantitative Thresholds
- Panaroo Bacterial Pangenome Workflow
- Minigraph-Cactus for Eukaryotic Pangenome
- vg Pangenome Genotyping
- Pan-GWAS with Scoary
- Reconciliation: When Methods Disagree
- Cohort Gotchas
- Anticipated Reviewer Pushback
- Common Errors
- Tool Installation Notes
- References
- Related Skills
Scripts executed separately
scripts/panaroo_bacterial_pangenome.sh
Files
SKILL.mdcore33,767 bytesSKILL.mdcore33,767 bytesSKILL.mdcore33,767 bytesSKILL.mdcore33,767 bytesSKILL.mdcore33,767 bytesSKILL.mdcore33,767 bytesSKILL.mdcore33,767 bytesreferences/usage-guide.mddoc5,938 bytesscripts/panaroo_bacterial_pangenome.shruns4,119 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: Panaroo 1.5.1+ (Tonkin-Hill 2020 Genome Biol 21:180), PPanGGOLiN 2.2.0+ (Gautreau 2020 PLoS Comp Biol 16:e1007732), PEPPAN 1.0.5+ (Zhou 2020 GR 30:1667), GET_HOMOLOGUES 25102023+, anvi'o 8.0+ (Eren 2021 Nat Microbiol 6:3), Minigraph-Cactus (Hickey 2024 Nat Biotech 42:663; bundled with Cactus 2.5+), PGGB 0.7.5+ (Garrison 2024 Nat Methods 21:2008), vg 1.59.0+ (Sirén J et al 2024 Nat Methods 21:2017), PanGenie 3.1.0+ (Ebler 2022 Nat Genet 54:518), PGR-TK 0.3.6+ (Chin 2023 Nat Methods 20:1213; cschin/pgr-tk; repo archived April 2026 transitioning to PANGEA), PANGEA (in development by DGI / Diploid Genomics as PGR-TK's successor for pangenome graph exploration + analysis -- check https://github.com/cschin/pgr-tk for current repository pointer), Bakta 1.10.4+ (annotation for input), Roary 3.13.0+ (DEPRECATED; use Panaroo), Scoary 1.6.16+, pyseer 1.3.11+, BUSCO 5.7+, FastTree 2.1.11+, RAxML-NG 1.2+. Python 3.10+ required for Panaroo / PPanGGOLiN.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `panaroo --version`; `ppanggolin --version`; `peppan --help`; `cactus-pangenome --help`; `pggb --version`; `vg version`
- Python: `pip show panaroo ppanggolin`
If code throws `Bakta annotation incompatible`, `GFA file inconsistent`, `vg index version mismatch`, the bacterial pangenome ecosystem expects consistent annotation; re-annotate all input genomes with the same tool and version before pangenome analysis.
# Pangenome Analysis
**"What genes are universal vs accessory across this set of genomes?"** -> The pangenome is the union of all genes across a sampled group; the **Tettelin partition** (Tettelin 2005 PNAS 102:13950) splits it into core (universal), shell (in many but not all), cloud (rare), and species-specific (private) genes. The fundamental dichotomy is **bacterial pangenome** (clusters genes into orthogroups; Panaroo / PPanGGOLiN / PEPPAN for compact genomes) vs **eukaryotic pangenome** (graph-based; Minigraph-Cactus / PGGB / vg for haplotype-resolved sequences). The choice depends on what's being represented: bacterial pangenome captures gene-content variation in a species/genus; eukaryotic pangenome graph captures haplotype-level structural and sequence variation. **Roary (Page 2015) is now deprecated** in favor of Panaroo, which handles annotation-error noise that previously inflated bacterial pangenomes substantially.
bio-comparative-genomics-positive-selection
When your agent uses it
Detect positive (diversifying / episodic / pervasive) selection using codon dN/dS frameworks. Implements PAML codeml site models (M0/M1a/M2a/M7/M8/M8a), branch models, branch-site model A (Zhang 2005), and HyPhy methods (BUSTED, BUSTED-S, BUSTED-MH, BUSTED-PH, MEME, FEL, FUBAR, aBSREL, SLAC, RELAX, GARD, FUBAR-MH). Includes McDonald-Kreitman framework (asymptotic alpha, impMKT, polyDFE, DFE-alpha, GRAPES) for within-species + divergence inference, RERconverge for trait-correlated rate shifts, CSUBST for convergent substitution, and PhyloAcc for accelerated noncoding evolution. Use when testing adaptive evolution at codons, branches, or full gene; running GARD recombination pre-screen; controlling alignment-error and gBGC false positives; reconciling PAML vs HyPhy results; or performing genome-scale selection scans.
What it loads into context
Frontmatter
333 tokensAlways available to the agent
SKILL.md
10,679 tokensWhen the skill is activated
Reference documents
1,559 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Decision Tree by Experimental Scenario
- Per-Method Failure Modes
- Quantitative Thresholds
- Selection Scan Standard Pipeline
- PAML Branch-Site Test (Operational)
- McDonald-Kreitman with Asymptotic Alpha
- RERconverge for Trait-Correlated Rate Shifts
- Reconciliation: When Methods Disagree
- Cohort Gotchas
- Anticipated Reviewer Pushback
- Common Errors
- Tool Installation Notes
- References
- Related Skills
Scripts executed separately
scripts/selection_analysis.py
Files
SKILL.mdcore38,859 bytesSKILL.mdcore38,859 bytesSKILL.mdcore38,859 bytesSKILL.mdcore38,859 bytesSKILL.mdcore38,859 bytesSKILL.mdcore38,859 bytesSKILL.mdcore38,859 bytesreferences/usage-guide.mddoc5,677 bytesscripts/selection_analysis.pyruns7,461 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: PAML 4.10.7+, HyPhy 2.5.62+ (BUSTED-MH from Lucaci 2023 MBE 40:msad150; FUBAR-MH from same), datamonkey.org 2024+ for web jobs, IQ-TREE 2.3.6+, MACSE V2.07+, PRANK 170427+, MAFFT 7.526+, PREQUAL 1.02+, HmmCleaner 0.243+, GARD (HyPhy bundled), RDP5 5.59+, ete4 4.1.0+, BioPython 1.84+, scipy 1.13+, polyDFE 2.0+, DFE-alpha 2.16+, GRAPES 1.1.1+, RERconverge 0.3.0+, CSUBST 1.6.0+, PhyloAcc 2.4.0+. Quest-for-Selection benchmark refreshed annually.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `codeml` (PAML; check by `codeml /dev/null` -- prints version banner), `hyphy --version`, `gard --help`
- Python: `pip show pyhyphy`; introspect ete4 API for tree-labeling
- R: `packageVersion('RERconverge')`; `?correlateWithBinaryPhenotype`
If code throws `branch-site test LRT non-positive`, `omega2 hit upper bound 999`, `MEME ML mixed gradient`, the most common cause is alignment error or saturated dS -- inspect alignment with TCS / Guidance2 and dS-vs-divergence-time. PAML 4.10 changed several control-file keywords from 4.9 (`getSE = 1` syntax tightened).
# Positive Selection Analysis
**"Is this gene / branch / site under positive selection?"** -> dN/dS (omega = nonsynonymous-to-synonymous substitution rate ratio) framework with explicit choice of WHICH question is being asked (gene-wide / branch-specific / site-specific / episodic) and WHICH null is being rejected. The "test failed because of selection" claim has more known confounders than any other comparative-genomics inference; **mandatory pre-screens are: recombination (GARD), alignment errors (PREQUAL or HmmCleaner), saturation (dS distribution), and gBGC (W->S substitution bias)**. Skipping any one inflates Type-I error to ~20-50% (Anisimova & Yang 2007 MBE 24:1219; Pond 2006 Mol Biol Evol 23:1891).bio-comparative-genomics-synteny-analysis
When your agent uses it
Detect syntenic blocks and structural rearrangements between genomes using MCScanX (Wang 2012), JCVI/MCScan (Tang 2008 Python), GENESPACE (Lovell 2022) for orthology-anchored riparian visualization, SyRI for structural variation, AnchorWave for sequence-level synteny, i-ADHoRe 3.0 for highly diverged species, SynNet for synteny networks, and ntSynt for multi-genome macrosynteny. Use when identifying collinear gene blocks across species, distinguishing macrosynteny from microsynteny, detecting inversions/translocations/duplications, anchoring orthology in WGD lineages, producing publication riparian plots, computing synteny block age via Ks (cross-references whole-genome-duplication), or running synteny-aware ortholog inference in polyploids.
What it loads into context
Frontmatter
283 tokensAlways available to the agent
SKILL.md
9,006 tokensWhen the skill is activated
Reference documents
1,625 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Decision Tree by Experimental Scenario
- Per-Tool Failure Modes
- Quantitative Thresholds
- MCScanX Standard Pipeline
- GENESPACE Plant-Focused Pipeline
- SyRI for Structural Rearrangement Inventory
- JCVI / MCScan Python for Publication Plots
- Reconciliation: When Methods Disagree
- Cohort Gotchas
- Anticipated Reviewer Pushback
- Common Errors
- Tool Installation Notes
- References
- Related Skills
Scripts executed separately
scripts/synteny_analysis.py
Files
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## Version Compatibility
Reference examples tested with: MCScanX 1.0+ (wyp1125/MCScanX commit 2020+), JCVI 1.4.21+ (Python port of MCScan), GENESPACE 1.4.0+ (Lovell 2022 eLife 11:e78526), SyRI 1.7.1+ (Goel 2019 Genome Biol 20:277), plotsr 1.1.1+, AnchorWave 1.2.5+ (Song 2022 PNAS 119:e2113075119), i-ADHoRe 3.0.01+, SynNet (Zhao 2017 Plant Cell 29:1278), ntSynt 1.0.4+ (2024), minimap2 2.28+, MUMmer 4.0.0+, OrthoFinder 3.0+, R 4.4+. plotsr requires pysam 0.22+ and seaborn 0.13+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `MCScanX -h`; `syri --version`; `python3 -m jcvi.compara.catalog ortholog --help`
- R: `packageVersion('GENESPACE')`; `?run_genespace`
- Python: `pip show jcvi`
If code throws `MCScanX: argument bad format`, `syri: input alignment file missing required columns`, or `GENESPACE: GFF parse error`, these tools have brittle input parsing: MCScanX requires 4-column `species_chr gene start end` BED (non-standard), JCVI expects 4-column simple BED, GENESPACE requires GFF3 with `gene` feature type. Pre-process with `jcvi.formats.gff bed` or custom AWK.
# Synteny Analysis
**"Compare genome architecture between these species"** -> Detect conserved gene order (synteny) and infer rearrangement history. Synteny is NOT the same as collinearity: synteny is "genes on same chromosome", collinearity is "same order on same chromosome" (modern usage often conflates them). The choice of tool depends on whether the question is **gene-level co-linearity** (MCScanX, JCVI), **whole-genome structural rearrangements** (SyRI, AnchorWave), **multi-genome macrosynteny** (GENESPACE, ntSynt), or **synteny-aware orthology** (GENESPACE, ProteinOrtho-synteny). Repeat-masking quality is the dominant determinant of result reliability -- unmasked TEs produce ~100x more false anchor pairs than real syntenic anchors.bio-comparative-genomics-whole-genome-alignment
When your agent uses it
Build whole-genome alignments using Progressive Cactus (Armstrong 2020 reference-free clade-level WGA), Minigraph-Cactus (Hickey 2024 pangenome-aware), LASTZ chain/net (UCSC pipeline), MUMmer4 (Marçais 2018 pairwise), minimap2 -x asm5/10/20 (Li 2018 fast pairwise), AnchorWave (Song 2022 WGD-aware), and Mauve / progressiveMauve (bacterial). Operates the HAL toolkit (Hickey 2013) for downstream extraction including halSynteny, halLiftover, halBranchMutations, and hal2maf. Use when constructing multi-species alignments for comparative-annotation projection (TOGA), synteny detection, conservation analyses (phyloP / PhastCons), or pangenome graph construction; selecting between reference-free (Cactus) and reference-anchored (LASTZ chains/nets) approaches; tuning sensitivity for closely vs distantly related genomes; or producing HAL files for genome-wide downstream tools.
What it loads into context
Frontmatter
327 tokensAlways available to the agent
SKILL.md
7,929 tokensWhen the skill is activated
Reference documents
1,397 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Decision Tree by Experimental Scenario
- Per-Tool Failure Modes
- Quantitative Thresholds
- Progressive Cactus Standard Workflow
- LASTZ + UCSC Chain/Net Standard Pipeline
- Minigraph-Cactus for Pangenome Construction
- Reconciliation: When Methods Disagree
- Cohort Gotchas
- Anticipated Reviewer Pushback
- Common Errors
- Tool Installation Notes
- References
- Related Skills
Scripts executed separately
scripts/progressive_cactus_wga.sh
Files
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## Version Compatibility
Reference examples tested with: Progressive Cactus 2.9.1+ (ComparativeGenomicsToolkit/cactus; Armstrong 2020 Nature 587:246), Minigraph-Cactus (Hickey 2024 Nat Biotech 42:663; bundled with Cactus 2.5+), HAL toolkit 2.3+ (Hickey 2013 Bioinformatics 29:1341), LASTZ 1.04.22+, UCSC kentUtils for chain/net (Kent 2003 PNAS 100:11484), MUMmer 4.0.0+, minimap2 2.28+, AnchorWave 1.2.5+, progressiveMauve 2.4.0+, sibeliaz 1.2.5+, winnowmap 2.03+ (Jain 2022 Nat Methods 19:705). Toil workflow runner 6.0+ for Cactus on HPC/cloud.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `cactus --help`, `cactus-pangenome --help`, `halStats --help`, `lastz --version`, `nucmer --version`, `minimap2 --version`
- Python: `pip show toil`, `toil --version`
If code throws `Toil workflow restart failure`, `HAL file corrupted`, `WDL workflow missing`, the Cactus pipeline is Toil-based and requires careful checkpointing; failed runs must be restarted with `--restart`. HAL file versions differ across hal-toolkit releases; pin the version that produced the file.
# Whole Genome Alignment
**"Align these multiple genomes at the base-pair level"** -> Choose between **reference-free** progressive alignment (Cactus / Minigraph-Cactus: produces HAL, no privileged reference) and **reference-anchored** pairwise alignment (LASTZ chains/nets, MUMmer, minimap2: one genome is the reference, queries align to it). The fundamental tradeoff is **scale vs structure**: pairwise pipelines scale linearly per pair but lose multi-way relationships; progressive Cactus scales linearly with a tree but quadratically without and produces ancestrally-coherent alignments. For comparative genomics at vertebrate / mammal scale, Cactus is now the standard substrate (Zoonomia, Christmas 2023 Science 380:eabn3943; Bird10000 Genomes); for pangenome graph construction, Minigraph-Cactus (Hickey 2024) is the production pipeline.
bio-comparative-genomics-whole-genome-duplication
When your agent uses it
Detect, date, and contextualize whole-genome duplication (WGD / paleopolyploidy) events using wgd v2 (Chen et al 2024), KsRates (Sensalari 2022 substitution-rate-corrected Ks dating), DupGen_finder (Qiao 2019), MAPS (Li 2018 phylogenomic), POInT (Conant 2008 ordered-block), SLEDGe (2024 ML-based), Whale.jl (Bayesian DL+WGD), and synteny-anchored paranome construction. Use when identifying ancient polyploidy from Ks distributions and synteny block analysis, positioning WGD events relative to speciation, distinguishing tandem from segmental from WGD duplications, dating the 2R/3R vertebrate / fish / salmonid WGDs, building paranome and Ks-age mixture models, applying KsRates substitution-rate correction across lineages, or testing alternative biased-fractionation / dosage-balance models post-WGD.
What it loads into context
Frontmatter
311 tokensAlways available to the agent
SKILL.md
7,998 tokensWhen the skill is activated
Reference documents
1,418 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Algorithmic Taxonomy
- Decision Tree by Experimental Scenario
- Per-Tool Failure Modes
- Quantitative Thresholds
- wgd v2 Standard Workflow
- KsRates Substitution-Rate-Corrected Workflow
- DupGen_finder for Duplication Class Assignment
- MAPS Phylogenomic WGD Placement
- Reconciliation: When Methods Disagree
- Cohort Gotchas
- Anticipated Reviewer Pushback
- Common Errors
- Tool Installation Notes
- References
- Related Skills
Scripts executed separately
scripts/wgd_v2_ks_pipeline.sh
Files
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## Version Compatibility
Reference examples tested with: wgd v2.0.31+ (heche-psb/wgd; Chen et al 2024 Bioinformatics 40:btae272), KsRates 1.1.3+ (VIB-PSB/ksrates; Sensalari 2022 Bioinformatics 38:530), DupGen_finder (Qiao 2019 Genome Biol 20:38), MAPS 1.0 (Li 2018), POInT (Conant lab), SLEDGe (bioRxiv 2024.01.17.574559), Whale.jl 2.0+, ksrates pip 1.1+, MCScanX 1.0+, PAML 4.10+ (yn00/codeml for Ks), BLAT 36+, DIAMOND 2.1+, R 4.4+, mclust 6.1+ (for mixture models). Python 3.10+ required for wgd v2.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `wgd --version`; `ksrates --version`; `wgd ksd --help`
- Python: `pip show wgd ksrates`
- R: `packageVersion('mclust')`
If code throws `wgd ksd: cannot find PAML output`, `KsRates: insufficient sister species`, `MAPS: missing tree`, these tools have specific input expectations: wgd needs codon-aware MAFFT/MUSCLE alignment; KsRates needs configured `config_ksrates.txt`; MAPS needs nucleotide tree. The deprecated arzwa/wgd v1 is replaced by heche-psb/wgd v2.
# Whole Genome Duplication Analysis
**"Are there WGD events in this lineage and when did they occur?"** -> WGD detection combines **Ks distributions** (synonymous-substitution rates between gene paralog pairs, showing peaks at past polyploidy events) and **synteny block analysis** (parallel collinear blocks within a genome). Modern best practice uses **wgd v2** (Chen et al 2024 Bioinformatics 40:btae272) as an integrated pipeline. **KsRates** (Sensalari 2022 Bioinformatics 38:530) is mandatory for cross-lineage comparison because substitution rates vary across the tree -- ignoring this places WGDs incorrectly relative to speciation events. The fundamental tradeoff: Ks plot peaks are visually obvious but biologically ambiguous between (1) small-scale tandem duplications, (2) segmental duplications, and (3) true WGD; combining Ks with synteny anchors disambiguates these.bio-copy-number-allele-specific-copy-number
When your agent uses it
Infer integer allele-specific copy number, tumor purity, and ploidy from tumor sequencing by jointly modeling read depth (logR) and B-allele frequency (BAF) with ASCAT, Sequenza, FACETS, PURPLE, and PureCN (tumor-only). Covers the purity-ploidy identifiability problem, the diploid-baseline (dipLogR) anchor, major/minor copy number, loss of heterozygosity, sunrise/contour fit diagnostics, and reconciliation of conflicting fits. Use when tumor analysis needs absolute copy number rather than relative log2, when estimating purity and ploidy, calling LOH or copy-neutral LOH, resolving whole-genome doubling, running tumor-only allele-specific calling, or choosing among ASCAT, Sequenza, FACETS, and PureCN.
What it loads into context
Frontmatter
264 tokensAlways available to the agent
SKILL.md
4,399 tokensWhen the skill is activated
Reference documents
1,180 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Identifiability Problem — Why This Is Hard
- Caller Taxonomy
- Decision Tree by Data Type
- FACETS — Tumor-Normal Allele-Specific CN
- Sequenza — Exome Tumor-Normal
- ASCAT — WGS / SNP Array
- PureCN — Tumor-Only
- Failure Modes
- Reconciliation: When Callers Disagree
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/run_facets.R
Files
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## Version Compatibility
Reference examples tested with: ASCAT 3.1+, Sequenza 3.0+ (sequenza-utils 3.0+), FACETS 0.6+ (snp-pileup), PureCN 2.6+, R 4.3+, Python 3.10+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('ASCAT')` / `'sequenza'` / `'facets'` / `'PureCN'`, then `?function`
- CLI: `sequenza-utils --version`, `snp-pileup --help`
Sequenza 3.0 depends on the `copynumber` Bioconductor package, REMOVED from Bioconductor 3.18+ (2023). Install a maintained fork (`ShixiangWang/copynumber` or `igordot/copynumber`) before Sequenza will load. ASCAT's GC-correction function was renamed across 2.x->3.x (`ascat.GCcorrect` -> `ascat.correctLogR`) — verify against the installed version.
# Allele-Specific Copy Number
**"How many copies of each allele, in what fraction of cells, at what tumor purity"** -> Jointly model read depth and B-allele frequency to fit tumor purity, ploidy, and integer major/minor copy number per segment. Depth alone gives only *relative* copy ratio; depth + BAF gives *absolute* allele-specific copy number. This skill is required whenever the question involves LOH, absolute copy number, purity, ploidy, or whole-genome doubling — CNVkit and GATK somatic CNV cannot answer those.
bio-copy-number-cnv-annotation
When your agent uses it
Annotate copy number variant segments with overlapping genes, dosage-sensitivity scores, cancer driver databases, population frequencies, and clinical-variant content. Covers bedtools/pybedtools interval intersection, AnnotSV comprehensive annotation and ranking, ClinGen haploinsufficiency/triplosensitivity scoring, gnomAD-SV/DGV frequency filtering, COSMIC Cancer Gene Census, and ClinVar overlap. Use when interpreting which genes a CNV affects, distinguishing the driver gene of a focal event from passengers, filtering against population CNVs, separating whole-gene from partial-gene overlap, or preparing CNVs for clinical classification.
What it loads into context
Frontmatter
220 tokensAlways available to the agent
SKILL.md
3,435 tokensWhen the skill is activated
Reference documents
947 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Annotation Strategy — Pick the Database for the Question
- The Core Distinction: Overlap Is Not Consequence
- Gene Overlap with bedtools
- Comprehensive Annotation with AnnotSV
- Dosage-Sensitivity and Driver Annotation
- Population-Frequency Filtering
- Pathway Enrichment of Affected Genes
- Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/annotate_cnvs.py
Files
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## Version Compatibility
Reference examples tested with: bedtools 2.31+, AnnotSV 3.4+, Python 3.10+ with pybedtools 0.9+, pandas 2.2+, pysam 0.22+; R 4.3+ with clusterProfiler 4.10+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `bedtools --version`, `AnnotSV --version`
- Python: `pip show pybedtools pandas pysam`
- R: `packageVersion('clusterProfiler')`
If code throws an error, introspect the installed package and adapt the example. AnnotSV output column names change between major versions — verify against the installed version.
# CNV Annotation
**"Annotate my CNV calls with the genes they affect"** -> Overlap CNV segments with gene models, dosage-sensitivity maps, and clinical databases. The hard part is not the intersection — it is deciding *which* genes matter. A focal amplification overlapping 30 genes usually has one driver (the peak gene); a deletion's consequence depends on whether each gene is dosage-sensitive and whether the whole gene or only part is removed.bio-copy-number-cnv-visualization
When your agent uses it
Visualize copy number profiles, segments, allele-specific tracks, and cohort patterns from CNVkit, GATK, ASCAT, FACETS, Sequenza, and other callers. Covers genome-wide and per-chromosome log2 scatter plots, B-allele-frequency/minor-allele-fraction tracks, ideograms, cohort heatmaps, circos views, and caller-native plots. Use when creating publication CNV figures, choosing which plot answers a given question, diagnosing a wrong diploid baseline visually, displaying loss of heterozygosity, or deciding what depth-only plots cannot reveal.
What it loads into context
Frontmatter
217 tokensAlways available to the agent
SKILL.md
3,601 tokensWhen the skill is activated
Reference documents
981 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Plot Selection — What Each View Reveals and Hides
- CNVkit Built-in Plots
- Genome-Wide log2 Profile with Segments
- Combined log2 + B-Allele-Frequency Panel
- Cohort Heatmap
- Caller-Native Diagnostic Plots
- Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/plot_cnv.py
Files
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## Version Compatibility
Reference examples tested with: matplotlib 3.8+, pandas 2.2+, numpy 1.26+, seaborn 0.13+, CNVkit 0.9.10+, GATK 4.5+; R 4.3+ with ggplot2 3.5+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show matplotlib pandas` then `help(function)` for signatures
- R: `packageVersion('ggplot2')` then `?function_name`
- CLI: `cnvkit.py version`, `gatk --version`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example rather than retrying.
# CNV Visualization
**"Plot my copy number profile"** -> A CNV figure is an argument, not a picture. The plot type, the y-axis quantity, and where the diploid baseline sits all determine what the reader can conclude. The single most important rule: a depth-only log2 plot cannot show loss of heterozygosity, cannot show tumor purity, and silently misleads if the diploid baseline is centered on a non-diploid mode.bio-copy-number-cnvkit-analysis
When your agent uses it
Detect somatic and germline copy number variants from targeted, exome, and whole-genome sequencing with CNVkit, a read-depth caller that combines on-target and off-target (antitarget) coverage. Covers panel-of-normals construction, flat-reference tumor-only calling, hybrid/amplicon/WGS modes, CBS vs HMM segmentation selection, purity-aware integer calling, and reconciliation against GATK and allele-specific callers. Use when calling CNVs from hybrid-capture panels or exomes, deciding whether CNVkit (depth-only) is the right tool versus an allele-specific caller, building a panel of normals, diagnosing flat-reference false positives, or interpreting log2 ratios into copy-number states.
What it loads into context
Frontmatter
241 tokensAlways available to the agent
SKILL.md
4,245 tokensWhen the skill is activated
Reference documents
1,048 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Where CNVkit Sits — Caller Taxonomy
- Decision Tree by Scenario
- Core Pipeline — Tumor-Normal Pair
- Panel of Normals — The Reference Determines Call Quality
- Step-by-Step Pipeline (Fine-Grained Control)
- Segmentation Method Selection
- Purity-Aware Integer Calling
- Failure Modes
- Reconciliation: When CNVkit Disagrees With Another Caller
- Quality Control
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/run_cnvkit.sh
Files
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## Version Compatibility
Reference examples tested with: CNVkit 0.9.10+, samtools 1.19+, bedtools 2.31+, Python 3.10+, R 4.3+ with DNAcopy 1.76+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `cnvkit.py version` then `cnvkit.py batch --help` to confirm flags
- Python: `pip show cnvkit` then `python3 -c "import cnvlib; help(cnvlib.read)"`
- R: `packageVersion('DNAcopy')` (CBS backend)
If a command throws an unrecognized-argument or AttributeError, introspect the installed version and adapt the example rather than retrying. CNVkit segmentation methods (`hmm`, `hmm-tumor`, `hmm-germline`) depend on `pomegranate`; CBS depends on Bioconductor `DNAcopy`.
# CNVkit Copy Number Analysis
**"Detect copy number variants from my exome / panel data"** -> Run a read-depth pipeline: normalize on-target and off-target coverage against a reference, segment the log2-ratio profile, and call gains/losses. CNVkit is a *depth-only* caller — it estimates **relative** copy number and cannot, on its own, resolve tumor purity, ploidy, or allele-specific state. Choosing CNVkit is a decision that the experiment does not require allelic resolution.bio-copy-number-copy-ratio-segmentation
When your agent uses it
Normalize read-depth copy-ratio profiles and segment them into copy-number regions using circular binary segmentation (CBS, DNAcopy), hidden Markov models, HaarSeg, and fused-lasso methods. Covers GC-content, mappability, and replication-timing (wave-artifact) bias correction, panel-of-normals/PCA denoising, diploid-baseline centering, and algorithm selection by sequencing depth and event size. Use when choosing a segmentation algorithm, correcting depth bias, diagnosing oversegmentation or a mis-centered baseline, tuning CBS or HMM parameters, or understanding why a downstream CNV caller produced fragmented or shifted segments.
What it loads into context
Frontmatter
226 tokensAlways available to the agent
SKILL.md
3,457 tokensWhen the skill is activated
Reference documents
944 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Stage 1: Why Depth Is Biased Before It Is Copy Number
- Stage 2: Segmentation Algorithm Taxonomy
- Decision Tree
- Bias Correction — GC Loess Normalization
- Segmentation — CBS with DNAcopy
- Failure Modes
- Reconciliation: When Segmentations Disagree
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/segment_cbs.R
Files
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## Version Compatibility
Reference examples tested with: R 4.3+ with DNAcopy 1.76+, Python 3.10+ with numpy 1.26+, pandas 2.2+; QDNAseq 1.38+ (optional, GC/mappability normalization).
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('DNAcopy')` then `?segment` to confirm arguments
- Python: `pip show numpy pandas`
If code throws an error, introspect the installed package and adapt the example. CBS lives in Bioconductor `DNAcopy`; HMM segmentation is provided by caller-specific backends (CNVkit uses `pomegranate`; HaarSeg has its own R/Python packages).
# Copy-Ratio Segmentation
**"Turn noisy per-bin depth into clean copy-number segments"** -> Two stages, both error-prone. First, normalize the depth profile so the only remaining variation is copy number (not GC, mappability, or replication timing). Second, partition the normalized profile into segments of constant copy number. The segmentation algorithm choice has a *predictable* bias signature, and the diploid-baseline choice can invert every call.
bio-copy-number-focal-amplification-ecdna
When your agent uses it
Resolve the architecture of focal oncogene amplifications — extrachromosomal DNA (ecDNA), breakage-fusion-bridge (BFB) cycles, homogeneously staining regions (HSR), and linear amplification — from whole-genome sequencing with AmpliconArchitect, the AmpliconSuite pipeline, and AmpliconClassifier. Covers copy-number seed selection, breakpoint-graph reconstruction, balanced-flow optimization, ecDNA classification, and the limits of depth-only amplification calls. Use when a focal amplification needs structural characterization, when distinguishing ecDNA from chromosomal amplification, suspecting ecDNA-driven oncogene amplification or therapy resistance, or selecting copy-number seeds for amplicon reconstruction.
What it loads into context
Frontmatter
239 tokensAlways available to the agent
SKILL.md
2,679 tokensWhen the skill is activated
Reference documents
949 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Why Architecture Matters — Four Amplicon Classes
- When to Suspect ecDNA
- The AmpliconSuite Workflow
- Failure Modes
- Reconciliation
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/run_ampliconsuite.sh
Files
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## Version Compatibility
Reference examples tested with: AmpliconSuite-pipeline 1.3+, AmpliconArchitect 1.3+, AmpliconClassifier 1.2+, CNVkit 0.9.10+, Python 3.10+, samtools 1.19+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `AmpliconSuite-pipeline.py --help`, `amplicon_classifier.py --help`
- AmpliconArchitect needs a `$AA_DATA_REPO` reference download and a Mosek license (free for academic use); confirm both are configured before running
Verify the reference build — AmpliconArchitect was historically hg19-centric; GRCh38 support and data repos exist but the build must be set explicitly and consistently.
# Focal Amplification and ecDNA
**"This oncogene is amplified — but how, structurally"** -> A depth caller reports "high focal amplification" and stops. The biology depends entirely on the *architecture*: extrachromosomal DNA (ecDNA) behaves utterly differently from a chromosomal homogeneously staining region. Resolving architecture needs the breakpoint graph, not depth.
bio-copy-number-gatk-cnv
When your agent uses it
Call copy number variants with the GATK best-practices workflows — the somatic CNV pipeline (CollectReadCounts, DenoiseReadCounts with tangent normalization, ModelSegments, CallCopyRatioSegments) and the germline GATK-gCNV pipeline (DetermineGermlineContigPloidy, GermlineCNVCaller cohort/case mode, PostprocessGermlineCNVCalls). Covers panel-of-normals construction, AnnotateIntervals/FilterIntervals, allelic-count integration, and QS-based filtering. Use when integrating CNV calling into a GATK variant pipeline, calling rare germline CNVs from an exome cohort, deciding between the somatic and germline GATK workflows, or diagnosing why tangent normalization removed a real event or why gCNV output has low precision.
What it loads into context
Frontmatter
263 tokensAlways available to the agent
SKILL.md
4,076 tokensWhen the skill is activated
Reference documents
1,073 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Critical: What GATK Somatic CNV Does NOT Provide
- Somatic vs Germline — Choosing the Workflow
- Decision Tree by Scenario
- Somatic CNV Pipeline
- Germline gCNV Pipeline
- Failure Modes
- Reconciliation: GATK vs Other Callers
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/run_gatk_cnv.sh
Files
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## Version Compatibility
Reference examples tested with: GATK 4.5+ (gatk4), Python 3.10+ (gcnv conda env), R 4.3+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `gatk --version` then `gatk <ToolName> --help` to confirm arguments
- gCNV requires a working `gatkcondaenv` (theano/tensorflow stack) — `gatk` will report if the Python environment is missing
GATK 4.5+ gCNV inference defaults are tuned for whole-exome data; whole-genome runs generally need parameter changes. If a tool reports an unrecognized argument, check the help for that exact GATK version rather than retrying.
# GATK CNV Workflows
**"Call CNVs the GATK way"** -> GATK has two *separate* CNV workflows that share almost no tools. Picking the wrong one is the most common mistake.
bio-copy-number-germline-cnv-interpretation
When your agent uses it
Classify constitutional (germline) copy number variants for clinical reporting using the 2019 ACMG/ClinGen technical standards points-based framework, with ClassifyCNV and AnnotSV for semi-automated scoring. Covers the separate copy-number-loss and copy-number-gain rubrics, the five-tier classification, ClinGen haploinsufficiency/triplosensitivity and dosage-sensitive regions, de novo and segregation evidence, and population-frequency benign evidence. Use when assigning pathogenic/likely-pathogenic/VUS/likely-benign/benign to a constitutional CNV, scoring a CNV against ACMG/ClinGen criteria, or distinguishing the automatable evidence from the case-specific evidence requiring manual input.
What it loads into context
Frontmatter
244 tokensAlways available to the agent
SKILL.md
2,881 tokensWhen the skill is activated
Reference documents
926 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Points Framework
- Classification Workflow
- Semi-Automated Scoring with ClassifyCNV
- Comprehensive Annotation Cross-Check with AnnotSV
- Failure Modes
- Reconciliation
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/classify_germline_cnv.py
Files
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## Version Compatibility
Reference examples tested with: ClassifyCNV 1.1+, AnnotSV 3.4+, Python 3.10+ with pandas 2.2+; bedtools 2.31+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `python ClassifyCNV.py --help`, `AnnotSV --version`
- Update the bundled ClinGen/dosage databases — ClassifyCNV ships an `update_clingen.sh`; dosage curation changes, and a stale database silently mis-scores.
This skill is for **constitutional/germline** CNVs only. Somatic tumor CNVs use a different framework (AMP/ASCO/CAP and OncoKB tiers) — do not apply ACMG/ClinGen constitutional scoring to a tumor.
# Germline CNV Interpretation
**"Is this constitutional CNV pathogenic"** -> Apply the 2019 ACMG/ClinGen technical standards: a semiquantitative, points-based rubric that sums evidence into one of five clinical categories. There are two separate rubrics — one for copy-number **loss**, one for copy-number **gain** — because the evidence for deletion and duplication pathogenicity is different. The total score maps to a five-tier classification.
bio-copy-number-hrd-scoring
When your agent uses it
Quantify homologous recombination deficiency (HRD) from tumor copy number using the three genomic-scar metrics — loss of heterozygosity (LOH), large-scale state transitions (LST), and telomeric allelic imbalance (TAI) — with scarHRD, and via the whole-genome HRDetect and CHORD models. Covers the genomic instability score, the PARP-inhibitor clinical context, whole-genome-doubling correction, and the scar-versus-state distinction. Use when computing an HRD score for PARP-inhibitor eligibility, deriving LOH/LST/TAI scars from allele-specific copy number, deciding between scar-based and mutational-signature HRD methods, or interpreting an HRD result in a BRCA-reverted or low-purity tumor.
What it loads into context
Frontmatter
261 tokensAlways available to the agent
SKILL.md
3,006 tokensWhen the skill is activated
Reference documents
995 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Three Genomic Scars
- Method Selection
- Computing Genomic Scars with scarHRD
- The Postdoc-Level Caveats
- Failure Modes
- Reconciliation
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/compute_hrd.R
Files
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## Version Compatibility
Reference examples tested with: R 4.3+ with scarHRD 0.1.1+, sequenza 3.0+ (allele-specific input); HRDetect / CHORD as their respective R packages where whole-genome data is available.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('scarHRD')` then `?scar_score` to confirm arguments
- scarHRD is GitHub-only (`sztup/scarHRD`); install with `remotes::install_github`
scarHRD consumes allele-specific copy number — a Sequenza `.seqz` file or an ASCAT/allele-specific segment table. It cannot run on relative log2 copy ratio.
# HRD Scoring
**"Is this tumor homologous-recombination deficient"** -> HRD leaves characteristic copy-number scars. Three are quantified and summed into an HRD score: loss of heterozygosity (LOH), large-scale state transitions (LST), and telomeric allelic imbalance (TAI). A high score predicts response to platinum chemotherapy and PARP inhibitors. The scar score is a *consequence* of past HR deficiency — which is both its strength (it integrates over tumor history) and its key limitation.
bio-copy-number-recurrent-cnv
When your agent uses it
Identify recurrent and driver copy number alterations across a tumor cohort with GISTIC2 (G-score, Ziggurat deconstruction, focal vs broad/arm-level analysis, q-values from permutation) and quantify copy-number signatures with the Steele 2022 COSMIC framework and the Drews 2022 CINSignatures framework. Covers driver-gene localization from recurrence peaks, distinguishing focal drivers from arm-level passengers, and the caller-sensitivity caveats of copy-number signatures. Use when finding recurrently amplified or deleted regions in a cohort, localizing driver genes, separating focal from broad events, running GISTIC2, or extracting copy-number mutational signatures.
What it loads into context
Frontmatter
227 tokensAlways available to the agent
SKILL.md
3,044 tokensWhen the skill is activated
Reference documents
924 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- How GISTIC2 Works — and Its Limits
- Decision Tree
- Running GISTIC2
- Copy-Number Signatures
- Failure Modes
- Reconciliation
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/run_gistic2.sh
Files
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## Version Compatibility
Reference examples tested with: GISTIC 2.0.23, R 4.3+ with CINSignatureQuantification 1.2+; Python 3.10+ with SigProfilerAssignment 0.1+ (optional, COSMIC CN signatures).
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `gistic2 --help` (GISTIC 2.0 is a MATLAB-compiled binary; needs the MCR runtime)
- R: `packageVersion('CINSignatureQuantification')`
- Python: `pip show SigProfilerAssignment`
GISTIC 2.0 has had no substantive release since ~2017; it is effectively frozen. It runs as a compiled binary against the MATLAB Compiler Runtime — there is no R or Python package. Verify the reference (`-refgene`) `.mat` file matches the genome build.
# Recurrent and Driver Copy Number Alteration
**"Which copy number changes recur across my cohort, and which gene is the driver"** -> A CNV in one tumor is an observation; a CNV recurring across many tumors beyond chance is evidence of selection. GISTIC2 separates recurrent driver events from passengers by modeling a background rate and scoring each locus by how often, and how strongly, it is altered. Copy-number signatures decompose the genome-wide pattern of alterations into the mutational processes that generated them.bio-copy-number-subclonal-copy-number
When your agent uses it
Resolve subclonal copy number, whole-genome doubling, and copy-number tumor evolution from bulk sequencing with Battenberg, TITAN, and MEDICC2. Covers clonal versus subclonal copy-number states, haplotype phasing for subclonal resolution, cancer cell fraction, whole-genome-doubling detection and timing relative to mutations, mirrored subclonal allelic imbalance, and copy-number phylogenies. Use when a tumor is heterogeneous and bulk data shows non-integer copy number, when calling subclonal CNAs, detecting or timing whole-genome doubling, reconstructing copy-number evolution, or deciding between Battenberg and TITAN.
What it loads into context
Frontmatter
233 tokensAlways available to the agent
SKILL.md
3,547 tokensWhen the skill is activated
Reference documents
988 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Clonal vs Subclonal — What the Tools Output
- Tool Selection
- Whole-Genome Doubling — Detection and Timing
- Calling Subclonal CN with Battenberg
- Calling Subclonal CN with TITAN
- Failure Modes
- Reconciliation
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/run_titan.R
Files
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## Version Compatibility
Reference examples tested with: R 4.3+ with Battenberg 2.2.10+ and TitanCNA 1.40+, MEDICC2 1.0+, Python 3.10+; impute2/Beagle phasing reference panels.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('Battenberg')` / `'TitanCNA')` then `?function`
- CLI: `medicc2 --help`
- Battenberg is GitHub-only (`Wedge-lab/battenberg`) and needs a 1000 Genomes impute/phasing reference and allele-counter; confirm reference data is installed
Battenberg and TITAN both consume allele-specific data (logR + BAF at heterozygous SNPs); they cannot run on relative copy ratio alone.
# Subclonal Copy Number and Tumor Evolution
**"This copy number is non-integer — is it noise, or are there subclones"** -> A tumor is a mixture of cell populations. When a copy-number change is present in only some cancer cells, bulk sequencing averages it into a *non-integer* state. A long non-integer segment is not noise — it is a subclonal copy-number alteration, and resolving it reveals the tumor's clonal architecture.bio-crispr-screens-bagel-essentiality
When your agent uses it
Identifies essential genes from CRISPR-Cas9 fitness screens using BAGEL2 (Kim & Hart 2021 Genome Med), a Bayesian classifier scoring per-gene Bayes Factors via log-likelihood ratios over per-sgRNA fold changes, calibrated against CEGv2 core-essentials (Hart 2017 G3, ~684 genes) and NEGv1 non-essentials (Hart 2014, ~927 genes). Covers the fc + bf + pr workflow, the linear-extrapolation improvement over BAGEL1 truncation, multi-target off-target correction, tumor-suppressor sensitivity (BAGEL2 detects enrichment), and BF calibration (BF >6 ≈ 90% posterior per Hart 2017; ~5% FDR by BAGEL convention). Use when classifying essential vs non-essential genes, calibrating BAGEL2 thresholds against PR curves, identifying tumor suppressors alongside essentials, comparing BAGEL2 hits to MAGeCK / drugZ, or generating publication-quality essentiality calls.
What it loads into context
Frontmatter
311 tokensAlways available to the agent
SKILL.md
3,956 tokensWhen the skill is activated
Reference documents
1,893 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- BAGEL2 Essentiality Analysis
- The BAGEL2 Bayesian Framework (under the hood)
- Calibration to CEGv2 / NEGv1
- Compute Per-Sample Fold Changes
- Compute Bayes Factors
- Precision-Recall Curve
- Interpret BAGEL2 Results
- Bayesian Reasoning Per Sgrna
- Comparing BAGEL2, MAGeCK, drugZ
- Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/run_bagel2.sh
Files
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## Version Compatibility
Reference examples tested with: BAGEL2 2.0 (hart-lab/bagel, build 115), pandas 2.2+, numpy 1.26+, scipy 1.12+, matplotlib 3.8+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `BAGEL.py fc --help`; `BAGEL.py bf --help`; `BAGEL.py pr --help`
- Python: BAGEL2 is distributed via `git clone` (no canonical PyPI release); confirm `BAGEL.py version` after checkout.
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
## BAGEL2 Essentiality Analysis
**"Identify essential genes from my CRISPR fitness screen using BAGEL2"** -> Compute per-sgRNA fold changes from counts, derive per-gene log-likelihood ratios against reference essential and non-essential gene sets, sum to Bayes Factor, and apply BF threshold calibrated by precision-recall against the reference.
bio-crispr-screens-base-editing-analysis
When your agent uses it
Analyzes base-editing screens for variant function. Covers library design (Hanna 2021 ClinVar-scale CBE screen benchmarked on BRCA1/2, Cuella-Martin 2021 DDR saturation), CBE vs ABE chemistry choice (BE3/BE4 vs ABE7.10/ABE8.20/ABE8e), editing-window math (positions 4-8 from PAM-distal end; 4-7 for ABE7.10), bystander-edit quantification and the variant-call ambiguity it creates, sgRNA-efficiency filtering before hit calling, indel byproduct interpretation, the substitution-vs-indel diagnostic, variant annotation against ClinVar / COSMIC, and the Broad be-validation-pipeline. Use when designing a BE variant screen, choosing CBE vs ABE for a specific edit, interpreting bystander-confounded hits, distinguishing functional signal from indel artifact, integrating CRISPResso2 output with screen scoring, or deciding BE vs PE for SNV installation.
What it loads into context
Frontmatter
312 tokensAlways available to the agent
SKILL.md
6,152 tokensWhen the skill is activated
Reference documents
2,331 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Base Editing Screen Analysis
- Base Editor Chemistry Selection
- Editing Window Math
- sgRNA Library Design for BE Screens
- Editing Efficiency Filtering (Critical Pre-Hit-Calling)
- Bystander Edit Attribution
- Hit Calling for Variant-Function Screens
- Hanna 2021 BRCA1/2 Variant-Function Screen Methodology
- Cuella-Martin 2021 DDR-Gene Variant Screening
- Cas9 vs Base Editor vs Prime Editor for Variant Installation
- Broad be-validation-pipeline
- Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/base_editing_analysis.sh
Files
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## Version Compatibility
Reference examples tested with: CRISPResso2 2.2.14+, BE-Hive 1.0+ (BE prediction), pandas 2.2+, biopython 1.83+, numpy 1.26+, scipy 1.12+, scikit-learn 1.4+; Broad be-validation-pipeline notebooks (repo HEAD).
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `CRISPResso --version`
- Python: `pip show CRISPResso2`; BE-Hive is a GitHub clone (maxwshen/be_predict_bystander), not a PyPI package
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
## Base Editing Screen Analysis
**"Analyze my base-editor variant-function screen"** -> Quantify per-sgRNA target-base conversion, bystander rate, and indel byproducts from amplicon sequencing; filter on editing efficiency; map each sgRNA to its intended SNV (target + bystander pattern); compute per-variant fitness from the screen log-fold change; reconcile target vs bystander variant attribution; annotate against ClinVar / COSMIC.
bio-crispr-screens-batch-correction
When your agent uses it
Batch effect correction for CRISPR screens covering ComBat empirical-Bayes, RUV, SVA, control-sgRNA normalization, and the model-based alternative of including batch as a covariate in MAGeCK MLE or Chronos. Covers screen-specific batch sources (passage cohort, library lot, infection day, sequencing run, Cas9 lot, FBS lot), PCA + variance-decomposition diagnostic to decide if correction is needed, when correction harms biology by over-correcting condition into batch, limma removeBatchEffect for visualization-only correction, and relationship to multi-condition design matrices. Use when combining screens for joint analysis, when passage cohort confounds biology, when DepMap-style panels need Chronos with batch covariates, when picking ComBat vs RUV, or when correction harms biology and should be replaced with explicit covariate modeling.
What it loads into context
Frontmatter
276 tokensAlways available to the agent
SKILL.md
3,969 tokensWhen the skill is activated
Reference documents
1,711 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Batch Correction for CRISPR Screens
- Batch Sources in CRISPR Screens
- Batch Effect Decision Tree
- Diagnose: PCA + Variance Decomposition
- ComBat Empirical-Bayes Correction
- RUV (Remove Unwanted Variation)
- SVA (Surrogate Variable Analysis)
- Batch as Explicit Covariate (Preferred for MAGeCK MLE / Chronos)
- Control-Sgrna Anchored Normalization
- When NOT to Correct
- Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/batch_correct.py
Files
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## Version Compatibility
Reference examples tested with: pyComBat 0.3.3+ (epigenelabs/pyComBat), MAGeCK 0.5.9+, R/limma 3.58+, sva 3.50+, RUVSeq 1.36+, pandas 2.2+, numpy 1.26+, scikit-learn 1.4+, scipy 1.12+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show combat`; `from combat.pycombat import pycombat`
- R: `packageVersion('sva')`; `?ComBat`; `packageVersion('RUVSeq')`; `?RUVg`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
## Batch Correction for CRISPR Screens
**"Correct batch effects in my CRISPR screens"** -> Diagnose the batch source, decide whether to remove via empirical-Bayes (ComBat), explicit covariate modeling (MAGeCK MLE / Chronos design matrix), control-guide-anchored normalization, or unwanted-variation decomposition (RUV, SVA), then apply only the correction that preserves biological condition signal.
bio-crispr-screens-combinatorial-screens
When your agent uses it
Designs and analyzes combinatorial CRISPR screens covering paired-Cas9 (Big Papi, Najm 2018), enhanced AsCas12a multiplex (enCas12a, DeWeirdt 2021), in4mer 4-guide-array Cas12a (Esmaeili Anvar N et al 2024 Nat Commun 15:3577) and the Inzolia paralog-pair library, paralog-buffering detection (Dede 2020 Genome Biol; Thompson 2021 Nat Commun 12:1302), genetic-interaction (GI) scoring as observed_double_LFC minus expected_additive_double_LFC, synthetic-lethal and synthetic-rescue interaction interpretation, the half-of-essentiality buffered by paralogs phenomenon, multiplex screen statistical analysis with MAGeCK MLE interaction terms, and the relationship to single-cell combinatorial Perturb-seq. Use when designing a paralog or pathway-pair screen, choosing between paired-Cas9 (Big Papi) and Cas12a multiplex (Inzolia), interpreting genetic interaction scores, identifying synthetic-lethal targets for drug development, or scaling beyond single-gene CRISPR screens.
What it loads into context
Frontmatter
343 tokensAlways available to the agent
SKILL.md
4,651 tokensWhen the skill is activated
Reference documents
1,920 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Combinatorial CRISPR Screen Analysis
- Combinatorial Architecture Decision Tree
- Cas9 vs Cas12a for Multiplex
- The Paralog Buffering Phenomenon
- Genetic Interaction (GI) Scoring
- Run Combinatorial Screen Analysis (MAGeCK MLE with Interaction Indicator)
- Inzolia / in4mer 4-Guide Array Analysis
- Failure Modes
- Cross-Modality Validation
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/gi_scoring.py
Files
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## Version Compatibility
Reference examples tested with: MAGeCK 0.5.9+ (for MLE with interaction terms), Inzolia library annotation (Esmaeili Anvar 2024), pandas 2.2+, numpy 1.26+, scipy 1.12+, matplotlib 3.8+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `mageck --version`; `mageck mle --help`
- For Cas12a libraries: verify against published Inzolia / in4mer / Big Papi annotations
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
## Combinatorial CRISPR Screen Analysis
**"Run a combinatorial CRISPR screen to find synthetic-lethal interactions"** -> Design a paired or multiplex library, screen for double-knockout fitness, score per-pair genetic interaction (GI = observed_double - expected_additive), and identify synthetic-lethal (negative GI) and synthetic-rescue (positive GI) interactions.
bio-crispr-screens-copy-number-correction
When your agent uses it
Corrects the gene-independent copy-number artifact in CRISPR-Cas9 screens (Aguirre 2016 / Munoz 2016 Cancer Discov) where amplified loci appear essential from DNA-damage burden of simultaneous cuts. Covers the gene-independent DNA-damage / G2-arrest mechanism, CRISPRcleanR (Iorio 2018) unsupervised pre-hoc correction, CERES (Meyers 2017) joint CN + gene-effect model, Chronos (Dempster 2021) DepMap-standard population-dynamics + CN model with lowest residual bias, the decision tree by data availability, the Spearman LFC-vs-CN diagnostic, focal-amplification examples (ERBB2 in HER2+, MYC in colorectal, FGFR1 in head and neck), and CRISPRi/a alternatives that bypass the artifact. Use when screening cancer cell lines, diagnosing essentiality at amplified loci, choosing CRISPRcleanR / CERES / Chronos, deciding whether CN correction is needed before MAGeCK / BAGEL2 / drugZ, or switching from Cas9 to CRISPRi.
What it loads into context
Frontmatter
338 tokensAlways available to the agent
SKILL.md
4,668 tokensWhen the skill is activated
Reference documents
1,855 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Copy-Number Bias Correction in CRISPR Screens
- The Copy-Number Artifact (Mechanism)
- Correction Method Decision Tree
- CRISPRcleanR (Iorio 2018) - Unsupervised Pre-Hoc
- Chronos (Dempster 2021) - Joint Population-Dynamics + CN Model
- CERES (Legacy, Superseded by Chronos)
- Detect Uncorrected CN Bias
- Reconciliation: When CN Correction Fails
- Apply CN Correction to Pipeline
- Failure Modes
- CRISPRi/a Alternative
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: CRISPRcleanR 3.0+ (R; github.com/francescojm/CRISPRcleanR), Chronos 2.0+ (https://github.com/broadinstitute/chronos), CERES (legacy, superseded by Chronos), pandas 2.2+, numpy 1.26+, scipy 1.12+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('CRISPRcleanR')`; `?ccr.GWclean`
- Python: `pip show crispr_chronos`; `python3 -c 'import chronos; print(chronos.__file__)'`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
## Copy-Number Bias Correction in CRISPR Screens
**"Correct copy-number artifacts in my cancer-cell-line screen"** -> Identify gene-independent depletion at amplified loci, apply CRISPRcleanR (pre-hoc, unsupervised, position-based) or Chronos (joint model, supervised with CN profile) to remove the artifact, then proceed to hit calling on corrected data.
bio-crispr-screens-crispresso-editing
When your agent uses it
Quantifies CRISPR editing outcomes with CRISPResso2 (Clement 2019 Nat Biotechnol) across Cas9-nuclease (indels, HDR), CBE and ABE base editors (target conversion + bystander), and prime editor (pegRNA-templated) modes. Covers single-amplicon (CRISPResso), multi-sample batch (CRISPRessoBatch), pooled-amplicon (CRISPRessoPooled), WGS off-target (CRISPRessoWGS), and sample-comparison (CRISPRessoCompare) workflows; quantification-window math that controls what is called edited; substitution-vs-indel diagnostic to distinguish BE from Cas9 contamination; MMEJ deletion pattern interpretation; allele-frequency tables; and failure modes from amplicon misalignment or contamination. Use when quantifying editing from amplicon sequencing, choosing CRISPResso mode by design, distinguishing intended edits from bystanders and indel byproducts, debugging low-alignment runs, or generating publication-grade editing reports.
What it loads into context
Frontmatter
321 tokensAlways available to the agent
SKILL.md
5,075 tokensWhen the skill is activated
Reference documents
2,250 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- CRISPResso2 Editing Quantification
- Mode Decision Tree
- The Quantification Window
- Single-Amplicon Cas9 Editing
- Base Editor Quantification
- Prime Editor Quantification
- Batch Mode (Multi-Sample, Same Amplicon)
- Pooled-Amplicon Mode
- WGS Off-Target Mode
- Parse Output in Python
- Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/crispresso_analysis.sh
Files
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## Version Compatibility
Reference examples tested with: CRISPResso2 2.2.14+ (pinellolab/CRISPResso2), pandas 2.2+, numpy 1.26+, matplotlib 3.8+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `CRISPResso --version`; `CRISPRessoBatch --help`; `CRISPRessoPooled --help`; `CRISPRessoWGS --help`; `CRISPRessoCompare --help`
- Python: `from CRISPResso2 import ...`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
## CRISPResso2 Editing Quantification
**"Quantify CRISPR editing from my amplicon sequencing"** -> Align amplicon reads against the reference, classify each read as unmodified / NHEJ / HDR / base-edited / prime-edited within the quantification window, and report per-edit-type frequencies, indel size distributions, allele-frequency tables, and substitution-position profiles.
bio-crispr-screens-drugz-chemogenomic
When your agent uses it
Analyzes CRISPR drug-modifier (chemogenomic) screens with drugZ (Colic et al. 2019 Genome Med), a bidirectional Z-score method that identifies synthetic-lethal sensitizing genes and resistance-conferring suppressor genes from vehicle vs drug comparisons. Covers vehicle-anchored design (not Day-0), the bidirectional Z math giving greater sensitivity to small-effect hits than MAGeCK / STARS / edgeR / RIGER on drug screens, per-gene sumZ and normZ, synth (sensitizer) vs supp (suppressor) FDR, multi-dose handling, integration with control sgRNAs, and comparison with MAGeCK MLE with dose covariate. Use when running a drug-modifier CRISPR screen, identifying sensitizing or resistance genes for a drug candidate, choosing drugZ vs MAGeCK MLE for chemogenomic analysis, troubleshooting low-effect drug screens where MAGeCK lacks sensitivity, or designing a drug-screen layout (vehicle vs drug arms).
What it loads into context
Frontmatter
314 tokensAlways available to the agent
SKILL.md
3,759 tokensWhen the skill is activated
Reference documents
1,938 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- drugZ Chemogenomic Analysis
- Why drugZ for Drug Screens (not MAGeCK)
- The drugZ Algorithm (under the hood)
- Run drugZ on a Drug-Modifier Screen
- Vehicle vs Day-0 Reference: Critical Decision
- Drug-Dose and Time-Course Designs
- Comparison: drugZ vs MAGeCK MLE for Drug Screen
- Removing Genes from Null Distribution
- Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/run_drugz.py
Files
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## Version Compatibility
Reference examples tested with: drugZ Aug-2019+ (hart-lab/drugz; Python 3.6+), MAGeCK 0.5.9+, pandas 2.2+, numpy 1.26+, scipy 1.12+, statsmodels 0.14+, matplotlib 3.8+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `python drugz.py --help` (the repo has no setup.py, so there is no `drugz` console script)
- GitHub: install via `git clone https://github.com/hart-lab/drugz`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
## drugZ Chemogenomic Analysis
**"Identify genes that sensitize or confer resistance to my drug in a CRISPR screen"** -> Compare drug-treated vs vehicle-treated arms (NOT Day-0 baseline) using bidirectional Z-scores per sgRNA, sum to per-gene normalized Z, and rank genes for sensitizer (synthetic lethal) vs suppressor (resistance) phenotype.
bio-crispr-screens-hit-calling
When your agent uses it
Cross-method decision tree for calling hits in pooled CRISPR screens. Catalogs statistical models (MAGeCK RRA, MAGeCK MLE, BAGEL2, drugZ, JACKS, Chronos, CERES), experimental designs each is built for, failure modes outside design domain, reconciliation when methods disagree, multiple-testing and effect-size thresholds, the order of operations (count -> QC -> CN-correct -> hit-call -> validate), the second-best-sgRNA conservative rule, and consensus-hit strategy. Use when choosing among MAGeCK / BAGEL2 / drugZ / JACKS / Chronos for a given design, reconciling disagreement across two or three methods on the same screen, deciding whether to require consensus, gating downstream validation by hit-confidence tier, or interpreting unstable hit lists across reruns.
What it loads into context
Frontmatter
267 tokensAlways available to the agent
SKILL.md
5,176 tokensWhen the skill is activated
Reference documents
2,121 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Hit Calling Decision Tree
- Statistical Models Compared
- RRA vs MLE Within MAGeCK
- Algorithmic Taxonomy: Why Each Was Built
- Run All Five on the Same Data (Consensus Strategy)
- Reconciliation: When Two Methods Disagree
- Second-Best sgRNA Conservative Rule
- Multiple-Testing Correction Conventions
- Order of Operations
- Custom z-score Hit Calling (when standard tools don't fit)
- Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/consensus_hits.py
Files
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## Version Compatibility
Reference examples tested with: MAGeCK 0.5.9+, BAGEL2 2.0, drugZ Aug 2019+, JACKS 0.2.0+, Chronos 2.0+ (DepMap), CERES 1.0+, pandas 2.2+, numpy 1.26+, scipy 1.12+, statsmodels 0.14+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `mageck --version`, `BAGEL.py version`, `python drugz.py --help`
- Python: `pip show crispr_chronos` (JACKS installs from GitHub, not PyPI)
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
## Hit Calling Decision Tree
**"Identify significant hits in my CRISPR screen"** -> Choose the analysis method that matches the experimental design, statistical assumptions, and quality grade of the screen. Reconcile across methods when high-stakes hits must be validated.
bio-crispr-screens-in-vivo-screens
When your agent uses it
Designs and analyzes in vivo CRISPR screens in animal tumor models, organoids, and immune-cell adoptive transfers. Covers bottleneck math (250x cells/sgRNA requires ~25M cells implanted; impossible for most syngeneic models, forcing focused libraries), focused library design (Manguso 2017 Nature 547:413 immune screen; Chen 2015 tumor screens), CRISPR-StAR intrinsic-control screening (Uijttewaal 2025 Nat Biotechnol 43:1848), clonal-dynamics-limited detection, tumor-explant DNA recovery, syngeneic vs xenograft vs PDX considerations, and the relationship to downstream MAGeCK / drugZ analysis. Use when designing in vivo CRISPR screens for tumor / immune / metastasis biology, choosing focused vs genome-wide for animal models, addressing bottleneck-induced clonal collapse, picking the syngeneic / xenograft / PDX model, integrating in vivo with in vitro results, or applying CRISPR-StAR for animal experiments.
What it loads into context
Frontmatter
318 tokensAlways available to the agent
SKILL.md
3,845 tokensWhen the skill is activated
Reference documents
2,016 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- In Vivo CRISPR Screen Analysis
- The In Vivo Bottleneck Problem
- Focused Library Design for In Vivo
- CRISPR-StAR (Stochastic Activation by Recombination; Uijttewaal 2025)
- Syngeneic vs Xenograft vs PDX
- Tumor DNA Extraction and Sequencing
- Hit Calling for In Vivo
- Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/per_animal_meta_analysis.py
Files
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## Version Compatibility
Reference examples tested with: MAGeCK 0.5.9+, MAGeCK-VISPR 0.5.6+, pandas 2.2+, numpy 1.26+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `mageck --version`
- Reference focused libraries: Manguso 2017, Chen 2015, public Addgene aliquots
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
## In Vivo CRISPR Screen Analysis
**"Design or analyze an in vivo CRISPR screen"** -> Account for the dramatic bottleneck during animal implantation and tumor growth; use focused libraries; recover DNA from tumor explants; analyze with bottleneck-adjusted hit calling.
bio-crispr-screens-jacks-analysis
When your agent uses it
Runs JACKS (Joint Analysis of CRISPR/Cas9 Knockout Screens; Allen et al 2019 Genome Research) which models per-sgRNA log-fold-change as the product of a treatment-dependent gene-essentiality term and a treatment-independent guide-efficacy term. Covers the Bayesian decomposition math, the hierarchical efficacy prior shared across screens performed with the same library, when JACKS outperforms MAGeCK (multi-screen joint analysis, libraries with broad efficacy variance) and when it does not (single screen, novel libraries with no prior efficacy), library-reuse efficacy transfer, downstream essentiality interpretation, and the 2.5x sample-size reduction enabled by efficacy-aware testing. Use when running multiple screens with the same library, when guide-level noise is suspected to dominate per-gene signal, when reusing published essentiality reference screens for efficacy priors, or when comparing screens performed across cell lines that share library but differ biologically.
What it loads into context
Frontmatter
291 tokensAlways available to the agent
SKILL.md
4,330 tokensWhen the skill is activated
Reference documents
1,978 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- JACKS CRISPR Screen Analysis
- The JACKS Model (under the hood)
- When JACKS Outperforms MAGeCK and BAGEL2
- When JACKS Is Not the Right Tool
- Run JACKS Joint Analysis
- Output Interpretation
- Build Library-Wide Efficacy Prior from Reference Screens
- Per-sgRNA Efficacy Diagnostics
- Comparing JACKS, MAGeCK, BAGEL2
- Failure Modes
- Reconciliation: When JACKS and Other Tools Disagree
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/run_jacks.py
Files
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## Version Compatibility
Reference examples tested with: JACKS 0.2.0+ (felicityallen/JACKS), pandas 2.2+, numpy 1.26+, scipy 1.12+, matplotlib 3.8+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `python run_JACKS.py --help` (run_JACKS.py at the JACKS repo root after clone)
- Python: from jacks.jacks_io import runJACKS; help(runJACKS)
- GitHub: install via `git clone https://github.com/felicityallen/JACKS && cd JACKS && pip install .`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
## JACKS CRISPR Screen Analysis
**"Analyze CRISPR screens with guide-level efficacy modeling"** -> Jointly model per-sgRNA log-fold-change across one or more screens as the product of gene essentiality and guide efficacy, sharing efficacy across screens with the same library so that low-quality guides are down-weighted automatically.bio-crispr-screens-library-design
When your agent uses it
Designs pooled sgRNA libraries for CRISPR knockout, interference (CRISPRi), activation (CRISPRa), Cas12a multiplex, base-editor, and prime-editor screens. Covers on-target scoring (Rule Set 2, Azimuth, DeepSpCas9, CRISPRon), off-target scoring (CFD, MIT), TSS-relative positioning for CRISPRi/a (Horlbeck, Dolcetto, Calabrese), PAM-variant chemistries, control-guide composition, oligo cloning architecture, and library QC. Use when choosing a genome-wide library (GeCKOv2 vs Avana vs Brunello vs TKOv3 vs Inzolia), designing a focused or paralog-focused custom library, picking CRISPRi vs CRISPRa TSS windows, deciding control-guide proportions, or diagnosing library skew and dropout in a freshly cloned pool.
What it loads into context
Frontmatter
282 tokensAlways available to the agent
SKILL.md
6,895 tokensWhen the skill is activated
Reference documents
2,317 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- sgRNA Library Design
- Library Chemistry Decision Tree
- On-Target Scoring: Algorithmic Taxonomy
- Off-Target Scoring
- Score and Rank sgRNAs for a Target Gene
- CRISPRi / CRISPRa TSS Targeting
- Genome-Wide Library Selection
- PAM Variants and Alternative Cas Enzymes
- Control Guides
- Library Composition for Specialized Screens
- Oligo Design for Pooled Synthesis
- Library QC After Cloning
- Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/design_library.py
Files
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## Version Compatibility
Reference examples tested with: CRISPOR 5.01+, BioPython 1.83+, pandas 2.2+, numpy 1.26+, Azimuth 2.0+ (Doench 2016), CRISPRon 1.0+ (Xiang 2021), DeepSpCas9 1.0+ (Kim 2019).
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `crispor.py --help` from the crisporWebsite clone
- Python: Azimuth has no console script; call `azimuth.model_comparison.predict(...)`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
## sgRNA Library Design
**"Design a CRISPR library for my screen"** -> Pick a chemistry (Cas9 KO, CRISPRi, CRISPRa, Cas12a, base or prime editor), score candidate guides for on-target activity and off-target liability, position them relative to gene/TSS, add appropriate controls, lay out the oligo for synthesis, and validate the cloned pool.
bio-crispr-screens-mageck-analysis
When your agent uses it
Analyzes pooled CRISPR screens with MAGeCK (Li et al 2014), covering count generation (mageck count), the RRA two-condition workflow (mageck test using alpha-RRA over per-sgRNA negative-binomial p-values), the MLE multi-condition workflow (mageck mle with explicit design matrix and beta-score output), normalization choice (median vs total vs control-sgRNA vs spike-in), sgRNA efficiency injection, paired-sample testing, time-course design, drug-screen versus dropout-screen design matrices, MAGeCKFlute and MAGeCK-VISPR downstream visualization, and decision logic for when to use MAGeCK vs JACKS / BAGEL2 / drugZ / Chronos. Use when running a fresh CRISPR screen analysis, picking RRA vs MLE for the experimental design, choosing a normalization method from QC signatures, debugging MLE convergence failure or NaN beta scores, comparing MAGeCK output across tools, or building a batch-aware multi-cell-line / multi-condition MLE design matrix.
What it loads into context
Frontmatter
315 tokensAlways available to the agent
SKILL.md
6,269 tokensWhen the skill is activated
Reference documents
2,098 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- MAGeCK CRISPR Screen Analysis
- RRA vs MLE Decision Tree
- The RRA Algorithm (under the hood)
- The MLE Model (under the hood)
- Count sgRNAs from FASTQ
- Normalization Decision
- MAGeCK Test (RRA for Two-Condition)
- MAGeCK MLE (Multi-Condition)
- Sample MAGeCK Test for Drug Screen with sgRNA Efficiency
- Time-Course Analysis
- Visualizing Results
- MAGeCKFlute Integration (R)
- MAGeCK-VISPR Interactive Dashboard
- Failure Modes
- Reconciliation: When MAGeCK Disagrees With Other Tools
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/mageck_workflow.sh
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: MAGeCK 0.5.9+, MAGeCKFlute 2.0+ (R/Bioconductor), MAGeCK-VISPR 0.5.6+, pandas 2.2+, numpy 1.26+, matplotlib 3.8+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `mageck --version`, `mageck count --help`, `mageck test --help`, `mageck mle --help`
- R: `packageVersion('MAGeCKFlute')`, `?FluteRRA`, `?FluteMLE`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
## MAGeCK CRISPR Screen Analysis
**"Run MAGeCK on my pooled CRISPR screen"** -> Count sgRNAs from FASTQ, normalize across samples, and rank genes by enrichment or depletion using either the robust rank aggregation (RRA) test for two-condition designs or the maximum-likelihood (MLE) model with explicit design matrix for multi-condition / time-course / drug screens.
bio-crispr-screens-perturb-seq-analysis
When your agent uses it
Analyzes single-cell pooled CRISPR screens (Perturb-seq, CROP-seq, Perturb-CITE-seq, ECCITE-seq, multiome) where each cell carries an sgRNA and a scRNA-seq / surface-protein / chromatin readout. Covers experimental design (direct-capture Perturb-seq Dixit 2016 vs CROP-seq 3'UTR-barcoded Datlinger 2017 vs ECCITE-seq vs Multiome), MOI for sgRNA assignment, escaper-cell filtering (Mixscape, Papalexi 2021), SCEPTRE NB GLM + permutation for low-MOI (Barry 2024 Genome Biol 25:124), the Pertpy framework, factor decomposition, genome-scale Perturb-seq (Replogle 2022 Cell, 2.5M cells), and per-perturbation single-cell DE. Use when running a single-cell CRISPR screen, choosing direct-capture vs CROP-seq architecture, filtering escaper cells, performing single-cell DE, integrating Perturb-seq with pathway analysis, scaling to GW CRISPRi via Replogle protocol, or analyzing multi-omics screens.
What it loads into context
Frontmatter
353 tokensAlways available to the agent
SKILL.md
4,939 tokensWhen the skill is activated
Reference documents
2,256 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Single-Cell Perturb-Seq Analysis
- Experimental Architecture Comparison
- MOI and sgRNA Assignment
- Escaper Cell Filtering (Mixscape)
- SCEPTRE for Low-MOI Differential Expression
- Pertpy Unified Framework
- Genome-Wide Perturb-Seq (Replogle 2022)
- Factor-Based Analysis
- Multiomic Perturb-seq (RNA + ATAC)
- Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/run_pertpy.py
Files
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## Version Compatibility
Reference examples tested with: Pertpy 0.6+, SCEPTRE 0.10+ (R / katsevich-lab/sceptre), Mixscape via Seurat 4.3+ or Pertpy, scanpy 1.10+, anndata 0.10+, pandas 2.2+, numpy 1.26+, scipy 1.12+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show pertpy scanpy anndata`
- R: `packageVersion('sceptre')`; `?sceptre`; `?Seurat::PrepLDA`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
## Single-Cell Perturb-Seq Analysis
**"Analyze a single-cell pooled CRISPR perturbation screen"** -> Assign sgRNAs to cells, filter unperturbed escapers, normalize counts, fit per-gene differential expression conditioned on perturbation, and rank perturbations by their molecular effect.
bio-crispr-screens-prime-editing-screens
When your agent uses it
Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2/PE3/PE3b/PEmax variants, MOSAIC in situ saturation mutagenesis, the PRIME pooled-screen methodology (Ren 2023; ~3,699 ClinVar variant screens), chromatin context as a major locus-level determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of variants.
What it loads into context
Frontmatter
304 tokensAlways available to the agent
SKILL.md
4,404 tokensWhen the skill is activated
Reference documents
2,159 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Prime-Editing Screen Analysis
- Prime Editor Chemistry Comparison
- pegRNA Architecture
- PRIDICT and PRIDICT2 pegRNA Efficiency Prediction
- PRIME Pooled Screen Methodology
- MOSAIC In Situ Saturation Mutagenesis
- Run PRIDICT2 on a Custom pegRNA Library
- Cross-Validate PE with Base Editor Screens
- CRISPResso2 for PE Quantification
- Failure Modes
- Cas9 vs BE vs PE for Variant Installation
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/design_pegrna_pridict2.py
Files
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## Version Compatibility
Reference examples tested with: PRIDICT2 v1.0+ (https://github.com/uzh-dqbm-cmi/PRIDICT2), CRISPResso2 2.2.14+, pandas 2.2+, biopython 1.83+, numpy 1.26+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `python pridict2_pegRNA_design.py single --help`; `python pridict2_pegRNA_design.py batch --help`
- Web: PRIDICT2 web interface at https://pridict.it/
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
## Prime-Editing Screen Analysis
**"Design or analyze a pooled prime-editor screen"** -> Design pegRNAs (spacer + scaffold + PBS + RTT) for intended edits, predict efficiency with PRIDICT2, filter pre-synthesis to efficient candidates, install variants in the screen, quantify intended-edit vs scaffold-incorporation vs indel via CRISPResso2, and aggregate to per-variant fitness scores.
bio-crispr-screens-screen-qc
When your agent uses it
Quality control for pooled CRISPR screens covering library representation, Gini index, log-skew, replicate Pearson and Spearman concordance, essentialome precision-recall AUC against CEGv2 (Hart 2017), Cas9 cut-toxicity diagnostics, copy-number amplicon detection (Aguirre 2016 / Munoz 2016), bottleneck propagation through plasmid pool, infection, selection, and endpoint stages, MOI verification, and DepMap-style screen-quality scoring. Use when assessing screen quality before hit calling, deciding whether to repeat or rescue a screen, diagnosing low-confidence hits, choosing between MAGeCK / BAGEL2 / Chronos based on quality grade, picking a normalization strategy from QC signatures, or evaluating whether an in-vivo screen retained adequate library complexity.
What it loads into context
Frontmatter
267 tokensAlways available to the agent
SKILL.md
6,509 tokensWhen the skill is activated
Reference documents
1,941 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- CRISPR Screen Quality Control
- QC Stage Hierarchy
- Library Representation Metrics
- Gini Coefficient
- Replicate Concordance
- Essentialome Recovery (CEGv2 PR-AUC)
- Copy-Number Amplicon Bias Diagnostic
- Sequencing Depth Audit
- MOI Verification
- PCA and Batch Effect Detection
- Composite DepMap-Style Quality Score
- Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/screen_qc.py
Files
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## Version Compatibility
Reference examples tested with: MAGeCK 0.5+ (count + VISPR), MAGeCKFlute 2.0+ (R), pandas 2.2+, numpy 1.26+, scikit-learn 1.4+, matplotlib 3.8+, seaborn 0.13+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `mageck --version` then `mageck count --help`; R: `packageVersion('MAGeCKFlute')`
- R: `packageVersion('MAGeCKFlute')` then `?BatchRemove` / `?FluteRRA`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
## CRISPR Screen Quality Control
**"Audit my CRISPR screen quality before hit calling"** -> Assess library representation, replicate concordance, depth, drift, and biological signal recovery using DepMap-grade metrics, then decide whether the screen is usable, salvageable, or must be repeated.
bio-data-visualization-circos-plots
When your agent uses it
Build circular genome visualizations using circlize (R), pyCirclize (Python), or Circos (Perl CLI) with ideogram tracks, multi-data tracks (scatter, histogram, heatmap), chord/link arcs for interactions, and explicit circos.clear() between plots. Covers when circular is appropriate vs when Cartesian wins (Cleveland-McGill 1984), karyograms, and chromosome adjacency in chord diagrams. Use when adjacency on the circle conveys meaning — chromosome-level overview, structural variants, Hi-C interactions, cross-genome comparisons.
What it loads into context
Frontmatter
214 tokensAlways available to the agent
SKILL.md
3,493 tokensWhen the skill is activated
Reference documents
996 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Circular Plots Often Hide What Cartesian Reveals
- circlize (R) — Modern Default
- The `circos.clear()` Trap
- pyCirclize (Python)
- Circos CLI (Perl) — Most Powerful, Steepest Curve
- Decision Tree by Use Case
- Ideogram + Karyogram Without Circos
- Per-Method Failure Modes
- Reconciliation
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/circos_basic.Rscripts/circos_basic.py
Files
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## Version Compatibility
Reference examples tested with: circlize 0.4.16+ (R), pyCirclize 1.4+ (Python), Circos 0.69-9 (Perl CLI), ComplexHeatmap 2.18+ (uses circlize for color mapping).
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name`
- Python: `pip show <package>` then `help(module.function)`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Circular Genome Plots (Circos)
**"Make a circos plot"** -> Render genome chromosomes around a circle with stacked tracks (histogram, scatter, heatmap) and arcs/chords showing interactions. Krzywinski 2009 *Genome Res* 19:1639 introduced the genre for genome-scale comparative views. The single decision that matters: **does the circular layout convey meaning that Cartesian cannot?**
bio-data-visualization-color-palettes
When your agent uses it
Select colormaps and qualitative palettes for scientific figures using perceptual-uniformity, color-vision-deficiency safety, and luminance-monotonicity criteria. Covers Crameri scientific colormaps, viridis/cividis/magma, Okabe-Ito categorical, ColorBrewer, and the rainbow/jet critique. Use when choosing palettes for heatmaps, scatter, networks, or any encoding where color carries quantitative or categorical meaning.
What it loads into context
Frontmatter
197 tokensAlways available to the agent
SKILL.md
4,016 tokensWhen the skill is activated
Reference documents
1,231 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Three Modern Standards
- Palette Type by Data Type
- The Crameri Scientific Colormaps
- viridis Family (matplotlib default since 3.0)
- Okabe-Ito Categorical Palette (Wong 2011)
- ColorBrewer (Harrower & Brewer 2003)
- Scientific Journal Brand Palettes
- CVD Simulation -- The Mandatory Check
- Grayscale Monotonicity Test
- Diverging Palette Setup (LFC, z-score)
- Custom Palette Construction
- Common Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Files
SKILL.mdcore14,756 bytesSKILL.mdcore14,756 bytesSKILL.mdcore14,756 bytesSKILL.mdcore14,756 bytesSKILL.mdcore14,756 bytesSKILL.mdcore14,756 bytesreferences/usage-guide.mddoc4,884 bytesscripts/palette_examples.Rasset2,729 bytesscripts/palettes_phd.Rasset3,183 bytes
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## Version Compatibility
Reference examples tested with: viridis 0.6+, RColorBrewer 1.1+, scico 1.5+ (Crameri colormaps in R), khroma 1.12+ (Tol/Crameri palettes in R), matplotlib 3.8+, colorcet 3.0+, ggsci 3.0+, colorspace 2.1+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Color Palettes for Scientific Visualization
**"Pick a color palette"** -> Choose a colormap that (a) is perceptually uniform along the relevant data axis, (b) remains interpretable under common color-vision deficiencies, (c) prints correctly to grayscale, and (d) matches the data type — sequential, diverging, cyclic, or qualitative.
bio-data-visualization-dimensionality-reduction-plots
When your agent uses it
Produce and interpret PCA, t-SNE, UMAP, and PHATE plots for high-dimensional omics data with rigor about which method preserves what (variance, local structure, manifold, transitions), hyperparameter sensitivity, and the well-documented limits of 2D embeddings. Covers PCA biplot/scree/loadings, t-SNE PCA initialization (Kobak-Berens 2019), UMAP n_neighbors/min_dist trade-offs, and the Chari-Pachter 2023 critique. Use when visualizing high-dimensional data — bulk PCA, single-cell embeddings, multi-omics integration projections.
What it loads into context
Frontmatter
222 tokensAlways available to the agent
SKILL.md
5,047 tokensWhen the skill is activated
Reference documents
1,298 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- 2D Embeddings Distort
- Algorithmic Taxonomy
- Decision Tree by Scenario
- PCA -- The Underused Workhorse
- t-SNE -- Kobak-Berens Modern Defaults
- UMAP -- Modern Defaults and the Random-Seed Trap
- PHATE -- For Continuous Trajectories
- Per-Method Failure Modes
- Reconciliation: When Methods Disagree
- Quantitative Thresholds
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/embedding_phd.py
Files
SKILL.mdcore19,610 bytesSKILL.mdcore19,610 bytesSKILL.mdcore19,610 bytesSKILL.mdcore19,610 bytesSKILL.mdcore19,610 bytesSKILL.mdcore19,610 bytesreferences/usage-guide.mddoc5,091 bytesscripts/embedding_phd.pyruns3,851 bytes
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## Version Compatibility
Reference examples tested with: scanpy 1.10+, anndata 0.10+, scikit-learn 1.4+, umap-learn 0.5+, openTSNE 1.0+, phate 1.0+, ggplot2 3.5+, PCAtools 2.16+, matplotlib 3.8+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Dimensionality-Reduction Plots
**"Make a PCA / UMAP / t-SNE plot"** -> Choose a projection method aligned with what the plot must reveal — variance explained (PCA), local neighborhood structure (t-SNE), manifold approximation with some global structure (UMAP), or continuous transitions (PHATE). Set hyperparameters deliberately. Communicate the projection's limits and refuse to over-interpret 2D distances.
bio-data-visualization-distribution-plots
When your agent uses it
Plot per-group distributions of continuous data using boxplots, violins, beeswarms, quasirandom jitter, and raincloud plots with sample-size honesty (Weissgerber 2015), KDE-bandwidth awareness, and N-aware encoding choices. Use when comparing distributions across a small number of groups — expression per cluster, biomarker per arm, scores per condition — and the bar-of-mean default is misleading.
What it loads into context
Frontmatter
185 tokensAlways available to the agent
SKILL.md
3,830 tokensWhen the skill is activated
Reference documents
991 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Bars of Means Lie
- Decision Tree by N per Group
- Box, Violin, Beeswarm, Raincloud -- The Four Standard Encodings
- Per-Method Failure Modes
- Reconciliation: When Encodings Disagree
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: ggplot2 3.5+, ggbeeswarm 0.7+, ggdist 3.3+, gghalves 0.1.4+, seaborn 0.13+, matplotlib 3.8+, ptitprince 0.3+ (Python raincloud).
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Distribution Plots
**"Plot the distribution per group"** -> Render boxplot, violin, beeswarm, or raincloud calibrated to N per group, the underlying distribution shape, and the audience's ability to read each encoding. The default `geom_bar(stat='summary')` is the canonical misleading choice — Weissgerber 2015 *PLOS Biol* documented that 703 top physiology papers use bar-of-mean despite multiple distinct distributions producing identical bars.
bio-data-visualization-flow-and-transition-plots
When your agent uses it
Build Sankey, alluvial, river, and CONSORT-style flow diagrams to visualize cohort transitions, cell-state changes, or pipeline filtering using ggalluvial, networkD3, plotly, and consort. Use when showing how entities move between categories across timepoints (cell states, drug response classes, patient flow through a trial) or filtering pipelines (variants filtered through QC stages).
What it loads into context
Frontmatter
179 tokensAlways available to the agent
SKILL.md
3,039 tokensWhen the skill is activated
Reference documents
998 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Sankey vs Alluvial Are Different
- Decision Tree by Use Case
- ggalluvial -- Modern R Default for Alluvial
- networkD3 -- Interactive Sankey
- plotly Sankey (Python)
- CONSORT Diagrams -- The Formal Trial-Flow Standard
- Per-Method Failure Modes
- Reconciliation: When Implementations Differ
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Files
SKILL.mdcore11,674 bytesSKILL.mdcore11,674 bytesSKILL.mdcore11,674 bytesSKILL.mdcore11,674 bytesSKILL.mdcore11,674 bytesSKILL.mdcore11,674 bytesSKILL.mdcore11,674 bytesreferences/usage-guide.mddoc4,237 bytesscripts/alluvial_phd.Rasset4,124 bytes
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## Version Compatibility
Reference examples tested with: ggalluvial 0.12+, networkD3 0.4+, plotly 4.10+, consort 0.2+ (CONSORT diagrams), pySankey 0.0.1+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name`
- Python: `pip show <package>` then `help(module.function)`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Flow and Transition Plots
**"Show how things flow between categories"** -> Render entities as ribbons whose width encodes count, flowing between ordered columns of categories. Sankey emphasizes total flow magnitude; alluvial emphasizes per-entity continuity (each row's path is traceable); CONSORT formalizes the trial-filtering convention. The decision space: which method (Sankey vs alluvial vs CONSORT), how to order categories within each column, and whether to highlight specific entity trajectories.
bio-data-visualization-forest-funnel-plots
When your agent uses it
Build forest plots (HR, OR, RR, beta-coefficient summaries with CIs) and funnel plots (meta-analysis publication-bias diagnostics) using forestplot, metafor, ggforest, and MendelianRandomization with proper axis-scaling, summary-diamond placement, subgroup nesting, and Egger / trim-and-fill asymmetry tests. Use when summarizing effects across subgroups, trials, or instruments — meta-analysis, Mendelian randomization, subgroup HRs.
What it loads into context
Frontmatter
196 tokensAlways available to the agent
SKILL.md
4,231 tokensWhen the skill is activated
Reference documents
996 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Heterogeneity Is the First Question
- Decision Tree by Analysis Type
- Fixed-Effect vs Random-Effects Meta-Analysis
- metafor::rma + forest -- The Reference Implementation
- ggforest for Cox Subgroup Forests
- Small-k Regime -- When Meta-Analysis Asymptotics Break
- Funnel Plot and Egger Test
- Per-Method Failure Modes
- Reconciliation: When Methods Disagree
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Files
SKILL.mdcore16,408 bytesreferences/usage-guide.mddoc4,248 bytesscripts/forest_phd.Rasset3,716 bytes
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## Version Compatibility
Reference examples tested with: metafor 4.4+, forestplot 3.1+, ggforestplot 0.1+ (subgroup forests), ggforest from survminer 0.4.9+, MendelianRandomization 0.10+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Forest and Funnel Plots
**"Summarize effects across studies / subgroups"** -> Render each effect estimate (HR, OR, RR, β) as a square (size = inverse variance / weight), horizontal bar (95% CI), and label, with an optional summary diamond at the bottom from a meta-analysis pool (fixed-effect or random-effects). The funnel plot diagnoses publication bias by plotting effect size vs precision; asymmetry indicates missing small-study-with-null-result publications (Egger 1997).
- R: `metafor::forest`, `metafor::funnel`, `forestplot::forestplot`, `survminer::ggforest` (Cox HR forests), `MendelianRandomization::mr_forest`bio-data-visualization-genome-tracks
When your agent uses it
Build genome-browser-style multi-track figures with pyGenomeTracks (config-driven), Gviz (R), and IGV batch screenshotting. Covers BigWig coverage tracks, BED/peak overlays, gene-model rendering, Hi-C matrix tracks, BedPE link arcs, spike-in-aware normalization, and the bamCoverage --normalizeUsing trap. Use when producing publication figures of genomic loci with stacked aligned tracks (coverage, peaks, genes, interactions) for ChIP-seq, ATAC-seq, RNA-seq, Hi-C, or generic locus visualization.
What it loads into context
Frontmatter
211 tokensAlways available to the agent
SKILL.md
3,583 tokensWhen the skill is activated
Reference documents
1,114 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Spike-In Normalization Cannot Be Done With --normalizeUsing
- pyGenomeTracks — Config-Driven, Reproducible
- Gviz (R Bioconductor)
- IGV Batch Scripting
- BigWig Generation — The Spike-In Trap
- Track Comparison Across Samples
- Per-Method Failure Modes
- Reconciliation: When Tracks Disagree
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: pyGenomeTracks 3.9+, Gviz 1.46+ (Bioconductor), deepTools 3.5+, GenomicRanges 1.54+, IGV 2.18+ (batch mode).
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)`
- R: `packageVersion('<pkg>')` then `?function_name`
- CLI: `<tool> --version` then `<tool> --help`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Genome Browser Tracks
**"Plot a genomic locus with multiple tracks"** -> Build a stacked figure where each track (coverage from BigWig, peaks from BED, genes from GTF, Hi-C from cool, loops from BedPE) is aligned to genome coordinates. The decisions that matter: track normalization (especially for ChIP-Rx spike-in), gene-model rendering style (UCSC vs FlyBase), y-axis sharing across samples, and which tool fits the workflow — pyGenomeTracks (config-driven, reproducible, headless), Gviz (R Bioconductor), IGV batch (interactive-tool screenshots).bio-data-visualization-ggplot2-fundamentals
When your agent uses it
Build publication-quality figures in R with ggplot2 using the grammar of graphics (data + aesthetics + geometries + scales + facets + themes) with CVD-safe palettes, cairo_pdf TrueType embedding, programmatic aes via tidy evaluation, and the theme_classic publication baseline. Use when producing static figures in R for papers, presentations, or reports.
What it loads into context
Frontmatter
184 tokensAlways available to the agent
SKILL.md
3,099 tokensWhen the skill is activated
Reference documents
1,032 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Three Modern Defaults
- Grammar in Layers
- Common Geoms
- Aesthetic Mappings
- Scales
- Facets
- Theme
- Programmatic Plots (Tidy Evaluation)
- Labels with ggtext (rich-text)
- Saving — TrueType Embedding
- Common Failure Modes
- References
- Related Skills
Files
SKILL.mdcore11,497 bytesreferences/usage-guide.mddoc4,026 bytesscripts/publication_figures.Rasset3,613 bytes
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## Version Compatibility
Reference examples tested with: ggplot2 3.5+, scales 1.3+, ggrepel 0.9.5+, ggtext 0.1.2+, viridis 0.6+, scico 1.5+, patchwork 1.2+ (axes='collect' requires 1.2.0+).
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# ggplot2 Fundamentals
**"Build a publication figure in R"** -> Express the figure as **data + aesthetic mappings + one or more geometries + scales + facets + theme**. The grammar of graphics (Wilkinson 2005; Wickham 2010 *J Comput Graph Stat* 19:3) makes each visual element separately addressable — change scales without rewriting geoms; swap geom_point for geom_violin without touching aesthetics.
- R: `ggplot(data, aes(x, y)) + geom_point() + scale_color_manual(...) + theme_classic()`bio-data-visualization-heatmaps-clustering
When your agent uses it
Build clustered heatmaps for expression matrices and other features-by-samples data with rigorous distance/linkage/scaling choices, robust color mapping, optimal leaf ordering, and ComplexHeatmap/pheatmap/seaborn rendering. Covers the ward.D vs ward.D2 trap, the row-vs-column scaling decision, multi-track annotations, oncoPrint, and raster rendering for large matrices. Use when visualizing expression patterns across samples or identifying co-regulated clusters.
What it loads into context
Frontmatter
192 tokensAlways available to the agent
SKILL.md
5,815 tokensWhen the skill is activated
Reference documents
1,447 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Distance, Linkage, and Scaling Are Three Independent Decisions
- The ward.D vs ward.D2 Trap (Murtagh-Legendre 2014)
- Decision Tree by Scenario
- Color Mapping -- The Quietly Most-Important Choice
- Optimal Leaf Ordering (Bar-Joseph 2001)
- Annotation Tracks -- ComplexHeatmap as the Reference
- seaborn.clustermap (Python)
- OncoPrint -- The Specialized Mutation-Matrix Heatmap
- Per-Method Failure Modes
- Reconciliation: When Methods Disagree
- Quantitative Thresholds
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Files
SKILL.mdcore23,509 bytesSKILL.mdcore23,509 bytesreferences/usage-guide.mddoc6,100 bytesscripts/expression_heatmap.Rasset3,484 bytesscripts/heatmap_phd.Rasset3,747 bytes
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## Version Compatibility
Reference examples tested with: ComplexHeatmap 2.18+, pheatmap 1.0.13 (still maintained as of 2025-06), circlize 0.4.16+, seaborn 0.13+, scipy 1.12+, scanpy 1.10+, ggplot2 3.5+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Heatmaps and Hierarchical Clustering
**"Make a clustered heatmap"** -> Render an expression / feature matrix as a colored grid with hierarchical-clustering dendrograms, after committing to (a) how to scale the data (row z-score vs raw vs robust), (b) which distance metric (Euclidean vs correlation vs Manhattan), (c) which linkage criterion (ward.D2 vs complete vs average), (d) how to order the leaves (default vs optimal leaf ordering), (e) how to map values to color (sequential vs diverging, robust quantile bounds), and (f) which package can handle the matrix size and annotation complexity.
bio-data-visualization-interactive-visualization
When your agent uses it
Build interactive HTML/web visualizations with plotly (Python/R), bokeh (Python), and gganimate/plotly frames for animation, with awareness of current Kaleido static-export model (post-orca-EOL), HTML file-size bloat, and the limits of interactive-only output for journal submission. Use when producing zoomable/hoverable plots for notebook EDA, supplementary HTML, dashboards, or animated time-course / iteration visualizations.
What it loads into context
Frontmatter
190 tokensAlways available to the agent
SKILL.md
2,989 tokensWhen the skill is activated
Reference documents
972 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Kaleido v1 and the Static-Export Pipeline
- Interactive vs Static — The Reproducibility Cost
- plotly (Python) — Standard Interactive
- plotly (R) — ggplotly Conversion
- bokeh (Python) — Server-Side / Streaming
- Animation — gganimate (R) and plotly frames (Python)
- htmlwidgets Ecosystem (R)
- Per-Method Failure Modes
- Reconciliation
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/interactive_volcano.py
Files
SKILL.mdcore12,047 bytesSKILL.mdcore12,047 bytesSKILL.mdcore12,047 bytesSKILL.mdcore12,047 bytesSKILL.mdcore12,047 bytesreferences/usage-guide.mddoc4,084 bytesscripts/interactive_volcano.pyruns1,947 bytes
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## Version Compatibility
Reference examples tested with: plotly 5.24+, plotly R 4.10+, bokeh 3.4+, kaleido 1.0+ (note: v1 dropped bundled Chrome), gganimate 1.0.9+, altair 5.4+, htmlwidgets 1.6+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)`
- R: `packageVersion('<pkg>')` then `?function_name`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Interactive Visualization
**"Build an interactive plot"** -> Render a zoomable, hoverable, panable HTML/web visualization, knowing that interactive output is a SUPPLEMENT to (not replacement for) the static figure needed for journal submission. Choose plotly for fastest onboarding and ggplot2 conversion (`ggplotly`); bokeh for streaming/server-side; altair for grammar-of-graphics; D3.js for full custom.
bio-data-visualization-lollipop-protein-maps
When your agent uses it
Plot per-gene mutation distributions on a protein-domain map (lollipop / needle plots) showing mutation position, recurrence count, and variant classification with maftools, g3-lollipop, trackViewer, and ProteinPaint. Use when visualizing recurrent mutation hotspots on a single gene's protein, marking domain boundaries from UniProt/Pfam, comparing missense vs truncating distributions, or contrasting two cohorts on the same lollipop.
What it loads into context
Frontmatter
188 tokensAlways available to the agent
SKILL.md
3,100 tokensWhen the skill is activated
Reference documents
1,043 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Hotspot Recurrence Drives the Plot
- Decision Tree by Question
- maftools::lollipopPlot
- trackViewer::lolliplot -- Fine Control over Track Layout
- g3viz / g3-lollipop -- Interactive HTML
- Domain Annotation Sources
- Per-Method Failure Modes
- Reconciliation: When Hotspots Disagree
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: maftools 2.18+, trackViewer 1.38+, g3-lollipop (JavaScript via R `g3viz` 1.2+), Bio.PDB 1.83+ (for domain coordinates). ProteinPaint is a hosted service.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name`
- Python: `pip show <package>` then `help(module.function)`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Lollipop / Needle Protein Maps
**"Plot mutations on a gene's protein"** -> Render a horizontal protein backbone with colored domain rectangles (from UniProt/Pfam/InterPro), then stack vertical lines ("stems") at mutated amino-acid positions, capped with circles ("lollipops") whose size reflects mutation count and whose color encodes variant class. The biological story is hotspot identification — a tall stack of recurrences at a single residue (e.g., KRAS G12, PIK3CA E545/H1047) is the visual signature of a driver mutation.
bio-data-visualization-manhattan-qq-locuszoom
When your agent uses it
Build Manhattan, Miami, QQ, and locuszoom-style regional plots from GWAS, TWAS, PWAS, and QTL summary statistics with correct genomic-inflation diagnostics, multi-trait overlays, lead-SNP labeling, and LD-aware regional rendering. Use when visualizing association results across the genome, comparing two traits, computing genomic inflation lambda, or zooming into a locus with LD coloring.
What it loads into context
Frontmatter
184 tokensAlways available to the agent
SKILL.md
4,976 tokensWhen the skill is activated
Reference documents
1,210 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- The Threshold Is Always Conditional
- Decision Tree by Analysis
- Genomic Inflation (λGC) -- The Mandatory QC Step
- Small-N and Rare-Variant Regimes -- When Standard Asymptotics Break
- Manhattan Plot -- Canonical Layout
- Extreme Tail Handling -- The Y-Axis Cap
- Miami Plot -- Two-Trait Comparison
- Locuszoom-Style Regional Plot
- Per-Method Failure Modes
- Reconciliation: When QC Metrics Disagree
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: qqman 0.1.9 (R), CMplot 4.5+ (R), matplotlib 3.8+, pandas 2.2+, scipy 1.12+, plinkQC 0.3+. For locuszoom-style: locuszoomr 0.3+ (R) or pyranges + matplotlib.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Manhattan, QQ, and Locuszoom Plots
**"Plot my GWAS results"** -> Render per-variant -log10(p) across the genome (Manhattan), compare expected vs observed p quantiles (QQ + λGC), overlay two traits with mirrored axes (Miami), and zoom into a locus with LD-colored points + recombination rate + gene track (locuszoom). The choices that matter: significance thresholds, axis truncation for ultra-significant peaks, lead-SNP labeling, and LD reference selection for regional plots.
bio-data-visualization-matplotlib-fundamentals
When your agent uses it
Build publication-quality figures with matplotlib using the object-oriented Figure/Axes API, constrained_layout, rcParams customization, TrueType (Type-42) font embedding for journal submission, and CVD-safe palettes. Covers seaborn integration, common chart types, axis formatting, and the small gotchas that distinguish reproducible matplotlib from notebook scratch. Use when producing publication figures in Python — RNA-seq scatter, single-cell embeddings, generic biological plotting.
What it loads into context
Frontmatter
198 tokensAlways available to the agent
SKILL.md
3,243 tokensWhen the skill is activated
Reference documents
1,044 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Three Modern Defaults
- Standard Setup for Publication
- Figure / Axes API
- Common Chart Types
- seaborn Integration
- Axis Formatting
- Color and Palette
- Saving
- Common Failure Modes
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/matplotlib_phd.py
Files
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## Version Compatibility
Reference examples tested with: matplotlib 3.8+, seaborn 0.13+, numpy 1.26+, pandas 2.2+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# matplotlib Fundamentals
**"Make a publication figure in Python"** -> Build via the **object-oriented Figure/Axes API** (not pyplot state-machine), with `constrained_layout` for axes alignment, `pdf.fonttype=42` for journal-compliant TrueType fonts, CVD-safe palettes, and rasterized point layers for large scatter. The pyplot interface is for notebook scratch; the Figure/Axes API is for reproducible figures.
- Python: `fig, ax = plt.subplots()` -> `ax.scatter` / `ax.plot` / `ax.bar`; `seaborn.objects` (new grammar API) for ggplot-likebio-data-visualization-multipanel-figures
When your agent uses it
Compose multi-panel publication figures with patchwork, cowplot, gridExtra (R), or matplotlib GridSpec/subfigures (Python) including shared axes/legends/guides collection, panel labels in Nature/Cell convention, and journal-spec sizing. Covers patchwork ≥1.2.0 axes='collect' feature, Type-42 font embedding, and the cairo_pdf save path. Use when composing 2+ subpanels into a single figure for journal submission.
What it loads into context
Frontmatter
197 tokensAlways available to the agent
SKILL.md
3,352 tokensWhen the skill is activated
Reference documents
1,073 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Axes Collection Requires patchwork ≥ 1.2.0
- patchwork -- Modern R Composition
- patchwork Operators
- cowplot -- Alternative with Alignment Focus
- matplotlib GridSpec (Python)
- matplotlib Subfigures
- Journal Sizing
- Panel Labels — Nature/Cell Convention
- Per-Method Failure Modes
- Reconciliation
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/multipanel_matplotlib.py
Files
SKILL.mdcore12,441 bytesSKILL.mdcore12,441 bytesSKILL.mdcore12,441 bytesSKILL.mdcore12,441 bytesreferences/usage-guide.mddoc4,187 bytesscripts/multi_panel_figure.Rasset3,449 bytesscripts/multipanel_matplotlib.pyruns5,495 bytes
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## Version Compatibility
Reference examples tested with: patchwork 1.2+ (axes='collect' requires this version, released 2024-01-05), cowplot 1.1+, ggplot2 3.5+, matplotlib 3.8+ (subfigures stable since 3.4).
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name`
- Python: `pip show <package>` then `help(module.function)`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Multi-Panel Figures
**"Combine plots into a multi-panel figure"** -> Arrange individual plots into a single composed figure with consistent sizing, shared legends/axes, and panel labels (a, b, c) in the Nature/Cell convention. The decision space: which composition library (patchwork most modern in R; matplotlib subfigures in Python), how to share legends and axes, and how to size at journal specifications.
bio-data-visualization-network-visualization
When your agent uses it
Visualize biological networks (PPI, gene-regulatory, co-expression, pathway) with layout algorithm choice (ForceAtlas2, Fruchterman-Reingold, Kamada-Kawai, hive plots), edge bundling, community-based coloring, and reproducible seeds using NetworkX, PyVis, igraph, and Cytoscape automation. Use when rendering biological networks for static publication, interactive HTML exploration, or Cytoscape-format export.
What it loads into context
Frontmatter
189 tokensAlways available to the agent
SKILL.md
3,690 tokensWhen the skill is activated
Reference documents
982 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Layout Is an Artifact, Not Biology
- Decision Tree by Network Type and Question
- Layout Algorithms
- Hive Plots (Krzywinski 2012) — Biology-Faithful
- Hierarchical Edge Bundling (Holten 2006)
- NetworkX + matplotlib — Standard Static
- PyVis — Interactive HTML
- Cytoscape Automation (py4cytoscape)
- Per-Method Failure Modes
- Reconciliation: When Layouts Disagree
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/cytoscape_automation.pyscripts/interactive_network.pyscripts/network_plots.py
Files
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## Version Compatibility
Reference examples tested with: networkx 3.2+, igraph 0.10+ (Python and R), pyvis 0.3+, py4cytoscape 1.9+, matplotlib 3.8+, datashader 0.16+ (for large-graph rasterization).
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Network Visualization
**"Plot a biological network"** -> Select a layout algorithm (force-directed for general; hive plot for comparative; ForceAtlas2 for scale-free; circular for small dense), encode node attributes (size by degree/centrality, color by community/module), and choose rendering tier (matplotlib for static publication; PyVis for interactive HTML; Cytoscape for journal-grade compositing). The dominant pitfall is treating layout as biology — node positions in force-directed plots are NOT biologically meaningful; only connectivity is.
bio-data-visualization-oncoprint-mutation-matrices
When your agent uses it
Build OncoPrint and co-mutation matrix plots from somatic-variant cohorts using ComplexHeatmap, maftools, and comut.py with alteration-type stacking, sample ordering by mutational burden, mutual-exclusivity overlays, and clinical annotation tracks. Use when visualizing per-sample mutation patterns across recurrent driver genes, comparing alteration classes, or identifying mutually-exclusive / co-occurring driver pairs.
What it loads into context
Frontmatter
185 tokensAlways available to the agent
SKILL.md
3,729 tokensWhen the skill is activated
Reference documents
1,041 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Cell Stacking Encodes Multiple Alterations Per Cell
- Decision Tree by Cohort and Question
- ComplexHeatmap::oncoPrint -- Canonical Implementation
- maftools::oncoplot -- Faster Onboarding
- Mutual Exclusivity and Co-Occurrence
- comut.py -- Python Equivalent
- Per-Method Failure Modes
- Small-Cohort Regime (N = 20-50)
- Reconciliation: When Implementations Differ
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: ComplexHeatmap 2.18+, maftools 2.18+, comut 0.0.3+, MAFtools requires R 4.0+; comut.py requires pandas 2.0+, matplotlib 3.8+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# OncoPrint and Mutation Matrix Plots
**"Plot mutations across a cohort"** -> Render a gene-by-sample matrix where each cell stacks colored rectangles encoding alteration class (missense, truncating, splice, copy-gain, copy-loss, fusion). Sort samples by burden, optionally split by clinical group, and overlay co-mutation / mutual-exclusivity annotations. OncoPrint (Cerami 2012 *Cancer Discov* 2:401; canonical at cBioPortal) is the genre-defining visualization.
bio-data-visualization-sequence-logos
When your agent uses it
Build sequence logos from aligned DNA, RNA, or protein motifs using ggseqlogo (R), Logomaker (Python), or WebLogo with explicit bits vs probability encoding, background-frequency correction, custom alphabets, and multi-logo stacking. Use when visualizing motif PWMs (TF binding, splice sites, CRISPR spacers), aligned-position composition, or comparing two motif sets.
What it loads into context
Frontmatter
178 tokensAlways available to the agent
SKILL.md
3,390 tokensWhen the skill is activated
Reference documents
953 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Bits vs Probability Are Different Visualizations
- Decision Tree by Use Case
- ggseqlogo (R) -- Canonical Bioinformatics Default
- Logomaker (Python) -- Most Flexible
- WebLogo (CLI / web)
- Background Composition Correction
- Per-Method Failure Modes
- Reconciliation: When Logos Differ
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Files
SKILL.mdcore13,256 bytesSKILL.mdcore13,256 bytesSKILL.mdcore13,256 bytesSKILL.mdcore13,256 bytesSKILL.mdcore13,256 bytesreferences/usage-guide.mddoc4,069 bytesscripts/seqlogo_phd.Rasset3,165 bytes
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## Version Compatibility
Reference examples tested with: ggseqlogo 0.2 (CRAN; per Wagih 2017), Logomaker 0.8+ (Python), WebLogo 3.7+ (CLI), Biopython 1.83+ (motif parsing), MEME suite 5.5+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Sequence Logos
**"Plot a sequence motif"** -> Render a per-position stack of letters whose total height encodes information content (Schneider-Stephens 1990 *Nucleic Acids Res* 18:6097) and individual letter height is proportional to base/aa frequency. The information-content encoding makes conserved positions visually tall and variable positions visually short — the visual is *the conservation profile*.
bio-data-visualization-statistical-annotation
When your agent uses it
Add p-value brackets, significance asterisks, and effect-size annotations to distribution plots using ggpubr, ggsignif, and statannotations with correct test selection (parametric vs non-parametric vs paired), multiple-testing adjustment, and rendering of negative results. Use when a boxplot/violin/raincloud needs in-figure statistical comparisons between groups.
What it loads into context
Frontmatter
175 tokensAlways available to the agent
SKILL.md
3,380 tokensWhen the skill is activated
Reference documents
991 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- The Test Must Match the Data
- Decision Tree for Test Selection
- Multiple Testing Adjustment
- ggpubr -- Standard ggplot2 Workflow
- ggsignif -- Lighter Alternative
- statannotations (Python)
- Per-Method Failure Modes
- Reconciliation: When Tests Disagree
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: ggpubr 0.6+, ggsignif 0.6+, rstatix 0.7+, statannotations 0.6+ (Python), seaborn 0.13+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name`
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Statistical Annotation
**"Add p-values to my plot"** -> Render pairwise group comparisons as brackets with the correct statistical test (parametric vs non-parametric, paired vs unpaired, independent vs nested), adjusted for multiple testing, with rendering of significance as either numerical p OR asterisks. The choices that matter: which test is appropriate for the data, what multiple-testing adjustment applies, and whether to show n.s. (non-significant) results.
bio-data-visualization-upset-plots
When your agent uses it
Build UpSet plots to visualize set intersections beyond 4 sets (where Venn fails) using ComplexUpset (modern, ggplot2-grammar) or the unmaintained UpSetR, with explicit cardinality vs degree sorting, attribute panels, and query highlighting. Use when comparing overlap across many gene sets, peak sets, variant lists, or any set membership matrix where Venn diagrams become illegible.
What it loads into context
Frontmatter
183 tokensAlways available to the agent
SKILL.md
3,008 tokensWhen the skill is activated
Reference documents
949 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- UpSetR Is Effectively Unmaintained
- ComplexUpset (Modern Default)
- Sorting -- Cardinality vs Degree
- Pre-Specified Queries / Highlighting
- Attribute Panels (ComplexUpset Strength)
- upsetplot (Python)
- UpSetR (Legacy — Use Only for Reproducibility)
- Per-Method Failure Modes
- Reconciliation: When Implementations Differ
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/upset_python.py
Files
SKILL.mdcore12,001 bytesSKILL.mdcore12,001 bytesreferences/usage-guide.mddoc3,921 bytesscripts/upset_gene_sets.Rasset3,253 bytesscripts/upset_python.pyruns3,678 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: ComplexUpset 1.3+ (R, Krassowski), UpSetR 1.4.0 (last 2019 release; effectively unmaintained), upsetplot 0.9+ (Python).
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name`
- Python: `pip show <package>` then `help(module.function)`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# UpSet Plots
**"Show set intersections for 4+ sets"** -> Replace Venn diagrams (which become illegible past 4 sets) with UpSet (Lex 2014 *IEEE TVCG* 20:1983). The display: a matrix of dots indicating which sets participate in each intersection, with a vertical bar above each column showing intersection size and horizontal bars on the left showing per-set total size. Sort by intersection size (cardinality) for "biggest overlaps first" or by degree (number of sets) for grouped layout.
bio-data-visualization-volcano-and-ma-plots
When your agent uses it
Build volcano and MA plots from differential-expression / association results with LFC shrinkage, FDR-adjusted thresholds, sensible label placement, and axis-truncation conventions. Covers EnhancedVolcano, ggplot2, matplotlib, and the apeglm/ashr/normal shrinkage decision. Use when visualizing differential-expression results (RNA-seq, ChIP-seq, ATAC-seq, proteomics) or any per-feature effect-size + p-value table.
What it loads into context
Frontmatter
196 tokensAlways available to the agent
SKILL.md
5,775 tokensWhen the skill is activated
Reference documents
1,495 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Plot Shrunken LFC
- Shrinkage Method Selection
- Decision Tree by Scenario
- Volcano with ggplot2 + ggrepel
- EnhancedVolcano -- Production Use and Its Gotchas
- MA Plot -- The Underused Diagnostic
- Per-Method Failure Modes
- Reconciliation: When Methods Disagree
- Quantitative Thresholds
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: DESeq2 1.42+, EnhancedVolcano 1.20+, ggplot2 3.5+, ggrepel 0.9.5+, matplotlib 3.8+, numpy 1.26+, adjustText 1.1+, apeglm 1.28+, ashr 2.2+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Volcano and MA Plots
**"Plot differential-expression results"** -> Place per-feature shrunken effect estimate on the x-axis and a significance measure (-log10 padj or -log10 p) on the y-axis. The decision space spans which effect estimate (raw vs shrunken), which significance measure (raw p vs adjusted vs s-value), how to encode categories (color by direction, not by gradient), how to label (top-N is rarely informative), and how to handle the tail (extreme p compresses the plot).
bio-batch-downloads
When your agent uses it
Download large datasets from NCBI efficiently using EPost, history server, batching, rate limiting, and retry logic. Use when bulk-fetching tens of thousands of sequences, pulling all results of a large ESearch, designing reproducible pipelines, comparing E-utilities to NCBI Datasets v2 CLI, or implementing checksum-validated downloads. Encodes WebEnv TTL (~8h), EPost 200-ID limit, retmax caps, parallelization design, and integrity verification.
What it loads into context
Frontmatter
193 tokensAlways available to the agent
SKILL.md
3,966 tokensWhen the skill is activated
Reference documents
1,117 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Required Setup
- Decision matrix: which retrieval strategy?
- Rate-limit math (precise)
- History server lifecycle (the long-running-job trap)
- EPost specifics
- Batch size guidelines per rettype
- Code patterns
- Failure modes
- Common errors
- References
- Related Skills
Scripts executed separately
scripts/batch_by_ids.pyscripts/batch_fasta.pyscripts/robust_download.py
Files
SKILL.mdcore15,770 bytesSKILL.mdcore15,770 bytesSKILL.mdcore15,770 bytesSKILL.mdcore15,770 bytesSKILL.mdcore15,770 bytesSKILL.mdcore15,770 bytesSKILL.mdcore15,770 bytesSKILL.mdcore15,770 bytesreferences/usage-guide.mddoc4,469 bytesscripts/batch_by_ids.pyruns2,958 bytesscripts/batch_fasta.pyruns2,395 bytesscripts/robust_download.pyruns3,849 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: BioPython 1.83+, NCBI Datasets CLI 16.0+, Entrez Direct 21.0+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show biopython` then `help(Bio.Entrez.efetch)` to check signatures
- CLI: `datasets --version` and `efetch -version`
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Batch Downloads
**"Download N thousand records from NCBI without getting blocked"** -> The right answer is rarely "parallelize requests". For >5000 records the answer is the **history server**: search once, fetch in chunks server-side. For >100,000 records or whole genomes, the modern answer is **NCBI Datasets v2 CLI** -- the E-utilities are not optimized for bulk genome/gene data anymore.bio-biomart-queries
When your agent uses it
Bulk-query Ensembl BioMart (and other BioMart instances) for cross-database ID mapping, gene/transcript/exon coordinates, and ortholog tables. Use when batch-converting Ensembl IDs to other namespaces (HGNC, RefSeq, UniProt, Entrez), pulling gene coordinate tables for thousands of genes, building ortholog wide-tables across species, or replacing slow Ensembl REST loops with one-shot bulk export. Encodes BioMart's XML query format, R biomaRt vs Python pybiomart trade-off, mart-vs-dataset hierarchy, and the URL endpoint that's BioMart-specific (separate from rest.ensembl.org).
What it loads into context
Frontmatter
231 tokensAlways available to the agent
SKILL.md
3,525 tokensWhen the skill is activated
Reference documents
1,085 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Installation
- BioMart hierarchy
- Decision matrix: BioMart vs Ensembl REST
- Common attribute selectors
- Common filter selectors
- Code patterns
- Failure modes
- Common errors
- References
- Related Skills
Scripts executed separately
scripts/bulk_id_mapping.pyscripts/coordinate_table.shscripts/ortholog_table.py
Files
SKILL.mdcore12,856 bytesSKILL.mdcore12,856 bytesSKILL.mdcore12,856 bytesSKILL.mdcore12,856 bytesSKILL.mdcore12,856 bytesSKILL.mdcore12,856 bytesSKILL.mdcore12,856 bytesSKILL.mdcore12,856 bytesreferences/usage-guide.mddoc4,151 bytesscripts/bulk_id_mapping.pyruns1,692 bytesscripts/coordinate_table.shruns1,699 bytesscripts/ortholog_table.pyruns1,658 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: pybiomart 0.9+, R biomaRt 2.58+ (Bioconductor); Ensembl BioMart (release 110+)
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show pybiomart`
- R: `packageVersion('biomaRt')`
The BioMart XML query format is stable across Ensembl releases; the underlying mart names and attribute IDs can change between Ensembl releases. For published work, pin the Ensembl release via `useEnsembl(version=110)`.
# BioMart Queries
**"Bulk-convert IDs / pull coordinate tables / extract ortholog wide tables"** -> BioMart is the right answer for any Ensembl-rooted query producing >5,000 rows. It is a separate service from the Ensembl REST API, with separate rate behavior and a different query model (XML-based, batch-oriented). For one-off lookups (<100 records), Ensembl REST is more convenient; for bulk anything, BioMart wins.
bio-blast-searches
When your agent uses it
Run remote BLAST searches against NCBI servers using Biopython Bio.Blast.NCBIWWW. Use when identifying unknown sequences, finding homologs, picking the correct BLAST program (blastn/blastp/blastx/tblastn/tblastx/psiblast/megablast/dc-megablast), interpreting Karlin-Altschul E-values, avoiding the max_target_seqs trap (Shah 2019), choosing composition-based statistics, or limiting searches by organism. Covers RID lifecycle, database choice (nt/nr/refseq_select/swissprot), word-size and CBS taxonomy.
What it loads into context
Frontmatter
222 tokensAlways available to the agent
SKILL.md
4,334 tokensWhen the skill is activated
Reference documents
1,195 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Required Setup
- Program decision (query vs database molecule)
- Database decision (search space)
- E-value interpretation (Karlin-Altschul)
- Composition-Based Statistics (CBS)
- The `max_target_seqs` trap
- Word size, gap costs, and matrix
- RID lifecycle
- Code patterns
- Failure modes
- Common errors
- References
- Related Skills
Scripts executed separately
scripts/basic_blast.pyscripts/blastp_filtered.pyscripts/save_and_parse.py
Files
SKILL.mdcore15,778 bytesSKILL.mdcore15,778 bytesSKILL.mdcore15,778 bytesSKILL.mdcore15,778 bytesSKILL.mdcore15,778 bytesSKILL.mdcore15,778 bytesSKILL.mdcore15,778 bytesSKILL.mdcore15,778 bytesSKILL.mdcore15,778 bytesreferences/usage-guide.mddoc4,721 bytesscripts/basic_blast.pyruns1,998 bytesscripts/blastp_filtered.pyruns1,590 bytesscripts/save_and_parse.pyruns2,152 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: BioPython 1.83+, NCBI BLAST+ 2.15+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show biopython` then `help(Bio.Blast.NCBIWWW.qblast)` to check signatures
- CLI: `blastn -version` then `blastn -help`
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# BLAST Searches (Remote)
**"Find similar sequences in NCBI's database"** -> Submit a query to NCBI's remote BLAST servers; receive a Request ID (RID); poll for completion; parse the XML hit table. Best for one-off identification of a few sequences. For >50 sequences, switch to `local-blast` or DIAMOND/MMseqs2 in `remote-homology`.bio-ensembl-rest
When your agent uses it
Query the Ensembl REST API for gene/transcript/protein lookup, sequence retrieval, comparative genomics (Compara), variant effect prediction (VEP), regulatory features, and cross-species ortholog/paralog calls. Use when pulling Ensembl-native data (Ensembl Gene IDs, version-pinned releases, archive endpoints for reproducibility), gene/transcript/exon structure with stable IDs, or VEP for variant annotation. Encodes the 15 req/sec rate limit, archive (e110.rest.ensembl.org) for reproducibility, Ensembl divisions (vertebrates / plants / fungi / metazoa / bacteria), and the symbol-vs-ID stability problem.
What it loads into context
Frontmatter
229 tokensAlways available to the agent
SKILL.md
4,004 tokensWhen the skill is activated
Reference documents
1,169 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Required Setup
- Ensembl divisions
- Version pinning
- Major endpoint groups
- The symbol-vs-ID stability problem
- Rate-limit math
- VEP (Variant Effect Predictor)
- Compara homology
- Code patterns
- Failure modes
- Common errors
- References
- Related Skills
Scripts executed separately
scripts/compara_homology.pyscripts/lookup_and_overlap.pyscripts/vep_annotation.py
Files
SKILL.mdcore14,813 bytesSKILL.mdcore14,813 bytesSKILL.mdcore14,813 bytesSKILL.mdcore14,813 bytesSKILL.mdcore14,813 bytesSKILL.mdcore14,813 bytesSKILL.mdcore14,813 bytesSKILL.mdcore14,813 bytesSKILL.mdcore14,813 bytesSKILL.mdcore14,813 bytesSKILL.mdcore14,813 bytesreferences/usage-guide.mddoc4,532 bytesscripts/compara_homology.pyruns2,700 bytesscripts/lookup_and_overlap.pyruns2,766 bytesscripts/vep_annotation.pyruns2,623 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: requests 2.31+, Ensembl REST API (release 110+); Ensembl release schedule is roughly quarterly
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show requests`
- API surface: check release notes at https://rest.ensembl.org
Each Ensembl release has an archive REST endpoint (e.g. `https://e110.rest.ensembl.org`) for reproducibility.
# Ensembl REST
**"Pull Ensembl-native gene / transcript / variant data programmatically"** -> Ensembl REST is distinct from NCBI Entrez and BioMart. It is the right answer for: stable Ensembl IDs, transcript / exon structure, VEP (Variant Effect Predictor) annotation, Compara orthologs at vertebrate scale, regulatory feature annotation, and any workflow rooted in Ensembl's coordinate system.
bio-entrez-fetch
When your agent uses it
Retrieve records from NCBI databases using Biopython Bio.Entrez (EFetch, ESummary). Use when downloading sequences, fetching GenBank/GenPept records, getting document summaries, parsing nested XML, navigating GI deprecation, choosing between rettype+retmode combinations, and parsing into Biopython SeqRecord/SwissProt objects. Covers nucleotide, protein, gene, pubmed, sra, gds, taxonomy, snp, clinvar.
What it loads into context
Frontmatter
189 tokensAlways available to the agent
SKILL.md
4,032 tokensWhen the skill is activated
Reference documents
1,076 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Required Setup
- Decision matrix: rettype + retmode per database
- GI deprecation (still bites in 2026)
- ESummary vs EFetch triage
- XML schema brittleness
- Code patterns
- Failure modes
- Common errors
- References
- Related Skills
Scripts executed separately
scripts/fetch_pubmed.pyscripts/fetch_sequences.pyscripts/fetch_summaries.py
Files
SKILL.mdcore14,939 bytesSKILL.mdcore14,939 bytesSKILL.mdcore14,939 bytesSKILL.mdcore14,939 bytesSKILL.mdcore14,939 bytesSKILL.mdcore14,939 bytesSKILL.mdcore14,939 bytesSKILL.mdcore14,939 bytesSKILL.mdcore14,939 bytesSKILL.mdcore14,939 bytesreferences/usage-guide.mddoc4,355 bytesscripts/fetch_pubmed.pyruns2,210 bytesscripts/fetch_sequences.pyruns2,267 bytesscripts/fetch_summaries.pyruns1,881 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: BioPython 1.83+, Entrez Direct 21.0+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show biopython` then `help(Bio.Entrez.efetch)` to check signatures
- CLI: `efetch -version` then `efetch -help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Entrez Fetch
**"Download a record by accession from NCBI"** -> EFetch returns the full record content in a chosen format (FASTA, GenBank, XML, MEDLINE, etc.). ESummary returns a lightweight "docsum" object — much faster when only metadata is needed.bio-entrez-link
When your agent uses it
Find cross-database references between NCBI databases using Biopython Bio.Entrez (ELink). Use when navigating gene to protein/structure, sequence to publication, PubMed to GEO, BioProject to SRA runs, or discovering all link relationships for a record. Covers linkname semantics, cmd= variants, asymmetric link warnings, neighbor_history for >200 input IDs, and per-database link tables.
What it loads into context
Frontmatter
178 tokensAlways available to the agent
SKILL.md
4,348 tokensWhen the skill is activated
Reference documents
1,076 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Required Setup
- The `linkname` decision (most important)
- Decision table: which `cmd` for which goal
- Asymmetric link warning
- Per-database link catalog (curated subset)
- Code patterns
- Failure modes
- Common errors
- References
- Related Skills
Scripts executed separately
scripts/basic_linking.pyscripts/chain_links.pyscripts/discover_links.py
Files
SKILL.mdcore15,554 bytesSKILL.mdcore15,554 bytesSKILL.mdcore15,554 bytesSKILL.mdcore15,554 bytesSKILL.mdcore15,554 bytesSKILL.mdcore15,554 bytesSKILL.mdcore15,554 bytesSKILL.mdcore15,554 bytesSKILL.mdcore15,554 bytesSKILL.mdcore15,554 bytesSKILL.mdcore15,554 bytesSKILL.mdcore15,554 bytesSKILL.mdcore15,554 bytesSKILL.mdcore15,554 bytesSKILL.mdcore15,554 bytesSKILL.mdcore15,554 bytesreferences/usage-guide.mddoc4,190 bytesscripts/basic_linking.pyruns2,744 bytesscripts/chain_links.pyruns2,750 bytesscripts/discover_links.pyruns1,175 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: BioPython 1.83+, Entrez Direct 21.0+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show biopython` then `help(Bio.Entrez.elink)` to check signatures
- CLI: `elink -version` then `elink -help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Entrez Link
**"Find records linked to this record in another NCBI database"** -> ELink walks the curated, weekly-maintained link tables between Entrez databases. A link is an asserted relationship (e.g. "this PubMed article describes this nucleotide sequence"), not a similarity hit.bio-entrez-search
When your agent uses it
Search NCBI databases using Biopython Bio.Entrez (ESearch, EInfo, EGQuery, ESpell). Use when finding records by keyword, building reproducible field-qualified queries, navigating the Entrez Query Translator, exploiting the history server for large result sets, handling retmax caps, or interpreting weekly index lag. Covers PubMed, Nucleotide, Protein, Gene, SRA, GEO, Assembly, Taxonomy, ClinVar, dbSNP.
What it loads into context
Frontmatter
189 tokensAlways available to the agent
SKILL.md
4,385 tokensWhen the skill is activated
Reference documents
1,061 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Required Setup
- What ESearch actually does
- Decision table: which utility for which question
- retmax silent caps
- History server (WebEnv/QueryKey) semantics
- Index lag
- Field-qualified query patterns (per database)
- Code patterns
- Failure modes
- Rate-limit math
- Common errors
- References
- Related Skills
Scripts executed separately
scripts/basic_search.pyscripts/database_info.pyscripts/global_query.py
Files
SKILL.mdcore16,094 bytesSKILL.mdcore16,094 bytesSKILL.mdcore16,094 bytesSKILL.mdcore16,094 bytesSKILL.mdcore16,094 bytesSKILL.mdcore16,094 bytesSKILL.mdcore16,094 bytesSKILL.mdcore16,094 bytesSKILL.mdcore16,094 bytesSKILL.mdcore16,094 bytesSKILL.mdcore16,094 bytesSKILL.mdcore16,094 bytesSKILL.mdcore16,094 bytesreferences/usage-guide.mddoc4,279 bytesscripts/basic_search.pyruns2,684 bytesscripts/database_info.pyruns1,269 bytesscripts/global_query.pyruns1,566 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: BioPython 1.83+, Entrez Direct 21.0+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show biopython` then `help(Bio.Entrez.esearch)` to check signatures
- CLI: `esearch -version` then `esearch -help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Entrez Search
**"Find NCBI records matching a query"** -> ESearch returns matching record UIDs (not full records) from one NCBI database; EGQuery returns counts across all databases; EInfo describes a database's searchable fields and update timestamp.bio-geo-data
When your agent uses it
Query and download from NCBI Gene Expression Omnibus (GEO) and EMBL-EBI's BioStudies/ArrayExpress mirror. Use when finding expression datasets, navigating SuperSeries vs SubSeries, choosing between series-matrix (submitter-normalized) and raw supplementary files, downloading via GEOparse (Python) or GEOquery (R/Bioconductor), linking GEO to SRA for raw reads, or distinguishing GSE/GSM/GPL/GDS record types. Encodes the SuperSeries trap, the series-matrix normalization-trust caveat, GEOmetadb deprecation, ArrayExpress migration to BioStudies, and processed-vs-raw decision matrix.
What it loads into context
Frontmatter
230 tokensAlways available to the agent
SKILL.md
4,776 tokensWhen the skill is activated
Reference documents
1,252 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Required Setup
- GEO record taxonomy
- The SuperSeries trap
- Decision matrix: processed vs raw vs SRA
- Series matrix files
- SOFT vs MINiML
- GEOparse vs GEOquery
- GEOmetadb status
- ArrayExpress -> BioStudies migration (2020)
- Code patterns
- Failure modes
- Common errors
- References
- Related Skills
Scripts executed separately
scripts/geo_from_pubmed.pyscripts/geo_to_sra.pyscripts/search_geo.py
Files
SKILL.mdcore17,934 bytesSKILL.mdcore17,934 bytesSKILL.mdcore17,934 bytesSKILL.mdcore17,934 bytesSKILL.mdcore17,934 bytesSKILL.mdcore17,934 bytesSKILL.mdcore17,934 bytesSKILL.mdcore17,934 bytesSKILL.mdcore17,934 bytesSKILL.mdcore17,934 bytesSKILL.mdcore17,934 bytesreferences/usage-guide.mddoc4,869 bytesscripts/geo_from_pubmed.pyruns1,438 bytesscripts/geo_to_sra.pyruns2,557 bytesscripts/search_geo.pyruns2,687 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: BioPython 1.83+, GEOparse 2.0+, R Bioconductor GEOquery 2.70+, pandas 2.2+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show biopython geoparse` then introspect signatures
- R: `packageVersion('GEOquery')`
If the GSE structure doesn't match expectations (missing fields, malformed series matrix), re-fetch from FTP directly and inspect the SOFT or MINiML file as source of truth.
# GEO Data
**"Pull expression data from GEO accession GSE..."** -> GEO stores Series (GSE), Samples (GSM), Platforms (GPL), and curated DataSets (GDS, frozen 2018). The single most consequential decision is **processed (series matrix) vs raw (supplementary files / linked SRA)** — the answer turns on how much trust the submitter's normalization deserves.
bio-interaction-databases
When your agent uses it
Query protein-protein and gene interaction databases (STRING, BioGRID, IntAct, SIGNOR, Reactome, HuRI, HuMAP, OmniPath, ConsensusPathDB, DIP). Use when building PPI networks, choosing between physical vs functional vs genetic interactions, signed/directed vs undirected, high-throughput vs curated, picking confidence thresholds, aggregating across resources, or navigating license constraints. Encodes the database decision matrix, STRING v12 channel semantics, OmniPath as meta-database, SIGNOR for signed signaling, and per-resource rate limits.
What it loads into context
Frontmatter
206 tokensAlways available to the agent
SKILL.md
5,525 tokensWhen the skill is activated
Reference documents
1,250 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Required Setup
- Decision matrix: which resource for which question?
- STRING (v12, channels, confidence)
- BioGRID (physical + genetic, curated + HT)
- IntAct (PSI-MI curated physical)
- SIGNOR (signed signaling)
- Reactome (curated pathways)
- HuRI / HuMAP (human-specific interactomes)
- OmniPath (meta-database)
- License gotchas (critical for commercial use)
- Code patterns
- Failure modes
- Common errors
- References
- Related Skills
Scripts executed separately
scripts/interaction_query.pyscripts/string_network.py
Files
SKILL.mdcore21,521 bytesSKILL.mdcore21,521 bytesSKILL.mdcore21,521 bytesSKILL.mdcore21,521 bytesSKILL.mdcore21,521 bytesSKILL.mdcore21,521 bytesSKILL.mdcore21,521 bytesSKILL.mdcore21,521 bytesSKILL.mdcore21,521 bytesSKILL.mdcore21,521 bytesSKILL.mdcore21,521 bytesSKILL.mdcore21,521 bytesreferences/usage-guide.mddoc5,510 bytesscripts/interaction_query.pyruns4,964 bytesscripts/string_network.pyruns2,823 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: requests 2.31+, pandas 2.2+, networkx 3.2+; STRING v12.0, BioGRID 4.4+, IntAct (live), SIGNOR 3.0+, OmniPath (live)
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show requests pandas networkx`
- API surface: confirm endpoint URLs match each resource's current docs
STRING URL is version-pinned (`version-12-0` as of 2024); older URLs (`version-11-5`) were deprecated 2023. The Cytoscape/Cytoscape.js ecosystem uses different version semantics; check the docs for the targeted version.
# Interaction Databases
**"Get protein-protein interactions for these genes"** -> The choice of database matters more than the choice of API. Different resources index different evidence (physical binding, functional association, genetic interaction, signed signaling), with different curation pipelines (manually curated vs high-throughput vs text-mined), different species coverage, and different licenses.
bio-local-blast
When your agent uses it
Build local BLAST databases and run searches using NCBI BLAST+ command-line tools. Use when running >50 queries, building custom databases with -parse_seqids and -taxid, downloading prebuilt NCBI databases via update_blastdb.pl, choosing -task variants (megablast/dc-megablast/blastn/blastn-short), tuning soft/hard masking, scaling threads, or extracting hits with blastdbcmd. Encodes BLAST v5 vs v4 database format, taxonomy filtering, makeblastdb pitfalls.
What it loads into context
Frontmatter
203 tokensAlways available to the agent
SKILL.md
4,783 tokensWhen the skill is activated
Reference documents
1,337 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Installation
- Database format: v5 vs v4
- `makeblastdb` flag taxonomy
- `-task` taxonomy (the most-misused BLAST setting)
- Soft vs hard masking
- Thread scaling
- Output format reference (`-outfmt`)
- Prebuilt NCBI databases via `update_blastdb.pl`
- Code patterns
- Failure modes
- Common errors
- References
- Related Skills
Scripts executed separately
scripts/blast_wrapper.pyscripts/create_database.shscripts/reciprocal_best.shscripts/run_blast.sh
Files
SKILL.mdcore16,517 bytesSKILL.mdcore16,517 bytesSKILL.mdcore16,517 bytesSKILL.mdcore16,517 bytesSKILL.mdcore16,517 bytesSKILL.mdcore16,517 bytesSKILL.mdcore16,517 bytesSKILL.mdcore16,517 bytesSKILL.mdcore16,517 bytesSKILL.mdcore16,517 bytesSKILL.mdcore16,517 bytesSKILL.mdcore16,517 bytesreferences/usage-guide.mddoc4,878 bytesscripts/blast_wrapper.pyruns3,443 bytesscripts/create_database.shruns1,248 bytesscripts/reciprocal_best.shruns1,965 bytesscripts/run_blast.shruns1,506 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: NCBI BLAST+ 2.15+
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `blastn -version` then `blastn -help` to confirm flags
- CLI: `makeblastdb -help` to confirm database build options
If a flag is unrecognized or behavior changes, introspect with `-help` and adapt the example to match the installed version rather than retrying.
# Local BLAST
**"Run BLAST locally for speed and control"** -> Build or download a BLAST+ database, run the appropriate program with carefully chosen `-task`, masking, and thread settings, parse tabular output. Local BLAST is the right tool when remote is rate-limited or when the database must be reproducible (frozen).
bio-ncbi-datasets-cli
When your agent uses it
Download genome assemblies, gene records, and ortholog data from NCBI using the modern Datasets v2 CLI (replaces assembly_summary.txt scraping and many EFetch workflows). Use when bulk-pulling genome assemblies, gene metadata across species, ortholog sets, or BLAST databases; when E-utilities are too slow for genome-scale work; or when automatic checksum verification, parallel download, and clean accession-driven retrieval are required. Encodes the JSON-lines output format, dataformat conversion, --dehydrated for cloud workflows, and when Datasets is/isn't the right tool.
What it loads into context
Frontmatter
217 tokensAlways available to the agent
SKILL.md
3,756 tokensWhen the skill is activated
Reference documents
1,171 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Installation
- What's in scope (use Datasets) vs out of scope (use E-utilities or other tools)
- Subcommand taxonomy
- Key parameters (download)
- JSON-lines output + dataformat
- When to use --dehydrated for cloud workflows
- Checksum verification (automatic)
- Code patterns
- Failure modes
- Common errors
- References
- Related Skills
Scripts executed separately
scripts/bulk_dehydrated.shscripts/download_genome.shscripts/gene_metadata.sh
Files
SKILL.mdcore14,514 bytesSKILL.mdcore14,514 bytesSKILL.mdcore14,514 bytesSKILL.mdcore14,514 bytesSKILL.mdcore14,514 bytesSKILL.mdcore14,514 bytesSKILL.mdcore14,514 bytesSKILL.mdcore14,514 bytesSKILL.mdcore14,514 bytesSKILL.mdcore14,514 bytesSKILL.mdcore14,514 bytesSKILL.mdcore14,514 bytesreferences/usage-guide.mddoc4,581 bytesscripts/bulk_dehydrated.shruns1,594 bytesscripts/download_genome.shruns1,056 bytesscripts/gene_metadata.shruns1,299 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: NCBI Datasets CLI 16.0+ (2024), dataformat 16.0+
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `datasets --version`, `dataformat --version`
- Subcommand help: `datasets <subcommand> --help`
If a subcommand or flag is unrecognized, run `datasets --help` and adapt. The CLI is under active development; major releases (v15 -> v16) added subcommands and renamed flags.
# NCBI Datasets CLI
**"Pull genome / gene / ortholog data from NCBI in 2026"** -> The Datasets v2 CLI (launched 2023) is the official, supported bulk endpoint for genome and gene-centric data. It replaces the prior best-practice of scraping `assembly_summary.txt` + parallel FTP + manual checksum verification. For genome-scale data, it is strictly better than E-utilities (EFetch).
bio-ortholog-inference
When your agent uses it
Pull pre-computed ortholog calls from public databases (OrthoDB, Ensembl Compara, OMA browser, eggNOG, PANTHER, KEGG Orthology, HomoloGene) via their REST APIs. Use when orthologs are already curated upstream, when the question is "what is the X ortholog of Y" rather than "how to infer orthology de novo", when batch-mapping gene IDs across species, or when comparing the resources for consensus calls. Encodes confidence-level semantics, 1:1 vs 1:many vs many:many, HomoloGene deprecation, and when to defect to de novo computation.
What it loads into context
Frontmatter
227 tokensAlways available to the agent
SKILL.md
4,981 tokensWhen the skill is activated
Reference documents
1,279 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Required Setup
- Decision matrix: which resource for which question?
- Per-resource API reference
- Confidence-level semantics
- The orthology conjecture (and why resources disagree)
- Code patterns
- Failure modes
- Common errors
- References
- Related Skills
Scripts executed separately
scripts/compara_orthologs.pyscripts/cross_resource.pyscripts/kegg_orthology.py
Files
SKILL.mdcore18,868 bytesSKILL.mdcore18,868 bytesSKILL.mdcore18,868 bytesSKILL.mdcore18,868 bytesSKILL.mdcore18,868 bytesSKILL.mdcore18,868 bytesSKILL.mdcore18,868 bytesSKILL.mdcore18,868 bytesSKILL.mdcore18,868 bytesSKILL.mdcore18,868 bytesSKILL.mdcore18,868 bytesSKILL.mdcore18,868 bytesreferences/usage-guide.mddoc5,147 bytesscripts/compara_orthologs.pyruns3,161 bytesscripts/cross_resource.pyruns2,714 bytesscripts/kegg_orthology.pyruns1,979 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: requests 2.31+, pandas 2.2+; OrthoDB v12 API, Ensembl REST (Ensembl release 112+), OMA REST API, eggNOG 6.0+, PANTHER v18+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show requests pandas`
- API surface: confirm endpoint URLs and JSON schema match the current API docs
If endpoints return 404 or unexpected JSON, check release notes for the resource; schema migrations happen with each major version (Ensembl release is the biggest moving target).
# Ortholog Inference (Database Access)
**"What is the X ortholog of gene Y?"** -> Many ortholog resources have already done the inference at scale. Pulling their answers is faster and often more reliable than re-computing. This skill is the **database-access** view: how to query the major orthology resources programmatically, what their confidence semantics mean, and when their disagreements matter.
bio-remote-homology
When your agent uses it
Detect distant homologs using profile and structure-aware methods that go beyond standard BLAST. Use when sequence identity falls into the twilight zone (<35% pairwise), when BLAST fails to find homologs that should exist, when working at metagenomic scale (DIAMOND, MMseqs2), or when structure beats sequence (Foldseek). Covers PSI-BLAST (iterative PSSM), jackhmmer (iterative HMM), HHblits/HHsearch (profile-profile), DIAMOND, MMseqs2, and Foldseek (3Di structural alphabet, van Kempen 2024).
What it loads into context
Frontmatter
220 tokensAlways available to the agent
SKILL.md
5,267 tokensWhen the skill is activated
Reference documents
1,296 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Required Setup
- Decision matrix: which method when
- Foldseek: the 2024 revolution
- PSI-BLAST: still useful, but watch the drift
- HMMER 3 (hmmsearch, jackhmmer)
- HHblits / HHsearch (HH-suite3)
- MMseqs2 (the modern protein search workhorse)
- DIAMOND (the modern blastp replacement)
- Iterative HMMER (jackhmmer)
- Code patterns
- Failure modes
- Common errors
- References
- Related Skills
Scripts executed separately
scripts/foldseek_search.shscripts/iterative_profile.shscripts/pfam_annotation.sh
Files
SKILL.mdcore18,802 bytesSKILL.mdcore18,802 bytesSKILL.mdcore18,802 bytesSKILL.mdcore18,802 bytesSKILL.mdcore18,802 bytesSKILL.mdcore18,802 bytesSKILL.mdcore18,802 bytesSKILL.mdcore18,802 bytesSKILL.mdcore18,802 bytesSKILL.mdcore18,802 bytesSKILL.mdcore18,802 bytesSKILL.mdcore18,802 bytesSKILL.mdcore18,802 bytesreferences/usage-guide.mddoc4,997 bytesscripts/foldseek_search.shruns1,801 bytesscripts/iterative_profile.shruns2,287 bytesscripts/pfam_annotation.shruns1,282 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: NCBI BLAST+ 2.15+, HMMER 3.4+, MMseqs2 15+, DIAMOND 2.1+, HH-suite3 3.3+, Foldseek 9+
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
- Python: `pip show <package>` then introspect signatures
If a flag is unrecognized or behavior changes, introspect with `--help` and adapt the example to match the installed version rather than retrying.
# Remote Homology
**"Find homologs my BLAST missed"** -> Standard BLAST detects similarity reliably down to ~35% pairwise identity (the "twilight zone", Rost 1999 *Protein Eng* 12:85). Below that, profile methods (PSSMs, HMMs) and structure-aware methods (Foldseek) recover homologs that pairwise alignment misses.
bio-sra-data
When your agent uses it
Download raw sequencing reads from NCBI SRA using sra-tools (prefetch, fasterq-dump, vdb-validate) or the ENA mirror. Use when pulling FASTQ for SRR/ERR/DRR accessions, deciding between SRA-direct, ENA mirror, or AWS/GCP cloud mirror (STRIDES), handling --include-technical for 10x and other single-cell records, validating with MD5/vdb-validate, navigating SRR/SRX/SRS/SRP/PRJNA hierarchy, or finding accessions via pysradb. Encodes SRA cloud-egress economics, the fasterq-dump uncompressed-scratch trap, and the --max-size default that silently truncates large prefetches.
What it loads into context
Frontmatter
246 tokensAlways available to the agent
SKILL.md
4,732 tokensWhen the skill is activated
Reference documents
1,267 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Required Setup
- Decision matrix: where to pull from
- SRA accession hierarchy
- fasterq-dump vs fastq-dump
- prefetch and the `--max-size` trap
- ENA mirror: direct FASTQ URLs
- Single-cell / 10x quirks
- MD5 / vdb-validate
- Cloud (STRIDES) access
- Code patterns
- Failure modes
- Common errors
- References
- Related Skills
Scripts executed separately
scripts/download_batch.shscripts/download_single.shscripts/find_sra_runs.pyscripts/prefetch_large.sh
Files
SKILL.mdcore16,137 bytesSKILL.mdcore16,137 bytesSKILL.mdcore16,137 bytesSKILL.mdcore16,137 bytesSKILL.mdcore16,137 bytesSKILL.mdcore16,137 bytesSKILL.mdcore16,137 bytesSKILL.mdcore16,137 bytesSKILL.mdcore16,137 bytesSKILL.mdcore16,137 bytesSKILL.mdcore16,137 bytesSKILL.mdcore16,137 bytesSKILL.mdcore16,137 bytesSKILL.mdcore16,137 bytesreferences/usage-guide.mddoc4,604 bytesscripts/download_batch.shruns2,192 bytesscripts/download_single.shruns1,206 bytesscripts/find_sra_runs.pyruns2,297 bytesscripts/prefetch_large.shruns1,668 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: sra-tools 3.0+ (fasterq-dump, prefetch, vdb-validate, vdb-config), pysradb 2.2+, ENA portal API 2.0+
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `fasterq-dump --version`, `prefetch --version`
- Python: `pip show pysradb`
If a flag is unrecognized or behavior changes, run `<tool> --help` and adapt.
# SRA Data
**"Download FASTQ from this SRA accession"** -> Two paths exist in 2026: the **SRA toolkit** (NCBI's official, with prefetch + fasterq-dump) and the **ENA mirror** (EMBL-EBI's mirror with direct FASTQ download, often faster). For >1 TB workflows, a third path: **AWS Open Data** (STRIDES program) where same-region EC2 pulls SRA data with zero egress cost.
bio-uniprot-access
When your agent uses it
Query UniProt's REST API (post-2022 endpoint at rest.uniprot.org) for protein sequences, annotations, GO terms, cross-references, ID mappings, and proteomes. Use when fetching UniProtKB entries, navigating the JSON schema, choosing between UniProtKB/UniRef/UniParc/Proteomes resources, deciding stream vs search endpoint for batch retrieval, running ID-mapping jobs with the async pattern, handling isoform suffixes, or filtering reviewed Swiss-Prot vs auto-annotated TrEMBL. Encodes the legacy URL migration (2022), the new JSON schema layout, and bulk-pull patterns.
What it loads into context
Frontmatter
223 tokensAlways available to the agent
SKILL.md
5,119 tokensWhen the skill is activated
Reference documents
1,249 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Required Setup
- Endpoint reference
- Search query syntax
- `?fields=` for bulk pulls
- Stream vs search vs accessions
- JSON schema navigation (the post-2022 layout)
- Isoform handling
- ID Mapping API (async)
- Code patterns
- Failure modes
- Common errors
- References
- Related Skills
Scripts executed separately
scripts/isoforms_and_xrefs.pyscripts/uniprot_query.py
Files
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## Version Compatibility
Reference examples tested with: requests 2.31+, pandas 2.2+; UniProt REST API as of 2024_06 release
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show requests pandas`
- API surface: confirm endpoint URLs match https://www.uniprot.org/help/api
The REST API JSON schema is stable within a release; major schema changes are documented at https://www.uniprot.org/release-notes. The 2022 migration broke the legacy `https://www.uniprot.org/uniprot/...` endpoints.
# UniProt Access
**"Get protein information from UniProt"** -> Two facts dominate every UniProt workflow in 2026: (1) **the API endpoint migrated in 2022** from `https://www.uniprot.org/uniprot/...` to `https://rest.uniprot.org/uniprotkb/...` with a substantially different JSON schema; pre-2022 code does not work as-is. (2) **`?fields=`** is essential — default JSON returns the full entry (~20-30 KB each); for bulk pulls, request only the fields actually needed.
bio-differential-expression-batch-correction
When your agent uses it
Handles batch effects in bulk RNA-seq via design-matrix inclusion (the correct path for DE), ComBat/ComBat-seq for visualization, SVA for unknown latent factors, RUVSeq for negative-control-gene-anchored unwanted variation, and limma::removeBatchEffect for plotting only. Encodes the Nygaard 2016 cardinal sin against testing on a batch-corrected matrix, the choice between SVA/RUVg/RUVs/RUVr, the confounding non-identifiability problem, the single-cell boundary (Harmony/MNN are NOT for bulk), and the Goh 2017 harmonization critique. Use when designing a DE analysis with batch structure, troubleshooting batch-dominated PCA, choosing ComBat vs ComBat-seq, handling unknown batch via SVA, integrating across studies, or deciding when (rarely) to subtract batch.
What it loads into context
Frontmatter
275 tokensAlways available to the agent
SKILL.md
5,711 tokensWhen the skill is activated
Reference documents
571 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- The Nygaard 2016 cardinal sin
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Standard Workflow -- Design-Matrix Inclusion
- ComBat-seq (Visualization or Cross-Study Counts)
- SVA -- Unknown Batch
- RUVSeq -- Negative-Control-Anchored UV
- removeBatchEffect -- Visualization Only
- Detecting Confounding Early
- The Single-Cell Boundary
- The Goh 2017 Critique on "Harmonization"
- Reconciliation: When Methods Disagree
- Per-Method Failure Modes
- Common errors
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: sva 3.50+ (includes ComBat + ComBat_seq), DESeq2 1.42+, edgeR 4.0+, limma 3.58+, RUVSeq 1.36+, ggplot2 3.5+, harmony 1.2+ (single-cell context only)
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Batch Effect Correction
**"Remove the batch effect before DE"** -> Almost always WRONG. Include batch as a covariate in the design formula (`~ batch + condition`) so DESeq2/edgeR/limma model it without subtracting. Subtraction is for visualization only.
bio-differential-expression-de-results
When your agent uses it
Extracts, filters, annotates, and exports differential expression results from DESeq2 or edgeR with proper handling of padj=NA (independent filtering, Cook's outliers, all-zero), multiple-testing correction choice (BH vs Storey q-value vs IHW vs lfsr), TREAT vs post-hoc fold-change filtering, p-value histogram diagnostics, gene annotation via org.db/biomaRt/mygene, GSEA preranked input, ORA background construction, replication reality (Schurch 2016 small-n result), and SABV/sex-stratified reporting. Use when extracting and interpreting DE results, troubleshooting padj=NA, choosing FDR method, preparing ranked lists for pathway analysis, annotating gene IDs, or comparing DESeq2 vs edgeR outputs.
What it loads into context
Frontmatter
259 tokensAlways available to the agent
SKILL.md
6,248 tokensWhen the skill is activated
Reference documents
612 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- `padj = NA` has three distinct meanings
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Extracting Results
- TREAT vs Post-hoc LFC Filtering
- IHW for Better Power
- P-value Histogram Diagnostics
- Filtering and Ordering
- Gene Annotation
- GSEA Preranked Input
- ORA Input
- Cross-Tool Concordance Check
- Per-Method Failure Modes
- Common errors
- References
- Related Skills
Files
SKILL.mdcore22,907 bytesreferences/usage-guide.mddoc2,410 bytesscripts/annotate_results.Rasset1,244 bytesscripts/export_excel.Rasset1,760 bytesscripts/filter_results.Rasset1,439 bytes
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## Version Compatibility
Reference examples tested with: DESeq2 1.42+, edgeR 4.0+, IHW 1.34+, qvalue 2.34+, ashr 2.2+, AnnotationDbi 1.66+, org.Hs.eg.db 3.18+, biomaRt 2.58+, mygene 1.38+ (Python), dplyr 1.1+, openxlsx 4.2+
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# DE Results
**"What are my significant genes?"** -> Extract DE estimates and p-values from the fitted model, handle missing padj correctly, apply FDR control appropriate to the design, and produce the table or ranked list the downstream tool actually needs.bio-differential-expression-de-visualization
When your agent uses it
Creates DE-specific diagnostic and result visualizations using DESeq2/edgeR built-in functions and lightweight ggplot2 wrappers. Covers MA plot (with the shrunken-LFC compression effect), volcano (with the apeglm caveat that p-values are unchanged), PCA on VST/rlog (never raw counts), sample distance heatmaps, top-DE-gene heatmaps with the row-scaling trap, dispersion / BCV plot interpretation, p-value histogram diagnostics, plotCounts for individual genes, blind=TRUE vs FALSE rationale, and the n=3 visualization stake. Use when generating DE diagnostic plots, choosing VST vs rlog for visualization, troubleshooting suspicious plot patterns (shifted MA cloud, batch-dominated PCA, anti-conservative p-value histogram), or building a standard QC figure panel.
What it loads into context
Frontmatter
261 tokensAlways available to the agent
SKILL.md
5,472 tokensWhen the skill is activated
Reference documents
853 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- Scope
- The Single Most Important Modern Insight -- A volcano with shrunken LFC compresses the cloud, but the p-values are unchanged
- Plot Taxonomy
- Decision Tree by Scenario
- Dispersion Diagnostic (Run This First)
- P-value Histogram (Run This Second)
- MA Plot (LFC vs Mean)
- Volcano with Shrunken LFC
- PCA on VST/rlog (Never on Raw Counts)
- Sample Distance Heatmap (for QC)
- Top-DE Heatmap and the Row-Scaling Trap
- Per-gene Plot
- UpSet for Multi-set Comparisons
- Per-Method Failure Modes
- Common errors
- References
- Related Skills
Files
SKILL.mdcore20,541 bytesreferences/usage-guide.mddoc3,537 bytesscripts/heatmap.Rasset1,335 bytesscripts/pca_plot.Rasset1,481 bytesscripts/volcano_plot.Rasset1,809 bytes
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## Version Compatibility
Reference examples tested with: DESeq2 1.42+, edgeR 4.0+, limma 3.58+, ggplot2 3.5+, pheatmap 1.0+, RColorBrewer 1.1+, ggrepel 0.9+, EnhancedVolcano 1.20+, matrixStats 1.2+
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# DE Visualization
**"Make the standard DE figure panel"** -> Use built-in functions or thin wrappers to produce diagnostic plots (dispersion, p-value histogram, PCA, sample distance) and result plots (MA, volcano, heatmap of top DE genes, per-gene counts), interpreted as diagnostics of the underlying model.
bio-differential-expression-deseq2-basics
When your agent uses it
Performs differential expression on bulk RNA-seq count data with DESeq2's negative-binomial GLM, Wald and LRT testing, apeglm/ashr/normal LFC shrinkage, independent filtering, Cook's outlier handling, VST/rlog transforms, and design formulas including paired, batch, and interaction terms. Use when running bulk DE, choosing DESeq2 over edgeR or limma-voom, building a paired or interaction design, applying LFC shrinkage for ranking or GSEA, choosing Wald vs LRT, troubleshooting padj=NA, picking VST vs rlog, importing salmon/kallisto via tximport, or analyzing prokaryotic RNA-seq.
What it loads into context
Frontmatter
244 tokensAlways available to the agent
SKILL.md
6,137 tokensWhen the skill is activated
Reference documents
780 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Shrunken LFC and the Wald p-value come from different models
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Standard Workflow
- Tximport (Salmon / kallisto / RSEM)
- Design Formulas and the resultsNames Trap
- Wald vs LRT
- LFC Shrinkage -- Three Flavors, Three Failure Modes
- Independent Filtering, Cook's Outliers, padj=NA
- VST vs rlog vs normTransform (Visualization Only)
- betaPrior Deprecation Timeline
- Size Factor Alternatives
- Per-Method Failure Modes
- PyDESeq2 (Python alternative)
- Prokaryotic RNA-seq
- Common errors
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: DESeq2 1.42+, apeglm 1.28+, ashr 2.2+, IHW 1.34+, tximport 1.30+, edgeR 4.0+ (for cross-comparison), PyDESeq2 0.5+
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# DESeq2 Basics
**"Find differentially expressed genes between conditions"** -> Fit a negative-binomial GLM per gene with shared dispersion shrinkage, test the coefficient of interest (Wald) or the joint effect of a factor (LRT), and report a shrunken effect-size estimate for ranking.bio-differential-expression-edger-basics
When your agent uses it
Performs differential expression on bulk RNA-seq count data with edgeR's negative-binomial GLM and quasi-likelihood F-test framework. Covers DGEList construction, filterByExpr, TMM/TMMwsp normalization, robust dispersion estimation, glmQLFit/glmQLFTest, TREAT for magnitude-bounded hypotheses, contrasts via no-intercept designs, voom and voomWithQualityWeights for heterogeneous samples, and the edgeR v4 bias-corrected APL changes. Use when running bulk DE with edgeR, choosing edgeR over DESeq2 (small n, transcript DE via catchSalmon, large samples), needing TREAT for a fold-change-threshold hypothesis, troubleshooting v3-to-v4 reproducibility, building paired or interaction designs, or handling library-quality heterogeneity.
What it loads into context
Frontmatter
261 tokensAlways available to the agent
SKILL.md
5,920 tokensWhen the skill is activated
Reference documents
804 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- edgeR v4 changed the QL framework and `legacy=FALSE` is now default
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Standard Workflow
- Filtering -- `filterByExpr` Internals
- Normalization -- TMM/TMMwsp Is an Offset, Not a Division
- QL F-test vs LRT vs Exact Test
- TREAT -- Testing Against a Fold-Change Threshold
- Contrasts via No-Intercept Designs
- voom and voomWithQualityWeights
- Transcript-Level DE via catchSalmon
- edgeR vs DESeq2 vs limma-voom
- Per-Method Failure Modes
- Common errors
- References
- Related Skills
Files
SKILL.mdcore22,209 bytesreferences/usage-guide.mddoc3,350 bytesscripts/basic_workflow.Rasset1,344 bytesscripts/batch_correction.Rasset1,341 bytesscripts/contrasts.Rasset1,499 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: edgeR 4.0+, limma 3.58+, statmod 1.5+ (for voom internals), tximport 1.30+ (for catchSalmon path)
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# edgeR Basics
**"Find differentially expressed genes between conditions"** -> Fit a negative-binomial GLM per gene with empirical-Bayes-moderated dispersions, test coefficients with the quasi-likelihood F-test (proper finite-sample FPR control), and report ranked DE lists.
bio-differential-expression-timeseries-de
When your agent uses it
Analyzes time-series and longitudinal RNA-seq for differential expression and trajectory structure. Covers DESeq2 LRT with reduced models, time as factor vs continuous vs natural splines, maSigPro (Nueda 2014 for RNA-seq), ImpulseDE2 with explicit impulse-model failure modes, DREAM for repeated measures via linear mixed models, pseudoreplication avoidance, conditional vs marginal modeling, and trajectory clustering with DPGP, Mfuzz (with Schwämmle 2010 fuzzifier estimation), and splines+k-means. Use when modeling time-course or longitudinal expression, choosing factor vs spline, handling repeated measures from the same subject, avoiding pseudoreplication, clustering temporal trajectories, or selecting between dedicated time-course tools and pairwise+LRT.
What it loads into context
Frontmatter
257 tokensAlways available to the agent
SKILL.md
5,867 tokensWhen the skill is activated
Reference documents
776 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Most dedicated time-course tools UNDERPERFORM pairwise + LRT on short series
- Algorithmic Taxonomy
- Decision Tree by Scenario
- DESeq2 LRT for Time -- The Canonical Pattern
- Time as Factor vs Continuous vs Spline
- limma-voom + Splines (Modern Alternative)
- DREAM for Repeated Measures
- maSigPro (Nueda 2014 for RNA-seq)
- ImpulseDE2
- Trajectory Clustering of DE Genes
- Time as the Only Variable (Developmental Series)
- Per-Method Failure Modes
- Common errors
- References
- Related Skills
Scripts executed separately
scripts/timeseries_limma.R
Files
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## Version Compatibility
Reference examples tested with: DESeq2 1.42+, edgeR 4.0+, limma 3.58+, splines (base R), maSigPro 1.74+, ImpulseDE2 1.10+ (Bioconductor archive; verify availability), variancePartition / dream 1.32+, Mfuzz 2.62+, TCseq 1.26+, ggplot2 3.5+, pheatmap 1.0+
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Time-Series Differential Expression
**"Find genes that change over time"** -> Define what "change" means -- any non-zero time effect (LRT), a smooth nonlinear trend (splines), a transient impulse (ImpulseDE2), or differing trajectories between groups (interaction LRT) -- and choose the model that asks that specific question while handling repeated-measures correctly.
bio-ecological-genomics-biodiversity-metrics
When your agent uses it
Quantifies biodiversity from species abundance/incidence tables using Hill numbers (iNEXT) with coverage-based rarefaction-extrapolation (Chao & Jost 2012), asymptotic richness via Chao1/ACE/jackknife as a lower bound, Baselga turnover/nestedness partition with the Podani alternative as sensitivity check, mandatory Hellinger transformation before ordination (Legendre & Gallagher 2001), Faith PD and SES_MPD/SES_MNTD with explicit null-model choice, and Maire 2015 functional-diversity dimensionality optimization. Use when comparing diversity across sites with unequal sampling effort, picking the right richness estimator for singleton-heavy amplicon data, partitioning beta diversity into turnover vs nestedness, reporting Hill-number effective species counts rather than raw entropies, computing SES_MPD with explicit null-model justification, or deciding whether to apply standard metrics to compositional amplicon data. Not for clinical 16S microbiome diversity (see microbiome/diversity-analysis).
What it loads into context
Frontmatter
308 tokensAlways available to the agent
SKILL.md
6,891 tokensWhen the skill is activated
Reference documents
2,020 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Standardize by COVERAGE not by sample size
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Hill Numbers and Why Raw Entropies Are Not Diversities
- Asymptotic Richness — Chao1 is a Lower Bound, NOT a Point Estimate
- Coverage-Based Rarefaction with the Doubling Rule
- The Double-Zero Problem and Hellinger Transformation
- Beta Diversity Partition — Run BOTH Baselga AND Podani
- Phylogenetic Diversity — Faith's PD, MPD, MNTD with Null-Model Choice
- Functional Diversity with Trait-Axis Dimensionality Optimization
- Per-Method Failure Modes
- Quantitative Thresholds
- Common errors
- References
- Related Skills
Files
SKILL.mdcore27,298 bytesreferences/usage-guide.mddoc8,877 bytesscripts/beta_partitioning.Rasset6,333 bytesscripts/inext_diversity.Rasset5,403 bytes
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## Version Compatibility
Reference examples tested with: iNEXT 3.0+, iNEXT.3D 1.0+, vegan 2.6+, betapart 1.6+, picante 1.8+, ggplot2 3.5+
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Biodiversity Metrics
**"Calculate species diversity for my ecological samples"** -> Compute Hill-number diversity (numbers-equivalent of richness, Shannon, Simpson) with coverage-based rarefaction/extrapolation, choose the richness estimator appropriate to the singleton/doubleton signature of the data, and partition beta diversity into turnover and nestedness components with a documented partition framework.
- R: `iNEXT::iNEXT()` for coverage-based rarefaction/extrapolationbio-ecological-genomics-community-ecology
When your agent uses it
Analyzes species-environment relationships with constrained ordination (CCA, RDA, db-RDA), variance partitioning, indicator species (indicspecies IndVal.g group-equalized), PERMANOVA paired MANDATORILY with PERMDISP (Anderson & Walsh 2013; dispersion confounds centroid tests), Joint Species Distribution Models (HMSC, sjSDM, gjam) with explicit rejection of "residual covariance equals biotic interaction", phylogenetic community ecology (SES_MPD/MNTD), trait-environment via RLQ + fourth-corner with corrected modeltype=6 (Dray 2014), bipartite network metrics (NODF, modularity) with curveball null (Strona 2014), and Mantel-test replacements (dbRDA, GDM) for spatial data. Use when testing how environmental gradients structure communities, identifying habitat indicator taxa, partitioning variance among predictors, deciding whether PERMANOVA significance is location vs dispersion, picking among HMSC/sjSDM/gjam, or replacing Mantel tests for landscape data.
What it loads into context
Frontmatter
321 tokensAlways available to the agent
SKILL.md
6,604 tokensWhen the skill is activated
Reference documents
2,152 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Always run PERMDISP alongside PERMANOVA
- Algorithmic Taxonomy
- Decision Tree by Scenario
- CCA vs RDA — Gradient-Length Decision
- PERMANOVA with the Mandatory PERMDISP Companion
- Joint Species Distribution Models — HMSC vs sjSDM
- Indicator Species with Group-Size Correction
- Mantel Replacement — dbRDA with Spatial Covariates
- Per-Method Failure Modes
- Quantitative Thresholds
- Common errors
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: vegan 2.6+, indicspecies 1.7+, Hmsc 3.0+, sjSDM 1.0+, ade4 1.7+, picante 1.8+, ggplot2 3.5+
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Community Ecology
**"Test how environmental gradients structure my species communities"** -> Constrained ordination (CCA / RDA / db-RDA) with explicit dispersion testing alongside PERMANOVA, indicator-species analysis with group-size correction, Joint Species Distribution Models for residual covariance and prediction, and trait-environment testing with statistically corrected permutation schemes.
- R: `vegan::rda()`, `vegan::dbrda()`, `vegan::adonis2()` for ordination and PERMANOVAbio-ecological-genomics-conservation-genetics
When your agent uses it
Assesses genetic health of populations for conservation with Ne estimation across time horizons (LDNe NeEstimator V2 option-file API + SNeP physical-linkage correction; recent trajectory via GONE/GONE2; deep history via Stairway Plot 2 / dadi / fastsimcoal2 / PSMC), F-statistics, runs of homozygosity binned by length class to date inbreeding, genetic-load decomposition (Bertorelle 2022 realized vs masked), the modern 100/1000 Ne rule (Frankham 2014), Ne/Nc 2-6 orders of magnitude in marine fish (Hauser & Carvalho 2008), tree-sequence forward simulations (SLiM 4 + pyslim + tskit), and the Sukumaran-Knowles caveat against MSC methods for management-unit definition. Use when estimating Ne by time horizon, detecting inbreeding via F_ROH, decomposing genetic load, justifying conservation thresholds, distinguishing ESU/MU/DPS, configuring NeEstimator V2, or correcting LDNe physical linkage.
What it loads into context
Frontmatter
322 tokensAlways available to the agent
SKILL.md
7,966 tokensWhen the skill is activated
Reference documents
2,372 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- There is No Single Best Ne Estimator
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Genetic Diversity and F-Statistics
- Runs of Homozygosity with Length-Class Dating
- NeEstimator V2 — Option-File API
- GONE2 — Recent Ne Trajectory with the cM-Column Trap
- SNeP — Physical-Linkage-Corrected LDNe for Genomic Data
- Deep Demographic History
- Genetic Load — Realized vs Masked Decomposition
- Forward Simulation with Tree-Sequence Recording
- Per-Method Failure Modes
- Quantitative Thresholds
- Common errors
- References
- Related Skills
Files
SKILL.mdcore28,745 bytesSKILL.mdcore28,745 bytesSKILL.mdcore28,745 bytesSKILL.mdcore28,745 bytesSKILL.mdcore28,745 bytesSKILL.mdcore28,745 bytesSKILL.mdcore28,745 bytesSKILL.mdcore28,745 bytesreferences/usage-guide.mddoc9,164 bytesscripts/conservation_diversity.Rasset4,075 bytesscripts/ne_estimation.Rasset6,480 bytes
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## Version Compatibility
Reference examples tested with: hierfstat 0.5+, adegenet 2.1+, detectRUNS 2.0+, poppr 2.9+, NeEstimator V2.1+, GONE2, SNeP 1.1+, SLiM 4+, msprime 1.3+, bcftools 1.19+
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Conservation Genetics
**"Assess the genetic health of my endangered population"** -> Estimate Ne by time horizon (contemporary LD, recent trajectory, deep demographic history), measure inbreeding via F_ROH with length-class thresholds, decompose realized vs masked genetic load, and interpret against the modern 100/1000 Ne rule.bio-ecological-genomics-edna-metabarcoding
When your agent uses it
Processes eDNA metabarcoding from raw paired-end reads to species tables, navigating ASV (DADA2, UNOISE3) vs OTU (swarm v2) decision (Callahan 2017 vs Schloss multi-copy-16S critique), marker/primer choice (Leray COI, MiFish 12S, 515F/806R 16S, ITS2) with primer-specific bias, OBITools3 v3 command-name break (obi stats plural; .tar.gz taxonomy), tag-jumping with dual-indexing (Schnell 2015; NovaSeq 10x MiSeq), decontam as screening-not-classifier (Davis 2018), read-counts-not-abundance critique (Lamb 2019), site-occupancy modeling (Ficetola 2015), Naive-Bayes calibration limits (Bokulich 2018), and eDNA decay (Strickler 2015). Use when going from raw eDNA FASTQ to species tables, picking marker + denoising pipeline, deciding whether read counts represent abundance, applying occupancy modeling, configuring OBITools3 v3, or interpreting decontam output. Not for clinical 16S microbiome (see microbiome/amplicon-processing).
What it loads into context
Frontmatter
365 tokensAlways available to the agent
SKILL.md
7,306 tokensWhen the skill is activated
Reference documents
2,404 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Read Counts Are NOT Abundance
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Primer Trimming with cutadapt
- DADA2 ASV Pipeline
- OBITools3 Pipeline — The v1 -> v3 Command Break
- Tag-Jumping Mitigation — Schnell 2015 + NovaSeq Caveat
- Contamination Screening with decontam
- Site-Occupancy Modeling — Correcting for Imperfect Detection
- Per-Method Failure Modes
- Quantitative Thresholds
- Common errors
- References
- Related Skills
Scripts executed separately
scripts/obitools3_edna_pipeline.sh
Files
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## Version Compatibility
Reference examples tested with: DADA2 1.30+, cutadapt 4.7+, OBITools3 (Python 3), decontam 1.20+, microDecon 1.0+, occumb 1.0+, vsearch 2.27+, swarm 3.1+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# eDNA Metabarcoding
bio-ecological-genomics-landscape-genomics
When your agent uses it
Tests genotype-environment associations and identifies adaptive loci while correcting for the four-confound landscape (structure, demography, background selection, sampling design) using LFMM2 with mandatory K via sNMF cross-entropy elbow (LEA 3), BayPass Core/AUX/C2/IS with Omega covariance matrix, RDA / pRDA for polygenic adaptation (Forester 2018; requires imputed genotypes), OutFLANK with trimmed FST null, pcadapt, gradient forests (Ellis-Smith-Pitcher 2012, NOT mis-cited Ellis-Manel), Capblancq & Forester 2021 RDA Swiss-army-knife, genomic-offset prediction with Lind & Lotterhos 2025 three-regime caveat, Lotterhos-Whitlock sampling optima, Wang & Bradburd 2014 IBD vs IBE, and Circuitscape + ResistanceGA. Use when identifying adaptive loci across gradients, choosing K for LFMM2, deciding among GEA methods, predicting maladaptation with the novel-environment caveat, distinguishing IBD vs IBE, or optimizing sampling design.
What it loads into context
Frontmatter
333 tokensAlways available to the agent
SKILL.md
6,475 tokensWhen the skill is activated
Reference documents
2,131 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- GEA is a Four-Confound Problem, Not a Signal-Plus-Noise Problem
- Algorithmic Taxonomy
- Decision Tree by Scenario
- LFMM2 with Mandatory K Selection
- RDA — Capblancq & Forester 2021 Swiss-Army-Knife Workflow
- BayPass — Core, AUX, C2, IS Models
- Genomic Offset — Lind & Lotterhos 2025 Three-Regime Framework
- Per-Method Failure Modes
- Quantitative Thresholds
- Common errors
- References
- Related Skills
Files
SKILL.mdcore24,834 bytesSKILL.mdcore24,834 bytesreferences/usage-guide.mddoc8,781 bytesscripts/lfmm_gea.Rasset5,319 bytesscripts/pcadapt_outliers.Rasset4,034 bytes
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## Version Compatibility
Reference examples tested with: LEA 3.14+ (lfmm2 via LEA 3), pcadapt 4.3+, OutFLANK 0.2+, vegan 2.6+, gradientForest 0.1-32+, terra 1.7+, qvalue 2.34+, BayPass 2.3+
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Landscape Genomics
**"Find loci associated with environmental adaptation in my populations"** -> Genotype-environment association with K-selected latent-factor correction, multivariate RDA for polygenic signal, paired with demography-calibrated FST outliers; genomic-offset prediction with explicit prediction-novelty regime characterization.bio-ecological-genomics-species-delimitation
When your agent uses it
Delimits putative species boundaries from molecular data within the de Queiroz 2007 unified-lineage framework using ASAP (Puillandre 2021 successor to ABGD), mPTP C++ (Kapli 2017 successor to bPTP; bPTP is Python NOT R), GMYC single/multi-threshold (Pons 2006; Fujisawa 2013), multilocus BPP v4 with prior calibration from data (NOT defaults; Yang 2015), SNAPP + BFD* for SNP delimitation, DELINEATE (Sukumaran 2021) speciation-process modeling to address Sukumaran & Knowles 2017 PNAS critique that MSC delimits structure not species, integrative-taxonomy congruence (Padial 2010; Carstens 2013), Dsuite for introgression testing before sister claims (Malinsky 2021), and Meyer & Paulay 2005 barcoding-gap-absence caveat. Use when delineating species from DNA barcoding data, resolving cryptic complexes, choosing among ASAP/mPTP/BPP/DELINEATE, calibrating BPP priors, distinguishing introgression from ILS, or applying the Sukumaran-Knowles oversplitting correction.
What it loads into context
Frontmatter
359 tokensAlways available to the agent
SKILL.md
7,189 tokensWhen the skill is activated
Reference documents
2,128 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- MSC Methods Delimit Structure, NOT Species
- Algorithmic Taxonomy
- Decision Tree by Scenario
- ASAP — The Modern Successor to ABGD
- mPTP — Multi-Rate PTP for Heterogeneous Intraspecific Rates
- bPTP — Legacy Tree-Based Delimitation (Python, NOT R)
- GMYC with Mandatory Ultrametric Tree
- BPP A10/A11 — Multilocus MSC Delimitation with Calibrated Priors
- DELINEATE — Addressing the Sukumaran-Knowles Oversplitting
- Dsuite — Introgression vs Incomplete Lineage Sorting
- Per-Method Failure Modes
- Quantitative Thresholds
- Common errors
- References
- Related Skills
Scripts executed separately
scripts/asap_delimitation.py
Files
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## Version Compatibility
Reference examples tested with: ASAP (current CLI), mPTP (current C++), bPTP (Python from github.com/zhangjiajie/PTP), splits 1.0+ for GMYC, BPP 4.7+, SNAPP/BEAST 2.7+, DELINEATE (current Python), Dsuite 0.5+, ape 5.7+, fossil 0.4+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Species Delimitation
bio-epidemiological-genomics-amr-surveillance
When your agent uses it
Detects acquired antimicrobial-resistance determinants and chromosomal point-mutation resistance in bacterial assemblies using AMRFinderPlus, ResFinder 4.0 (acquired + PointFinder), CARD-RGI, abritAMR, staramr, and species-specific callers (TB-Profiler, Mykrobe). Harmonises cross-tool output via hAMRonization, contextualises determinants with mobile-genetic-element annotation (MOB-suite, PlasmidFinder, MobileElementFinder, ICEberg), predicts phenotype against EUCAST or CLSI breakpoints, and translates calls into WHO GLASS reporting categories. Use when screening clinical or surveillance isolates for AMR, distinguishing acquired vs intrinsic vs point-mutation resistance, calling rpoB / katG / pncA / gyrA / mgrB mutations, reconciling AMRFinderPlus vs RGI vs ResFinder disagreement, contextualising carbapenemases or mcr alleles on plasmids, predicting susceptibility from genotype against the WHO Mtb 2nd-edition catalogue, or building a hAMRonized multi-lab AMR surveillance pipeline.
What it loads into context
Frontmatter
328 tokensAlways available to the agent
SKILL.md
7,848 tokensWhen the skill is activated
Reference documents
2,469 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Acquired AMR detection is not species-agnostic
- Algorithmic Taxonomy
- Decision Tree by Scenario
- AMRFinderPlus With Species Mode
- TB-Specific Workflow
- hAMRonization for Cross-Tool Reporting
- Mobile-Genetic-Element Context
- Per-Method Failure Modes
- Reconciliation: When AMR Tools Disagree
- Quantitative Thresholds
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/amr_surveillance.py
Files
SKILL.mdcore30,675 bytesSKILL.mdcore30,675 bytesSKILL.mdcore30,675 bytesSKILL.mdcore30,675 bytesSKILL.mdcore30,675 bytesreferences/usage-guide.mddoc10,097 bytesscripts/amr_surveillance.pyruns7,150 bytes
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## Version Compatibility
Reference examples tested with: ncbi-amrfinderplus 4.0+, resfinder 4.5+, rgi 6.0+ (CARD 3.3+), abritamr 1.0.14+, staramr 0.10+, hamronization 1.1+, tb-profiler 6.2+, mykrobe 0.13+, mob_suite 3.1+, plasmidfinder 2.1+, MobileElementFinder 1.0+, pandas 2.2+, BioPython 1.84+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
- AMRFinderPlus: `amrfinder --list_organisms` for the current `--organism` catalogue
- WHO Mtb catalogue: `tb-profiler list_db` and verify the bundled WHO edition
- AMR database freshness: `amrfinder -u` (NCBI ReferenceGeneCatalog) and `tb-profiler update_tbdb`
If a flag or column name does not match (`--species` vs `--organism`, `barcode_build` vs `barcode-build`), introspect the installed package rather than retrying. AMRFinderPlus, RGI, and ResFinder all renamed columns between major releases.
# AMR Surveillancebio-epidemiological-genomics-pathogen-typing
When your agent uses it
Assigns isolate identity at the right resolution for the question -- ANI/Mash species triage, 7-locus MLST historical comparability, cgMLST/wgMLST outbreak resolution (chewBBACA, BIGSdb, Ridom SeqSphere, EnteroBase HierCC), in-silico serotyping (SISTR/SeqSero2 Salmonella, SerotypeFinder E. coli, Kaptive Klebsiella, SeroBA pneumococcus, spa+SCCmec S. aureus), and lineage callers (TB-Profiler/Mykrobe barcode for MTBC, Pangolin + Nextclade for SARS-CoV-2, PopPUNK GPSC for S. pneumoniae). Use when typing bacterial isolates for surveillance or outbreak investigation, choosing between cgMLST allele distance and core-SNP distance for cluster definition, harmonising calls across schemas/database versions, assigning MTBC lineage with the Napier 90-SNP barcode, calling Salmonella serovar via SISTR with monophasic Typhimurium awareness, running Pangolin UShER mode with pangolin-data version pinning, or selecting a typing resolution to match the surveillance question.
What it loads into context
Frontmatter
345 tokensAlways available to the agent
SKILL.md
9,282 tokensWhen the skill is activated
Reference documents
2,514 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- cgMLST and core-SNP distance answer different questions
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Running 7-Locus MLST and cgMLST
- SNP-Based Outbreak Cluster Definition
- Lineage Calling for Mtb and SARS-CoV-2
- Per-Method Failure Modes
- Reconciliation: When Typing Methods Disagree
- Quantitative Thresholds
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/mlst_typing.py
Files
SKILL.mdcore35,146 bytesSKILL.mdcore35,146 bytesSKILL.mdcore35,146 bytesSKILL.mdcore35,146 bytesSKILL.mdcore35,146 bytesSKILL.mdcore35,146 bytesSKILL.mdcore35,146 bytesSKILL.mdcore35,146 bytesSKILL.mdcore35,146 bytesSKILL.mdcore35,146 bytesSKILL.mdcore35,146 bytesSKILL.mdcore35,146 bytesreferences/usage-guide.mddoc10,042 bytesscripts/mlst_typing.pyruns7,464 bytes
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## Version Compatibility
Reference examples tested with: mlst 2.23+, chewBBACA 3.3+, SISTR 1.1+, SeqSero2 1.3+, SerotypeFinder 2.0+, Kleborate 3.0+, Kaptive 3.0+, SeroBA 1.0+, PopPUNK 2.7+, pangolin 4.3+ (pangolin-data 1.30+), nextclade 3.8+, tb-profiler 6.2+, mykrobe 0.13+, mash 2.3+, skani 0.2+, snippy 4.6+, snp-dists 0.8+, pandas 2.2+, BioPython 1.84+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name`
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
- Pangolin: `pangolin --all-versions` records pangolin + pangolin-data + scorpio + constellations
- Nextclade dataset: `nextclade dataset list --tag latest sars-cov-2`
- chewBBACA schema: schemas are versioned independently; record the schema source AND the schema-fetch date for any published call
- TB-Profiler bundled DB: `tb-profiler list_db` for the WHO catalogue edition currently bundled
If a tool reports an unexpected serovar / lineage / ST, the schema or barcode version is the first thing to check; introspect the installed package and database date before re-running.bio-epidemiological-genomics-phylodynamics
When your agent uses it
Estimates time-scaled phylogenies, molecular-clock rates, effective reproduction number R_e, and population dynamics from dated pathogen genomes using TreeTime (maximum-likelihood) and BEAST2 (Bayesian; strict/relaxed clocks; coalescent, Bayesian-Skyline, Skygrid, Birth-Death-Skyline, and sampled-ancestor priors; structured coalescent via MASCOT). Covers root-to-tip clock QC via TempEst, date-randomisation tests, recombination masking via Gubbins/ClonalFrameML before clock inference for recombining bacteria, BDSKY origin-vs-rootHeight pitfalls, sampling-bias correction, multi-chain convergence diagnostics, and reconciling phylodynamic R_e with case-based R_t. Use when dating outbreak origins, estimating substitution rates, inferring R_e through time, building time-calibrated Nextstrain Augur trees, choosing between strict and relaxed clocks, fitting Birth-Death-Skyline models, diagnosing temporal-signal failure, running MASCOT for structured-population analyses, or using UShER for pandemic-scale placement.
What it loads into context
Frontmatter
329 tokensAlways available to the agent
SKILL.md
8,541 tokensWhen the skill is activated
Reference documents
2,644 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Recombination passed unmasked into clock inference inflates the clock rate 2-5x and breaks every downstream estimate
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Time-Scaling With TreeTime
- BDSKY in BEAST2 With Recombination Masking First
- MASCOT Structured Coalescent
- Per-Method Failure Modes
- Reconciliation: When Methods Disagree
- Quantitative Thresholds
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/treetime_analysis.py
Files
SKILL.mdcore32,418 bytesSKILL.mdcore32,418 bytesSKILL.mdcore32,418 bytesSKILL.mdcore32,418 bytesSKILL.mdcore32,418 bytesSKILL.mdcore32,418 bytesSKILL.mdcore32,418 bytesSKILL.mdcore32,418 bytesreferences/usage-guide.mddoc10,659 bytesscripts/treetime_analysis.pyruns8,497 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: BEAST 2.7.6+, BDSKY 1.5+, BEASTLabs 2.0+, feast 9.5+, ORC 1.1.2+, MASCOT 3.0+, BEAGLE 4.0+, TreeTime 0.11+, IQ-TREE 2.3.6+, Gubbins 3.3+, ClonalFrameML 1.13+, UShER 0.6+, matUtils 0.6+, BactDating 1.1+ (R), bdskytools 1.1+ (R), coda 0.19+ (R), ape 5.8+ (R), ggplot2 3.5+, BioPython 1.84+, dendropy 4.6+, baltic 0.2+.
Before using code patterns, verify installed versions match. If versions differ:
- BEAST: `beast -version`; `packagemanager -list` for BEAST2 packages and versions
- Python: `pip show treetime`; `help(treetime.TreeTime)`
- R: `packageVersion('bdskytools')`; `?bdskytools::bdskytools_plot`
- CLI: `gubbins --version`; `iqtree --version`; `clonalframeml --version`
If BEAST2 throws `IllegalArgumentException` on XML load, the BDSKY / feast / BEASTLabs minor version probably moved; check the XML against the installed package's example XML in `~/beast/examples/`. BEAST2 XML is NOT robust across minor releases; pin the BEAST package version in any published analysis.
# Phylodynamics
bio-epidemiological-genomics-transmission-inference
When your agent uses it
Infers person-to-person transmission from pathogen genomes using outbreaker2, TransPhylo, phybreak, BadTrIP, SCOTTI, BEASTLIER, and SNP-distance / cluster-picker approaches (HIV-TRACE for HIV; transcluster). Defines outbreak clusters using pathogen-specific SNP thresholds (NOT a universal cutoff -- TB <=12 SNPs; MRSA <=15; C. difficile <=2; Klebsiella <=21), models within-host diversity and transmission bottlenecks, integrates contact-tracing data, distinguishes generation from serial interval, and attributes source via Bayesian source attribution (islandR). Use when investigating outbreaks for who-infected-whom, defining SNP-cluster outbreak definitions, accounting for unsampled intermediates, choosing between outbreaker2 (rich epi data) and TransPhylo (genomic-only after a dated phylogeny), running source attribution between host populations, calling HIV-TRACE thresholds appropriate to the local subtype, or distinguishing recent transmission from reactivation in TB or chronic HIV.
What it loads into context
Frontmatter
311 tokensAlways available to the agent
SKILL.md
8,621 tokensWhen the skill is activated
Reference documents
2,557 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- There is no universal SNP cutoff for transmission
- Algorithmic Taxonomy
- Decision Tree by Scenario
- outbreaker2 With Contact Data
- TransPhylo From a Dated Tree
- SNP-Cluster Definition With Pathogen-Specific Thresholds
- Per-Method Failure Modes
- Reconciliation: When Methods Disagree
- Quantitative Thresholds
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/transmission_inference.py
Files
SKILL.mdcore34,425 bytesSKILL.mdcore34,425 bytesSKILL.mdcore34,425 bytesSKILL.mdcore34,425 bytesSKILL.mdcore34,425 bytesSKILL.mdcore34,425 bytesSKILL.mdcore34,425 bytesSKILL.mdcore34,425 bytesSKILL.mdcore34,425 bytesSKILL.mdcore34,425 bytesSKILL.mdcore34,425 bytesSKILL.mdcore34,425 bytesSKILL.mdcore34,425 bytesSKILL.mdcore34,425 bytesSKILL.mdcore34,425 bytesSKILL.mdcore34,425 bytesSKILL.mdcore34,425 bytesSKILL.mdcore34,425 bytesSKILL.mdcore34,425 bytesSKILL.mdcore34,425 bytesSKILL.mdcore34,425 bytesreferences/usage-guide.mddoc11,231 bytesscripts/transmission_inference.pyruns8,635 bytes
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## Version Compatibility
Reference examples tested with: TransPhylo 1.4+ (R), outbreaker2 1.2+ (R), phybreak 0.5+ (R), BadTrIP via BEAST 2.7+ package manager, BEASTLIER via BEAST 1.10+, transcluster 1.0+ (R), HIV-TRACE 1.5+, snp-dists 0.8+, ape 5.8+ (R), igraph 1.6+ (R), TreeTime 0.11+, BactDating 1.1+ (R), BEAST 2.7.6+, lofreq 2.1+, deepSNV via Bioconductor 3.18+, pandas 2.2+, BioPython 1.84+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('TransPhylo')`; `?inferTTree` to confirm arg names
- R: `packageVersion('outbreaker2')`; `?create_config` -- iteration count is set via `n_iter` in the `config` object, NOT as `iters` to `outbreaker()`
- Python: `pip show lofreq`; check whether deep variant calling supports the target MAF
- CLI: `snp-dists --help`; `hiv-trace --help`
If R rejects an argument, the function signature changed between minor releases; `?function_name` is authoritative.
# Transmission Inference
bio-epidemiological-genomics-variant-surveillance
When your agent uses it
Assigns pathogen lineages (SARS-CoV-2 Pangolin UShER mode; Nextclade clade + QC; pango-designation alias resolution) and tracks variant frequencies over time using Nextstrain (Augur + Auspice), wastewater deconvolution (Freyja, COJAC, alcov, lineagespot), lineage-fitness modelling (multinomial logistic), and recombinant detection (3SEQ, RDP4, Bolotie). Covers Pangolin pangolin-data and Nextclade dataset version pinning (mandatory; lineage-defining mutations change with dataset), Freyja barcode forward-only date constraint, ARTIC primer scheme churn (V3/V4/V4.1/V5.3.2/Midnight) with dropout regions, and recombinant X-prefix designation lag. Use when assigning Pango lineages and Nextclade clades to viral consensus sequences, building Nextstrain Augur surveillance pipelines, deconvolving wastewater into lineage frequencies with Freyja, tracking lineage frequencies over time, handling ARTIC primer dropouts, or running surveillance for SARS-CoV-2/influenza/Mpox/RSV/H5N1/measles.
What it loads into context
Frontmatter
346 tokensAlways available to the agent
SKILL.md
8,438 tokensWhen the skill is activated
Reference documents
2,597 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Lineage assignment is dataset-version-dependent
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Pangolin Lineage Assignment With Version Pinning
- Nextclade With Dataset Pinning
- Wastewater Lineage Deconvolution With Freyja
- COJAC for Co-Occurrence Detection
- Nextstrain Augur Pipeline
- Per-Method Failure Modes
- Reconciliation: When Methods Disagree
- Quantitative Thresholds
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/variant_surveillance.py
Files
SKILL.mdcore34,608 bytesSKILL.mdcore34,608 bytesSKILL.mdcore34,608 bytesSKILL.mdcore34,608 bytesSKILL.mdcore34,608 bytesSKILL.mdcore34,608 bytesSKILL.mdcore34,608 bytesSKILL.mdcore34,608 bytesreferences/usage-guide.mddoc10,889 bytesscripts/variant_surveillance.pyruns8,190 bytes
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## Version Compatibility
Reference examples tested with: pangolin 4.3+ (pangolin-data 1.30+), nextclade 3.8+, augur 24.0+, freyja 1.4+, cojac 0.9+, lineagespot 1.6+ (Bioconductor), usher 0.6+, matUtils 0.6+, samtools 1.20+, lofreq 2.1+, ivar 1.4+, ARTIC pipeline 1.3+, snakemake 8.5+, pandas 2.2+, BioPython 1.84+, jq 1.7+.
Before using code patterns, verify installed versions match. If versions differ:
- `pangolin --all-versions` -- prints pangolin + pangolin-data + scorpio + constellations versions
- `nextclade dataset list --tag latest sars-cov-2` -- list current dataset tags
- `freyja --version`; `freyja barcode-build --help` (note: HYPHEN, not underscore; some legacy docs show `barcode_build`)
- `nextclade run --help` -- v3+ syntax replaced v2; old `nextclade` invocation no longer works
- `augur --version`; `augur refine --help` for current root-strategy flags
If `pangolin --inference usher` is rejected, the flag is `--analysis-mode usher` (no `--inference`). If `nextclade --input-dataset DIR` works, the installed version may be v2; v3 accepts both but `--dataset NAME` is the modern form for built-in datasets. Pangolin and Nextclade output column names differ between major releases -- introspect rather than retry.
# Variant Surveillancebio-epitranscriptomics-m6a-differential
When your agent uses it
Identifies differential m6A methylation between conditions from MeRIP-seq paired IP/input data using exomePeak2 (GC-bias-aware differential via its bam_ip/bam_input control + bam_treated_ip/bam_treated_input treatment arms), QNB beta-binomial, MeTDiff HMM, and RADAR, plus the paired-symmetric edgeR/DESeq2-on-peak-counts route when batch/lot covariates need fixed-effect handling that exomePeak2's API does not accept. Covers paired vs unpaired vs interaction designs, batch confounding and per-lot meta-analysis, the stoichiometry-vs-expression-vs-IP-efficiency confound, and effect-size filtering against under-powered N=2 designs. Use when comparing m6A across two or more conditions, choosing between exomePeak2/QNB/RADAR/MeTDiff for a design, handling batch confounding when exomePeak2's API is too rigid, distinguishing real hyper/hypo-methylation from expression shifts, applying effect-size thresholds, or planning orthogonal stoichiometry validation (GLORI/SAC-seq/m6Anet mod_ratio).
What it loads into context
Frontmatter
346 tokensAlways available to the agent
SKILL.md
8,853 tokensWhen the skill is activated
Reference documents
1,650 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- IP fold-change between conditions conflates stoichiometry change with expression change and IP efficiency drift
- Algorithmic Taxonomy
- Decision Tree by Scenario
- exomePeak2 Differential Workflow
- QNB Beta-Binomial for Small-N Designs
- RADAR Reproducibility-Aware Differential
- Volcano Plot of Differential Peaks
- Per-Method Failure Modes
- Reconciliation: When Differential Methods Disagree
- Quantitative Thresholds
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Files
SKILL.mdcore34,651 bytesSKILL.mdcore34,651 bytesreferences/usage-guide.mddoc6,884 bytesscripts/differential_m6a.Rasset3,465 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: exomePeak2 1.14+ (Bioconductor 3.18+), QNB 1.1.11 (GitHub `lzcyzm/QNB`), MeTDiff (GitHub, bundled with MeTPeak), RADAR 0.2.4+ (GitHub `scottzijiezhang/RADAR`), DESeq2 1.42+, edgeR 4.0+, GenomicFeatures 1.54+, ggplot2 3.5+, GenomicAlignments 1.38+, Rsubread 2.16+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('exomePeak2')` then `?exomePeak2` to verify parameters
- R: `packageVersion('QNB')` then `?qnbtest` to confirm argument signature
If R throws `unused argument` or `argument is missing`, the API moved between Bioconductor minor releases; consult `?exomePeak2` directly. QNB function signature has been stable since 2017 but the GitHub source has occasional changes; pin the commit SHA.
exomePeak2's differential interface is NOT a `mode=` argument — populate the `bam_treated_ip=` and `bam_treated_input=` arguments alongside the standard `bam_ip=` / `bam_input=` (control arm) to trigger paired differential calling. `peak_calling_mode` is a separate argument controlling locus scope (`exon` | `full_transcript` | `whole_genome`). Verify against `?exomePeak2` and Bioconductor 3.20+ release notes. QNB is on GitHub only; install via `devtools::install_github('lzcyzm/QNB')`. RADAR is on GitHub only; install via `devtools::install_github('scottzijiezhang/RADAR')`; its differential workflow is `countReads -> normalizeLibrary -> adjustExprLevel -> filterBins -> diffIP -> reportResult`.
# Differential m6A Analysis
bio-epitranscriptomics-m6a-peak-calling
When your agent uses it
Calls m6A peaks from MeRIP-seq / m6A-seq paired IP-vs-input data using exomePeak2 (transcript-aware, GC-bias-corrected Poisson GLM), MeTPeak (HMM over sliding windows), MACS3/MACS2 with --nomodel --broad --keep-dup all (genome-wide broad alternative), and DRACH motif enrichment via HOMER or ggseqlogo as a sanity check (NOT a filter). Covers BED12 vs narrowPeak output, exonic vs intronic peak handling, multi-tool reconciliation (intersection vs union), the m6A-vs-m6Am ambiguity at 5'UTR peaks that antibody methods cannot resolve, and orthogonal validation (miCLIP/GLORI/m6A-SAC-seq/m6Anet). Use when calling peaks from paired IP/input genome BAMs, choosing exomePeak2 (transcript-aware default) vs MACS3 (broad genomic) vs MeTPeak (HMM-smoothed low-coverage), confirming DRACH enrichment as a sanity check on the peak set, reconciling differing peak sets across tools, validating MeRIP peaks against single-base methods, interpreting 5' peaks where m6Am contamination is possible, or recommending a consensus strategy.
What it loads into context
Frontmatter
376 tokensAlways available to the agent
SKILL.md
9,561 tokensWhen the skill is activated
Reference documents
1,632 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- MeRIP cannot distinguish m6A from m6Am near the 5' end, and DRACH is a sanity check not a filter
- Algorithmic Taxonomy
- Decision Tree by Scenario
- exomePeak2 Standard Workflow
- MeTPeak HMM-Smoothed Alternative
- MACS3 Broad-Peak Alternative
- DRACH Motif Confirmation (Sanity Check, NOT a Filter)
- Multi-Tool Consensus
- Per-Method Failure Modes
- Reconciliation: When Peak Callers Disagree
- Quantitative Thresholds
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/macs3_peaks.sh
Files
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## Version Compatibility
Reference examples tested with: exomePeak2 1.14+ (Bioconductor 3.18+), MeTPeak (GitHub commit SHA-pinned; no Bioconductor release), MACS3 3.0+, MACS2 2.2.9+, samtools 1.19+, GenomicFeatures 1.54+, BSgenome.Hsapiens.UCSC.hg38 1.4+, HOMER 4.11+, ggseqlogo 0.2+, rtracklayer 1.62+, GenomicRanges 1.54+, ChIPseeker 1.38+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('exomePeak2')` then `?exomePeak2` to verify parameters
- CLI: `macs3 callpeak --help`, `findMotifsGenome.pl` to confirm flags
If R throws `unused argument` or `argument is missing`, the exomePeak2 / MeTPeak API may have moved between releases; consult `?exomePeak2` and the installed package's NAMESPACE.
exomePeak2 differential is triggered by populating `bam_treated_ip` / `bam_treated_input` alongside the control-arm `bam_ip` / `bam_input` (see m6a-differential); there is NO `mode=` argument. MeTPeak is GitHub-only and unversioned — pin a commit SHA in any reproducible analysis; defaults are `WINDOW_WIDTH=50, SLIDING_STEP=50, FRAGMENT_LENGTH=100`. MACS3 supersedes MACS2 in development activity but both are widely used; default `--keep-dup` is 1 in BOTH and MUST be overridden to `all` for MeRIP. HOMER `findMotifsGenome.pl` is mature; the `-rna` flag is the RNA-mode entry.
# m6A Peak Calling
bio-epitranscriptomics-m6anet-analysis
When your agent uses it
Detects m6A modifications from Oxford Nanopore direct-RNA-seq (DRS) signal using m6Anet (multiple-instance-learning over DRACH 5-mer signal). Covers the upstream pipeline (Dorado/Guppy basecalling -> minimap2 map-ont -> nanopolish eventalign -> m6anet dataprep -> m6anet inference), per-site vs per-read probability including the mod_ratio stoichiometry column, the DRACH-only constraint, minimum-coverage thresholds (20-50 reads/site), multi-condition comparison via xPore/Nanocompore/ELIGOS, Dorado native modification calling (RNA004, 2024+), and the cDNA-vs-DRS distinction (cDNA Nanopore CANNOT detect modifications). Use when calling m6A from ONT DRS without immunoprecipitation, choosing m6Anet vs xPore vs Nanocompore vs ELIGOS vs Dorado native, interpreting probability_modified vs mod_ratio vs per-read probabilities, deciding between m6Anet (known DRACH sites) and Dorado/Remora (genome-wide screening), pinning RNA002 vs RNA004 chemistry and basecaller versions, or troubleshooting eventalign/dataprep failures.
What it loads into context
Frontmatter
362 tokensAlways available to the agent
SKILL.md
9,435 tokensWhen the skill is activated
Reference documents
1,800 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- m6Anet scores per-site DRACH probabilities, but reliability depends on coverage and the model is DRACH-only
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Full m6Anet Pipeline (RNA002 / RNA004 chemistry)
- Filtering and Interpreting m6Anet Results
- xPore Two-Condition Differential
- Nanocompore Comparative Modification Detection
- Per-Method Failure Modes
- Reconciliation: When Methods Disagree
- Quantitative Thresholds
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/m6anet_workflow.py
Files
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## Version Compatibility
Reference examples tested with: m6anet 2.1+ (PyPI; project capitalisation `m6Anet`), nanopolish 0.14+, minimap2 2.26+, samtools 1.19+, Dorado 0.5+, xpore 2.1+, nanocompore 1.0.4+, ELIGOS 2 (GitHub `novoalab/Eligos2`), CHEUI (GitHub `comprna/CHEUI`), pandas 2.2+, pyranges 0.0.129+, pysam 0.22+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show m6anet` then `m6anet --help`, `m6anet dataprep --help`, `m6anet inference --help`
- CLI: `nanopolish --version`, `minimap2 --version`, `dorado --version`
If `m6anet` is invoked as a hyphenated command (`m6anet-dataprep`), the v1.x CLI is installed; v2.x uses subcommand syntax (`m6anet dataprep`). Pin m6anet version explicitly in any reproducible analysis. Dorado modification models are versioned independently of the basecaller (`m6A_DRACH@v0.1`, `m6A_DRACH@v1`, ...); model versions are NOT directly comparable across releases. RNA002 vs RNA004 nanopore chemistry use different signal characteristics — models trained on RNA002 do NOT transfer to RNA004.
# m6Anet Direct-RNA m6A Detection
**"Detect m6A from my Nanopore direct RNA data without IP"** -> Run the full pipeline from POD5 / FAST5 signal to per-site m6A modification probabilities: Dorado basecalling -> minimap2 alignment to TRANSCRIPTOME (not genome) with `-ax map-ont -uf -k14` -> nanopolish eventalign with `--scale-events --signal-index` (the m6Anet-required pair) -> m6anet dataprep -> m6anet inference. Report per-site `probability_modified` (model posterior that any reads at the site are modified) AND `mod_ratio` (per-site stoichiometry, the fraction of reads called modified) with coverage. CRITICAL: m6Anet is DRACH-only — non-DRACH sites are invisible to the model; cDNA-Nanopore data CANNOT be used (PCR erases the signal); RNA002 and RNA004 chemistry require different model versions.
bio-epitranscriptomics-merip-preprocessing
When your agent uses it
Aligns and QCs methylated-RNA-immunoprecipitation (MeRIP / m6A-seq) IP and input libraries using STAR or HISAT2 splice-aware mapping, samtools sort/index, IP/input matched-pair tracking, antibody-lot metadata recording, replicate concordance via deepTools multiBamSummary + plotCorrelation, IP enrichment QC via plotFingerprint and per-transcript IP/input ratio distributions, library-complexity saturation curves via PreSeq, and the explicit do-NOT-deduplicate convention for standard non-UMI MeRIP. Use when preparing paired IP and input BAM files for exomePeak2 / MeTPeak / MACS3 peak calling, evaluating MeRIP replicate concordance and IP enrichment, deciding whether to deduplicate (standard MeRIP typically NOT), choosing genome-vs-transcriptome alignment for downstream peak vs m6Anet workflows, recording antibody clone and lot metadata for cross-batch reconciliation, detecting failed IPs via saturation curves and IP/input distribution shape, or generating IP-over-Input bigWig tracks for visualisation.
What it loads into context
Frontmatter
326 tokensAlways available to the agent
SKILL.md
8,659 tokensWhen the skill is activated
Reference documents
1,583 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Peak counts are library-size-dependent; saturation curves are the only honest cross-library comparison
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Adapter Trimming for MeRIP
- STAR Splice-Aware Alignment for IP and Input
- HISAT2 Alternative for Memory-Constrained Environments
- Per-Sample QC: flagstat and idxstats
- Replicate Concordance via deepTools
- IP Enrichment via plotFingerprint
- Library Complexity / Saturation Curves via PreSeq
- IP-over-Input bigWig for Downstream Visualisation
- Per-Method Failure Modes
- Reconciliation: When QC Signals Disagree
- Quantitative Thresholds
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/align_merip.shscripts/merip_qc.py
Files
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## Version Compatibility
Reference examples tested with: STAR 2.7.11+, HISAT2 2.2.1+, samtools 1.19+, deepTools 3.5+, PreSeq 3.2+, fastp 0.23+, Trim Galore 0.6.10+, Picard 3.1+, MultiQC 1.25+, bowtie2 2.5+, BWA-MEM2 2.2+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
- Python: `pip show <package>` then `help(module.function)`
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
STAR `--outSAMtype` accepts `BAM SortedByCoordinate` since 2.5.x; check the `Log.final.out` file for input/output statistics. deepTools `bamCompare --operation log2` is the modern syntax (older `--ratio log2` still works but is being phased out). PreSeq `c_curve` and `lc_extrap` have stable interfaces; HISAT2 reports unique vs multi-mapped in the summary log.
# MeRIP-seq Preprocessing
bio-epitranscriptomics-modification-visualization
When your agent uses it
Visualises RNA-modification data with transcript-feature metagene plots (Guitar GuitarPlot; MetaPlotR; deepTools computeMatrix scale-regions), peak-centred heatmaps (ComplexHeatmap; deepTools plotHeatmap), IP-vs-input paired browser tracks (log2 IP/input bigWig via deepTools bamCompare; pyGenomeTracks; Gviz; IGV/UCSC track hubs), DRACH sequence-logo plots (ggseqlogo; MEME), feature-distribution stacked bars, and volcano/MA plots for differential modification. Establishes stop-codon enrichment in the metagene plot as the biological QC anchor for any MeRIP dataset (Dominissini 2012; Meyer 2012). Use when producing the canonical metagene plot with stop-codon enrichment as a QC anchor, building paired IP/input genome-browser tracks at single-locus resolution, plotting peak-centred heatmaps clustered by condition, summarising peak distribution across transcript features, generating DRACH motif logos as sanity checks, rendering volcano plots of differential m6A, or reproducing the stop-codon enrichment plot.
What it loads into context
Frontmatter
327 tokensAlways available to the agent
SKILL.md
8,571 tokensWhen the skill is activated
Reference documents
1,546 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Stop-codon enrichment in the metagene plot is the biological QC anchor — a MeRIP dataset without it is suspect
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Guitar Transcript-Feature Metagene (THE Canonical Plot)
- deepTools Genome-Coordinate Metagene
- Peak-Centred Heatmap
- IP-vs-Input Paired Browser Tracks via pyGenomeTracks
- ggcoverage ggplot2-Native Browser Track
- DRACH Sequence Logo
- 5'UTR / CDS / 3'UTR Stacked Bar
- Per-Method Failure Modes
- Reconciliation: When Plots Disagree
- Quantitative Thresholds
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Scripts executed separately
scripts/deeptools_metagene.sh
Files
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## Version Compatibility
Reference examples tested with: Guitar 2.18+ (Bioconductor), MetaPlotR (GitHub, unversioned), deepTools 3.5+, ggcoverage 1.4+, pyGenomeTracks 3.8+, Gviz 1.46+, ComplexHeatmap 2.18+, ggseqlogo 0.2+, ggplot2 3.5+, rtracklayer 1.62+, GenomicFeatures 1.54+, BSgenome.Hsapiens.UCSC.hg38 1.4+, TxDb.Hsapiens.UCSC.hg38.knownGene 3.18+, ChIPseeker 1.38+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('Guitar')` then `?GuitarPlot` to verify parameters
- CLI: `<tool> --help`; `deepTools <tool> --help`
If R throws `unused argument` or `argument is missing`, the API moved between Bioconductor minor releases. Guitar's older API used `txdb=` while newer uses `txTxdb=`; verify with `?GuitarPlot`. deepTools `bamCompare --operation log2` is the modern syntax (older `--ratio log2` is being phased out).
# RNA Modification Visualisation
**"Make the canonical m6A metagene plot for my paper"** -> Render the 5'UTR / CDS / 3'UTR transcript-feature metagene with stop-codon enrichment for visual confirmation of the canonical m6A topology (THE smoke test that the antibody-IP captured real m6A), the peak-feature-distribution stacked bar ("where do my peaks land?" Figure 1), peak-centred heatmaps clustered by condition, IP-over-Input paired browser tracks at specific loci, the DRACH sequence logo as a sanity check on antibody specificity, and the volcano / MA plots for differential modification. CRITICAL: the stop-codon enrichment plot is a biological-QC anchor — a MeRIP dataset that does NOT show enrichment at and around the stop codon indicates IP failure, wrong antibody, wrong protocol, or the assay captured a different modification (e.g., m1A is centred at TSS, not stop). Build this plot FIRST as a smoke test BEFORE any downstream visualisation.
bio-experimental-design-batch-design
When your agent uses it
Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.
What it loads into context
Frontmatter
287 tokensAlways available to the agent
SKILL.md
3,784 tokensWhen the skill is activated
Reference documents
834 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- No Post-Hoc Method Recovers a Confounded Design
- Confounding vs Blocking vs Nuisance -- the Causal-Graph View
- Algorithmic Taxonomy -- Design Strategies for Technical Variation
- Decision Tree by Scenario
- Confounded vs Balanced -- the Canonical Contrast
- Constrained Sample-to-Batch Assignment
- Downstream Correction -- Choose by Design, Execute Elsewhere
- Detecting Hidden Batch Effects (SVA)
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- Reproducibility Metadata
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: designit 0.5+, OSAT 1.50+ (Bioconductor), sva 3.50+, RUVSeq 1.36+, limma 3.58+, edgeR 4.0+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws an error, introspect the installed package and adapt to the actual API. Notes: OSAT uses `optimal.shuffle()` on a setup object (there is no bare `osat()` function); designit is R6 (`BatchContainer$new()`, `optimize_design()`, `*_score_generator()`) and its signatures drift between releases; `sva::ComBat_seq()` is for integer counts while `ComBat()` expects log-normalized values. Confirm each against the installed vignette before relying on it.
# Batch Design
**"Design my experiment so batch effects don't ruin it"** -> Assign samples to batches/lanes/plates so the biological variable is balanced against (orthogonal to) every technical nuisance factor, making batch estimable rather than confounded — because no post-hoc correction recovers a design where batch and condition are aliased.
- R: `designit::optimize_design()`, `OSAT::optimal.shuffle()` — constrained assignment at design time
- R: `sva::sva()`/`num.sv()` — detect hidden batches; `sva::ComBat_seq()`, `RUVSeq::RUVg()` — DOWNSTREAM correction (executed in differential-expression/batch-correction)bio-experimental-design-multiple-testing
When your agent uses it
Controls error rates across thousands of simultaneous tests in genomics discovery using false-discovery-rate methods (Benjamini-Hochberg 1995; Benjamini-Yekutieli 2001 for arbitrary dependence; Storey q-value with pi0 estimation; local FDR; independent filtering Bourgon 2010; covariate-weighted FDR via IHW Ignatiadis 2016), plus family-wise error control (Bonferroni, Holm) and the GWAS genome-wide threshold. Covers the FDR-versus-FWER choice as the discovery-versus-confirmatory distinction, the dependence assumptions behind BH (PRDS) versus BY, pi0 estimation, the independent-filtering and false-coverage-rate traps, and reproducibility ranking via IDR (Li 2011). Use when correcting p-values from genome-wide tests, choosing between BH/BY/q-value/Bonferroni, setting an FDR threshold, applying IHW or independent filtering, or interpreting q-values. For confirmatory trials with few pre-specified endpoints (closed testing, graphical/gatekeeping), see clinical-biostatistics/multiplicity-graphical.
What it loads into context
Frontmatter
337 tokensAlways available to the agent
SKILL.md
3,576 tokensWhen the skill is activated
Reference documents
845 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- FDR vs FWER Is a Choice About Which Error Matters
- Algorithmic Taxonomy
- Decision Tree by Scenario
- FDR -- Benjamini-Hochberg and the q-value
- Dependence -- When BH Is Not Enough (BY)
- Covariate-Weighted FDR -- IHW
- Independent Filtering -- Power for Free, If the Filter Is Independent
- Python Equivalent (mind the default)
- GWAS and the Family-Definition Problem
- Reconciliation: When Methods Disagree
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: qvalue 2.34+, IHW 1.30+, R stats (base) p.adjust, statsmodels 0.14+, scipy 1.12+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws an error, introspect the installed package and adapt to the actual API. Note: `statsmodels.stats.multitest.multipletests` defaults to `method='hs'` (Holm-Sidak, an FWER method), NOT Benjamini-Hochberg — always pass `method='fdr_bh'`/`'fdr_by'`/`'bonferroni'`/`'holm'` explicitly.
# Multiple Testing Correction
**"Correct p-values for testing thousands of features"** -> Choose an error rate appropriate to the regime (FDR for discovery, FWER for confirmatory), apply a procedure whose dependence assumptions match the data, and report the adjusted quantity with its interpretation.
- R: `p.adjust(p, method = 'BH')`, `qvalue::qvalue()`, `IHW::ihw()`bio-experimental-design-power-analysis
When your agent uses it
Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.
What it loads into context
Frontmatter
308 tokensAlways available to the agent
SKILL.md
3,747 tokensWhen the skill is activated
Reference documents
896 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Genomics Power Is Per-Gene; Simulate, and Never Report Observed Power
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Closed-Form NB Power -- RNASeqPower
- Simulation-Based Power -- the Honest Default for Counts
- Depth vs Replicates -- the Budget Question
- CV / Dispersion Guidelines (estimate from pilot when possible)
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: RNASeqPower 1.42+, PROPER 1.34+, powsimR 1.2+ (GitHub), DESeq2 1.42+, edgeR 4.0+, pwr 1.3+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws an error, introspect the installed package and adapt to the actual API. Notes: `RNASeqPower::rnapower()` solves for whichever of `n` or `power` is omitted; PROPER is a multi-step pipeline (`RNAseq.SimOptions.2grp` -> `simRNAseq` -> `runSims` -> `comparePower`); powsimR is GitHub-only and its `estimateParam`/`Setup`/`simulateDE` signatures drift — pin a commit SHA for reproducible work. Verify each against the installed help before relying on argument names.
# Power Analysis for Genomics Experiments
**"How many replicates does my sequencing experiment need?"** -> Compute the probability of detecting a biologically meaningful effect given replicate number, sequencing depth, and biological variability — modeling counts as negative-binomial and recognizing that power is a per-gene quantity, not one number for the whole transcriptome.
- R: `RNASeqPower::rnapower()` — closed-form NB power/sample size; `PROPER`, `powsimR` — simulation from the mean-dispersion trend
bio-experimental-design-randomization-blocking
When your agent uses it
Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.
What it loads into context
Frontmatter
296 tokensAlways available to the agent
SKILL.md
4,025 tokensWhen the skill is activated
Reference documents
870 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- The Experimental Unit, Not the Measurement, Is the n
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Choosing and Counting the Experimental Unit
- Randomization Mechanics
- Blocking and Local Control
- Split-Plot and Nested Designs -- the Genomics Trap
- Factorial Designs and Interactions
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: designit 0.5+, lme4 1.1-35+, lmerTest 3.1+, pwr 1.3+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws an error, introspect the installed package and adapt the example to the actual API rather than retrying. designit is an R6 package whose `BatchContainer$new()`, `optimize_design()`, and `*_score_generator()` signatures evolve between releases; confirm against the installed vignette (`vignette(package = 'designit')`) before relying on argument names.
# Randomization and Blocking
**"Design the experiment so the statistics will be valid"** -> Decide what the experimental unit is, randomize treatments to those units, replicate the unit (not the measurement), and remove known nuisance variation by blocking — so that the analysis model mirrors how the experiment was actually run.
- R: `designit::optimize_design()` for constrained randomization; `lme4::lmer()` / `lmerTest` for the matching mixed model
- The design and the analysis are one decision: "analyze as randomized"bio-experimental-design-sample-size
When your agent uses it
Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experiments using ssizeRNA, PROPER, powsimR for scRNA-seq, and pilot-data dispersion estimation from DESeq2/edgeR. Covers the biological-versus-technical replication distinction (technical replicates do not add degrees of freedom for biological inference), replicate-number-versus-sequencing-depth budgeting, scRNA-seq sample-versus-cell allocation under a pseudobulk model, and the critique that "n=3" is a publication convention rather than a power calculation. Use when budgeting a sequencing experiment, writing the sample-size justification in a grant, estimating replicates from pilot data, allocating a fixed budget between samples and depth, or planning scRNA-seq cohort size. For clinical-trial sample size see clinical-biostatistics/power-and-sample-size; for the power-given-n direction see experimental-design/power-analysis.
What it loads into context
Frontmatter
291 tokensAlways available to the agent
SKILL.md
3,590 tokensWhen the skill is activated
Reference documents
861 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- The Biological Replicate Is the Unit, and n=3 Is a Convention
- Algorithmic Taxonomy
- Decision Tree by Scenario
- FDR-Aware NB Sample Size -- ssizeRNA
- Pilot Dispersions Drive Honest Sample Size
- Biological vs Technical Replication
- Replicates vs Depth Under a Fixed Budget
- Sample Size by Assay (floors under favorable conditions, not targets)
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- Anticipated Reviewer Pushback
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: ssizeRNA 1.3+, PROPER 1.34+, powsimR 1.2+ (GitHub), DESeq2 1.42+, edgeR 4.0+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws an error, introspect the installed package and adapt to the actual API. Notes: ssizeRNA provides `ssizeRNA_single()` (one mean/dispersion for all genes), `ssizeRNA_vary()` (genes vary), and `check.power()` (average power and true FDR for a given n); powsimR is GitHub-only with drifting signatures. Verify against the installed help before use.
# Sample Size for Genomics Experiments
**"How many samples do I need?"** -> Find the smallest number of biological replicates per group that achieves a target marginal power at a target FDR, given the dispersion and effect-size distribution expected for the assay — counting biological units, not measurements.
- R: `ssizeRNA::ssizeRNA_vary()`, `ssizeRNA::check.power()` — FDR-aware NB sample size; pilot dispersions from `DESeq2`/`edgeR`
bio-expression-matrix-counts-ingest
When your agent uses it
Imports gene expression count matrices from featureCounts, HTSeq, STAR ReadsPerGene, Salmon/kallisto via tximport or tximeta, RSEM, 10X Genomics MTX/H5, AnnData H5AD, and RDS. Handles silent-miscounting traps (featureCounts -p v2.0.2 API break, STAR strandedness column choice, salmon NumReads-sum without tximport, RSEM non-integer expected_count, GENCODE _PAR_Y suffix, zero-length-transcript TPM divide-by-zero), and encodes the tximport countsFromAbundance decision tree with the "lengthScaledTPM is not TPM" warning. Use when assembling a gene-by-sample count matrix from aligner or quantifier output, importing salmon/kallisto for DESeq2 vs limma-voom, choosing strandedness column for STAR, debugging zero-count panics, or building tx2gene mapping.
What it loads into context
Frontmatter
290 tokensAlways available to the agent
SKILL.md
7,304 tokensWhen the skill is activated
Reference documents
1,880 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- `lengthScaledTPM` is NOT TPM; the naming has fooled many
- Algorithmic Taxonomy
- Decision Tree by Scenario
- featureCounts -- The `-p` and `-O` Traps
- STAR `--quantMode GeneCounts`
- Salmon / kallisto via tximport
- tximeta -- Automatic Provenance
- HTSeq-count
- RSEM expected_count
- kallisto + sleuth (Uncertainty-Aware DE)
- Salmon Selective Alignment
- 10X Genomics
- Annotation Pre-Filtering -- rRNA, Mt-rRNA, Pseudogenes
- GENCODE vs Ensembl -- What Differs at the File Level
- Filter Low-Count Genes
- Per-Method Failure Modes
- Common errors
- References
- Related Skills
Scripts executed separately
scripts/load_counts.py
Files
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## Version Compatibility
Reference examples tested with: pandas 2.2+, numpy 1.26+, scanpy 1.10+, anndata 0.10+, tximport 1.30+, tximeta 1.20+, GenomicFeatures 1.54+, Subread/featureCounts 2.0.6+ (post-v2.0.2 API), STAR 2.7.10+, Salmon 1.10+, kallisto 0.48+, RSEM 1.3.3+, HTSeq 2.0+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Count Matrix Ingestion
bio-expression-matrix-gene-id-mapping
When your agent uses it
Maps between gene identifier systems (Ensembl, Entrez, HGNC symbol, UniProt, RefSeq, MANE) using AnnotationDbi, biomaRt, mygene, pyensembl, and Ensembl REST. Encodes Ensembl version stripping with GENCODE _PAR_Y preservation, the Ziemann 2016 Excel autocorrect debacle and Bruford 2020 HGNC renames (SEPT*->SEPTIN*, MARCH*->MARCHF*, MARC*->MTARC*, DEC1->DELEC1), OCT4/POU5F1 alias resolution, biomaRt archive endpoints for release pinning, the `filters` (plural) gotcha, MANE Select for clinical reporting, cross-species orthology via Ensembl Compara / OMA / OrthoDB, and tx2gene construction for tximport. Use when converting gene IDs across systems, handling renamed symbols, building tx2gene, pinning to a specific Ensembl release for reproducibility, or mapping cross-species orthologs.
What it loads into context
Frontmatter
320 tokensAlways available to the agent
SKILL.md
6,872 tokensWhen the skill is activated
Reference documents
2,172 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Excel autocorrect renamed real genes
- Algorithmic Taxonomy
- Decision Tree by Scenario
- AnnotationDbi + org.db
- biomaRt with Version Pinning
- mygene (Python)
- pyensembl
- Apply Mapping to Count Matrix -- Handling One-to-Many
- Handle Unmapped IDs
- MANE Select for Clinical Reporting
- Cross-Species Orthologs
- PAR Gene Complications
- Build tx2gene for tximport
- ID Type Reference
- Per-Method Failure Modes
- Common errors
- References
- Related Skills
Scripts executed separately
scripts/map_gene_ids.py
Files
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## Version Compatibility
Reference examples tested with: biomaRt 2.58+, AnnotationDbi 1.66+, org.Hs.eg.db 3.18+, org.Mm.eg.db 3.18+, GenomicFeatures 1.54+, mygene 1.38+ (Python), pyensembl 2.3+, pandas 2.2+, rtracklayer 1.62+
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Gene ID Mapping
**"Convert gene IDs from X to Y"** -> Query the appropriate annotation source (local org.db for speed, biomaRt for Ensembl-specific attributes, mygene for cross-database aliases, Ensembl REST for low-level access), with version pinning for reproducibility and explicit handling of one-to-many mappings, withdrawn symbols, and species-specific naming.bio-expression-matrix-metadata-joins
When your agent uses it
Aligns sample metadata with count matrices and constructs design matrices for downstream DE, handling the alphabetical-reference-level trap (relevel BEFORE DESeq), LRT reduced-model rules, the interaction-term resultsNames trap, continuous-covariate scaling and splines, repeated measures via duplicateCorrelation or dream, high-cardinality categorical pseudo-singular designs, sample swap detection via XIST/RPS4Y1 expression and somalier/NGSCheckMate genotypes, SABV (sex-as-biological-variable) mandate, Simpson's-paradox collapsing of technical replicates, and the `~ 0 + group` parameterization for clean contrasts. Use when building a design matrix, troubleshooting reversed fold-change direction, encoding paired or repeated-measures designs, detecting sample swaps, deciding sex-as-covariate, or aggregating technical replicates.
What it loads into context
Frontmatter
265 tokensAlways available to the agent
SKILL.md
6,496 tokensWhen the skill is activated
Reference documents
1,791 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Alphabetical reference levels invert fold changes silently
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Basic Join
- Reference Level (Critical -- Set Before DESeq)
- Paired Designs
- Interaction Terms -- the resultsNames Trap
- LRT and the Reduced Model
- Continuous Covariates
- Repeated Measures -- duplicateCorrelation vs dream
- Sample Swap Detection (Mandatory for Cohort >= 20)
- SABV -- Sex as Biological Variable
- Simpson's Paradox -- Collapsing Technical Replicates
- High-Cardinality Categorical Covariates
- ~ 0 + group Parameterization
- Create DESeq2 / edgeR / AnnData Containers
- Per-Method Failure Modes
- Common errors
- References
- Related Skills
Scripts executed separately
scripts/join_metadata.py
Files
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## Version Compatibility
Reference examples tested with: pandas 2.2+, DESeq2 1.42+, edgeR 4.0+, limma 3.58+, variancePartition / dream 1.32+, somalier 0.2.18+ (CLI), pyensembl 2.3+, anndata 0.10+
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Metadata Joins
bio-expression-matrix-normalization
When your agent uses it
Normalizes and transforms RNA-seq count matrices for DE, visualization, clustering, and ML. Covers between-sample (TMM, TMMwsp, RLE/median-of-ratios, upper quartile), within-sample (TPM, FPKM/RPKM), variance-stabilizing (VST, rlog, log-CPM), GC-content correction (cqn, EDASeq), and single-cell (scran deconvolution, scanpy normalize_total). Encodes the composition-bias rationale, the "most genes not DE" assumption and its catastrophic failure modes (MYC amplification, apoptosis, viral host shutoff, prokaryotic stress), the "lengthScaledTPM is not TPM" naming trap, the "TPM is not for DE" rule, the blind=TRUE vs FALSE decision, ERCC spike-in normalization (SBN), and the single-cell zero-inflation breakdown of TMM/RLE. Use when choosing or applying normalization, debugging shifted-MA-plot diagnostics, handling zero-heavy single-cell data, or correcting GC bias.
What it loads into context
Frontmatter
320 tokensAlways available to the agent
SKILL.md
6,485 tokensWhen the skill is activated
Reference documents
1,886 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- TMM/RLE assume most genes are NOT DE; that assumption is wrong in MYC, apoptosis, viral host shutoff, and prokaryotic stress
- Normalization Decision Table
- Between-Sample Normalization
- Within-Sample Normalization (TPM, FPKM)
- Variance-Stabilizing Transformations (VST, rlog)
- GC Content and Length Bias (cqn, EDASeq)
- Single-Cell Normalization
- Pre-Filtering Before Normalization
- Per-Method Failure Modes
- Common errors
- References
- Related Skills
Scripts executed separately
scripts/normalization_workflow.py
Files
SKILL.mdcore24,945 bytesSKILL.mdcore24,945 bytesSKILL.mdcore24,945 bytesSKILL.mdcore24,945 bytesreferences/usage-guide.mddoc7,364 bytesscripts/normalization_workflow.pyruns3,121 bytes
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## Version Compatibility
Reference examples tested with: DESeq2 1.42+, edgeR 4.0+, limma 3.58+, pandas 2.2+, numpy 1.26+, scanpy 1.10+, scran 1.30+, scater 1.30+, EDASeq 2.36+, cqn 1.48+
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Expression Matrix Normalization
**"Normalize my counts for X"** -> Pick a method that matches the downstream task (raw counts for DE; TMM/RLE-scaled CPM for cross-sample comparison; VST/rlog for visualization/ML; scran for single-cell) and the data structure (bulk vs single-cell, zero-heavy or not, length-biased or not).bio-expression-matrix-sparse-handling
When your agent uses it
Stores and operates on sparse expression matrices for single-cell and large bulk RNA-seq, covering dgCMatrix/dgRMatrix/dgTMatrix when-each-is-fast, the dgCMatrix (CSC, R) <-> CSR (Python) implicit transpose, AnnData (cells-rows) <-> SingleCellExperiment (cells-cols) orientation flip, HDF5/h5ad vs Zarr cloud-native shift, HDF5SummarizedExperiment + DelayedArray for out-of-memory bulk, scanpy backed mode for large h5ad, the ~10-15% density crossover where dense beats sparse, 10X format proliferation (MTX vs CellRanger H5 vs h5ad), the dense-conversion memory blow-up, and Dask + Zarr for consortium-scale matrices. Use when choosing sparse format, working with single-cell-sized matrices, importing/exporting 10X, debugging R/Python interop transposes, processing matrices too large for RAM, or building cloud-native pipelines.
What it loads into context
Frontmatter
304 tokensAlways available to the agent
SKILL.md
6,112 tokensWhen the skill is activated
Reference documents
1,624 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- The R/Python interop transpose is silent and catastrophic
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Check Sparsity
- dgCMatrix / scipy CSC / CSR
- HDF5 vs Zarr -- The Cloud-Native Shift
- HDF5SummarizedExperiment + DelayedArray (Bioconductor)
- scanpy Backed Mode
- 10X Genomics Format Proliferation
- Dense Conversion -- The Memory Blow-Up
- SCE vs AnnData vs MuData -- Where Bulk Fits
- Dask + Zarr for Consortium-Scale Matrices
- Sparse Operations
- CPM Normalization on Sparse
- Save / Load Sparse Matrices
- Per-Method Failure Modes
- Common errors
- References
- Related Skills
Scripts executed separately
scripts/sparse_operations.py
Files
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## Version Compatibility
Reference examples tested with: numpy 1.26+, scipy 1.12+, pandas 2.2+, anndata 0.10+, scanpy 1.10+, Matrix R package 1.6+, HDF5Array 1.30+ (Bioconductor), DelayedArray 0.28+, zellkonverter 1.12+, zarr-python 2.18+, dask 2024.1+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Sparse Matrix Handling
**"Store / compute on a single-cell matrix without blowing up memory"** -> Pick the sparse format that matches the access pattern (CSC for column ops, CSR for row ops), respect the R/Python convention difference (Bioconductor stores cells in columns; AnnData stores cells in rows), and use HDF5/Zarr backed mode for matrices too large for RAM.bio-flow-cytometry-bead-normalization
When your agent uses it
Bead-based signal normalization and cross-batch harmonization for CyTOF and high-parameter cytometry - EQ four-element bead normalization of instrument sensitivity drift (CATALYST normCytof, premessa), and reference-anchor cross-batch normalization (CytoNorm, per-cluster quantile splines). Covers the distinction between within-run drift correction and between-batch correction, the mandatory anchor/reference sample, why normalization is per-cluster with many quantiles, and the over-correction risk. Use when correcting CyTOF signal drift, harmonizing multi-batch or multi-site studies, or deciding whether to normalize data versus model batch in the design.
What it loads into context
Frontmatter
233 tokensAlways available to the agent
SKILL.md
2,030 tokensWhen the skill is activated
Reference documents
883 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Two Different Layers; Anchor Controls Are the Guarantee
- Why Per-Cluster and Many (99) Quantiles
- EQ-Bead Normalization (drift)
- Cross-Batch Normalization (CytoNorm)
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: CATALYST 1.26+, CytoNorm 2.0+, flowCore 2.14+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
`normCytof()` returns a LIST (`$data`, `$beads`, `$removed`, ...), not a flowFrame; `beads="dvs"` encodes EQ masses 140,151,153,165,175. Confirm with `?normCytof` before relying on slot names.
# Bead Normalization
**"Normalize my CyTOF data"** -> Correct instrument sensitivity drift with EQ beads (within/across runs), then harmonize batches with a reference anchor.
- R (drift): `CATALYST::normCytof()` (EQ-bead-based) or `premessa`
- R (batch): `CytoNorm::CytoNorm.train()` + `CytoNorm.normalize()` (per-cluster quantile splines)bio-flow-cytometry-clustering-phenotyping
When your agent uses it
Unsupervised clustering and cell-type identification for high-dimensional flow, spectral, and mass cytometry - FlowSOM, PhenoGraph, FlowSOM-via-CATALYST, with UMAP/tSNE for visualization. Covers the type-vs-state marker distinction (cluster on lineage, test state within clusters), over-provision-then-metacluster, the Weber-Robinson benchmark, seed dependence and metacluster stability, why embeddings are for looking not measuring, and median-heatmap annotation/merging. Use when discovering populations without predefined gates, choosing a clustering algorithm, selecting the number of metaclusters, or annotating clusters into cell types.
What it loads into context
Frontmatter
237 tokensAlways available to the agent
SKILL.md
2,438 tokensWhen the skill is activated
Reference documents
852 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Cluster on Type Markers; the Embedding Is for Looking, Not Measuring
- Algorithm Taxonomy
- Why Over-Provision the Grid, Then Metacluster
- CATALYST Clustering Pipeline
- PhenoGraph (graph-based alternative)
- Dimensionality Reduction (visualization only) and Annotation
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: CATALYST 1.26+, FlowSOM 2.10+, flowCore 2.14+; Rphenograph (GitHub: JinmiaoChenLab/Rphenograph).
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
`Rphenograph` is GitHub-only (`remotes::install_github('JinmiaoChenLab/Rphenograph')`) and returns a list - membership is `igraph::membership(out[[2]])`, not a vector. Adapt rather than retrying.
# Clustering and Phenotyping
**"Cluster my cytometry data to find cell types"** -> Discover populations in high-dimensional data without gates, then annotate them by marker expression.
- R: `CATALYST::cluster()` (wraps FlowSOM + ConsensusClusterPlus) - the field default
- R: `FlowSOM::FlowSOM()` directly, or `Rphenograph()` for graph-based clusteringbio-flow-cytometry-compensation-transformation
When your agent uses it
Corrects fluorophore spillover (conventional compensation) or spectral overlap (spectral unmixing) and applies variance-stabilizing transforms (logicle/biexponential, arcsinh, log) for flow and mass cytometry. Covers spillover-matrix estimation from single-stain controls, AutoSpill, the spillover spreading matrix and why panel design (not compensation) bounds resolution, compensate-then-transform ordering, and arcsinh cofactor choice (5 for CyTOF, ~150 for fluorescence, per-channel via flowVS). Use when correcting spectral overlap, preparing data for gating/clustering, choosing logicle vs arcsinh, deciding a cofactor, or distinguishing compensation from spectral unmixing.
What it loads into context
Frontmatter
244 tokensAlways available to the agent
SKILL.md
2,873 tokensWhen the skill is activated
Reference documents
913 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Compensation Corrects the Mean; It Cannot Remove Spreading Error
- Method Taxonomy
- Decision Tree by Scenario
- Compensate-Then-Transform Ordering (load-bearing)
- Apply or Compute Compensation
- Logicle / Biexponential Transform (fluorescence)
- Arcsinh Transform (CyTOF cofactor 5; fluorescence/spectral ~150 or per-channel)
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: flowCore 2.14+, flowStats 4.14+, flowWorkspace 4.14+, CATALYST 1.26+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
Notes that bite: `estimateLogicle()` lives in flowWorkspace (not flowCore). `flowCore::spillover()` on a flowFrame returns a LIST of keyword matrices (index `[[1]]`); `flowStats::spillover()` on single-stain controls returns the matrix DIRECTLY (not a list) - do not index it with `$`.
If code throws an error, introspect the installed package and adapt rather than retrying.
# Compensation and Transformation
**"Compensate and transform my cytometry data"** -> Remove spillover (matrix subtraction, conventional) or unmix the full spectrum (least squares, spectral), then apply a transform so populations separate.bio-flow-cytometry-cytometry-qc
When your agent uses it
Quality control for flow, spectral, and mass cytometry - time-based anomaly cleaning (flowAI, flowCut, PeacoQC, flowClean), margin/boundary event removal, signal-drift detection, dead-cell exclusion, CyTOF Gaussian/DNA/event-length checks, instrument calibration/standardization (MESF, CS&T, peak-2), and batch-level outlier flagging. Use when assessing acquisition quality, choosing a cleaning tool, ordering QC relative to compensation, deciding margin removal before density-based steps, or flagging problematic samples before clustering or differential analysis.
What it loads into context
Frontmatter
217 tokensAlways available to the agent
SKILL.md
2,693 tokensWhen the skill is activated
Reference documents
851 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- The Time Parameter Is the Master QC Axis, and Order Matters
- Cleaning-Tool Taxonomy
- Run flowAI (with the correct API)
- Margins First, Then PeacoQC
- Dead-Cell, Drift, and CyTOF Checks
- Calibration and Standardization (cross-study comparability)
- Batch-Level Outlier Flagging
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: flowAI 1.32+, PeacoQC 1.12+, flowCore 2.14+, flowDensity 1.36+, CATALYST 1.26+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
Counterintuitive defaults to confirm: flowAI checks are FR/FS/FM (FM = dynamic range, not "flow"); PeacoQC `MAD`/`IT_limit` are LESS strict when HIGHER. Verify with `?flow_auto_qc` and `?PeacoQC` before tuning.
# Cytometry QC
**"Run quality control on my cytometry data"** -> Detect and remove acquisition artifacts (flow-rate instability, signal drift, margin events, dead cells, CyTOF doublets) on the Time axis, then flag outlier samples.
- R (flow/spectral): `flowAI::flow_auto_qc()`, `PeacoQC::PeacoQC()` (+ `RemoveMargins()`)
- R (mass): `CATALYST::normCytof()` beads + Gaussian/DNA/event-length gatingbio-flow-cytometry-differential-analysis
When your agent uses it
Differential abundance (DA) and differential state (DS) analysis for flow and mass cytometry - tests which cell populations change in frequency or marker expression between conditions using diffcyt (edgeR/voom/GLMM for DA, limma/LMM for DS), with cydar, CITRUS, and compositional methods (sccomp, scCODA, DCATS) as alternatives. Covers the sample-is-the-experimental-unit principle, design/contrast and mixed-model formulas, compositionality of cluster proportions, and FDR across clusters. Use when comparing populations between groups, choosing a DA method, handling paired/batch designs, or deciding whether compositional correction is needed.
What it loads into context
Frontmatter
239 tokensAlways available to the agent
SKILL.md
2,380 tokensWhen the skill is activated
Reference documents
829 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- The Sample Is the Experimental Unit, Not the Cell
- DA vs DS, and the type/state marker link
- Method Taxonomy
- Run diffcyt DA and DS
- Paired / Repeated-Measures (mixed models)
- Compositional Re-Check
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: diffcyt 1.22+, CATALYST 1.26+, edgeR 4.0+, limma 3.58+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
`testDA_edgeR`/`testDS_limma` are diffcyt functions operating on count/median objects from `calcCounts`/`calcMedians`; the CATALYST-integrated path is the `diffcyt()` wrapper on the SCE. Confirm the signature with `?diffcyt` before relying on it.
# Differential Analysis
**"Compare cell populations between my conditions"** -> Test cluster frequencies (DA) and within-cluster marker expression (DS) between groups, with the sample (not the cell) as the unit.
- R: `diffcyt::diffcyt(sce, analysis_type='DA', method_DA='diffcyt-DA-edgeR', design, contrast)`
- R: `diffcyt(sce, analysis_type='DS', method_DS='diffcyt-DS-limma', ...)`bio-flow-cytometry-doublet-detection
When your agent uses it
Detects and removes doublets/aggregates from flow, spectral, and mass cytometry before clustering or quantification. Covers FSC-A vs FSC-H singlet discrimination (the Area-Height non-proportionality, not a 1D area gate), FSC-W/SSC width gating, CyTOF Gaussian discrimination parameters (Center/Offset/Width/Residual/Event_length) and DNA intercalator gating, and the residual heterotypic conjugates that survive scatter gating and masquerade as double-positive populations. Use when filtering aggregates before phenotyping, choosing a doublet method for flow vs CyTOF, or diagnosing a suspicious double-positive cluster.
What it loads into context
Frontmatter
231 tokensAlways available to the agent
SKILL.md
2,118 tokensWhen the skill is activated
Reference documents
852 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Doublets Are Caught by Area-vs-Height Non-Proportionality, and Scatter Gating Is Necessary but Not Sufficient
- Method Taxonomy
- FSC-A vs FSC-H Singlet Gating (flow/spectral)
- CyTOF Doublet Removal
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: flowCore 2.14+, CATALYST 1.26+, ggplot2 3.5+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws an error, introspect the installed package and adapt rather than retrying.
# Doublet Detection
**"Remove doublets from my cytometry data"** -> Discriminate single cells from aggregates using pulse geometry (flow) or ion-cloud parameters (CyTOF), before any clustering or quantification.
- R (flow/spectral): `flowCore` gate on the FSC-A vs FSC-H diagonal (+ FSC-W/SSC-W)
- R (mass/CyTOF): gate on DNA intercalator + Gaussian/Event_length parametersbio-flow-cytometry-fcs-handling
When your agent uses it
Reads, inspects, and writes Flow Cytometry Standard (FCS) files from conventional, spectral, and mass cytometry (CyTOF), and parses FlowJo/Cytobank/Diva workspaces. Covers FCS 2.0/3.0/3.1/3.2 internals ($PnE linear-vs-log, $DATATYPE, $SPILLOVER vs SPILL vs $COMP, $TIMESTEP), channel/parameter metadata, the silent linearize/truncate defaults, and R (flowCore, flowWorkspace, CytoML) plus Python (FlowKit, readfcs) readers. Use when loading flow or mass cytometry data, mapping detector channels to antibodies, extracting the event matrix, choosing a reader, or bridging FCS to the scanpy/AnnData ecosystem before preprocessing.
What it loads into context
Frontmatter
270 tokensAlways available to the agent
SKILL.md
2,713 tokensWhen the skill is activated
Reference documents
905 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- read.FCS Silently Transforms by Default
- FCS Standard Internals (what the keywords mean)
- Reader Taxonomy
- Load and Inspect FCS (R)
- Access the Compensation Matrix from Keywords
- Load FCS in Python (FlowKit / readfcs)
- Rename Channels, Subset, Write, Annotate Samples
- Per-Method Failure Modes
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/load_fcs.py
Files
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## Version Compatibility
Reference examples tested with: flowCore 2.14+, flowWorkspace 4.14+, CytoML 2.14+; Python flowkit 1.1+, readfcs 1.1+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# FCS File Handling
**"Load my FCS files and inspect the channels"** -> Parse FCS format into event matrix + parameter metadata, map detector channels to antibodies, and choose a reader appropriate to the instrument and downstream ecosystem.bio-flow-cytometry-gating-analysis
When your agent uses it
Defines cell populations in flow and spectral cytometry through manual gates (rectangle, polygon, quadrant, boolean) and reproducible automated gating (openCyto gating templates, flowDensity data-driven thresholds, flowClust model-based gates), organized as a hierarchical GatingSet (flowWorkspace) and round-tripped with FlowJo via CytoML. Covers the canonical gate order (time -> debris -> singlets -> live -> lineage), FMO-vs-isotype boundary setting, gate-order dependence and recompute semantics, rare-event/MRD gating, and per-population statistics. Use when building a gating strategy, automating a manual FlowJo scheme across samples, choosing manual vs data-driven gates, or extracting population frequencies.
What it loads into context
Frontmatter
243 tokensAlways available to the agent
SKILL.md
2,442 tokensWhen the skill is activated
Reference documents
841 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- FMO, Not Isotype, Sets the Boundary; and Gate Order Is a Funnel
- Automated-Gating Taxonomy
- Build a Gating Hierarchy
- Automated Gating with an openCyto Template
- Rare-Event / MRD Gating
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: flowWorkspace 4.14+, openCyto 2.14+, flowDensity 1.36+, flowCore 2.14+, CytoML 2.14+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
openCyto gating-method names drift across versions - confirm with `gt_list_methods()` on the installed package (e.g. `gate_flowclust_2d` vs `flowClust.2d`). Adapt rather than retrying.
# Gating Analysis
**"Gate my data to identify cell populations"** -> Define populations by drawing boundaries in marker space, organized as a hierarchy, manually or with reproducible data-driven methods.
- R (manual + hierarchy): `flowCore` gates -> `flowWorkspace::GatingSet` -> `gs_pop_add` -> `recompute`
- R (automated): `openCyto` gating template (CSV) or `flowDensity::deGate`bio-gene-regulatory-networks-coexpression-networks
When your agent uses it
Build weighted gene co-expression networks to identify modules of co-regulated genes, relate them to phenotypes, and find hub genes using WGCNA, hdWGCNA, MEGENA, CEMiTool, and Gaussian graphical models. Covers signed-network choice, soft-threshold selection, module preservation, and the marginal-vs-partial-correlation distinction. Use when finding co-expression modules, identifying hub genes, relating gene networks to clinical or experimental traits, or building single-cell co-expression networks. For directed TF-target inference see scenic-regulons and grn-inference; for condition rewiring see differential-networks.
What it loads into context
Frontmatter
230 tokensAlways available to the agent
SKILL.md
4,236 tokensWhen the skill is activated
Reference documents
967 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Co-expression Is Marginal Correlation; Regulation Is Conditional Dependence
- Co-expression Method Taxonomy
- Decision Tree by Scenario
- WGCNA: Build a Signed Network
- WGCNA: Eigengenes, Hubs, and Trait Relationships
- WGCNA: Module Preservation (the step that makes a module real)
- hdWGCNA: Single-Cell Co-expression
- Direct vs Indirect Edges: Gaussian Graphical Model
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: WGCNA 1.72+, hdWGCNA 0.3+, CEMiTool 1.26+, GENIE3-adjacent GGM via GeneNet 1.2.16+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
WGCNA argument defaults differ by entry point: `pickSoftThreshold()` and `blockwiseModules()` default to `networkType='unsigned'`. The signed-network choice (below) must be set identically at every step or the soft power and modules silently mismatch.
# Co-expression Networksbio-gene-regulatory-networks-differential-networks
When your agent uses it
Compare gene co-expression and regulatory networks between biological conditions to find rewired relationships using DiffCorr, DiffCoEx, DINGO/iDINGO, and CoDiNA. Covers the differential-connectivity-is-not-differential-expression distinction, the pairwise multiple-testing explosion, marginal vs partial (direct) rewiring, and the underpowered-rewiring failure mode. Use when comparing co-expression networks between disease vs control, treatment, or developmental stages, or finding hub genes that rewire without changing mean expression. For single-condition modules see coexpression-networks; for differential expression of means see differential-expression/de-results.
What it loads into context
Frontmatter
229 tokensAlways available to the agent
SKILL.md
3,136 tokensWhen the skill is activated
Reference documents
887 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Differential Connectivity Is Not Differential Expression
- Differential-Network Method Taxonomy
- Decision Tree by Scenario
- DiffCorr: Pairwise Differential Correlation (R)
- DINGO: Direct Differential Rewiring (R)
- Python: Fisher z Differential Network
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/differential_network.py
Files
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## Version Compatibility
Reference examples tested with: DiffCorr 0.4.1+, DINGO/iDINGO 1.0.4+, CoDiNA 1.1.2+; Python path uses scipy 1.12+, statsmodels 0.14+, networkx 3.0+.
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
In statsmodels, `multipletests()` defaults to method `'hs'` (Holm-Sidak), NOT Benjamini-Hochberg. Always pass `method='fdr_bh'` explicitly for differential-correlation FDR.
# Differential Networksbio-gene-regulatory-networks-grn-inference
When your agent uses it
Infer gene regulatory networks from bulk or general expression data with mutual-information (ARACNe) and tree-ensemble (GENIE3, GRNBoost2) methods, and infer transcription-factor protein activity from regulons with VIPER and msVIPER. Covers the activity-not-edges paradigm, the undirected-association caveat, the DREAM5 wisdom-of-crowds and method-complementarity result, AUPRC-over-AUROC evaluation, and gold-standard incompleteness. Use when inferring a regulatory network from a bulk expression matrix, finding master regulators, or scoring TF activity from a signature. For single-cell motif-pruned regulons see scenic-regulons; for co-expression modules see coexpression-networks.
What it loads into context
Frontmatter
250 tokensAlways available to the agent
SKILL.md
3,543 tokensWhen the skill is activated
Reference documents
900 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Activity, Not Edges: A Regulon Is a Multiplexed Reporter
- Method Taxonomy
- Decision Tree by Scenario
- Edge Inference with GENIE3 (R)
- Edge Inference with ARACNe-AP (Java CLI)
- TF Activity with VIPER / msVIPER (R)
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Files
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## Version Compatibility
Reference examples tested with: VIPER 1.36+ (Bioconductor), GENIE3 1.24+ (Bioconductor), ARACNe-AP (Java, build from source), arboreto 0.1.6+ (Python GRNBoost2).
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
GENIE3 expects the expression matrix as genes-in-rows, samples-in-columns (the transpose of the WGCNA convention); a transposed matrix silently produces a meaningless network.
bio-gene-regulatory-networks-multiomics-grn
When your agent uses it
Build enhancer-driven gene regulatory networks (eGRNs) by integrating single-cell RNA-seq and ATAC-seq using SCENIC+, CellOracle base GRNs, Pando, FigR, DIRECT-NET, TRIPOD, and scMEGA. Covers the accessibility-defines-enhancers principle, peak-to-gene linking and its cell-composition confound, the paired-vs-unpaired decision, and TF-region-gene eRegulon triplets. Use when analyzing 10x multiome or paired/unpaired scRNA+scATAC to infer cis-regulatory GRNs. For RNA-only regulons see scenic-regulons; for in silico TF perturbation see perturbation-simulation.
What it loads into context
Frontmatter
250 tokensAlways available to the agent
SKILL.md
3,843 tokensWhen the skill is activated
Reference documents
1,083 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Accessibility Defines the Candidate Enhancers, but Every Inference Arrow Leaks
- eGRN Method Taxonomy
- Decision Tree by Scenario
- SCENIC+ Pipeline (current Snakemake workflow)
- CellOracle Base GRN (works without paired multiome)
- FigR (paired, DORC-based, R)
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/scenicplus_multiome.py
Files
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## Version Compatibility
Reference examples tested with: SCENIC+ (current Snakemake workflow), pycisTopic 2.0+, pycistarget 1.0+, scanpy 1.10+, MACS3 3.0+; FigR/Signac/ArchR (R).
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
SCENIC+ has undergone major API churn: the manual-object API (cisTopicObject, separate pycistarget, SCENICPLUS objects) is superseded by a Snakemake workflow (`scenicplus init_snakemake`). Pre-2024 tutorials are stale; verify against scenicplus.readthedocs.io before coding.
# Multiomics GRN Inferencebio-gene-regulatory-networks-perturbation-simulation
When your agent uses it
Simulate transcription factor perturbation effects on cell state in silico with CellOracle and Dynamo, and predict transcriptional responses to genetic perturbations with GEARS, scGen, and CPA. Covers the direction-not-magnitude principle, local-linear validity, the GRN/velocity error it inherits, baseline discipline (mean and additive baselines), and the validation gap. Use when predicting TF knockout or overexpression effects, ranking driver TFs for fate transitions, or planning perturbation experiments. For GRN construction see multiomics-grn; for experimental Perturb-seq see single-cell/perturb-seq.
What it loads into context
Frontmatter
227 tokensAlways available to the agent
SKILL.md
3,518 tokensWhen the skill is activated
Reference documents
968 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- These Methods Predict a Direction, Locally, and Inherit Every Error of the Underlying Model
- Method Taxonomy
- Decision Tree by Scenario
- CellOracle: Build the GRN
- CellOracle: Simulate and Score
- Dynamo: Vector-Field Perturbation
- Baseline Discipline (always run these)
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/celloracle_simulation.py
Files
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## Version Compatibility
Reference examples tested with: CellOracle 0.18+, scanpy 1.10+, anndata 0.10+; Dynamo (dynamo-release 1.4+), GEARS, CPA (scvi-tools ecosystem) where used.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Perturbation Simulation
**"Predict what happens if I knock out this transcription factor"** -> Propagate a forced expression change through a learned GRN (or a learned velocity vector field), then project the shift onto the cell-state manifold to predict the DIRECTION cells move.bio-gene-regulatory-networks-scenic-regulons
When your agent uses it
Infer transcription factor regulons from single-cell RNA-seq with pySCENIC by combining GRNBoost2 co-expression, cisTarget motif-enrichment pruning, and AUCell per-cell activity scoring. Covers the motif-pruning-as-directionality principle, regulon specificity scoring, run-to-run stability, and database/species matching. Use when identifying TF regulons, scoring TF activity per cell, finding master regulators of cell identity, or comparing regulon activity across conditions. For enhancer-driven multiomic GRNs see multiomics-grn; for bulk inference and VIPER protein-activity see grn-inference.
What it loads into context
Frontmatter
229 tokensAlways available to the agent
SKILL.md
3,634 tokensWhen the skill is activated
Reference documents
1,107 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Motif Pruning Is What Converts Co-expression into Directed Regulation
- Pipeline Taxonomy
- Decision Tree by Scenario
- Required Databases
- Step 1: GRN Inference (use the multiprocessing wrapper)
- Step 2: Prune to Regulons by Motif Enrichment
- Step 3: AUCell Per-Cell Activity
- Interpretation: Specificity and Binarization
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/pyscenic_workflow.pyscripts/scenic_visualization.py
Files
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## Version Compatibility
Reference examples tested with: pySCENIC 0.12+, ctxcore 0.2+, arboreto 0.1.6+, scanpy 1.10+, loompy 3.0+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
The motif-DB machinery lives in `ctxcore`; a ctxcore/feather-format version mismatch is the most common silent failure. pySCENIC is most reliable on a dedicated Python 3.10 environment.
# SCENIC Regulonsbio-genome-annotation-annotation-qc
When your agent uses it
Assesses the quality and completeness of a genome annotation with BUSCO (conserved single-copy ortholog recovery), OMArk (proteome completeness, consistency, and contamination), CheckM2 (prokaryotic completeness/contamination), and a gene-set sanity panel (gene count, mono-exonic fraction, protein-length distribution, mRNA:gene ratio, coding density). Covers the assembly-BUSCO-vs-proteome-BUSCO diagnostic, what BUSCO-Duplicated really means, why gene count is a vanity metric, and the QC of transferred annotations. Use when judging whether an annotation is good enough to publish or submit, diagnosing a suspect annotation, or comparing annotation completeness across pipelines.
What it loads into context
Frontmatter
240 tokensAlways available to the agent
SKILL.md
3,878 tokensWhen the skill is activated
Reference documents
1,059 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- BUSCO Measures the Easy 10%; the Diagnostic Is Genome-vs-Proteome
- Tool Taxonomy
- Decision Tree by Scenario
- BUSCO and How to Read It
- OMArk (Proteome Consistency and Contamination)
- CheckM2 (Prokaryotic Completeness/Contamination)
- Gene-Set Sanity Panel with Python
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/busco_genome_vs_proteome.shscripts/gene_set_sanity.py
Files
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## Version Compatibility
Reference examples tested with: BUSCO 5.5+, OMArk 0.3+, CheckM2 1.0+, compleasm 0.2.6+, gffutils 0.12+, matplotlib 3.8+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
- Python: `pip show <package>` then `help(module.function)` to check signatures
BUSCO results depend on the **lineage dataset** (e.g. `vertebrata_odb10` vs the shallow `eukaryota_odb10`) and the OrthoDB version - record them; a 99% on a shallow ~255-gene set and a 99% on a deep ~5,500-gene clade set are very different claims. If code throws an error, introspect the installed tool and adapt rather than retrying.
# Annotation QC
**"Is my genome annotation any good?"** -> Measure conserved-gene completeness, proteome consistency/contamination, and gene-set sanity, and decide whether the limiting factor is the assembly or the predictor.
- CLI: `busco -i proteins.faa -m proteins -l <lineage>_odb10` (proteome) and `busco -i genome.fa -m genome -l <lineage>_odb10` (assembly), `omark`, `checkm2 predict` (prokaryotes)bio-genome-annotation-annotation-transfer
When your agent uses it
Transfers gene annotations between genome assemblies via coordinate liftover (UCSC liftOver, CrossMap for same-species version updates) or feature/sequence projection (Liftoff for same/close species, miniprot for protein-level cross-species, TOGA/GeMoMa/CAT for distant clades). Covers the coordinate-vs-projection decision by divergence, why a successful lift is not biological confirmation, reference bias, the silent-dropping of unmapped features, build/PAR/MHC/inversion hazards, and transfer-vs-de-novo validation. Use when annotating a new assembly of a species with an existing reference, harmonizing coordinates across builds, or mapping annotations across related species.
What it loads into context
Frontmatter
242 tokensAlways available to the agent
SKILL.md
4,137 tokensWhen the skill is activated
Reference documents
1,025 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- A Lift Is Geometry, Not Biology
- Two Paradigms
- Decision Tree by Divergence
- Liftoff (Same / Close Species, Nucleotide)
- miniprot (Cross-Species, Protein)
- TOGA (Distant Species, Orthology + Gene Loss)
- Validating a Transfer with Python
- Hazards and the 1% That Matters
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/compare_annotations.pyscripts/liftoff_transfer.shscripts/miniprot_crossspecies.sh
Files
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## Version Compatibility
Reference examples tested with: Liftoff 1.6.3+, LiftoffTools 0.4+, miniprot 0.13+, CrossMap 0.7+, UCSC liftOver (current), BioPython 1.83+, gffutils 0.12+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
- Python: `pip show <package>` then `help(module.function)` to check signatures
The **chain file must match the exact assembly pair** (build and patch); the **source and target build must be recorded** with every coordinate (a coordinate without a build is unusable). If code throws an error, introspect the installed tool and adapt rather than retrying.
# Annotation Transfer
**"Transfer annotations from a reference to my new assembly"** -> Map gene models from a well-annotated reference onto a target, by coordinate liftover (same-species, fast) or by re-aligning the actual gene sequence (cross-assembly/species, structure-aware), then validate against the target.
- CLI: `liftoff -g ref.gff3 -o out.gff3 -u unmapped.txt target.fa reference.fa` (note: target before reference), `liftOver in.bed map.chain out.bed unmapped` (intervals)bio-genome-annotation-eukaryotic-gene-prediction
When your agent uses it
Predicts protein-coding gene structures (exons, introns, UTRs) in eukaryotic genomes with BRAKER3 (RNA-seq + protein evidence), BRAKER1/BRAKER2, GALBA (protein-only), Funannotate (fungi), GeMoMa (homology projection), or Helixer/Tiberius (deep-learning ab initio). Covers the evidence-first tool decision, mandatory soft-masking, the training-set-quality-dominates principle, OrthoDB clade-partition selection, the one-isoform-per-locus and missing-UTR traps, merge/split errors, and reference bias against orphan genes. Use when annotating a newly assembled eukaryotic genome, choosing a gene-prediction pipeline based on available evidence, or diagnosing a poor annotation.
What it loads into context
Frontmatter
271 tokensAlways available to the agent
SKILL.md
4,726 tokensWhen the skill is activated
Reference documents
1,238 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Training-Set Quality and Evidence Dominate, Not the Algorithm
- Tool Taxonomy
- Decision Tree by Scenario
- Soft-Masking Is Mandatory (Prerequisite)
- BRAKER3 (RNA-seq + Protein)
- GALBA (Protein-Only, Close Relatives)
- Funannotate (Fungi)
- Gene-Model Sanity Statistics with Python
- Hard Biology the Pipeline Gets Wrong
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/braker3_pipeline.shscripts/galba_protein_only.sh
Files
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## Version Compatibility
Reference examples tested with: BRAKER 3.0+, GALBA 1.0.11+, AUGUSTUS 3.5+, Funannotate 1.8+, HISAT2 2.2.1+, STAR 2.7.11+, BUSCO 5.5+, samtools 1.19+, gffutils 0.12+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
- Python: `pip show <package>` then `help(module.function)` to check signatures
Reproducibility is engineered, not assumed: run BRAKER/GALBA/Funannotate from their official containers, and pin the **OrthoDB partition version** (v11 vs v12 give different hints -> different models), the repeat library, and the Dfam release. GeneMark (inside BRAKER) historically required an expiring academic `.gm_key`; the requirement has relaxed in recent versions but check the installed version's docs. If code throws an error, introspect the installed tool and adapt rather than retrying.
# Eukaryotic Gene Prediction
**"Predict genes in my eukaryotic genome"** -> Identify protein-coding gene structures from a soft-masked assembly using RNA-seq and/or protein evidence to train and guide a gene finder.
- CLI: `braker.pl --genome=masked.fa --prot_seq=orthodb_clade.fa --rnaseq_sets_ids=SRR... --softmasking` (BRAKER3)bio-genome-annotation-functional-annotation
When your agent uses it
Assigns GO terms, Pfam/InterPro domains, KEGG orthologs, EC numbers, and product names to predicted proteins using eggNOG-mapper (orthology), InterProScan (domain signatures), and KofamScan (KEGG), routing specialized functions to dbCAN/antiSMASH/AMRFinderPlus/SignalP. Covers the orthology-vs-domain-vs-homology paradigms, the annotation-error percolation cascade, domain-presence-is-not-function, GO IEA circularity in enrichment, evidence tiering, and bit-score/coverage thresholds. Use when adding functional annotation to predicted genes, choosing between eggNOG-mapper and InterProScan, or judging how much to trust a functional label.
What it loads into context
Frontmatter
253 tokensAlways available to the agent
SKILL.md
4,357 tokensWhen the skill is activated
Reference documents
1,241 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Annotation Is a Propagated Hypothesis, Not a Measurement
- Tool Taxonomy
- Decision Tree by Scenario
- eggNOG-mapper
- InterProScan
- Reconciling Multi-Tool Output with Python
- Ontology Rigor and the IEA Circularity
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/functional_annotation.shscripts/merge_annotations.py
Files
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## Version Compatibility
Reference examples tested with: eggNOG-mapper 2.1.15 (pin for reproducibility), InterProScan 5.66+, KofamScan 1.3+, pandas 2.2+, AGAT 1.4+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
- Python: `pip show <package>` then `help(module.function)` to check signatures
Annotation content tracks **database release**: record the eggNOG DB version, InterPro/Pfam release, and KEGG/KofamScan profile date, and note whether InterProScan used the EBI precalculated lookup service. eggNOG-mapper v3 is under testing (not production) - pin v2.1.15. If code throws an error, introspect the installed tool and adapt rather than retrying.
# Functional Annotation
**"Functionally annotate my predicted proteins"** -> Transfer GO/KEGG/Pfam/EC/product labels from characterized proteins by orthology and domain signatures, attaching a confidence tier and provenance to each.
- CLI: `emapper.py -i proteins.faa --itype proteins -m diamond` (eggNOG-mapper), `interproscan.sh -i proteins.faa -f TSV,GFF3 -goterms -pa` (InterProScan)bio-genome-annotation-ncrna-annotation
When your agent uses it
Identifies non-coding RNAs (tRNA, rRNA, snoRNA, snRNA, riboswitches, sRNAs) using Infernal covariance-model search against Rfam, tRNAscan-SE 2.0 for tRNA, barrnap for rRNA, and ARAGORN for tmRNA, plus the small-RNA-seq boundary for miRNA and the transcript-assembly boundary for lncRNA. Covers the structure-conserved-not-sequence-conserved principle (why BLAST fails), GA-threshold and clan-competition correctness, tRNAscan-SE domain modes and pseudogene flags, rDNA copy-number collapse, and why homology annotation is a recall floor. Use when performing genome-wide ncRNA annotation, choosing the right tool for an RNA class, or interpreting ncRNA counts.
What it loads into context
Frontmatter
263 tokensAlways available to the agent
SKILL.md
4,267 tokensWhen the skill is activated
Reference documents
1,111 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- ncRNA Homology Is Structure-Conserved, Not Sequence-Conserved
- Tool Taxonomy
- Decision Tree by Scenario
- Infernal / cmscan (the General ncRNA Engine)
- tRNAscan-SE 2.0
- barrnap (rRNA)
- Parsing and Combining ncRNA Calls with Python
- The miRNA Disaster and lncRNA Non-Annotatability
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/ncrna_annotation.shscripts/parse_ncrna.py
Files
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## Version Compatibility
Reference examples tested with: Infernal 1.1.4+, Rfam 15+ (CM library), tRNAscan-SE 2.0.12+, barrnap 0.9+, ARAGORN 1.2+, pandas 2.2+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
- Python: `pip show <package>` then `help(module.function)` to check signatures
The **Rfam release version** drives results (Rfam 15 has ~4,200+ families); record it. The downloaded `Rfam.cm` ships pre-calibrated, so only `cmpress` is needed before `cmscan`. If code throws an error, introspect the installed tool and adapt rather than retrying.
# Non-Coding RNA Annotation
**"Find non-coding RNAs in my genome"** -> Scan an assembly for structured ncRNA families using covariance models (sequence + secondary structure jointly), with specialist detectors for tRNA and the expression boundary for miRNA/lncRNA.
- CLI: `cmscan --cut_ga --rfam --nohmmonly --fmt 2 --clanin Rfam.clanin Rfam.cm genome.fa` (Infernal), `tRNAscan-SE -B genome.fa`bio-genome-annotation-prokaryotic-annotation
When your agent uses it
Annotates bacterial and archaeal genomes (isolates, MAGs, plasmids) with Bakta (active versioned databases, NCBI-compliant output) or Prokka (legacy), producing GFF3/GenBank/EMBL/FASTA with INSDC locus tags. Covers Bakta-vs-Prokka-vs-PGAP-vs-DFAST choice, light-vs-full database tiers, translation-table selection (11/4/25), archaeal and leaderless-gene caveats, the small-ORF blind spot, pseudogene-vs-phase-variation, the pangenome re-annotation trap, and submission compliance. Use when annotating a newly assembled prokaryotic genome, choosing an annotation tool, re-annotating a collection for pangenomics, or preparing annotations for NCBI/DDBJ submission.
What it loads into context
Frontmatter
275 tokensAlways available to the agent
SKILL.md
4,561 tokensWhen the skill is activated
Reference documents
1,221 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Gene Calling Is Near-Solved; Function Is Not, and Wrong Labels Propagate
- Tool Taxonomy
- Decision Tree by Scenario
- Bakta (Default)
- Prokka (Legacy)
- Coding Density and CDS Extraction with Python
- Hard Cases the Caller Gets Wrong
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/bakta_annotation.shscripts/parse_annotation.py
Files
SKILL.mdcore17,065 bytesSKILL.mdcore17,065 bytesSKILL.mdcore17,065 bytesSKILL.mdcore17,065 bytesreferences/usage-guide.mddoc5,082 bytesscripts/bakta_annotation.shruns1,960 bytesscripts/parse_annotation.pyruns3,571 bytes
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## Version Compatibility
Reference examples tested with: Bakta 1.9+, Prokka 1.14.6 (legacy), tRNAscan-SE 2.0+, CheckM2 1.0+, gffutils 0.12+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
- Python: `pip show <package>` then `help(module.function)` to check signatures
Annotation content tracks the **database version**, not just the binary: a Bakta full DB (schema v5+, ~30 GB zipped) and a Bakta light DB give different functional calls, and two runs months apart can differ purely from DB updates. Record the Bakta DB version in methods. Prokka's bundled databases are frozen (~2019-2021). If code throws an error, introspect the installed tool and adapt rather than retrying.
# Prokaryotic Genome Annotation
**"Annotate my bacterial genome"** -> Call protein-coding genes, tRNAs/rRNAs/ncRNAs, and other features, then assign function by database identity, and emit submission-ready files.
- CLI: `bakta --db db/ assembly.fa` (default), `prokka --outdir out assembly.fa` (legacy), NCBI PGAP (submission/RefSeq-grade)bio-genome-annotation-repeat-annotation
When your agent uses it
Discovers, classifies, and masks repetitive elements and transposable elements with RepeatModeler2 (de novo family library), RepeatMasker (masking against a library), EDTA (plant/structural TEs), or EarlGrey (auto-curating wrapper), and quantifies TE expression from RNA-seq with TEtranscripts/SQuIRE. Covers de-novo-library-as-curation-project, soft-vs-hard masking, the domesticated-gene over-masking massacre, Dfam-vs-RepBase, TE classification (Class I/II, family-vs-copy), Kimura repeat landscapes, LAI, and the RNA-seq multimapping problem. Use when masking repeats before gene prediction, building a TE library for a non-model genome, or analyzing transposable-element content or expression.
What it loads into context
Frontmatter
261 tokensAlways available to the agent
SKILL.md
4,653 tokensWhen the skill is activated
Reference documents
1,041 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- The Library Is the Experiment, and the Assembly Caps It
- Tool Taxonomy
- Decision Tree by Scenario
- RepeatModeler2 -> RepeatMasker (the canonical pair)
- Soft vs Hard Masking (Critical, and the Over-Masking Massacre)
- TE Classification (Wicker-compatible)
- Repeat Statistics and Age Landscape with Python
- TE Expression from RNA-seq (the Multimapping Minefield)
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/repeat_annotation.shscripts/repeat_stats.pyscripts/te_expression.py
Files
SKILL.mdcore18,460 bytesSKILL.mdcore18,460 bytesSKILL.mdcore18,460 bytesreferences/usage-guide.mddoc4,401 bytesscripts/repeat_annotation.shruns1,985 bytesscripts/repeat_stats.pyruns4,993 bytesscripts/te_expression.pyruns3,181 bytes
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## Version Compatibility
Reference examples tested with: RepeatModeler 2.0.5+, RepeatMasker 4.1.5+, EDTA 2.1+, EarlGrey 4.0+, TEtranscripts 2.2+, matplotlib 3.8+, pandas 2.2+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
- Python: `pip show <package>` then `help(module.function)` to check signatures
The **library database version matters as much as the binary**: RepeatMasker now ships with Dfam (open); RepBase has been paywalled since May 2019, so any pipeline that "requires RepBase" is a reproducibility/access hazard - record the Dfam release and library provenance. If code throws an error, introspect the installed tool and adapt rather than retrying.
# Repeat and Transposable Element Annotation
**"Mask repeats in my genome assembly"** -> Build a de novo repeat-family library, annotate copies genome-wide, and soft-mask them as a prerequisite for gene prediction.
- CLI: `RepeatModeler -database mydb -LTRStruct` (library), `RepeatMasker -lib lib.fa -xsmall assembly.fa` (soft-mask), or `EarlGrey`/`EDTA.pl` (wrappers)bio-genome-assembly-assembly-polishing
When your agent uses it
Decides whether and how to polish a draft genome assembly to raise consensus accuracy (QV) with read-type-matched tools - Racon and medaka (ONT consensus), dorado polish, Polypolish and pypolca (Illumina, repeat-aware), Pilon (legacy short-read), NextPolish/NextPolish2, Hapo-G (haplotype-aware), ntEdit, and DeepPolisher/PEPPER-Margin-DeepVariant for human. Covers the do-not-polish-HiFi rule, the medaka basecaller-model footgun, held-out Merqury QV as the only honest stop signal, and the haplotype-collapse trap. Use when correcting homopolymer indels or residual SNPs in a long-read assembly, deciding if a HiFi assembly needs polishing, or choosing an ONT vs hybrid vs short-read polishing chain.
What it loads into context
Frontmatter
279 tokensAlways available to the agent
SKILL.md
5,228 tokensWhen the skill is activated
Reference documents
1,330 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Polishing Is Being Engineered Out, and Reflexive Polishing Does Net Harm
- Tool Taxonomy
- Decision Tree by Scenario
- The Canonical ONT Chain: Racon -> medaka
- Hybrid / Short-Read Polishing: Polypolish + pypolca
- Pilon (Legacy Short-Read)
- Measuring the Gain: Held-Out Merqury QV
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/polish_assembly.sh
Files
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## Version Compatibility
Reference examples tested with: Racon 1.5+, medaka 2.0+, minimap2 2.26+, bwa 0.7.17+, samtools 1.19+, Polypolish 0.6+, pypolca 0.3+, Pilon 1.24+, Merqury 1.3+, meryl 1.4+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
The **medaka consensus model** matters more than the medaka binary version: the model must match the basecaller + pore chemistry + caller mode + version (`-m r1041_e82_400bps_sup_v5.0.0`); a mismatched model silently degrades the consensus. List options with `medaka tools list_models`. medaka's status is a moving target - ONT now steers toward `dorado polish` and has deprecated medaka's diploid-variant workflow in favour of Clair3; verify current guidance and the `medaka_consensus` wrapper vs newer subcommands. If code throws an error, introspect the installed tool and adapt rather than retrying.
# Assembly Polishing
**"Polish my genome assembly"** -> Decide whether base-level consensus correction is warranted, then apply a read-type-matched polisher and measure the gain with held-out k-mers - or, for an already-accurate assembly, decline.
- CLI: `racon reads.fq aln.sam draft.fa` then `medaka_consensus -i reads.fq -d draft.fa -o out -m <model>` (ONT); `polypolish polish draft.fa f1.sam f2.sam` + `pypolca run` (hybrid/short); `merqury.sh reads.meryl asm.fa out` (measure QV before/after)
bio-genome-assembly-assembly-qc
When your agent uses it
Evaluates genome assembly quality across the three orthogonal axes - contiguity (QUAST auN/NG50/NGx, not bare N50), completeness (BUSCO/compleasm gene-space plus Merqury k-mer completeness), and correctness (reference-free Merqury QV, Inspector/CRAQ structural errors, asmgene false-duplication/collapse). Covers why N50 is the most-gamed metric, why QV measured on the polishing reads is circular, distinguishing uncollapsed haplotigs from real WGD, and the EBP/VGP 6.C.Q40 standard. Use when judging whether an assembly is good enough to annotate or publish, comparing assemblers, diagnosing a fragmented or duplicated assembly, or assessing a phased diploid assembly.
What it loads into context
Frontmatter
255 tokensAlways available to the agent
SKILL.md
5,362 tokensWhen the skill is activated
Reference documents
1,550 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Quality Is Three Orthogonal Axes; N50 Is the Most-Gamed One
- Tool Taxonomy
- Decision Tree by Scenario
- Contiguity -- auN/NGx (not bare N50)
- Completeness -- BUSCO / compleasm + Merqury k-mer completeness
- Correctness -- Merqury QV (reference-free) and structural validation
- EBP/VGP standards and phased QC
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/run_qc.shscripts/summarize_three_axes.py
Files
SKILL.mdcore19,829 bytesSKILL.mdcore19,829 bytesSKILL.mdcore19,829 bytesSKILL.mdcore19,829 bytesSKILL.mdcore19,829 bytesSKILL.mdcore19,829 bytesreferences/usage-guide.mddoc6,439 bytesscripts/run_qc.shruns2,782 bytesscripts/summarize_three_axes.pyruns3,535 bytes
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## Version Compatibility
Reference examples tested with: QUAST 5.2+, BUSCO 5.5+ (and 6.x for odb12 lineages), compleasm 0.2.6+, Merqury 1.3+, meryl 1.4+, minimap2 2.26+, Inspector 1.2+, CRAQ 1.0+, merfin 1.0+, GenomeScope2 2.0+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
- Python: `pip show <package>` then `help(module.function)` to check signatures
Results depend on inputs that outlive the binary version - record them:
- BUSCO/compleasm depend on the **lineage dataset** and OrthoDB generation. `_odb10` (BUSCO 5) and `_odb12` (BUSCO 6 default) gene sets are not comparable across the version boundary; a 99% on the shallow `eukaryota_odb10` (~255 genes) is a different claim from 99% on a deep clade set (~5,500+).
- Merqury QV/completeness depend on the **k-mer size** (from `best_k.sh <genome_size>`, not hardcoded) and the **read set** used for the k-mer DB (use accurate reads; see the circularity warning below).
- NG50/NGx/auNG depend on the **expected genome-size estimate** (GenomeScope2 / flow cytometry / a congener).
If code throws an error, introspect the installed tool and adapt rather than retrying.bio-genome-assembly-contamination-detection
When your agent uses it
Detects and removes contamination in genome assemblies via two disjoint workflows - foreign-sequence screening of a single-organism (eukaryote/isolate) assembly with NCBI FCS-GX (GenBank-submission-mandatory), FCS-adaptor, and BlobToolKit blob plots; and MAG/bin quality assessment with CheckM2 plus GUNC (chimerism) plus GTDB-Tk taxonomy, judged against MIMAG. Covers why CheckM2 alone is blind to disjoint-marker chimeras, the FCS-GX RAM wall, organelle/NUMT triage, strain heterogeneity, and the HGT-vs-contamination (tardigrade) trap. Use when screening an assembly for foreign contamination before GenBank submission, assessing MAG completeness/contamination/chimerism, deciding which contigs to remove, or distinguishing real HGT from contaminant contigs.
What it loads into context
Frontmatter
284 tokensAlways available to the agent
SKILL.md
5,781 tokensWhen the skill is activated
Reference documents
1,585 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- "Contamination" Is Two Disjoint Problems With Disjoint Toolsets
- Tool Taxonomy
- Decision Tree by Scenario
- FCS-adaptor (Run First - Cheap and Unambiguous)
- FCS-GX (The GenBank-Mandatory Foreign Screen)
- BlobToolKit (The Blob Plot - Visual Triage)
- Merge CheckM2 and GUNC
- GUNC and the RRS Trap
- The Tardigrade Lesson (HGT vs Contamination)
- Organelles and NUMTs (A Third Category)
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/fcs_screen.shscripts/mag_qc_pipeline.sh
Files
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## Version Compatibility
Reference examples tested with: FCS-GX 0.5+, FCS-adaptor 0.5+, BlobToolKit 4.3+, CheckM2 1.0+, GUNC 1.0+, GTDB-Tk 2.4+, pandas 2.2+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
- Python: `pip show <package>` then `help(module.function)` to check signatures
Database release drives results more than the binary version. Record: the **FCS-GX GX database** release (it grows each release; ~470 GiB and rising), the **CheckM2 DIAMOND DB** version, the **GUNC reference** (proGenomes 2.1 default vs GTDB), and the **GTDB-Tk reference package release** (e.g. R220) - GTDB-Tk fails loudly if the DB release does not match the binary. If code throws an error, introspect the installed tool and adapt rather than retrying.
# Contamination Detection
**"Is my assembly contaminated?"** -> First decide which of two unrelated questions is being asked - "which contigs are not my organism?" (foreign-sequence screen) or "how complete/clean/chimeric is this bin?" (MAG quality) - then run the matching toolset; crossing them returns confidently wrong numbers.
- CLI (foreign): `run_fcsadaptor.sh` then `fcs.py screen genome --fasta asm.fa --gx-db <db> --tax-id <taxid>`, `blobtools create/add/view`bio-genome-assembly-genome-profiling
When your agent uses it
Profiles a genome from raw reads BEFORE assembly with a k-mer spectrum (KMC or Jellyfish histogram), then models it with GenomeScope2 to estimate genome size, heterozygosity, repeat content, and ploidy, and Smudgeplot to infer ploidy from heterozygous k-mer pairs (diploid AB vs triploid AAB vs tetraploid AABB). Covers choosing k via Merqury best_k.sh, the k-mer-coverage vs sequencing-coverage confusion, reading het/repeat/contamination/organelle peaks, why noisy ONT must not be used for counting, and how the estimate becomes the NG50 denominator, the Flye -g value, the hifiasm --hom-cov/purge setting, and the 1.5-2x-too-big haplotig sanity check. Use when starting any de novo assembly, deciding whether short reads can work, estimating genome size for an unknown organism, diagnosing ploidy, or sanity-checking an assembly's size against expectation.
What it loads into context
Frontmatter
311 tokensAlways available to the agent
SKILL.md
4,417 tokensWhen the skill is activated
Reference documents
1,387 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Profile the Genome Before Assembling, or the Assembler Guesses For Itself
- Tool Taxonomy
- Decision Tree by Scenario
- Choosing k
- Counting K-mers and Running GenomeScope2
- K-mer Coverage vs Sequencing Coverage (the confusion that breaks the fit)
- Ploidy with Smudgeplot
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/profile_genome.sh
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: GenomeScope2 2.0+, KMC 3.2+, Jellyfish 2.3+, meryl 1.4+ (Merqury 1.3+), Smudgeplot 0.2.5+, KAT 2.4+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
Smudgeplot changed its backend: classic releases (0.2.x) run `smudgeplot.py hetkmers`/`smudgeplot.py plot` on a KMC dump; newer releases (0.3+) run `smudgeplot hetmers`/`smudgeplot all` on a FastK database. Confirm which interface is installed (`smudgeplot.py --version` or `smudgeplot --version`) before scripting. GenomeScope2 ships as `genomescope2` and as `genomescope.R`; both take the same flags. meryl `best_k.sh` lives in the Merqury install. If code throws an error, introspect the installed tool and adapt rather than retrying.
# Genome Profiling
**"What am I about to assemble, and what should I expect?"** -> Build a k-mer spectrum from raw accurate reads and model it to estimate genome size, heterozygosity, repeat content, and ploidy, which set every downstream assembly expectation and parameter.
- CLI: `kmc -k21 ... reads kmc_db tmp/ && kmc_tools transform kmc_db histogram reads.histo` (count) then `genomescope2 -i reads.histo -o gs_out -k 21 -p 2` (model); `smudgeplot.py hetkmers` / `smudgeplot.py plot` (ploidy)
bio-genome-assembly-hifi-assembly
When your agent uses it
Assembles haplotype-resolved diploid and telomere-to-telomere (T2T) genomes from PacBio HiFi reads with hifiasm (HiFi-only, Hi-C, or trio phasing) and verkko (HiFi + ultralong ONT for T2T), extracting contigs from GFA and routing phasing QC to k-mer/trio metrics. Covers why a primary assembly is a haplotype mosaic that exists in no cell, partial-vs-full phasing (the .bp. vs .dip. filename convention), the purge-default trap on inbred samples, the --hom-cov coverage-estimate alarm, and verkko-vs-hifiasm for T2T. Use when assembling a diploid eukaryote from HiFi, phasing haplotypes with parents (trio) or Hi-C, deciding whether to chase T2T, or diagnosing switch errors invisible to N50/BUSCO/QV.
What it loads into context
Frontmatter
303 tokensAlways available to the agent
SKILL.md
5,382 tokensWhen the skill is activated
Reference documents
1,629 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- A Primary Assembly Is a Mosaic Chimera, Not a Haplotype
- Tool Taxonomy
- Decision Tree by Scenario
- hifiasm Invocations (the fragile commands - run as written)
- Output Filenames: the .bp. vs .dip. Convention (read the prefix, it encodes the mode)
- GFA Is Not FASTA (extract S lines before anything downstream)
- verkko (T2T, when the goal genuinely needs it)
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/hifi_assembly_workflow.sh
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: hifiasm 0.25.0+, yak 0.1+, verkko 2.3+, gfatools 0.5+, meryl 1.4+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
hifiasm output filenames AND the default purge level are version-dependent: confirm the `.bp.*`/`.dip.*` prefixes and `-l` defaults against `hifiasm --help` and the man page for the installed build before scripting around them. The k-mer-size convention also matters more than the binary version: hifiasm/yak trio uses k=31; verkko/Merqury hap-mers commonly use k=30 - mixing them silently produces garbage hap-mer matching. If a command errors, introspect the installed tool and adapt rather than retrying.
# HiFi Assembly
**"Assemble a diploid genome from HiFi reads"** -> Build two cleanly phased haplotypes (not one mosaic primary) from accurate long reads, choosing the phasing mechanism from the sample type and available data, and validate phasing with k-mer QC the headline metrics cannot see.
- CLI: `hifiasm -o prefix -t 32 reads.hifi.fq.gz` (HiFi-only), `--h1/--h2` (Hi-C), `-1/-2 *.yak` (trio); `verkko --hifi ... --nano ...` (T2T)
bio-genome-assembly-long-read-assembly
When your agent uses it
Assembles genomes de novo from noisy long reads (Oxford Nanopore R9/R10/Dorado, PacBio CLR) with Flye (repeat graph), Canu (correct-trim-assemble OLC), NextDenovo, Shasta, Raven, wtdbg2, or miniasm, and reconciles bacterial assemblies into a consensus with Trycycler/Autocycler. Covers matching the input flag to the basecaller era (--nano-hq vs --nano-raw), why a raw long-read assembly is contiguous but low-QV and not finished until polished, haplotig false-duplication and purge_dups, coverage and read-N50 as non-substitutable inputs, and mid-read adapter de-chimerization. Use when assembling a bacterial or eukaryotic genome from ONT or PacBio noisy reads, choosing a long-read assembler, or diagnosing an over-collapsed or duplicated assembly. For PacBio HiFi use hifi-assembly instead.
What it loads into context
Frontmatter
299 tokensAlways available to the agent
SKILL.md
5,107 tokensWhen the skill is activated
Reference documents
1,365 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- The Basecaller Era Picks the Flag, and the Assembly Is Only Half-Done Until Polished
- Tool Taxonomy
- Decision Tree by Scenario
- Flye (the default)
- Canu (thorough), wtdbg2 / Raven (fast), miniasm (layout only)
- Haplotig False-Duplication and purge_dups
- Pre-Assembly: Mid-Read Adapter De-Chimerization
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/flye_assembly.sh
Files
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## Version Compatibility
Reference examples tested with: Flye 2.9+, Canu 2.2+, NextDenovo 2.5+, Shasta 0.11+, Raven 1.8+, wtdbg2 2.5+, miniasm 0.3+, minimap2 2.26+, purge_dups 1.2+, Porechop_ABI 0.5+, Trycycler 0.5+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
Two behaviors are version-dependent and load-bearing: Flye `--genome-size` became optional (auto-estimated) and `--scaffold` flipped OFF by default in recent releases - confirm against the installed Flye. Shasta `--config` names are dated and chemistry-specific (e.g. `Nanopore-R10-Fast-Nov2022`) - list with `shasta --command listConfigurations` rather than hard-coding one. Canu read-type flag spellings and `correctedErrorRate` defaults changed across major versions. If a command errors, introspect the installed tool and adapt rather than retrying.
# Long-Read Assembly (Noisy Reads)
**"Assemble a genome from Nanopore/PacBio-CLR long reads"** -> Build a contiguous de novo assembly whose input mode matches the basecaller error regime, then hand off to polishing because the raw consensus is contiguous but not yet accurate.
- CLI: `flye --nano-hq reads.fq.gz --out-dir out -t 16` (modern ONT R10/Dorado), `canu -p asm -d out genomeSize=4.6m -nanopore reads.fq.gz` (thorough), `wtdbg2 -x ont -g 4.6m -i reads.fq.gz -fo asm && wtpoa-cns -i asm.ctg.lay.gz -fo asm.fa` (fast draft)
bio-genome-assembly-metagenome-assembly
When your agent uses it
Assembles microbial-community sequencing into metagenome-assembled genomes (MAGs) with metaFlye (ONT), metaSPAdes/MEGAHIT (Illumina), and hifiasm-meta/metaMDBG (PacBio HiFi), then recovers genomes via multi-binner consolidation (MetaBAT2, MaxBin2, CONCOCT, SemiBin2, VAMB -> DAS_Tool) and QCs them against MIMAG with CheckM2, GUNC, and GTDB-Tk. Covers why a metagenome is not a genome (uneven coverage, micro-diversity, strain collapse to consensus), differential-coverage binning, co-assembly vs per-sample, the rRNA-operon collapse that fails short-read MAGs, and strain resolution with inStrain. Use when reconstructing genomes from a microbiome, soil, ocean, or gut community, recovering MAGs, or resolving strain-level variation.
What it loads into context
Frontmatter
297 tokensAlways available to the agent
SKILL.md
5,547 tokensWhen the skill is activated
Reference documents
1,351 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- A Metagenome Is Not a Genome; the Assembler Cannot Assume Uniform Coverage
- Assembler Taxonomy
- Binner Taxonomy
- Decision Tree by Scenario
- metaFlye (ONT / Long Reads)
- metaSPAdes / MEGAHIT (Illumina)
- HiFi (the transformative case)
- Coverage for Binning, then Bin
- Consolidate (DAS_Tool), then QC
- Strain Resolution
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/metagenome_workflow.sh
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: Flye 2.9+, SPAdes 3.15+ (metaSPAdes), MEGAHIT 1.2+, hifiasm-meta 0.3+, metaMDBG 1.0+, MetaBAT2 2.15+, MaxBin 2.2.7+, CONCOCT 1.1+, SemiBin 2.0+, VAMB 4.1+, DAS_Tool 1.1.6+, CheckM2 1.0+, GUNC 1.0+, GTDB-Tk 2.4+, inStrain 1.7+, minimap2 2.26+, samtools 1.19+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
- Python: `pip show <package>` then `help(module.function)` to check signatures
GTDB-Tk results track the reference-package RELEASE (e.g. R214 vs R220); the DB release MUST match the GTDB-Tk binary or classification silently fails. CheckM2 and GUNC each download their own DIAMOND DB. SemiBin2's pretrained `--environment` models are versioned. If code throws an error, introspect the installed tool and adapt rather than retrying.
# Metagenome Assembly
**"Assemble genomes from my metagenome"** -> Co-assemble a community at uneven, strain-mixed coverage, then bin the contigs into a set of consensus population genomes (MAGs) and QC each against MIMAG. The deliverable is MAGs, not a single assembly.
- CLI: `flye --meta --nano-hq reads.fq` (ONT), `spades.py --meta -1 R1.fq -2 R2.fq` or `megahit -1 R1.fq -2 R2.fq` (Illumina), `hifiasm_meta`/`metaMDBG` (HiFi); then binners -> `DAS_Tool` -> `checkm2 predict` + `gunc run` + `gtdbtk classify_wf`bio-genome-assembly-scaffolding
When your agent uses it
Orders and orients assembled contigs into chromosome-scale scaffolds from long-range linking data, inserting N-gap spacers (adds no sequence). Covers Hi-C/Omni-C scaffolding (YaHS, SALSA2, 3D-DNA/Juicer), Hi-C read-mapping prerequisites (map each end separately, no mate rescue, dedup, enzyme-aware), reading the contact map for misjoins/inversions/false-duplications, manual curation in Juicebox/PretextView (the VGP/DToL standard), reference-guided scaffolding (RagTag) and its karyotype-erasure hazard, genetic-map (ALLMAPS) and Bionano optical-map integration, chimera-breaking before scaffolding, gap-filling, and telomere/contig-vs-scaffold-N50 QC (tidk). Use when turning contigs into chromosomes with Hi-C, integrating a linkage map or optical map, choosing a scaffolder by available linking data, or judging whether a chromosome-scale assembly is trustworthy.
What it loads into context
Frontmatter
309 tokensAlways available to the agent
SKILL.md
5,940 tokensWhen the skill is activated
Reference documents
1,622 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Automated Scaffolding Produces a Draft; the Genome Is Trustworthy Only After Manual Curation of the Contact Map
- Linking-Data Modality Taxonomy
- Decision Tree by Available Linking Data
- Hi-C Read Mapping (the step people botch)
- YaHS (the current default)
- SALSA2 (graph-aware, iterative, breaks chimeras)
- Reference-Guided Scaffolding (RagTag) -- Power and Peril
- Genetic/Linkage Maps (ALLMAPS) and Optical Maps (Bionano)
- Reading the Contact Map (the skill nobody writes down)
- Chimera-Breaking, Gap-Filling, and Phasing-vs-Scaffolding
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/yahs_scaffolding.sh
Files
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## Version Compatibility
Reference examples tested with: YaHS 1.2+, SALSA2 2.3+, juicer_tools 1.22/2.0+, bwa 0.7.17+, chromap 0.2+, samtools 1.19+, RagTag 2.1+, tidk 0.2.3+, seqkit 2.6+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
hifiasm output filenames and YaHS/SALSA2 enzyme and resolution defaults have changed across releases; confirm output names and `-e`/`-r` behaviour against the installed version. The Hi-C-mapping flag set (`bwa mem -5SP` vs the Arima/VGP per-end pipeline) varies by vendor (Arima, Dovetail/Omni-C, Phase Genomics) - check the current mapping guide before pasting flags. If a command errors, introspect the tool and adapt rather than retrying.
# Genome Scaffolding
**"Turn my contigs into chromosomes"** -> Order and orient contigs by long-range linking signal, insert N-gap spacers, then curate the contact map - scaffolding adds no sequence, so the output is a draft chromosome structure, not finished sequence.
- CLI: `yahs contigs.fa hic_to_contigs.bam` (Hi-C default), `run_pipeline.py -a contigs.fa -b sorted.bed -m yes` (SALSA2), `ragtag.py scaffold ref.fa contigs.fa` (reference-guided), `allmaps path` (linkage maps)
bio-genome-assembly-short-read-assembly
When your agent uses it
Assembles a genome de novo from Illumina short reads with SPAdes (isolate/careful/sc/meta/plasmid/rna modes), MEGAHIT (low-memory, huge datasets), Unicycler (bacterial finishing/hybrid), MaSuRCA (large hybrid), ABySS (Bloom-filter), and Platanus (heterozygous diploids), using multi-k de Bruijn graphs. Covers the repeat-resolution limit, why N50 plateaus at the genome not the depth, GenomeScope2 k-mer profiling first, the heterozygosity/haplotig trap, error-correction erasing rare alleles, GC dropout, and NG50/auN/BUSCO reporting. Use when assembling a bacterial isolate, fungal, small-eukaryotic, single-cell, or metagenome genome from Illumina reads, or when deciding whether short reads can even produce the assembly being asked for.
What it loads into context
Frontmatter
293 tokensAlways available to the agent
SKILL.md
5,259 tokensWhen the skill is activated
Reference documents
1,479 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Short Reads Cannot Span Repeats, So Contiguity Is Capped by the Genome, Not the Depth
- Tool Taxonomy
- Decision Tree by Scenario
- SPAdes -- the dominant short-read assembler (the mode is a graph choice, not a cosmetic flag)
- MEGAHIT, Unicycler, and the others
- Pre-Assembly Genome Profiling (run this FIRST)
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/bacterial_assembly.sh
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: SPAdes 4.0+, MEGAHIT 1.2+, Unicycler 0.5+, ABySS 2.3+, GenomeScope2 2.0+, KMC 3.2+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
SPAdes 4.0 (June 2024) requires Python >=3.8, defaults to GFA v1.2 output with circular-path tags (`TP:Z:circular`), and is stated by the maintainers to be the last major feature release - pin the version in methods, because k auto-selection and the `--isolate`/`--careful` recommendation flipped across 3.x. MEGAHIT preset k-lists (`meta-sensitive`, `meta-large`) have changed across versions - confirm against `megahit -h` before hard-coding. If code throws an error, introspect the installed tool and adapt rather than retrying.
# Short-Read Assembly
**"Assemble a genome from Illumina reads"** -> Build a multi-k de Bruijn graph from short reads and walk it into contigs, knowing the contiguity ceiling is set by the genome's repeat structure, not the depth.
- CLI: `spades.py --isolate -1 R1.fq.gz -2 R2.fq.gz -o out -t 16` (bacterial isolate, the genuine sweet spot)
- CLI: `megahit -1 R1.fq.gz -2 R2.fq.gz -o out -t 16` (huge/low-memory, metagenome)bio-genome-engineering-base-editing-design
When your agent uses it
Designs cytosine (CBE, C-to-T) and adenine (ABE, A-to-G) base-editor guides by positioning the target base at the activity-peak of the editing window (protospacer positions ~5-7, PAM-distal numbering), minimizing bystander edits for product purity, reading dinucleotide context (APOBEC1 TC favored / GC disfavored), and selecting the editor variant (BE4max, ABEmax, ABE8e, YE1/SECURE, TadCBE, CGBE, SpG/SpRY-BE). Covers knockout by premature stop (CRISPR-STOP/iSTOP) and splice-site disruption, the three off-target classes (Cas-dependent, Cas-independent DNA, RNA), outcome prediction (BE-Hive/DeepBE), and the base-vs-prime-vs-HDR decision. Use when installing a transition mutation without a double-strand break, knocking out a gene without indels, or choosing CBE vs ABE. Generic guide scoring, prime editing, and HDR donors are separate skills.
What it loads into context
Frontmatter
319 tokensAlways available to the agent
SKILL.md
5,392 tokensWhen the skill is activated
Reference documents
1,203 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- the job is product PURITY, not editing efficiency
- Mechanism & the Position-Numbering Convention (get this right)
- The Window Is a Gradient, Editor-Specific -- not a box
- Editor Variant Selection
- The Three Off-Target Classes -- ask "which of the three?" first
- Knockout by Base Editing (no DSB)
- Decision Tree by Scenario
- Find Editable Guides and Read the Window
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/base_editing_design.py
Files
SKILL.mdcore19,450 bytesSKILL.mdcore19,450 bytesSKILL.mdcore19,450 bytesreferences/usage-guide.mddoc4,669 bytesscripts/base_editing_design.pyruns4,911 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: BioPython 1.83+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
BE-Hive is NOT a pip package -- it is a web tool (crisprbehive.design) plus clone-only repos (`maxwshen/be_predict_efficiency`, `be_predict_bystander`) pinning old dependencies (scikit-learn 0.20.3, BioPython 1.73). Use the web tool for a quick answer, the clone for batch use. Editing windows and outcome predictions are **editor-variant- and cell-type- specific** and are activity gradients, not hard boxes -- record which editor model was used.
# Base Editing Design
**"Install a transition mutation without a double-strand break"** -> Write the edit as a base-pair change to pick the editor family, find a PAM that lands the target base at the window peak while keeping bystanders out, read sequence context, choose the editor variant, and report the predicted genotype spectrum -- not a lone efficiency number.bio-genome-engineering-grna-design
When your agent uses it
Designs and ranks guide RNAs (sgRNAs) for CRISPR-Cas9/Cas12a gene knockout by scanning a target for PAM sites (NGG SpCas9, NNGRRT SaCas9, TTTV Cas12a, NG SpCas9-NG, near-PAMless SpRY), enumerating candidate spacers, applying hard filters (Pol-III TTTT terminator, 5' G, GC), ranking on-target activity with the context-appropriate model (Rule Set 2/Azimuth for U6/lentiviral, CRISPRscan for T7/embryo, DeepHF for high-fidelity variants, DeepCpf1 for Cas12a), and predicting the indel/frameshift outcome (Bae out-of-frame score, inDelphi, FORECasT, Lindel). Use when selecting sgRNAs to knock out a gene, choosing a nuclease/PAM for a constrained locus, picking which exon to target, or shortlisting guides before an off-target check. Off-target specificity, base/prime editing, and HDR donors are separate skills.
What it loads into context
Frontmatter
325 tokensAlways available to the agent
SKILL.md
5,461 tokensWhen the skill is activated
Reference documents
1,212 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- a guide produces a reproducible indel *distribution*, not "a cut", and knockout success is a property of that distribution
- On-Target Score Taxonomy -- each model is valid for ONE context
- Nuclease & PAM Taxonomy -- expanding PAM range trades away activity/specificity
- Decision Tree by Scenario
- Enumerate and Filter Candidate Guides
- Rank On-Target Activity in the Valid Context
- Choose the Cut Site by Exon Biology (the under-used lever)
- Predict the Editing Outcome (frameshift fraction decides KO)
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/grna_design.py
Files
SKILL.mdcore19,938 bytesSKILL.mdcore19,938 bytesSKILL.mdcore19,938 bytesSKILL.mdcore19,938 bytesreferences/usage-guide.mddoc5,059 bytesscripts/grna_design.pyruns5,747 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: BioPython 1.83+, CRISPOR 5.0+ (web/CLI).
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Output depends on inputs more than tool versions: on-target scores are **model-specific and not interchangeable** (a 0.7 Azimuth score is not a 0.7 CRISPRscan score), and the **valid model is set by how the guide is delivered/transcribed**, not by preference. Record the nuclease, the delivery context (U6/lentiviral vs in-vitro T7/RNP), and the reference genome build used for any off-target step.
# Guide RNA Design
bio-genome-engineering-hdr-template-design
When your agent uses it
Designs donor/repair templates for precise CRISPR knock-ins -- choosing the format (ssODN, long-ssDNA/Easi-CRISPR, dsDNA/plasmid, AAV6), sizing homology arms, placing the cut within ~10 bp of the edit, and adding a mandatory codon-checked blocking (PAM/seed) mutation so the edited allele is not re-cut. Frames the HDR-vs-NHEJ-vs-MMEJ pathway competition, the MMEJ (PITCh) and homology-independent (HITI/HMEJ) alternatives for post-mitotic cells, ssODN strand/asymmetry choice, phosphorothioate end-protection, and ranked HDR enhancers. Use when designing a donor for a point mutation, epitope/fluorophore tag, allele replacement, or knock-in, or when HDR efficiency is low. Guide design and base/prime editing are separate skills.
What it loads into context
Frontmatter
297 tokensAlways available to the agent
SKILL.md
4,367 tokensWhen the skill is activated
Reference documents
1,242 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- HDR is the minority pathway, and a donor without a blocking mutation is a self-destructing one
- The Pathway Competition (everything follows from this)
- Donor Format Decision
- Homology Arms, Edit-to-Cut Distance, and the Blocking Mutation (the most-botched trio)
- ssODN Strand & Asymmetry (an over-cited rule) + the reliable win
- MMEJ / Homology-Independent Routes (when HDR is the wrong tool)
- HDR Enhancers -- ranked experiments, not multipliers
- Generate the Donor, Block Re-cutting (codon-checked), and Design Validation Primers
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/hdr_template_design.py
Files
SKILL.mdcore16,223 bytesSKILL.mdcore16,223 bytesSKILL.mdcore16,223 bytesreferences/usage-guide.mddoc5,018 bytesscripts/hdr_template_design.pyruns5,621 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: BioPython 1.83+, primer3-py 2.0+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
primer3-py designs PRIMERS (use `primer3.bindings.design_primers(seq_args, global_args)`; the camelCase `designPrimers` is deprecated since 1.0.0), not homology arms -- arm extraction and codon-aware blocking are the skill's own BioPython code. Design arms and guide against the **actual cell line's sequence**, not GRCh38 -- a SNP in an arm reduces annealing and a SNP in the PAM/seed can mean the guide does not cut.
# HDR Template Design
**"Design a donor for my CRISPR knock-in"** -> Decide the format by edit size and whether the cell cycles, size the arms, confirm a guide cuts within ~10 bp of the edit, and add a codon-checked blocking mutation so the corrected allele cannot be re-cut.bio-genome-engineering-off-target-prediction
When your agent uses it
Nominates and assesses CRISPR off-target sites genome-wide. Enumerates candidate sites by mismatch and bulge tolerance with Cas-OFFinder/CRISPRitz, ranks them with the published CFD score (SpCas9-only, relative ranker) or MIT/CRISTA/energy models, runs variant-aware screening against gnomAD/individual genomes (CRISPRme), and frames the empirical genome-wide discovery assays (GUIDE-seq, CIRCLE-seq, CHANGE-seq, DISCOVER-seq, Digenome-seq) and high-fidelity nuclease choice (HiFi Cas9, Sniper-Cas9, eSpCas9, SpCas9-HF1). Use when assessing guide RNA specificity, choosing among candidate guides, screening a therapeutic guide against population variation, or planning empirical off-target validation. Distinguishes predicted vs detected vs validated. On-target activity scoring and deaminase (Cas-independent) base/prime-editor off-targets are separate skills.
What it loads into context
Frontmatter
304 tokensAlways available to the agent
SKILL.md
6,001 tokensWhen the skill is activated
Reference documents
1,204 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- in-silico enumeration nominates *candidates*; it does not measure *which sites are cut*
- In-Silico Taxonomy -- enumerate, then score, then aggregate
- Empirical Discovery Assays -- each has a characteristic bias; concordance is partial
- Decision Tree by Scenario
- High-Fidelity Nucleases -- often a bigger lever than guide reselection
- Variant-Aware Screening (reference-only is a clinical liability)
- Enumerate Candidate Sites with Cas-OFFinder
- Score Candidates with the Published CFD Tables
- Structural Consequences Amplicon Panels Miss
- The Evidence Ladder & Limit of Detection
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/off_target_analysis.py
Files
SKILL.mdcore22,398 bytesSKILL.mdcore22,398 bytesSKILL.mdcore22,398 bytesreferences/usage-guide.mddoc4,987 bytesscripts/off_target_analysis.pyruns4,982 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: Cas-OFFinder 3.0+, pandas 2.2+, Python 3.10+.
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Results depend on inputs far more than tool versions: the candidate list is bounded by the **reference genome build, the mismatch/bulge tolerance, and the PAM pattern** searched, not by the Cas-OFFinder version. The **CFD matrix is SpCas9/NGG-specific and a relative ranker, not a calibrated cutting probability**. Load the published CFD tables (Doench 2016 / CRISPOR distribution) rather than hand-typing values. Cas-OFFinder is the maintained `snugel/cas-offinder` repository (native DNA/RNA bulge support from v3.0.0).
# Off-Target Prediction
bio-genome-engineering-prime-editing-design
When your agent uses it
Designs pegRNAs and nicking guides for prime editing (PE) -- choosing the nick/strand, tuning the primer-binding site (PBS) and reverse-transcription template (RTT) as a per-locus panel, selecting the PE system (PE2/PE3/PE3b/PE4/PE5/PEmax/PE7), adding MMR-evading and PAM-disrupting silent edits, appending epegRNA 3' motifs (tevopreQ1/mpknot), and ranking with PRIDICT/DeepPrime. Covers twinPE/PASTE for large insertions and the prime-vs-base-editing decision. Use when designing a scarless point mutation, small insertion/deletion, or any of the 12 base conversions without a double-strand break, when efficiency is low and MMR inhibition or pegRNA stabilization is needed, or when routing a large insertion to an integrase method. Generic guide scoring and base editing are separate skills.
What it loads into context
Frontmatter
300 tokensAlways available to the agent
SKILL.md
5,372 tokensWhen the skill is activated
Reference documents
1,195 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- there is no universal PBS/RTT optimum, and the *system* choice carries the order of magnitude
- Mechanism (the design rules fall out of it)
- The PE System Stack -- orthogonal axes, not a "bigger number is better" ranking
- pegRNA Parameters & the Free Wins
- epegRNA 3' Motifs & pegRNA Stability
- Outcome Prediction (rank, but still test)
- Large / Advanced Edits (single-pegRNA PE runs out of room)
- Decision Tree by Scenario
- Generate Designs with PrimeDesign (verified notation)
- Sweep a PBS x RTT Panel and Enforce the Hard Rules
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/prime_editing_design.py
Files
SKILL.mdcore19,232 bytesSKILL.mdcore19,232 bytesSKILL.mdcore19,232 bytesreferences/usage-guide.mddoc4,578 bytesscripts/prime_editing_design.pyruns5,323 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: BioPython 1.83+, PrimeDesign 1.2+ (Docker), PRIDICT2.0 (web/code).
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
PrimeDesign is Docker-only (no pip) and takes the edit inline in a single string with exact parenthesis notation (below) -- the most-hallucinated thing in PE tooling; verify it against the repo, never reconstruct from memory. Outcome-prediction models are trained mostly on HEK293T + small edits (<=3 bp); their scores are priors, not measurements, and degrade off-distribution. The PE *system* (MMR status, expressed vs synthetic pegRNA) drives efficiency more than any oligo tweak.
# Prime Editing Design
bio-genome-intervals-bed-file-basics
When your agent uses it
Handles BED-format genomic intervals (BED3 through BED12, narrowPeak/broadPeak) and the coordinate-system substrate the whole interval category rests on, with bedtools (CLI) and pybedtools/pyranges/pandas (Python). Covers the 0-based half-open vs 1-based-closed convention boundary and the start-1/end-unchanged conversion, the silent failures (chrom-name mismatch, CRLF, lexicographic-vs-version sort under -sorted), genome/chrom.sizes generation, sorting contracts, BED12 block invariants, validation, makewindows, cross-assembly liftover (liftOver/CrossMap), and BED<->VCF/BAM/FASTA conversion. Use when reading, creating, validating, sorting, lifting between genome builds, or converting interval files, preparing inputs for bedtools/tabix/bigBed, or debugging an off-by-one or empty-overlap result.
What it loads into context
Frontmatter
287 tokensAlways available to the agent
SKILL.md
4,871 tokensWhen the skill is activated
Reference documents
1,186 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- A Coordinate Is a Bare Integer With No Self-Describing Convention
- Tool Taxonomy
- Decision Tree by Scenario
- BED Columns (BED3 -> BED12)
- Create and Read BED Files
- Generate the Genome / chrom.sizes File
- Convert Across Format Boundaries
- Sort, Validate, and Make Windows
- Cross-Assembly Liftover (a different problem from convention conversion)
- BED12 Block Invariants
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/create_bed.pyscripts/validate_bed.sh
Files
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## Version Compatibility
Reference examples tested with: bedtools 2.31+, pybedtools 0.10+, pyranges 0.x (the `pyranges1` rewrite ships as a separate package), samtools 1.19+, pandas 2.2+, UCSC liftOver / CrossMap 0.7+ (the bare `CrossMap` entry point replaced `CrossMap.py` at 0.7.0).
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
- Python: `pip show <package>` then `help(module.function)` to check signatures
pyranges has a major-version API split: pyranges 0.x and the `pyranges1` rewrite differ in method names and DataFrame access; both keep `Chromosome/Start/End` columns and 0-based half-open coordinates. Check `import pyranges; pyranges.__version__` before chaining methods. Operations that need chromosome lengths (`slop`, `complement`, `shuffle`, `makewindows -g`, `-sorted` ordering) require a genome/chrom.sizes file. If code throws an error, introspect the installed tool and adapt rather than retrying.
# BED File Basics
**"Work with this interval file without shifting everything by one base"** -> Establish the coordinate convention from the format, read/create/validate/sort the intervals, and convert across format boundaries with the correct base shift.
- CLI: `bedtools sort -i in.bed`, `bedtools getfasta`, `bedtools makewindows -g genome.txt -w 10000`, `sort -k1,1 -k2,2n`bio-genome-intervals-bedgraph-handling
When your agent uses it
Generates, normalizes, and converts bedGraph signal tracks (4-column chrom/start/end/value, 0-based half-open) with bedtools genomecov, deepTools bamCoverage/bamCompare/bigwigCompare, bedtools unionbedg, and UCSC bedGraphToBigWig. Covers why a raw coverage bedGraph is not comparable across samples until normalized, the CPM/RPKM/BPM/RPGC normalization menu and the conserved-total assumption that makes them wrong under a global perturbation, the strict sorted-non-overlapping-chrom.sizes bedGraphToBigWig contract that silently corrupts a bigWig, effective-genome-size selection, and bin-size aliasing. Use when building or normalizing a coverage/signal track from a BAM, comparing tracks across samples or conditions, converting bedGraph to a browser-ready bigWig, or diagnosing a track that looks plausible but reports wrong heights.
What it loads into context
Frontmatter
287 tokensAlways available to the agent
SKILL.md
5,350 tokensWhen the skill is activated
Reference documents
1,293 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- A Raw Coverage bedGraph Is a Library-Size Artifact, and the Wrong Normalization Is Worse Than None
- Normalization Taxonomy
- Decision Tree by Scenario
- Generate a Normalized Track with bamCoverage (the modern default)
- Generate with bedtools genomecov (text, flexible, no normalization)
- Convert bedGraph -> bigWig (the silent-corruption trap)
- Multi-Sample Arithmetic
- Read/Extract Signal with pyBigWig
- Effective Genome Size (the two-table trap)
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/bam_to_bigwig.shscripts/bedgraph_operations.py
Files
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## Version Compatibility
Reference examples tested with: deeptools 3.5+, bedtools 2.31+, ucsc-bedgraphtobigwig 445+, pyBigWig 0.3.22+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
- Python: `pip show <package>` then `help(module.function)` to check signatures
bedGraphToBigWig has a hard, under-advertised input contract: the bedGraph must be `LC_COLLATE=C`-sorted by chrom then start, contain non-overlapping intervals, and ship with a chrom.sizes derived from the exact assembly the reads were aligned to. deepTools effective-genome-size tables are occasionally updated between releases - re-check the installed version's table. If code throws an error, introspect the installed tool and adapt the example to match the actual API rather than retrying.
# bedGraph Handling
**"Make me a coverage/signal track I can compare across samples and load in a browser"** -> Generate a per-bin signal track, normalize it onto a common scale (or decide a spike-in is required), then convert the text bedGraph to an indexed bigWig under the strict sort/overlap/chrom.sizes contract.
- CLI: `bamCoverage -b s.bam -o s.bw --normalizeUsing RPGC --effectiveGenomeSize <N>`; `bedtools genomecov -ibam s.bam -bga`; `LC_COLLATE=C sort -k1,1 -k2,2n in.bdg | bedGraphToBigWig /dev/stdin chrom.sizes out.bw`bio-genome-intervals-bigwig-tracks
When your agent uses it
Reads, queries, and writes bigWig indexed binary signal tracks (coverage, fold-change, conservation, methylation-rate) with pyBigWig (Python) and the UCSC Kent tools (bedGraphToBigWig, bigWigToBedGraph, bigWigInfo, bigWigSummary, bigWigAverageOverBed) and deepTools (multiBigwigSummary, computeMatrix, bigwigCompare). Covers the central trap that a wide query returns a precomputed zoom-level summary (by default the mean, which annihilates narrow peaks) not per-base data, when exact=True/values() is mandatory, the NaN-not-zero gap-handling fork, choosing mean vs max vs sum vs coverage by biological question, and the sorted-bedGraph plus chrom.sizes build requirement. Use when extracting signal at regions, computing mean signal per gene/peak, building a browser track from bedGraph, comparing tracks, or building TSS/gene-body metaprofiles.
What it loads into context
Frontmatter
303 tokensAlways available to the agent
SKILL.md
5,309 tokensWhen the skill is activated
Reference documents
1,364 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- A Wide Query Returns a Zoom-Level Summary, Not the Underlying Data
- Tool Taxonomy
- Decision Tree by Scenario
- Inspect a File Before Trusting It
- Mean Signal per Region (the most common task)
- Peak Height vs Total vs Coverage (statistic = question)
- Per-Base Values (NaN is not zero)
- Build a Valid bigWig
- Compare and Profile Tracks (deepTools)
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/bedgraph_to_bigwig.shscripts/pybigwig_demo.py
Files
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## Version Compatibility
Reference examples tested with: pyBigWig 0.3.22+, numpy 1.26+, ucsc-bedgraphtobigwig/ucsc-tools 469+, deeptools 3.5+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` (or `bigWigInfo` with no args for usage) then `<tool> --help` to confirm flags
- Python: `pip show pyBigWig` then `help(pyBigWig.bigWigFile.stats)` to check signatures
Building any bigWig needs a chrom.sizes file (`name<TAB>length`) and a coordinate-sorted bedGraph; pyBigWig's numpy return path requires numpy present at compile time. If code throws an error, introspect the installed tool and adapt rather than retrying.
# BigWig Tracks
**"Get the signal from my bigWig over these regions / build a browser track."** -> Query an indexed binary signal track, choosing the summary statistic and exactness that match the biological question, or build one from a sorted bedGraph + chrom.sizes.
- CLI: `bigWigAverageOverBed in.bw regions.bed out.tab`, `bigWigSummary in.bw chr s e N -type=max`, `bedGraphToBigWig in.sorted.bedGraph chrom.sizes out.bw`, `bigWigInfo in.bw`bio-genome-intervals-coverage-analysis
When your agent uses it
Computes and interprets sequencing read depth and coverage over a genome, windows, or target regions with mosdepth (windowed depth, cumulative distribution, --quantize callable BEDs), bedtools genomecov/coverage (bedGraph tracks, per-target stats), samtools depth/coverage (per-base depth, per-contig depth+breadth). Covers the breadth-vs-mean distinction, the cumulative-coverage curve, evenness (CV/Fano/fold-80/Gini), what each tool silently counts (duplicates, secondary/supplementary, MAPQ, read span vs fragment, mate-overlap), the samtools-depth 8000-cap version trap, and the bedtools coverage -a/-b orientation flip. Use when assessing sequencing adequacy, building coverage tracks, computing breadth at a depth threshold, defining callable regions, or QCing target-capture uniformity.
What it loads into context
Frontmatter
277 tokensAlways available to the agent
SKILL.md
4,919 tokensWhen the skill is activated
Reference documents
1,286 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Mean Depth Is a Budget, Not a Result; Report Breadth Off a Cumulative Curve
- Tool Taxonomy
- Decision Tree by Scenario
- mosdepth -- The Modern Default
- bedtools genomecov -- Tracks and the Histogram Default
- bedtools coverage -- Per-Target Stats (mind the orientation)
- samtools depth / coverage
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/bedgraph_from_bam.shscripts/coverage_analysis.py
Files
SKILL.mdcore18,044 bytesSKILL.mdcore18,044 bytesSKILL.mdcore18,044 bytesSKILL.mdcore18,044 bytesSKILL.mdcore18,044 bytesSKILL.mdcore18,044 bytesreferences/usage-guide.mddoc5,425 bytesscripts/bedgraph_from_bam.shruns2,580 bytesscripts/coverage_analysis.pyruns3,054 bytes
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## Version Compatibility
Reference examples tested with: bedtools 2.31+, mosdepth 0.3+, samtools 1.19+, pybedtools 0.10+, numpy 1.26+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
- Python: `pip show <package>` then `help(module.function)` to check signatures
`samtools depth` behaviour changed across versions: pre-1.13 capped depth at 8000 and truncated silently (`-d 0` = unlimited); 1.13+ rewrote the subcommand with NO cap and `-d/-m` deprecated/ignored. Always check `samtools --version` before trusting a max-depth number. If code throws an error, introspect the installed tool and adapt rather than retrying.
# Coverage Analysis
**"Is my sequencing deep enough to answer the question?"** -> Measure depth as a distribution over positions, then report median, breadth at a depth threshold, and an evenness number -- never the mean alone.
- CLI: `mosdepth --by 500 prefix in.bam` (windowed depth + cumulative dist), `samtools coverage in.bam` (per-contig depth+breadth), `bedtools genomecov -ibam in.bam -bga` (bedGraph track)bio-genome-intervals-gtf-gff-handling
When your agent uses it
Parses, queries, converts, and extracts from GTF and GFF3 gene-model annotation files - walking the gene/transcript/exon/CDS hierarchy with gffutils (queryable SQLite DB), converting formats and extracting transcript/CDS/protein FASTA with gffread, slurping to dataframes with gtfparse/pyranges, and sanitizing malformed files with AGAT. Covers the 1-based-inclusive vs 0-based BED coordinate conversion (start-1 only), deriving implicit features (introns/UTRs/TSS), phase-not-frame, the stop-codon-in-or-out-of-CDS convention, and the chr1-vs-1 seqid and gene-ID-version mismatches that silently produce all-zero count matrices and dropped joins. Use when extracting features or sequences from an annotation, converting GTF<->GFF3 or GTF->BED, traversing the gene tree, or diagnosing a coordinate/provenance mismatch upstream of counting or DE.
What it loads into context
Frontmatter
302 tokensAlways available to the agent
SKILL.md
4,779 tokensWhen the skill is activated
Reference documents
1,428 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- A GTF/GFF3 Is a Serialized Gene-Model Tree, Not a Table of Intervals
- Tool Taxonomy
- Decision Tree by Scenario
- Walk the Gene Tree and Derive Introns (gffutils)
- Convert Formats and Extract Sequences (gffread)
- Convert GTF to BED with the Right Coordinate Shift
- Sanitize a Malformed File First (AGAT)
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/gffread_convert.shscripts/parse_gtf.py
Files
SKILL.mdcore18,628 bytesSKILL.mdcore18,628 bytesSKILL.mdcore18,628 bytesSKILL.mdcore18,628 bytesSKILL.mdcore18,628 bytesreferences/usage-guide.mddoc5,777 bytesscripts/gffread_convert.shruns1,821 bytesscripts/parse_gtf.pyruns2,244 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: gffutils 0.13+, gffread 0.12+, gtfparse 2.x, pyranges 0.1+ (or 1.0+ - see note), AGAT 1.4+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
- Python: `pip show <package>` then `help(module.function)` to check signatures
Two version landmines specific to this skill: (1) **gtfparse changed its return type** - older releases returned a pandas DataFrame, gtfparse >=2.x returns a **polars** DataFrame by default; pass `result_type='pandas'` before chaining pandas idioms (`.copy()`, boolean masks). (2) **pyranges has a major-version API split** - pyranges 0.x and the 1.0 rewrite differ in method names and attribute access; check `import pyranges; pyranges.__version__` before pasting code. gffutils stores **1-based** coordinates while pyranges stores **0-based** - their `start` fields differ by one by design. If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt rather than retrying.
# GTF/GFF Handling
**"Pull these features (or their sequences) out of my annotation, convert it, or find out why my counts are wrong."** -> Treat the file as a serialized gene-model tree: walk gene->transcript->exon/CDS, derive implicit features, and reconcile coordinate and namespace conventions before trusting any number.
- CLI: `gffread in.gtf -T -o out.gtf` (convert), `gffread -w tx.fa -g genome.fa in.gtf` (FASTA), `agat_convert_sp_gxf2gxf.pl` (sanitize)bio-genome-intervals-interval-arithmetic
When your agent uses it
Performs set operations on genomic intervals - intersect (-wa/-wb/-wo/-wao/-loj/-c/-v/-u), subtract (-A), merge (-d, -c/-o), complement, cluster, multiinter, unionbedg, map, and groupby - with bedtools (CLI) and pybedtools/pyranges/bioframe (Python). Covers the sorted-input contract and the -sorted chromosome-order footgun, reciprocal/fractional overlap (-f/-F/-r/-e) and the A-vs-B asymmetry, -split for spliced/BED12/BAM features, and jaccard/fisher as mechanics only. Use when finding overlapping or unique regions between BED/peak/feature files, building consensus peaksets, removing blacklisted regions, transferring annotation values onto intervals, or computing interval-set similarity; route overlap-significance testing to overlap-significance.
What it loads into context
Frontmatter
282 tokensAlways available to the agent
SKILL.md
4,141 tokensWhen the skill is activated
Reference documents
1,569 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- The Arithmetic Is Exact; the Danger Is the Silent Preconditions
- Tool Taxonomy
- Decision Tree by Scenario
- Intersect - the Workhorse
- Subtract, Merge, Complement, Cluster
- Map - Transfer Values, and Groupby - Aggregate
- Multi-Sample: Multiinter and Unionbedg
- Jaccard and Fisher - Mechanics Only
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/intersect_examples.shscripts/interval_operations.py
Files
SKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesSKILL.mdcore15,700 bytesreferences/usage-guide.mddoc6,268 bytesscripts/intersect_examples.shruns2,230 bytesscripts/interval_operations.pyruns2,278 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: bedtools 2.31+, pybedtools 0.10+, pyranges 0.1+ (or 1.0+ - see note), bioframe 0.7+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `bedtools --version` then `bedtools <subcommand> --help` to confirm flags
- Python: `pip show <package>` then `help(module.function)` to check signatures
pyranges has a major-version API split: pyranges 0.x and the 1.0 rewrite (package `pyranges1`) differ in method names and return shapes. Verify with `import pyranges; pyranges.__version__` before pasting v0 idioms. If code throws an error, introspect the installed package and adapt rather than retrying.
# Interval Arithmetic
**"Which of my peaks overlap promoters, and how do I combine/subtract/annotate interval sets?"** -> Apply exact, deterministic set operations to sorted interval files, guarding the preconditions (prior `sort`, the `-sorted` chromosome-order contract, `-split`) that otherwise corrupt the answer.
- CLI: `bedtools intersect -a a.bed -b b.bed -u`, `bedtools merge`, `bedtools subtract`, `bedtools map -c 4 -o mean`bio-genome-intervals-overlap-significance
When your agent uses it
Tests whether two genomic interval sets overlap (colocalize) more than expected by chance using a permutation test against a structured-genome null model. Covers bedtools fisher (analytic 2x2 screen), bedtools shuffle + jaccard permutation, GAT (isochore/GC-conditioned simulation with FDR), regioneR (flexible permutation, randomizeRegions vs circularRandomizeRegions, localZScore), LOLA (universe-relative Fisher against a region database), and GREAT/rGREAT (regulatory-domain binomial + hypergeometric for ontology-from-regions). Stresses the universe/background choice, matched background, blacklist exclusion, and multiple-testing control. Use when asking whether peaks/regions are enriched at enhancers/TFBS/features, scoring region-set colocalization or region-set enrichment, comparing CNV/SV concordance, or turning an overlap count into a defensible p-value.
What it loads into context
Frontmatter
284 tokensAlways available to the agent
SKILL.md
5,613 tokensWhen the skill is activated
Reference documents
1,414 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- A Raw Overlap Count Is an Observation in Search of a Null, and the Universe Choice Dominates
- Method Taxonomy
- Decision Tree by Scenario
- bedtools fisher - The Analytic Screen (weakest null)
- Permutation Null with bedtools shuffle (entry-level)
- GAT - Isochore/GC-Conditioned Simulation
- regioneR - Flexible Permutation in R
- LOLA - Universe-Relative Region-Set Enrichment
- rGREAT - Ontology Enrichment From Regions
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/permutation_overlap.pyscripts/shuffle_permutation_test.sh
Files
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## Version Compatibility
Reference examples tested with: bedtools 2.31+, pybedtools 0.10+, regioneR 1.36+ (Bioconductor 3.18+), GAT 1.3+, LOLA 1.30+, rGREAT 2.4+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('regioneR')` then `?permTest` to verify parameters
Bioconductor packages (regioneR, LOLA, rGREAT) are version-pinned to the Bioconductor release, not just the package version - record the Bioconductor release with results. rGREAT 2.x runs GREAT locally with general background handling; rGREAT 1.x only proxied the (whole-genome-default) web server. If code throws an error, introspect the installed package and adapt the example to match the actual API rather than retrying.
# Overlap Significance
**"My peaks overlap enhancers a lot - is that more than chance?"** -> Compare the observed overlap to a null distribution from a structured-genome model, not to a uniform-random expectation, and report a permutation p-value/z-score, not a raw count.bio-genome-intervals-proximity-operations
When your agent uses it
Performs proximity operations on genomic intervals with bedtools (closest, window, flank, slop) and pybedtools - nearest-feature queries with signed/strand-aware distance, fixed-radius window searches, strand-aware promoter construction, and interval extension. Covers the closest -d/-D a/b/ref/-t/-k/-io/-iu/-id flags, the -D ref strand sign-flip, silent chromosome-end clipping in slop/flank, -t all tie double-counting, and the critical distinction between a geometry answer (nearest TSS) and a biology answer (which gene an element regulates). Use when assigning peaks or variants to genes, defining promoters from a gene model, building distance-to-TSS distributions, finding features within a window, or extending intervals - and when deciding whether nearest-gene is a fair prior (GWAS locus) or a trap (distal enhancer).
What it loads into context
Frontmatter
279 tokensAlways available to the agent
SKILL.md
5,022 tokensWhen the skill is activated
Reference documents
1,461 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- closest Answers a GEOMETRY Question Misread as a BIOLOGY Question
- Operation Taxonomy
- Decision Tree by Scenario
- closest - Nearest Feature with Signed, Strand-Aware Distance
- window - Features Within a Search Radius
- slop / flank - Extend or Find Adjacent Regions (genome file REQUIRED)
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/create_promoters.shscripts/proximity_analysis.py
Files
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## Version Compatibility
Reference examples tested with: bedtools 2.31+, pybedtools 0.10+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `bedtools --version` then `bedtools <subcommand> --help` to confirm flags
- Python: `pip show pybedtools` then `help(pybedtools.BedTool.closest)` to check signatures
`flank` and `slop` REQUIRE a chrom-sizes (`genome.txt`, two columns: chrom<TAB>length) file via `-g`; `closest` requires both inputs coordinate-sorted (`sort -k1,1 -k2,2n`). If code throws an error, introspect the installed tool and adapt rather than retrying.
# Proximity Operations
**"Which gene is nearest to each peak, and is that the gene it regulates?"** -> Compute interval geometry (nearest feature, signed distance, window membership, strand-aware promoters) with bedtools, then decide honestly whether geometry answers the biological question.
- CLI: `bedtools closest -D b -t first -a peaks.bed -b genes.bed`, `bedtools window -w 50000`, `bedtools slop -s -l 2000 -r 200 -g genome.txt`bio-hi-c-analysis-compartment-analysis
When your agent uses it
Detects A/B chromatin compartments from balanced Hi-C contact matrices via eigenvector decomposition of the distance-normalized, Pearson-correlated cis matrix with cooltools (eigs_cis), then orients (phases) the compartment eigenvector against a GC or gene-density track so the active (A) sign is not arbitrary. Covers the eigenvector-is-a-choice problem (per-arm view_df to remove the centromere gradient; picking the eigenvector by max correlation with activity, not by eigenvalue), GC phasing with bioframe.frac_gc, resolution choice (100kb-1Mb), saddle plots and saddle_strength for compartmentalization strength, the cohesin-loss-strengthens-compartments result, subcompartments (SNIPER/Calder/dcHiC), and cross-condition compartment switching. Use when calling A/B compartments, computing E1/eigenvectors, phasing the eigenvector, building saddle plots, choosing a compartment resolution, quantifying compartment strength, or comparing compartmentalization across conditions.
What it loads into context
Frontmatter
304 tokensAlways available to the agent
SKILL.md
5,433 tokensWhen the skill is activated
Reference documents
1,222 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- E1 Is a Choice, Not an Output, and Its Sign Is Arbitrary Until Phased
- Method / Output Taxonomy
- Decision Tree by Scenario
- Per-Arm Eigenvector with GC Phasing
- Compartment Strength via Saddle Plot
- Compare Compartments Across Conditions
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/call_compartments.py
Files
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## Version Compatibility
Reference examples tested with: cooler 0.10+, cooltools 0.7+, bioframe 0.7+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
cooltools had a major API shift around 0.5 -> 0.7+ (functions standardized on `view_df`/viewframe arguments; `eigs_cis`, `expected_cis`, `saddle` signatures changed). The cooler MUST be balanced before any compartment analysis: `clr.matrix(balance=True)` requires a stored `weight` column. A `.mcool` is multi-resolution -- pass a single-resolution URI (`file.mcool::/resolutions/100000`), not the bare `.mcool`. The `phasing_track` MUST share the cooler's exact binning or phasing silently no-ops. If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
# A/B Compartment Analysis
**"Which regions of my genome are in the active vs inactive compartment?"** -> Distance-normalize the cis matrix, take an eigenvector of its Pearson correlation matrix, then orient it by GC/gene density so positive = A (active) -- but verify the kept eigenvector is the compartment one, not an arm gradient.
- Python: `cooltools.eigs_cis(clr, gc_track, view_df=arms, n_eigs=3, sort_metric='pearsonr')`
bio-hi-c-analysis-contact-pairs
When your agent uses it
Turns Hi-C/Micro-C FASTQ into a deduplicated, filtered .pairs file with pairtools and decides whether the library worked. Covers the bwa mem -SP5M / bwa-mem2 / chromap --preset hic alignment idiom (mates mapped as independent single-end reads), pairtools parse vs parse2 and the walks-policy choice (5unique pairwise vs all for Pore-C/Micro-C concatemers), pair-type classification (keep UU and rescued UC), dedup (PCR vs optical/by-tile), select by pair_type/MAPQ/distance, restriction-fragment handling (restrict, Arima dual-enzyme, Micro-C/DNase fragment-free), and allele-specific phasing (pairtools phase to two coolers). The library-QC decision uses % long-range cis as the one-number quality metric, trans as the noise floor, orientation balance as fragment-map-free dangling-end/self-circle QC, and % duplicates as a complexity proxy. Use when processing Hi-C/Micro-C/Omni-C reads into pairs, judging library quality, handling multi-enzyme or restriction-agnostic protocols, or generating allele-specific contacts.
What it loads into context
Frontmatter
330 tokensAlways available to the agent
SKILL.md
5,088 tokensWhen the skill is activated
Reference documents
1,390 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- The Read Count Is a Lie Until Pairs Are Classified; Library Quality Is Decided in pairtools, Not in the Aligner
- Aligner Taxonomy
- Decision Tree by Scenario
- Align: Mates as Independent Single-End Reads
- Parse, Sort, Dedup, Select: the pairtools Core
- Library QC: the Decision This Skill Owns
- Restriction Fragments: Opt-In, Not Default
- Allele-Specific Contacts: pairtools phase -> Two Coolers
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/fastq_to_pairs.sh
Files
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## Version Compatibility
Reference examples tested with: pairtools 1.1+, bwa 0.7.17+ (or bwa-mem2 2.2+), chromap 0.2+, samtools 1.19+, cooler 0.10+
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
pairtools defaults have shifted across releases (e.g. `parse --max-molecule-size` is 750 bp in 1.1.x; `dedup --backend` defaults to scipy). `parse` and `parse2` report DIFFERENT positions by default - confirm `--report-position` before mixing outputs. If a command errors, introspect with `pairtools <subcommand> --help` and adapt rather than retrying.
# Hi-C Contact Pairs
**"Turn my Hi-C reads into clean contacts and tell me if the library worked"** -> Align mates independently through ligation junctions, classify and deduplicate pairs to a 5'-canonical .pairs file, then read the cis/trans and orientation statistics to decide library quality before any matrix is built.
- CLI: `bwa mem -SP5M ref.fa R1.fq R2.fq | pairtools parse -c chrom.sizes | pairtools sort | pairtools dedup | pairtools stats`
bio-hi-c-analysis-hic-data-io
When your agent uses it
Loads, converts, and manipulates Hi-C contact matrices in cooler format (.cool/.mcool/.scool) and Juicer .hic, using cooler (Python + CLI), hic2cool, and hictk. Covers the single-resolution mcool URI (file.mcool::/resolutions/<bp>), the load-bearing divisive-vs-multiplicative weight-naming rule (KR/VC/VC_SQRT auto-divisive vs cooler's multiplicative weight), what survives .hic<->.cool conversion (FRAG matrices and norm vectors do not), raw-vs-balanced coarsening, the .pairs upper-triangle/chromsize-order contract, and chrom-naming/bin-table provenance. Use when loading a cooler, converting .hic to .mcool, selecting a resolution, building a cooler from pairs or a matrix, coarsening/zoomifying, importing Juicer norm vectors, or debugging all-NaN balanced matrices and chr1-vs-1 empty fetches.
What it loads into context
Frontmatter
303 tokensAlways available to the agent
SKILL.md
5,087 tokensWhen the skill is activated
Reference documents
1,220 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- cooler Stores Observed Counts; .hic Bakes In Norms, So Conversion Is Never Lossless Both Ways
- Format and Tool Taxonomy
- Decision Tree by Scenario
- Load a Cooler and Select a Resolution
- Fetch Balanced vs Raw Pixels
- Convert .hic to cooler (and Back)
- Build a Cooler from a Matrix (Vectorized)
- Coarsen Correctly (Sum Raw, Re-balance Per Resolution)
- Export, Merge, and Inspect
- Reference, Blacklist, and Chrom-Naming Provenance
- Multi-Way and Single-Cell Contacts (Decompose or Defer)
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/load_cooler.py
Files
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## Version Compatibility
Reference examples tested with: cooler 0.10+, hic2cool 1.0+, hictk 1.0+, bioframe 0.7+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Two version boundaries change BEHAVIOUR, not just signatures: hic2cool >= 0.5.0 stores Juicer norms un-inverted (divisive); < 0.5.0 inverted them to multiplicative, so two coolers made from one .hic by different hic2cool versions disagree numerically. cooler standardized creation/balance signatures around 0.8-0.10 (`balance_cooler` keyword-only after `clr`; `store=False` default). Record the converter version in provenance.
# Hi-C Data I/Obio-hi-c-analysis-hic-differential
When your agent uses it
Compares Hi-C contact maps between conditions across the right scale -- differential bin-pair contacts (multiHiCcompare, diffHic), differential A/B compartments (dcHiC), differential TAD boundaries (delta insulation), and differential loops (diffloop, DiffHiChIP) -- with distance-stratified between-sample normalization, replicate-aware NB-GLM FDR, HiCRep SCC reproducibility gating, and CNV correction for cancer/aneuploid samples. Use when comparing Hi-C between treatment and control, finding differential contacts/compartments/boundaries/loops, normalizing two maps of unequal depth, choosing a replicate-aware test, gating replicates with SCC, or correcting copy-number artifacts before a tumor-vs-normal comparison.
What it loads into context
Frontmatter
252 tokensAlways available to the agent
SKILL.md
5,736 tokensWhen the skill is activated
Reference documents
1,282 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- balancing makes a matrix self-consistent, NOT cross-comparable
- Differential-Method Taxonomy (scale-matched -- one tool cannot do all four)
- Decision Tree by Scenario
- Replicate QC Gate with HiCRep SCC (do this FIRST)
- Differential Bin-Pair Contacts with multiHiCcompare
- Differential A/B Compartments with dcHiC
- Differential TAD Boundaries via Delta Insulation
- CNV Correction Before Cancer Comparisons
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Files
SKILL.mdcore22,124 bytesSKILL.mdcore22,124 bytesSKILL.mdcore22,124 bytesSKILL.mdcore22,124 bytesreferences/usage-guide.mddoc5,520 bytesscripts/differential_contacts.Rasset3,854 bytes
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## Version Compatibility
Reference examples tested with: cooler 0.10+, cooltools 0.7+, bioframe 0.7+, multiHiCcompare 1.20+, diffHic 1.34+, dcHiC (2022 release), hicrep (Bioconductor) 1.26+, edgeR 4.0+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Note: cooltools provides the Python feature-extraction parts (expected, eigenvectors, insulation, pileups) but NO turnkey two-condition test -- the differential statistics live in R/Bioconductor (multiHiCcompare, diffHic, dcHiC, hicrep). The `get.scc` signature changed between the TaoYang-dev and current Bioconductor/qunhualilab releases; verify with `?get.scc` before calling. A `.mcool` is multi-resolution: pass a single-resolution URI (`file.mcool::/resolutions/10000`), not the bare file.
bio-hi-c-analysis-hic-visualization
When your agent uses it
Renders Hi-C contact matrices honestly and reproducibly with matplotlib, cooltools, HiCExplorer, pyGenomeTracks, FAN-C, CoolBox, and plotgardener. Covers the raw/ICE-balanced/observed-over-expected transform choice, LogNorm vs symmetric-diverging colormaps with vmax/percentile clipping, resolution-to-feature matching (compartments 100-500kb, TADs 10-40kb, loops 5-10kb), square vs rotated-triangle track-stacking, NaN/white-stripe handling, virtual 4C, APA/saddle/on-diagonal pileups, two-condition side-by-side and log2-ratio maps, and interactive (HiGlass) vs scripted-static publication figures. Use when plotting a contact matrix, choosing a normalization or color scale, building a multi-track Hi-C figure, making a virtual 4C profile, piling up loops/boundaries, or comparing two conditions.
What it loads into context
Frontmatter
291 tokensAlways available to the agent
SKILL.md
5,527 tokensWhen the skill is activated
Reference documents
1,242 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- The Colorscale Is Where Hi-C Figures Lie
- Transform Taxonomy
- Layout Taxonomy
- Decision Tree by Scenario
- Square Contact Map (Balanced, Log-Scaled, NaN Shown)
- Observed/Expected Divergent Map
- Rotated Triangle for Track-Stacking
- Virtual 4C from a Viewpoint
- APA Pileup over a Loop Set
- Two-Condition Comparison
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/plot_hic.py
Files
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## Version Compatibility
Reference examples tested with: cooler 0.10+, cooltools 0.7+, matplotlib 3.8+, bioframe 0.7+, HiCExplorer 3.7+, pyGenomeTracks 3.9+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Notes specific to this skill: `.mcool` is multi-resolution -- pass a single-resolution URI (`file.mcool::/resolutions/10000`), never the bare path. A cooler must be balanced (`cooler balance`) before `matrix(balance=True)` returns anything but NaN. `cooltools.pileup` returns a stack of shape `(n_features, D, D)` -- aggregate over `axis=0`. cooltools standardised on `view_df`/`expected_df` arguments around 0.7+; verify with `help(cooltools.pileup)` before chaining.
# Hi-C Visualizationbio-hi-c-analysis-hichip-plac-loops
When your agent uses it
Calls significant loops from protein-directed and targeted 3C assays (HiChIP, PLAC-seq, Capture Hi-C/PCHi-C, ChIA-PET) where the contact background is peak-anchored and coverage-biased, so generic Hi-C loop callers (cooltools dots, Juicer HiCCUPS) use the wrong null. Covers FitHiChIP (config-driven coverage+distance-decay spline regression, peak-to-peak vs peak-to-all foreground, loose vs stringent background, coverage vs ICE bias), MAPS (positive Poisson regression on bias factors for PLAC-seq/HiChIP), hichipper (restriction-site-distance bias model + library QC), CHiCAGO (Delaporte two-component Brownian+technical background for asymmetric bait x other-end Capture Hi-C), the with/without separate-ChIP anchor decision, and differential loops via diffloop. Use when calling loops from HiChIP/PLAC-seq/Capture Hi-C, choosing FitHiChIP/MAPS/CHiCAGO, picking peak-to-all vs peak-to-peak, setting the loop FDR, supplying ChIP peaks as anchors, QCing a HiChIP library, or comparing loops between conditions.
What it loads into context
Frontmatter
351 tokensAlways available to the agent
SKILL.md
5,764 tokensWhen the skill is activated
Reference documents
1,780 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Generic Hi-C Loop Callers Use the Wrong Null on Peak-Anchored Data
- Method Taxonomy
- Decision Tree by Scenario
- FitHiChIP - Coverage + Distance Spline Regression (default)
- MAPS - Positive Poisson Regression on Bias Factors
- CHiCAGO - Two-Component Background for Capture Hi-C
- Differential Loops with diffloop
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/run_fithichip.sh
Files
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## Version Compatibility
Reference examples tested with: FitHiChIP 11.0+, MAPS 1.1+, hichipper 0.7+, CHiCAGO 1.20+ (Bioconductor 3.18+), HiC-Pro 3.1+, diffloop 1.18+ (Bioconductor 3.18+).
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
- FitHiChIP: read the shipped `configfile` comments; parameter names and defaults change between releases
- R: `packageVersion('diffloop')`/`packageVersion('Chicago')` then `?function` to check signatures
FitHiChIP is driven entirely by a key=value config file passed with `-C`; the loop caller, background, and bias model are set there, not on the command line. MAPS, hichipper, and CHiCAGO each expect a specific upstream format (HiC-Pro valid pairs / .allValidPairs, or HiCUP+capture design files for CHiCAGO). If a tool errors, introspect the installed version's config/help and adapt the example rather than retrying.
# Protein-Directed and Targeted 3C Loop Calling
**"My HiChIP/PLAC-seq/Capture Hi-C has loops anchored at CTCF/H3K27ac/promoters - which contacts are real?"** -> Call loops with a method whose null jointly models the per-anchor coverage bias AND the distance-decay, not the uniform/donut background that generic Hi-C callers assume.bio-hi-c-analysis-loop-calling
When your agent uses it
Detects focal chromatin loops (point interactions / corner-dots) in balanced Hi-C and Micro-C contact maps and aggregates/validates a loop set. Covers de-novo calling with cooltools dots (HiCCUPS-style 4-background local enrichment with lambda-chunked FDR), chromosight (template-correlation), and Mustache (scale-space blob detection); aggregate peak analysis (APA) via cooltools pileup for confirmation; the depth/resolution prerequisite (de-novo needs ~5-10kb resolution = hundreds of millions to billions of valid pairs); consensus across callers and convergent-CTCF support as validation; and differential loops via union anchors plus chromosight quantify. Use when calling chromatin loops or dots from a cooler, deciding whether a map is deep enough to call de-novo vs running APA on known CTCF/cohesin anchors, building an aggregate peak pileup, comparing loops across conditions, or validating loop calls. For HiChIP/PLAC-seq/PCHi-C protein-anchored data use FitHiChIP/MAPS, not dots.
What it loads into context
Frontmatter
320 tokensAlways available to the agent
SKILL.md
5,786 tokensWhen the skill is activated
Reference documents
1,358 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Loop Calling Is Depth-Limited, Not Algorithm-Limited; the First Question Is "How Deep Is the Map?"
- Loop-Caller Taxonomy
- Decision Tree by Scenario
- De-Novo Loop Calling with cooltools dots
- Template-Matching with chromosight
- Scale-Space with Mustache
- Aggregate Peak Analysis (APA) -- Confirm, Don't Discover
- Differential Loops -- Union Anchors, Not a Bin-Level Tool
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/call_loops.py
Files
SKILL.mdcore22,304 bytesSKILL.mdcore22,304 bytesSKILL.mdcore22,304 bytesSKILL.mdcore22,304 bytesSKILL.mdcore22,304 bytesSKILL.mdcore22,304 bytesreferences/usage-guide.mddoc5,584 bytesscripts/call_loops.pyruns2,594 bytes
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## Version Compatibility
Reference examples tested with: cooltools 0.7+, cooler 0.10+, bioframe 0.7+, chromosight 1.6+, mustache 1.3+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
The `.cool` must be BALANCED before calling loops -- `dots`/`pileup` read the `weight` column and raw counts are unsupported. An `.mcool` is multi-resolution; pass a single-resolution URI (`file.mcool::/resolutions/10000`), not the bare `.mcool`. cooltools changed signatures around 0.5 -> 0.7 (view_df/expected_value_col conventions); verify `help(cooltools.dots)` for the installed version. The `view_df` passed to `expected_cis` MUST be the same one passed to `dots`/`pileup`.
# Chromatin Loop Callingbio-hi-c-analysis-matrix-operations
When your agent uses it
Balances Hi-C contact matrices (ICE via cooler.balance_cooler, KR/SCALE/VC context), computes distance-decay expected with cooltools (expected_cis per-diagonal P(s), expected_trans scalar), builds observed/expected (O/E) matrices, and diagnoses polymer state from the P(s) log-derivative. Covers the within-matrix-vs-cross-sample distinction (balancing is NOT a normalizer), the equal-visibility assumption that CNV/aneuploidy violates (use raw counts for copy-number), cis-only balancing, mad_max/blacklist masking before balancing, multiplicative cooler weights vs divisive juicer weights, and the resolution-vs-depth budget. Use when balancing a .cool/.mcool, computing expected or P(s), making O/E matrices for compartments/loops, deciding ICE vs KR vs SCALE, choosing a resolution for a given depth, or troubleshooting NaN/all-NaN balanced matrices; route cross-sample comparison to hic-differential.
What it loads into context
Frontmatter
301 tokensAlways available to the agent
SKILL.md
4,931 tokensWhen the skill is activated
Reference documents
1,221 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Balancing Makes ONE Matrix Self-Consistent; It Does NOT Make Two Matrices Comparable
- Normalization-Method Taxonomy
- Decision Tree by Scenario
- Balance a Matrix (ICE)
- Expected: cis P(s) Curve and trans Scalar
- Observed/Expected Matrix
- P(s) Log-Derivative -- the Polymer-State Diagnostic
- Resolution-vs-Depth Budget
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/balance_and_oe.py
Files
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## Version Compatibility
Reference examples tested with: cooler 0.10+, cooltools 0.7+, bioframe 0.7+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
cooltools standardized its API around 0.7 (functions take a `view_df` viewframe; `expected_cis` defaults to `smooth=True, aggregate_smoothed=True`). `.mcool` is multi-resolution: analysis functions take a single-resolution URI (`file.mcool::/resolutions/10000`), never the bare `.mcool`. A matrix must be balanced (a stored `weight` column) before O/E, compartments, insulation, or dots; `clr.matrix(balance=True)` on an unbalanced cooler returns all-NaN.
# Hi-C Matrix Operationsbio-hi-c-analysis-tad-detection
When your agent uses it
Detects TAD boundaries from balanced Hi-C contact matrices via the diamond-window insulation score (cooltools insulation) and HiCExplorer hicFindTADs, returning a continuous log2 insulation track, valley-prominence boundary_strength, and Li/Otsu-thresholded is_boundary flags across a list of window sizes. Covers the multi-scale window sweep (sub-TAD to compartment-domain), why the boundary is reproducible but the domain partition is not, cross-condition comparison via differential SCORE not differential partition, and the insulation-vs-compartment orthogonality. Use when calling TADs or domain boundaries, computing insulation scores, choosing a window size, ranking boundary strength, comparing boundaries across conditions, or annotating CTCF-backed boundaries; route domain rendering to hic-visualization and boundary-feature overlap to genome-intervals.
What it loads into context
Frontmatter
265 tokensAlways available to the agent
SKILL.md
4,839 tokensWhen the skill is activated
Reference documents
1,173 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- The Boundary Is Real; the Domain Is Mostly an Averaging Artifact
- TAD-Caller Taxonomy
- Decision Tree by Scenario
- Insulation Score and Boundaries (multi-scale)
- Domains and FDR with hicFindTADs (CLI)
- Cross-Condition: Differential SCORE, Never Differential Partition
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/call_tads.py
Files
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## Version Compatibility
Reference examples tested with: cooler 0.10+, cooltools 0.7+, bioframe 0.7+, HiCExplorer 3.7+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
cooltools changed its API around 0.5 -> 0.7+ (functions standardized on `view_df`/viewframe arguments; `insulation` returns the `boundary_strength_{W}`/`is_boundary_{W}` columns). A `.cool` MUST be balanced (a stored `weight` column) before insulation; `clr.matrix(balance=True)` on an unbalanced cooler returns all-NaN. A `.mcool` is multi-resolution: pass a single-resolution URI (`file.mcool::/resolutions/10000`), never the bare `.mcool`.
# TAD Detectionbio-imaging-mass-cytometry-cell-segmentation
When your agent uses it
Segment single cells from multiplexed IMC/MIBI tissue images using Mesmer/DeepCell, Cellpose, or ilastik+CellProfiler, covering whole-cell vs nuclear segmentation, the summed-membrane-channel decision, nuclear-expansion bias, lateral spillover, resolution-floor parameters, and downstream-proxy evaluation. Use when delineating cells after preprocessing, choosing a segmentation model, building a cell mask for quantification, diagnosing impossible double-positive populations, or troubleshooting over/under-segmentation.
What it loads into context
Frontmatter
206 tokensAlways available to the agent
SKILL.md
3,488 tokensWhen the skill is activated
Reference documents
761 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- segmentation is the largest irreversible error source, not a preprocessing step
- Methods Landscape
- Decision Tree by Scenario
- Whole-Cell Segmentation with Mesmer
- Build the Summed Membrane Channel
- Orchestrate via steinbock
- Nuclear Segmentation with Constrained Expansion (fallback)
- Per-Tool Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/segment_cells.py
Files
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## Version Compatibility
Reference examples tested with: steinbock 0.16+, DeepCell 0.12+ (Mesmer), Cellpose 3.0+, numpy 1.26+, scikit-image 0.22+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Notes specific to this skill: Mesmer expects `(batch, y, x, 2)` with channel 0 = nuclear, channel 1 = membrane, and `image_mpp` set to the TRUE acquisition resolution (~1.0 for IMC) -- it was trained at `model_mpp ~= 0.5` and rescales the input, so a wrong mpp degrades everything. Cellpose `cyto` trains at 30-px and `nuclei` at 17-px diameter; steinbock feeds nuclear-first (reversed vs native Cellpose). Recent steinbock cellpose containers default to the `cpsam` (Cellpose-SAM) model -- pin the version.
# Cell Segmentation for IMCbio-imaging-mass-cytometry-data-preprocessing
When your agent uses it
Load and preprocess imaging mass cytometry (IMC) and MIBI data from raw MCD/TXT through hot-pixel removal, spillover compensation, and variance-stabilizing transformation, covering readimc/steinbock ingestion, NNLS spillover compensation (CATALYST), IMC-Denoise, and the IMC arcsinh-cofactor question. Use when starting analysis from raw MCD files, building per-channel TIFF stacks, compensating channel spillover, choosing an arcsinh cofactor, or preparing single-cell intensities for phenotyping.
What it loads into context
Frontmatter
220 tokensAlways available to the agent
SKILL.md
4,190 tokensWhen the skill is activated
Reference documents
852 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- IMC pixels are ion counts, and non-negativity is physics, not a preference
- Preprocessing Pipeline Order (load-bearing)
- Denoising Taxonomy
- Decision Tree by Scenario
- Read Raw Acquisitions
- Extract and Hot-Pixel Filter with steinbock
- Spillover Compensation (NNLS)
- Transform and Normalize
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/preprocess_imc.py
Files
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## Version Compatibility
Reference examples tested with: steinbock 0.16+, readimc 0.7+, numpy 1.26+, CATALYST 1.28+ (R/Bioconductor), cytomapper 1.16+ (R)
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Notes specific to this skill: IMC pixels are integer ion COUNTS, not fluorescence intensities. `CATALYST::compCytof` defaults to `method='nnls'` (non-negativity preserved); pass `cofactor=1` explicitly for IMC single-cell means (the compCytof default is `NULL`/5, the suspension value). The spillover-matrix and SCE channel names must both be `(metal)(mass)Di` (e.g. `Sm152Di`) or compensation silently no-ops. steinbock's hot-pixel filter is `steinbock preprocess imc images --hpf 50` (a signed 8-neighbor difference, not a median filter).
bio-imaging-mass-cytometry-differential-analysis
When your agent uses it
Compare cell-type composition and spatial features across conditions in IMC/MIBI cohorts with the patient as the experimental unit, covering pseudoreplication, per-patient aggregation, mixed models, compositional (Dirichlet/scCODA) differential abundance, diffcyt, per-image-to-patient spatial differential testing (SpaceANOVA), batch covariates, and FDR. Use when testing whether a cell type or spatial niche differs between groups, avoiding cell-level pseudoreplication, choosing a differential-abundance method, or correctly powering an IMC cohort comparison.
What it loads into context
Frontmatter
221 tokensAlways available to the agent
SKILL.md
3,376 tokensWhen the skill is activated
Reference documents
810 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- the replicate is the patient, not the cell, and the million-cell count is a red herring
- Differential Question Taxonomy
- Decision Tree by Scenario
- Aggregate to the Patient (the spine)
- Differential Abundance with a Mixed Model
- Compositional Differential Abundance (scCODA)
- Differential Spatial Feature
- Differential State Within a Type
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/differential_abundance.py
Files
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## Version Compatibility
Reference examples tested with: diffcyt 1.22+ (R), lme4 1.1+ (R), statsmodels 0.14+, scanpy 1.10+, sccoda 0.1.9+, numpy 1.26+, pandas 2.2+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Notes specific to this skill: cell-type proportions are compositional (they sum to 1), so a real increase in one type forces apparent decreases in others -- a Dirichlet/CLR-aware method (scCODA) or a reference cell type is needed, not independent per-type tests. statsmodels `mixedlm` fits patient as a random effect. diffcyt operates on per-sample cluster counts (DA) and per-sample median marker expression (DS).
# IMC Differential Analysisbio-imaging-mass-cytometry-interactive-annotation
When your agent uses it
Interactive cell annotation and image QC for IMC/MIBI using napari, napari-imc, Mantis Viewer, and cytomapper, covering the pixels-to-cell-table bridge, overlaying masks to catch segmentation/spillover artifacts, inter-annotator variability as the accuracy ceiling, contrast-as-threshold, and building class-balanced ground-truth label sets. Use when manually labeling cells, generating training data for a classifier, QC-ing segmentation on the image, confirming clusters are spatially real, or choosing an annotation viewer.
What it loads into context
Frontmatter
212 tokensAlways available to the agent
SKILL.md
2,963 tokensWhen the skill is activated
Reference documents
752 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- annotation is the pixels-to-cell-table bridge, and the image vetoes the table
- Viewer Landscape
- Decision Tree by Scenario
- Open Raw IMC and Overlay the Mask
- Paint Clusters Back onto Tissue
- Build a Class-Balanced Ground-Truth Set
- Per-Trap Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/napari_annotation.py
Files
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Read SKILL.md
## Version Compatibility
Reference examples tested with: napari 0.4.18+, napari-imc 0.7+, numpy 1.26+, scikit-learn 1.4+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Notes specific to this skill: core napari cannot open `.mcd` -- the `napari-imc` plugin reads raw Fluidigm/Standard BioTools files in machine coordinates. napari `add_labels` edits integer masks (segmentation QC); `add_points` with a `features` table holds per-cell categorical labels. Mantis Viewer is a standalone Electron app from CANDELbio/Parker Institute (NOT the Bodenmiller group). napari has no journal paper -- cite the Zenodo DOI.
# Interactive Annotation
bio-imaging-mass-cytometry-phenotyping
When your agent uses it
Assign cell types from marker expression in IMC/MIBI data using clustering (PhenoGraph/FlowSOM/Leiden/Pixie), marker-based probabilistic classifiers (Astir), or image-context CNNs (CellSighter), covering the double-positive segmentation artifact, lineage-vs-state markers, the two spillover types, and why a "cell type" in imaging is conditioned on a segmentation guess. Use when phenotyping segmented IMC cells, choosing clustering vs classification, diagnosing implausible double-positive populations, separating lineage from functional markers, or transferring labels across a cohort.
What it loads into context
Frontmatter
223 tokensAlways available to the agent
SKILL.md
3,459 tokensWhen the skill is activated
Reference documents
782 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- a "cell type" in imaging is an inference conditioned on a segmentation guess
- Phenotyping Approach Taxonomy
- Decision Tree by Scenario
- Load and Transform
- Marker-Based Classification with Astir
- Cluster on Lineage Markers Only
- Diagnose Double-Positive Populations
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/phenotype_cells.py
Files
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## Version Compatibility
Reference examples tested with: scanpy 1.10+, anndata 0.10+, astir 0.1.4+, numpy 1.26+, scikit-learn 1.4+, FlowSOM 2.10+ (R)
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Notes specific to this skill: arcsinh cofactor for IMC single-cell means is ~1, not the suspension-CyTOF 5 -- do not hard-code 5. Astir assigns a per-cell probability and routes below-threshold cells (default 0.7) to "Unknown" rather than forcing a call. CellSighter consumes raw multi-channel image crops + masks (not a mean matrix). FlowSOM consensus metaclustering can override `set.seed()` via ConsensusClusterPlus.
# Cell Phenotyping for IMCbio-imaging-mass-cytometry-quality-metrics
When your agent uses it
Quality control for IMC/MIBI data across pixel, channel, image, slide, and batch levels, covering Poisson-count SNR (cell-level Gaussian-mixture and empty-channel comparison), spillover-matrix QC (the three physical sources), drift and the missing EQ-bead analog, acquisition artifacts, and sample-of-origin batch effects. Use when deciding whether to keep or drop a channel, ROI, or slide, distinguishing a dim antibody from a failed one, reading a spillover matrix, or diagnosing batch-driven clustering before analysis.
What it loads into context
Frontmatter
213 tokensAlways available to the agent
SKILL.md
3,624 tokensWhen the skill is activated
Reference documents
795 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- QC is multi-level, and every metric is blind at some level
- Multi-Level QC Framework
- Decision Tree by Scenario
- Cell-Level SNR (the decision-relevant number)
- Spillover Matrix QC
- Batch / Sample-of-Origin QC
- Per-Source Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/run_qc.py
Files
SKILL.mdcore14,757 bytesSKILL.mdcore14,757 bytesSKILL.mdcore14,757 bytesreferences/usage-guide.mddoc3,593 bytesscripts/run_qc.pyruns2,937 bytes
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## Version Compatibility
Reference examples tested with: numpy 1.26+, scikit-learn 1.4+, scanpy 1.10+, CATALYST 1.28+ (R), spillR 1.0+ (R)
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Notes specific to this skill: IMC values are integer ion (dual) counts, so SNR must respect Poisson statistics, not fluorescence intuition; biology lives in 1-2 count differences. There is no EQ-bead in-line drift normalizer for ablated tissue. CATALYST `normCytof` is for SUSPENSION bead normalization, not IMC images -- do not apply it to image data. Compensate raw pixels before transformation.
# IMC Quality Metricsbio-imaging-mass-cytometry-spatial-analysis
When your agent uses it
Analyze spatial cell-cell interactions, neighborhoods, and niches in IMC/MIBI data with squidpy and imcRtools, covering neighborhood-enrichment permutation nulls, the abundance-vs-density confound, inhomogeneous Ripley's K, cellular-neighborhood discovery, graph-construction (contact vs proximity), and edge effects. Use when testing whether cell types co-locate, choosing a spatial null, building a neighbor graph, discovering tissue niches, or deciding whether a spatial pattern is real or a density/segmentation artifact.
What it loads into context
Frontmatter
210 tokensAlways available to the agent
SKILL.md
3,473 tokensWhen the skill is activated
Reference documents
759 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- a spatial interaction is a hypothesis test, and the null silently decides what is measured
- Spatial Methods Taxonomy
- Decision Tree by Scenario
- Build the Spatial Graph (contact vs proximity)
- Neighborhood Enrichment with a Named Null
- Cellular Neighborhoods (niche discovery)
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/spatial_analysis.py
Files
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## Version Compatibility
Reference examples tested with: squidpy 1.3+, scanpy 1.10+, anndata 0.10+, numpy 1.26+, imcRtools 1.8+ (R), spatstat 3.0+ (R)
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Notes specific to this skill: squidpy's `nhood_enrichment` z-score is unbounded and scales with graph degree, so it is NOT comparable across images of different cell counts. squidpy does not implement Ripley's K (its `ripley` and `co_occurrence` are loose analogs); the inhomogeneous K that conditions on tissue density lives in R `spatstat::Kinhom`/`Kcross.inhom`. Build the graph with `gr.spatial_neighbors(coord_type='generic')` before any test.
# Spatial Analysis for IMCbio-immunoinformatics-epitope-prediction
When your agent uses it
Predict B-cell and T-cell epitopes for vaccine antigen design and epitope mapping with BepiPred-3.0, DiscoTope-3.0, the IEDB tools, and EL-mode MHC presentation. Encodes the load-bearing asymmetry that T-cell epitope prediction is mature (it reduces to MHC presentation, AUC>0.9) while B-cell prediction is unreliable (linear predictors ~AUC 0.6 because ~90% of real epitopes are conformational) — so structure-based DiscoTope-3.0 on AlphaFold models is the only defensible B-cell path, propensity scales are obsolete, and NetChop is largely redundant on EL-trained models. Use when mapping epitopes or selecting vaccine antigens. MHC binding lives in mhc-binding-prediction.
What it loads into context
Frontmatter
275 tokensAlways available to the agent
SKILL.md
3,468 tokensWhen the skill is activated
Reference documents
884 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- "epitope prediction" is two fields at different maturity, wrongly conflated
- Tool Taxonomy
- Decision Tree by Scenario
- Predict Linear B-Cell Epitopes (BepiPred-3.0)
- Predict Conformational B-Cell Epitopes (DiscoTope-3.0)
- T-Cell Epitopes Reduce to MHC Presentation
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/epitope_prediction.py
Files
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## Version Compatibility
Reference examples tested with: BepiPred-3.0, pandas 2.2+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Notes specific to this skill: BepiPred-3.0 ships as the `bepipred3` package and auto-downloads ESM-2 weights on first run; its default threshold is 0.1512 (NOT 0.5). DiscoTope-3.0, ElliPro, SEPPA, NetChop, and NetCTLpan are standalone/web (IEDB or DTU). The IEDB classic and next-generation REST APIs wrap most predictors. Re-verify thresholds and the supported-method list against current docs.
# Epitope Predictionbio-immunoinformatics-immunogenicity-scoring
When your agent uses it
Rank and prioritize neoantigen/epitope candidates by likely T-cell response using NeoFox feature annotation, PRIME2.0, BigMHC-IM, the Łuksza/Balachandran fitness model (agretopicity + foreignness), and pVACtools tiering. Encodes the field's hard truths that immunogenicity is the least-solved layer (dedicated scores ~AUROC 0.6-0.7, modest PPV), that scores are valid only for RANKING within one patient (never absolute go/no-go or cross-patient), that DAI has anchor-inflation and WT-denominator traps, and that stacking weak correlated scores into one number is a red flag. Use when ordering a candidate list for a vaccine. Binding lives in mhc-binding-prediction; calling in neoantigen-prediction.
What it loads into context
Frontmatter
279 tokensAlways available to the agent
SKILL.md
3,633 tokensWhen the skill is activated
Reference documents
821 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- this is the least-solved layer; rank within a patient, never threshold
- Why "Best Binder" Lost to "Best Quality"
- Tool Taxonomy
- Decision Tree by Scenario
- Annotate Features, Then Rank Within Patient
- Compute Agretopicity (DAI) Defensively
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/immunogenicity_scoring.py
Files
SKILL.mdcore14,559 bytesSKILL.mdcore14,559 bytesSKILL.mdcore14,559 bytesSKILL.mdcore14,559 bytesreferences/usage-guide.mddoc3,501 bytesscripts/immunogenicity_scoring.pyruns2,733 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: NeoFox 1.0+, pVACtools 4.1+, pandas 2.2+, numpy 1.26+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Notes specific to this skill: NeoFox annotates ~16 published features (it does not rank candidates automatically); PRIME 2.x requires MixMHCpred v3.0+ on PATH; BigMHC has separate `-m el` and `-m im` heads. ImmunoBERT is a PRESENTATION model, not an immunogenicity predictor — do not use it here. PRIME2.0 is the *Cell Systems* 2023 paper (the *Cell Reports Medicine* 2021 paper is PRIME v1). Re-verify tool versions and the supported-allele lists before scoring.
# Immunogenicity Scoringbio-immunoinformatics-mhc-binding-prediction
When your agent uses it
Predict peptide-MHC class I binding and natural presentation with MHCflurry, NetMHCpan-4.1, and MixMHCpred to nominate candidate CD8 T-cell epitopes. Covers the binding-affinity (BA) vs eluted-ligand (EL/presentation) distinction, why %Rank beats raw nM for cross-allele work, the MS abundance bias that misranks low-expression neoantigens, allele-coverage inequity, and length bias. Use when scanning a protein or peptide set for class I epitopes, scoring neoantigen candidates, or choosing a binding predictor. For CD4/HLA class II see mhc-class-ii-prediction.
What it loads into context
Frontmatter
247 tokensAlways available to the agent
SKILL.md
3,565 tokensWhen the skill is activated
Reference documents
879 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- a strong predicted binder is a candidate for the next experiment, not an epitope
- Tool Taxonomy (Class I)
- BA vs EL -- the conceptual axis that determines which score to read
- Decision Tree by Scenario
- Predict Presentation with MHCflurry
- Interpret with %Rank, Not Raw nM
- Scan a Protein for Class I Epitopes
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/mhc_binding.py
Files
SKILL.mdcore14,113 bytesSKILL.mdcore14,113 bytesSKILL.mdcore14,113 bytesSKILL.mdcore14,113 bytesSKILL.mdcore14,113 bytesreferences/usage-guide.mddoc3,480 bytesscripts/mhc_binding.pyruns3,846 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: MHCflurry 2.1+, pandas 2.2+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Notes specific to this skill: MHCflurry 2.2.0+ switched its backend from TensorFlow to PyTorch (Python 3.10+) — confirm `mhcflurry-downloads fetch` succeeded and the backend imports before scoring. NetMHCpan-4.1 and MixMHCpred are standalone academic binaries, not pip-installable; the IEDB REST API wraps NetMHCpan if a local install is unavailable. Tool versions move fast — re-verify supported-allele lists and default %Rank thresholds against current docs.
# MHC Binding Predictionbio-immunoinformatics-mhc-class-ii-prediction
When your agent uses it
Predict peptide-MHC class II (HLA-DR/DQ/DP) binding and presentation for CD4 T-cell epitopes with NetMHCIIpan-4.3 and MixMHC2pred-2.0. Covers why class II is far less reliable than class I (open binding groove, 9-mer register ambiguity, sparse noisy training data, DR>DP>DQ accuracy asymmetry), the DQ/DP heterodimer alpha/beta pairing trap, and the looser 1%/5% %Rank thresholds. Use when predicting CD4 epitopes for vaccine help, mapping class II neoantigens, or scoring long peptides against DR/DQ/DP. For CD8/class I see mhc-binding-prediction.
What it loads into context
Frontmatter
261 tokensAlways available to the agent
SKILL.md
3,161 tokensWhen the skill is activated
Reference documents
901 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- class II is basically broken, and that must be stated plainly
- The DQ/DP heterodimer pairing trap
- Tool Taxonomy (Class II)
- Decision Tree by Scenario
- Running the Predictions (the fragile commands - run as written)
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/class_ii_pairing.pyscripts/run_class_ii.sh
Files
SKILL.mdcore11,589 bytesSKILL.mdcore11,589 bytesSKILL.mdcore11,589 bytesreferences/usage-guide.mddoc3,246 bytesscripts/class_ii_pairing.pyruns2,587 bytesscripts/run_class_ii.shruns1,291 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: NetMHCIIpan 4.3+, MixMHC2pred 2.0+, pandas 2.2+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Notes specific to this skill: NetMHCIIpan and MixMHC2pred are standalone academic binaries (not pip-installable); the IEDB MHC-II REST API wraps NetMHCIIpan if a local install is unavailable. Allele nomenclature differs sharply between tools and between isotypes (DR single-chain vs DQ/DP heterodimer) — confirm the exact string format against the installed tool before scoring. Class II %Rank thresholds (1%/5%) are LOOSER than class I (0.5%/2.0%); do not copy class I cutoffs.
# MHC Class II Predictionbio-immunoinformatics-neoantigen-prediction
When your agent uses it
Identify tumor neoantigens from somatic variants with pVACtools (pVACseq/pVACfuse/pVACbind/pVACvector/pVACview) for personalized cancer vaccines and checkpoint biomarkers. Encodes the field's hard truth that binding prediction is the easy, near-solved part and single-digit-percent PPV lives downstream — so it centers clonality/CCF, HLA LOH (the silent invalidator), expression, proximal-variant phasing, agretopicity/foreignness quality, and the predicted->presented->immunogenic validation tiers. Use when nominating vaccine targets, ranking neoantigens, or building a tumor-to-candidate pipeline. Binding details in mhc-binding-prediction; ranking in immunogenicity-scoring.
What it loads into context
Frontmatter
259 tokensAlways available to the agent
SKILL.md
4,209 tokensWhen the skill is activated
Reference documents
919 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- binding is the easy part; PPV lives downstream
- The pVACtools Suite
- Upstream Chain (every link can silently poison the output)
- Decision Tree by Scenario
- Run VEP, Then pVACseq
- Compute Agretopicity (DAI) Correctly
- Drop Candidates on Lost HLA Alleles (LOHHLA)
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/neoantigen_prediction.py
Files
SKILL.mdcore15,723 bytesSKILL.mdcore15,723 bytesSKILL.mdcore15,723 bytesSKILL.mdcore15,723 bytesreferences/usage-guide.mddoc3,611 bytesscripts/neoantigen_prediction.pyruns3,539 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: Ensembl VEP 111+, pVACtools 4.1+, MHCflurry 2.1+, VAtools 5+, pandas 2.2+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Notes specific to this skill: pVACtools is now at 7.x; positional CLI args are stable across 4.x-7.x but defaults and the supported-algorithm list change between releases — always run `pvacseq run --help` against the installed build. pVACseq requires the Wildtype and Frameshift VEP plugins; the Downstream plugin was replaced by Frameshift in pVACtools 2.0, so 4.x+ pipelines must NOT use Downstream. A local IEDB install (`--iedb-install-directory`) is strongly preferred over the rate-limited public API for patient data.
# Neoantigen Predictionbio-immunoinformatics-tcr-epitope-binding
When your agent uses it
Infer or annotate TCR antigen specificity by unsupervised clustering (TCRdist/tcrdist3, GLIPH2, clusTCR, GIANA) and database lookup (VDJdb, IEDB, McPAS-TCR), and rank candidates with supervised predictors (ERGO-II, NetTCR-2.x, pMTnet) under explicit caveats. Encodes the central truth that general TCR-epitope prediction for UNSEEN epitopes essentially does not work (collapses to near-random; IMMREP22, Grazioli 2022) because labeled data is dominated by a few immunodominant epitopes and there is no true negative set — so clustering for discovery is the honest task and de-novo binding needs wet-lab validation. Use when annotating TCR specificity or grouping a repertoire. Epitope/MHC context lives in mhc-binding-prediction.
What it loads into context
Frontmatter
294 tokensAlways available to the agent
SKILL.md
3,798 tokensWhen the skill is activated
Reference documents
833 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- general prediction for unseen epitopes does not work; clustering does
- Tool Taxonomy
- Reference Databases (training set AND lookup table)
- Decision Tree by Scenario
- Cluster TCRs by Specificity (tcrdist3)
- Annotate by Database Lookup
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/tcr_epitope.py
Files
SKILL.mdcore14,192 bytesSKILL.mdcore14,192 bytesSKILL.mdcore14,192 bytesSKILL.mdcore14,192 bytesreferences/usage-guide.mddoc3,386 bytesscripts/tcr_epitope.pyruns2,985 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: tcrdist3 0.2+, pandas 2.2+, scipy 1.12+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Notes specific to this skill: tcrdist3 expects IMGT-style columns (`cdr3_b_aa`, `v_b_gene`, `j_b_gene`, and the `_a_` analogs, plus `count`). Tools disagree on whether CDR3 keeps the leading Cys / trailing Phe-Trp — a common silent input mismatch. Supervised predictors (ERGO-II, NetTCR, pMTnet) are separate repos with pretrained weights; their reported AUCs depend heavily on the train/test split and negative-sampling scheme. Re-verify before trusting any number.
# TCR-Epitope Bindingbio-analytical-validation
When your agent uses it
Treats a ctDNA assay as a molecule-counting experiment at the Poisson edge and builds its analytical-validation case the measurement-science way. Covers the genome-equivalent currency (~330 haploid copies/ng), the lambda = input_GE x VAF sampling ceiling (lambda>=3 for ~95% detection), the error-suppression ladder (raw NGS ~1e-3 -> single-strand UMI ~1e-4/1e-5 -> duplex <1e-7), the CLSI EP17 LoB/LoD/LoD95/LoQ framework, the per-locus-vs-panel-integrated LoD distinction that lets bespoke MRD reach ppm, contrived/SEQC2 reference standards, and honest LoD reporting conditioned on input mass + consensus depth + replicate detection rate. Use when stating or trusting a sensitivity claim, designing a dilution-series validation, deciding how many genome equivalents are needed at a target VAF, choosing a single-locus vs panel-integrated LoD, or auditing a "detects 0.1% VAF" claim.
What it loads into context
Frontmatter
316 tokensAlways available to the agent
SKILL.md
4,481 tokensWhen the skill is activated
Reference documents
910 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- LoD Is Set by Genome Equivalents Sampled and Error Suppression, NOT by Sequencing Depth or the Caller
- Methods Landscape
- Decision Tree by Scenario
- Genome-Equivalent and Poisson Detection Calculator
- LoB and LoD95 from a Dilution Series (CLSI EP17 style)
- Per-Locus to Panel-Integrated LoD
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/detection_limits.py
Files
SKILL.mdcore16,942 bytesSKILL.mdcore16,942 bytesSKILL.mdcore16,942 bytesSKILL.mdcore16,942 bytesSKILL.mdcore16,942 bytesreferences/usage-guide.mddoc3,796 bytesscripts/detection_limits.pyruns4,075 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: numpy 1.26+, scipy 1.12+, statsmodels 0.14+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Analytical Validation and Detection Limits
**"What is the real limit of detection of my ctDNA assay, and can I trust the number I am about to report?"** -> Quantify the Poisson sampling ceiling, the error-suppression floor, and the LoB/LoD/LoQ that together define a defensible sensitivity claim.
- Python: `scipy.stats.poisson` for detection-probability math, `scipy.stats.norm` for CLSI LoB/LoD, `statsmodels` Probit/Logit for a dilution-series LoD95 fit.bio-cfdna-preprocessing
When your agent uses it
Decides how to preprocess plasma cfDNA sequencing data so the recoverable signal survives - library-prep-aware fragment expectations (dsDNA vs ssDNA/adaptase prep), UMI/duplex consensus with fgbio (ExtractUmisFromBam, GroupReadsByUmi --strategy paired for duplex, CallMolecularConsensusReads vs CallDuplexConsensusReads, FilterConsensusReads min-reads "total s1 s2"), the align->group->consensus->RE-align ordering, and the cfDNA dedup trap where naive coordinate dedup collapses nucleosome-coincident independent molecules. Covers when single-strand consensus suffices vs when duplex is mandatory, the singleton/sensitivity tax at low input, and reading the insert-size histogram as a pre-analytical QC instrument. Use when processing plasma cfDNA reads before fragmentomics, ctDNA mutation calling, or tumor-fraction estimation.
What it loads into context
Frontmatter
278 tokensAlways available to the agent
SKILL.md
4,784 tokensWhen the skill is activated
Reference documents
943 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Pre-Analytics and Library Prep Set the Ceiling; Consensus Is Error Suppression, Not Just Dedup
- Library-Prep and Consensus Landscape
- Decision Tree by Scenario
- The fgbio Consensus Pipeline
- Reading the Insert-Size Histogram (QC)
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/preprocess_cfdna.py
Files
SKILL.mdcore18,894 bytesSKILL.mdcore18,894 bytesSKILL.mdcore18,894 bytesSKILL.mdcore18,894 bytesreferences/usage-guide.mddoc4,127 bytesscripts/preprocess_cfdna.pyruns5,327 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: bwa 0.7.17+, fgbio 2.1+, numpy 1.26+, pysam 0.22+, samtools 1.19+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Notes specific to this skill: fgbio flag semantics drift across major versions - in `CallDuplexConsensusReads` the `--min-reads` is a permissive PRE-filter (fgbio issue #1009), and in `FilterConsensusReads` the `--min-reads` (`-M`) takes up to three values "total strand1 strand2" where if values two and three differ the more stringent value must come first. Confirm both against the installed `fgbio --help` before scripting.
# cfDNA Preprocessingbio-ctdna-mutation-detection
When your agent uses it
Detects somatic mutations in circulating tumor DNA, treating low-VAF detection as a signal-versus-noise problem set by error suppression and molecules sampled, not by the choice of caller. Distinguishes de novo CALLING (scanning a panel for unknown variants, bounded by per-locus error and multiple testing) from tumor-informed DETECTION (tracking a pre-specified variant set, where panel integration reaches single-ppm). Covers VarDict and Mutect2 for de novo calling, UMI-aware callers, and a pysam-based known-variant VAF tracker, with matched-WBC subtraction as the mandatory defense against clonal hematopoiesis (the dominant false positive). Use when calling or tracking tumor mutations from plasma cfDNA, setting a VAF threshold, or deciding whether a low-VAF call is tumor versus CHIP.
What it loads into context
Frontmatter
267 tokensAlways available to the agent
SKILL.md
5,539 tokensWhen the skill is activated
Reference documents
950 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- detection is a signal-vs-noise problem, and tracking is not the same problem as calling
- Methods Landscape
- Decision Tree by Scenario
- CHIP -- the dominant false positive, not background
- De Novo Calling with VarDict
- De Novo Calling with Mutect2 and the Orientation-Bias Model
- Track Known Mutations Across Serial Samples
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/detect_ctdna_mutations.py
Files
SKILL.mdcore20,696 bytesSKILL.mdcore20,696 bytesSKILL.mdcore20,696 bytesSKILL.mdcore20,696 bytesSKILL.mdcore20,696 bytesreferences/usage-guide.mddoc3,988 bytesscripts/detect_ctdna_mutations.pyruns4,894 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: pysam 0.22+, pandas 2.2+, VarDictJava 1.8+, GATK 4.5+, Ensembl VEP 111+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Notes specific to this skill: VarDict's `-c -S -E -g` are 1-based BED COLUMN INDICES, not genomic coordinates; var2vcf_valid.pl's `-E` suppresses the END tag (opposite meaning to VarDict's `-E`). VEP gnomAD flags are `--af_gnomade` (exomes)/`--af_gnomadg` (genomes); the bare `--af_gnomad` is a legacy alias that returns only exome AF, so prefer the explicit forms.
# ctDNA Mutation Detectionbio-fragment-analysis
When your agent uses it
Extracts cfDNA fragmentomics features (DELFI genome-wide short/long ratios, WPS nucleosome positioning, Griffin GC-corrected accessibility profiles, end-motifs/MDS, OCF) for cancer detection and tissue-of-origin from plasma WGS. Centers on the nuclease-footprint reframe (every feature re-reads one nucleosome object), the mandatory GC correction, and the cross-protocol non-comparability that breaks naive classifiers. Runs FinaleToolkit (real CLI/Python, MIT) and the Griffin Snakemake pipeline; DELFI is a method, not a package. Use when deriving fragment-based signal from cfDNA, choosing a feature family for detection vs subtyping, or diagnosing why a fragmentomic model failed validation.
What it loads into context
Frontmatter
245 tokensAlways available to the agent
SKILL.md
4,383 tokensWhen the skill is activated
Reference documents
807 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- Fragmentomics measures nucleosome positioning, not sequence; the biology is real but it can be destroyed in the wet lab or left un-GC-corrected
- Methods / Feature-Family Landscape
- Decision Tree by Scenario
- Genome-Wide Short/Long Ratio (custom DELFI-style)
- GC-Corrected DELFI Score (FinaleToolkit)
- Griffin Nucleosome Profiling (Snakemake pipeline)
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/fragment_analysis.py
Files
SKILL.mdcore17,365 bytesSKILL.mdcore17,365 bytesSKILL.mdcore17,365 bytesSKILL.mdcore17,365 bytesSKILL.mdcore17,365 bytesSKILL.mdcore17,365 bytesreferences/usage-guide.mddoc3,495 bytesscripts/fragment_analysis.pyruns5,359 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: numpy 1.26+, pandas 2.2+, pysam 0.22+, finaletoolkit 0.7+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Notes specific to this skill: FinaleToolkit CLI subcommands are hyphenated (`frag-length-bins`, `end-motifs`, `delfi-gc-correct`); the Python functions are underscored in `finaletoolkit.frag`. The filter subcommand is `filter-file`, NOT `filter-bam`. Griffin is a Snakemake pipeline, not an importable function. DELFI is a methodology and a company (DELFI Diagnostics), not a `pip install`-able tool.
# Fragment Analysisbio-longitudinal-monitoring
When your agent uses it
Tracks ctDNA across serial liquid-biopsy timepoints for molecular residual disease (MRD) and treatment-response monitoring, treating MRD as a binary integrated detection call across the patient's full variant set (with a defined LoD95 and per-sample specificity) rather than a per-timepoint VAF threshold, and handling undetectable samples as left-censored at the per-sample limit of detection rather than true zeros. Covers tumor-informed bespoke vs tumor-naive design, landmark vs surveillance sampling, molecular-response definitions and their non-standardization, censoring-aware clearance kinetics, and the multiple-testing structure of repeated surveillance. Use when monitoring ctDNA during therapy, calling molecular relapse before imaging, or estimating clearance half-life from serial samples.
What it loads into context
Frontmatter
241 tokensAlways available to the agent
SKILL.md
5,099 tokensWhen the skill is activated
Reference documents
847 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- MRD is a binary integrated detection call, and "undetectable" is left-censored, not zero
- Design Decision: tumor-informed vs tumor-naive, landmark vs surveillance
- ctDNA Kinetics Biology -- why timing and shedding gate interpretation
- Molecular-Response Definitions -- and the non-standardization caveat
- Clinical Evidence and Lead Time
- Tumor-Fraction Trend with Baseline and Nadir
- Mutation Tracking and Censoring-Aware Clearance Kinetics
- Molecular-Relapse Calling
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/longitudinal_monitoring.py
Files
SKILL.mdcore20,797 bytesSKILL.mdcore20,797 bytesSKILL.mdcore20,797 bytesSKILL.mdcore20,797 bytesSKILL.mdcore20,797 bytesreferences/usage-guide.mddoc3,851 bytesscripts/longitudinal_monitoring.pyruns6,786 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: numpy 1.26+, pandas 2.2+, scipy 1.12+, matplotlib 3.8+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Notes specific to this skill: this skill is statistical, not tool-bound. The hard parts are interpretive (left-censoring, multiple testing, lead-time bias), not API calls. `scipy.stats.linregress` returns a named tuple whose `.slope`/`.pvalue` attributes are stable across recent versions; the censoring-aware fit below uses only `linregress` on the uncensored decay phase plus a manual interval check, so version drift is low-risk.
# Longitudinal Monitoring
bio-methylation-based-detection
When your agent uses it
Detects cancer and infers tissue-of-origin from cfDNA methylation by choosing conversion chemistry (bisulfite vs EM-seq vs TAPS vs cfMeDIP), calling read-level methylation haplotypes rather than averaged beta values, and deconvolving a hematopoietic-dominated cfDNA mixture against a methylation atlas via NNLS/quadratic programming. Encodes the GRAIL/CCGA thesis that thousands of tissue-specific markers make methylation outperform sparse mutations for multi-cancer early detection (MCED) and localization, and that single concordantly-methylated fragments give ppm-level sensitivity. Uses MethylDackel for extraction (mbias-then-extract), MEDIPS/QSEA for enrichment data, scipy.optimize.nnls for deconvolution. Use when building an MCED or methylation-MRD assay, picking a conversion chemistry for low-input plasma, or deconvolving tissue-of-origin from cfDNA.
What it loads into context
Frontmatter
291 tokensAlways available to the agent
SKILL.md
4,141 tokensWhen the skill is activated
Reference documents
930 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- methylation beats mutations, and read-level haplotypes beat averaged beta
- Conversion Chemistry Tradeoffs
- Decision Tree by Scenario
- Extract Per-CpG Methylation with MethylDackel
- Tissue-of-Origin Deconvolution Against an Atlas
- Region-Level DMR Discovery
- cfMeDIP Enrichment Analysis
- Per-Method Failure Modes
- Quantitative Thresholds
- References
- Related Skills
Scripts executed separately
scripts/cfdna_methylation.py
Files
SKILL.mdcore16,490 bytesSKILL.mdcore16,490 bytesSKILL.mdcore16,490 bytesSKILL.mdcore16,490 bytesreferences/usage-guide.mddoc3,878 bytesscripts/cfdna_methylation.pyruns4,099 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: MethylDackel 0.6+, Bismark 0.24+, numpy 1.26+, pandas 2.2+, scipy 1.12+, statsmodels 0.14+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Notes specific to this skill: MethylDackel `extract` bedGraph column order is fixed (chrom / start / end / methylation-% rounded to integer / count-methylated / count-unmethylated); always run `MethylDackel mbias` first and feed its suggested `--OT/--OB` trimming into `extract`. cfMeDIP data are coverage, not conversion — do not feed them into per-CpG bisulfite pipelines.
# Methylation-Based Detectionbio-tumor-fraction-estimation
When your agent uses it
Estimates tumor fraction (the genome-wide proportion of cfDNA molecules that are tumor-derived, the cfDNA analogue of bulk-tumor purity) from shallow whole-genome sequencing with ichorCNA, an HMM over 1 Mb bins that jointly EM-estimates tumor fraction, ploidy, and subclonal prevalence over a normal/ploidy grid. Encodes the load-bearing reframes: tumor fraction is the quantity that travels across assays and is NOT mutation VAF (clonal-het VAF approximately TF/2), CNA-based estimation has a hard ~3 percent limit-of-detection floor, and near-diploid or copy-neutral-LOH genomes return a falsely low value. Selects the estimator by data type (sWGS to ichorCNA, deep panel to max-VAF, methylation to deconvolution, sub-3 percent to fragmentomics or methylation). Use when quantifying tumor burden from a liquid biopsy, picking a tumor-fraction estimator for a given assay, or reconciling a TF estimate against a panel VAF.
What it loads into context
Frontmatter
318 tokensAlways available to the agent
SKILL.md
4,402 tokensWhen the skill is activated
Reference documents
959 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- tumor fraction is the quantity that travels across assays; it is NOT VAF and it is blind below ~3 percent
- Estimator Landscape
- Decision Tree by Data Type
- ichorCNA Mechanics
- Unit Confusion -- TF vs VAF vs ctDNA%
- Per-Method Failure Modes
- Quantitative Thresholds
- References
- Related Skills
Scripts executed separately
scripts/estimate_tumor_fraction.R
Files
SKILL.mdcore15,528 bytesSKILL.mdcore15,528 bytesreferences/usage-guide.mddoc3,671 bytesscripts/estimate_tumor_fraction.Rruns3,551 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: ichorCNA 0.6.0+ (GavinHaLab fork), HMMcopy 1.40+, R 4.2+
Before using code patterns, verify installed versions match. If versions differ:
- R: `packageVersion('<pkg>')` then `?function_name` to verify parameters
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
Notes specific to this skill: ichorCNA is NOT an importable R function `runIchorCNA()` — it is a command-line script invoked as `Rscript scripts/runIchorCNA.R` with `optparse` flags, preceded by HMMcopy `readCounter` to build the WIG. Use the GavinHaLab fork (v0.6.0, 22 Nov 2024) for new work; the original broadinstitute/ichorCNA holds the wiki. The flag `--repTimeWig` does not exist — do not invent it.
# Tumor Fraction Estimationbio-long-read-sequencing-basecalling
When your agent uses it
Basecalls raw Oxford Nanopore signal (POD5/FAST5) into reads with Dorado, choosing the chemistry-matched model and accuracy tier (fast/hac/sup), requesting modified bases (5mCG_5hmCG, 6mA, m6A) at basecall time, and handling duplex, demultiplexing, trimming, and HERRO read correction. Covers why the model+version is an irreversible analysis decision, why methylation cannot be recovered later, and why downstream polish/variant models must match the basecaller. Use when converting POD5/FAST5 to reads, picking a Dorado model for R9/R10 or RNA004, enabling methylation calling, basecalling duplex, demultiplexing barcoded runs, or correcting reads for assembly.
What it loads into context
Frontmatter
262 tokensAlways available to the agent
SKILL.md
3,905 tokensWhen the skill is activated
Reference documents
1,140 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Version Compatibility
- The Single Most Important Modern Insight -- There Is No "The Reads," Only "The Reads As Called By This Model"
- Dorado Subcommand Taxonomy
- Model Naming Scheme (load-bearing)
- Decision Tree by Scenario
- Core Commands
- Per-Method Failure Modes
- Quantitative Thresholds
- Common Errors
- References
- Related Skills
Scripts executed separately
scripts/basecall_pipeline.shscripts/dorado_basecall.sh
Files
SKILL.mdcore14,270 bytesSKILL.mdcore14,270 bytesSKILL.mdcore14,270 bytesreferences/usage-guide.mddoc4,428 bytesscripts/basecall_pipeline.shruns1,864 bytesscripts/dorado_basecall.shruns1,725 bytes
Read SKILL.md
## Version Compatibility
Reference examples tested with: Dorado 1.0+, pod5 0.3+, samtools 1.19+, chopper 0.7+.
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
Results depend on inputs that outlive the binary version - record them:
- The basecaller MODEL string (e.g. `dna_r10.4.1_e8.2_400bps_sup@v5.2.0`) sets the entire error profile and must be propagated to every downstream tool. Pin it.
- Modified-base models carry a SECOND version (`..._sup@v5.0.0_5mCG_5hmCG@v3`); the mod version can lag the simplex version - check `dorado download --list`.
- R9.4.1 and RNA002 models were removed from Dorado v1.0 defaults; legacy data needs an archived model path.
If code throws an error, introspect the installed tool (`dorado --help`, `dorado basecaller --help`) and adapt the example to the actual API rather than retrying.
Skills included
- admet_genetic
- alphafold2
- audit-dataset
- bioprobench
- bio-alignment-io
- bio-alignment-trimming
- bio-alignment-amplicon-clipping
- bio-alignment-filtering
- bio-alignment-indexing
- bio-alignment-sorting
- bio-alignment-validation
- bio-bam-statistics
- bio-duplicate-handling
- bio-pileup-generation
- bio-reference-operations
- bio-sam-bam-basics
- bio-alignment-msa-parsing
- bio-alignment-msa-statistics
- bio-alignment-multiple
- bio-alignment-pairwise
- bio-alignment-structural
- bio-differential-splicing
- bio-isoform-switching
- bio-long-read-splicing
- bio-outlier-splicing-detection
- bio-sashimi-plots
- bio-single-cell-splicing
- bio-splice-variant-prediction
- bio-splicing-qc
- bio-splicing-quantification
- bio-atac-seq-allele-specific-accessibility
- bio-atac-seq-atac-peak-calling
- bio-atac-seq-atac-qc
- bio-atac-seq-co-accessibility
- bio-atac-seq-consensus-peakset
- bio-atac-seq-deep-learning-atac
- bio-atac-seq-differential-accessibility
- bio-atac-seq-enhancer-gene-linking
- bio-atac-seq-footprinting
- bio-atac-seq-motif-deviation
- bio-atac-seq-nucleosome-positioning
- bio-atac-seq-single-cell-atac
- bio-causal-genomics-colocalization-analysis
- bio-causal-genomics-effector-gene-prioritization
- bio-causal-genomics-fine-mapping
- bio-causal-genomics-genetic-correlation
- bio-causal-genomics-genomic-sem
- bio-causal-genomics-heritability-partitioning
- bio-causal-genomics-mediation-analysis
- bio-causal-genomics-mendelian-randomization
- bio-causal-genomics-pleiotropy-detection
- bio-causal-genomics-proteome-mr-drug-target
- bio-causal-genomics-transcriptome-wide-association
- bio-admet-prediction
- bio-conformer-generation
- bio-covalent-design
- bio-free-energy-calculations
- bio-generative-design
- bio-ml-docking-rescoring
- bio-molecular-descriptors
- bio-molecular-io
- bio-molecular-standardization
- bio-pharmacophore-modeling
- bio-pose-validation
- bio-protac-degraders
- bio-qsar-modeling
- bio-reaction-enumeration
- bio-retrosynthesis
- bio-scaffold-analysis
- bio-shape-similarity
- bio-similarity-searching
- bio-substructure-search
- bio-virtual-screening
- bio-chipseq-allele-specific-binding
- bio-chipseq-chip-deep-learning
- bio-chipseq-qc
- bio-chipseq-visualization
- bio-chipseq-chromatin-state-segmentation
- bio-chipseq-cut-and-run-tag
- bio-chipseq-differential-binding
- bio-chipseq-motif-analysis
- bio-chipseq-peak-annotation
- bio-chipseq-peak-calling
- bio-chipseq-spike-in-normalization
- bio-chipseq-super-enhancers
- bio-clinical-biostatistics-adaptive-designs
- bio-clinical-biostatistics-bayesian-trials
- bio-clinical-biostatistics-categorical-tests
- bio-clinical-biostatistics-cdisc-data
- bio-clinical-biostatistics-effect-measures
- bio-clinical-biostatistics-logistic-regression
- bio-clinical-biostatistics-missing-data
- bio-clinical-biostatistics-multiplicity-graphical
- bio-clinical-biostatistics-power-sample-size
- bio-clinical-biostatistics-subgroup-analysis
- bio-clinical-biostatistics-survival-analysis
- bio-clinical-biostatistics-trial-reporting
- bio-clinical-databases-acmg-classification
- bio-clinical-databases-clinvar-lookup
- bio-clinical-databases-dbsnp-queries
- bio-clinical-databases-gnomad-frequencies
- bio-clinical-databases-hla-typing
- bio-clinical-databases-msi-detection
- bio-clinical-databases-myvariant-queries
- bio-clinical-databases-pharmacogenomics
- bio-clinical-databases-polygenic-risk
- bio-clinical-databases-somatic-signatures
- bio-clinical-databases-tumor-mutational-burden
- bio-clinical-databases-variant-prioritization
- bio-clip-seq-ago-clip-mirna-targets
- bio-clip-seq-binding-site-annotation
- bio-clip-seq-clip-alignment
- bio-clip-seq-clip-deep-learning
- bio-clip-seq-clip-motif-analysis
- bio-clip-seq-clip-peak-calling
- bio-clip-seq-clip-preprocessing
- bio-clip-seq-clip-qc
- bio-clip-seq-crosslink-site-detection
- bio-clip-seq-differential-clip
- bio-clip-seq-m6a-clip
- bio-clip-seq-stamp-antibody-free
- bio-comparative-genomics-ancestral-reconstruction
- bio-comparative-genomics-comparative-annotation-projection
- bio-comparative-genomics-gene-family-evolution
- bio-comparative-genomics-gene-tree-species-tree-reconciliation
- bio-comparative-genomics-genome-distance-and-species-delineation
- bio-comparative-genomics-hgt-detection
- bio-comparative-genomics-introgression-detection
- bio-comparative-genomics-ortholog-inference
- bio-comparative-genomics-pangenome-analysis
- bio-comparative-genomics-positive-selection
- bio-comparative-genomics-synteny-analysis
- bio-comparative-genomics-whole-genome-alignment
- bio-comparative-genomics-whole-genome-duplication
- bio-copy-number-allele-specific-copy-number
- bio-copy-number-cnv-annotation
- bio-copy-number-cnv-visualization
- bio-copy-number-cnvkit-analysis
- bio-copy-number-copy-ratio-segmentation
- bio-copy-number-focal-amplification-ecdna
- bio-copy-number-gatk-cnv
- bio-copy-number-germline-cnv-interpretation
- bio-copy-number-hrd-scoring
- bio-copy-number-recurrent-cnv
- bio-copy-number-subclonal-copy-number
- bio-crispr-screens-bagel-essentiality
- bio-crispr-screens-base-editing-analysis
- bio-crispr-screens-batch-correction
- bio-crispr-screens-combinatorial-screens
- bio-crispr-screens-copy-number-correction
- bio-crispr-screens-crispresso-editing
- bio-crispr-screens-drugz-chemogenomic
- bio-crispr-screens-hit-calling
- bio-crispr-screens-in-vivo-screens
- bio-crispr-screens-jacks-analysis
- bio-crispr-screens-library-design
- bio-crispr-screens-mageck-analysis
- bio-crispr-screens-perturb-seq-analysis
- bio-crispr-screens-prime-editing-screens
- bio-crispr-screens-screen-qc
- bio-data-visualization-circos-plots
- bio-data-visualization-color-palettes
- bio-data-visualization-dimensionality-reduction-plots
- bio-data-visualization-distribution-plots
- bio-data-visualization-flow-and-transition-plots
- bio-data-visualization-forest-funnel-plots
- bio-data-visualization-genome-tracks
- bio-data-visualization-ggplot2-fundamentals
- bio-data-visualization-heatmaps-clustering
- bio-data-visualization-interactive-visualization
- bio-data-visualization-lollipop-protein-maps
- bio-data-visualization-manhattan-qq-locuszoom
- bio-data-visualization-matplotlib-fundamentals
- bio-data-visualization-multipanel-figures
- bio-data-visualization-network-visualization
- bio-data-visualization-oncoprint-mutation-matrices
- bio-data-visualization-sequence-logos
- bio-data-visualization-statistical-annotation
- bio-data-visualization-upset-plots
- bio-data-visualization-volcano-and-ma-plots
- bio-batch-downloads
- bio-biomart-queries
- bio-blast-searches
- bio-ensembl-rest
- bio-entrez-fetch
- bio-entrez-link
- bio-entrez-search
- bio-geo-data
- bio-interaction-databases
- bio-local-blast
- bio-ncbi-datasets-cli
- bio-ortholog-inference
- bio-remote-homology
- bio-sra-data
- bio-uniprot-access
- bio-differential-expression-batch-correction
- bio-differential-expression-de-results
- bio-differential-expression-de-visualization
- bio-differential-expression-deseq2-basics
- bio-differential-expression-edger-basics
- bio-differential-expression-timeseries-de
- bio-ecological-genomics-biodiversity-metrics
- bio-ecological-genomics-community-ecology
- bio-ecological-genomics-conservation-genetics
- bio-ecological-genomics-edna-metabarcoding
- bio-ecological-genomics-landscape-genomics
- bio-ecological-genomics-species-delimitation
- bio-epidemiological-genomics-amr-surveillance
- bio-epidemiological-genomics-pathogen-typing
- bio-epidemiological-genomics-phylodynamics
- bio-epidemiological-genomics-transmission-inference
- bio-epidemiological-genomics-variant-surveillance
- bio-epitranscriptomics-m6a-differential
- bio-epitranscriptomics-m6a-peak-calling
- bio-epitranscriptomics-m6anet-analysis
- bio-epitranscriptomics-merip-preprocessing
- bio-epitranscriptomics-modification-visualization
- bio-experimental-design-batch-design
- bio-experimental-design-multiple-testing
- bio-experimental-design-power-analysis
- bio-experimental-design-randomization-blocking
- bio-experimental-design-sample-size
- bio-expression-matrix-counts-ingest
- bio-expression-matrix-gene-id-mapping
- bio-expression-matrix-metadata-joins
- bio-expression-matrix-normalization
- bio-expression-matrix-sparse-handling
- bio-flow-cytometry-bead-normalization
- bio-flow-cytometry-clustering-phenotyping
- bio-flow-cytometry-compensation-transformation
- bio-flow-cytometry-cytometry-qc
- bio-flow-cytometry-differential-analysis
- bio-flow-cytometry-doublet-detection
- bio-flow-cytometry-fcs-handling
- bio-flow-cytometry-gating-analysis
- bio-gene-regulatory-networks-coexpression-networks
- bio-gene-regulatory-networks-differential-networks
- bio-gene-regulatory-networks-grn-inference
- bio-gene-regulatory-networks-multiomics-grn
- bio-gene-regulatory-networks-perturbation-simulation
- bio-gene-regulatory-networks-scenic-regulons
- bio-genome-annotation-annotation-qc
- bio-genome-annotation-annotation-transfer
- bio-genome-annotation-eukaryotic-gene-prediction
- bio-genome-annotation-functional-annotation
- bio-genome-annotation-ncrna-annotation
- bio-genome-annotation-prokaryotic-annotation
- bio-genome-annotation-repeat-annotation
- bio-genome-assembly-assembly-polishing
- bio-genome-assembly-assembly-qc
- bio-genome-assembly-contamination-detection
- bio-genome-assembly-genome-profiling
- bio-genome-assembly-hifi-assembly
- bio-genome-assembly-long-read-assembly
- bio-genome-assembly-metagenome-assembly
- bio-genome-assembly-scaffolding
- bio-genome-assembly-short-read-assembly
- bio-genome-engineering-base-editing-design
- bio-genome-engineering-grna-design
- bio-genome-engineering-hdr-template-design
- bio-genome-engineering-off-target-prediction
- bio-genome-engineering-prime-editing-design
- bio-genome-intervals-bed-file-basics
- bio-genome-intervals-bedgraph-handling
- bio-genome-intervals-bigwig-tracks
- bio-genome-intervals-coverage-analysis
- bio-genome-intervals-gtf-gff-handling
- bio-genome-intervals-interval-arithmetic
- bio-genome-intervals-overlap-significance
- bio-genome-intervals-proximity-operations
- bio-hi-c-analysis-compartment-analysis
- bio-hi-c-analysis-contact-pairs
- bio-hi-c-analysis-hic-data-io
- bio-hi-c-analysis-hic-differential
- bio-hi-c-analysis-hic-visualization
- bio-hi-c-analysis-hichip-plac-loops
- bio-hi-c-analysis-loop-calling
- bio-hi-c-analysis-matrix-operations
- bio-hi-c-analysis-tad-detection
- bio-imaging-mass-cytometry-cell-segmentation
- bio-imaging-mass-cytometry-data-preprocessing
- bio-imaging-mass-cytometry-differential-analysis
- bio-imaging-mass-cytometry-interactive-annotation
- bio-imaging-mass-cytometry-phenotyping
- bio-imaging-mass-cytometry-quality-metrics
- bio-imaging-mass-cytometry-spatial-analysis
- bio-immunoinformatics-epitope-prediction
- bio-immunoinformatics-immunogenicity-scoring
- bio-immunoinformatics-mhc-binding-prediction
- bio-immunoinformatics-mhc-class-ii-prediction
- bio-immunoinformatics-neoantigen-prediction
- bio-immunoinformatics-tcr-epitope-binding
- bio-analytical-validation
- bio-cfdna-preprocessing
- bio-ctdna-mutation-detection
- bio-fragment-analysis
- bio-longitudinal-monitoring
- bio-methylation-based-detection
- bio-tumor-fraction-estimation
- bio-long-read-sequencing-basecalling
Showing 300 of 612 skills. See GitHub for the complete repository.
Repository facts
- License
- MIT
- Created
- Jul 6, 2026
- Last pushed
- Nov 3, 2026
- Forks
- 78
- Open issues
- 0
- Project activity
- Active
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Before you install
Network or broad shellAutomated signals, not a guarantee
Checked Nov 4, 2026
Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.
Show 3,636 notes
skills/admet_genetic/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/admet_genetic/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/admet_genetic/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/admet_genetic/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/admet_genetic/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/admet_genetic/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/admet_genetic/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/admet_genetic/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/admet_genetic/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/admet_genetic/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedskills/admet_genetic/examples/candidates_final.csv:2Analysis incomplete: physical or joined line exceeds character limitskills/admet_genetic/examples/candidates_final.csv:1Analysis incomplete: physical or joined line exceeds character limitskills/admet_genetic/examples/candidates_final.csv:3Analysis incomplete: physical or joined line exceeds character limitskills/admet_genetic/examples/candidates_final.csv:4Analysis incomplete: physical or joined line exceeds character limitskills/admet_genetic/examples/candidates_final.csv:5Analysis incomplete: physical or joined line exceeds character limitskills/admet_genetic/examples/generation_log.csv:2Analysis incomplete: physical or joined line exceeds character limitskills/admet_genetic/examples/generation_log.csv:1Analysis incomplete: physical or joined line exceeds character limitskills/admet_genetic/examples/generation_log.csv:3Analysis incomplete: physical or joined line exceeds character limitskills/admet_genetic/examples/generation_log.csv:4Analysis incomplete: physical or joined line exceeds character limitskills/admet_genetic/examples/generation_log.csv:5Analysis incomplete: physical or joined line exceeds character limit
- No broad tool permissions
- 41 dangerous shell commandsin 37 files: bio-clinical-databases-tumor-mutational-burden/SKILL.md:423 bio-crispr-screens-copy-number-correction/SKILL.md:146 bio-database-access-sra-data/scripts/prefetch_large.sh:52 and 34 more
- 4,328 network referencesin 971 files: skills/retrosynthesis_planning/reaction_model_deployment.py:67 bio-database-access-uniprot-access/SKILL.md:9 bio-database-access-ensembl-rest/SKILL.md:9 and 968 more
- No instruction overrides
- 725 executable scriptsin 718 files: skills/retrosynthesis_planning/atom_mapping_benchmark.py:1 skills/retrosynthesis_planning/condition_benchmark.py:1 skills/retrosynthesis_planning/forward_benchmark.py:1 and 715 more
- 101 references to secretsin 41 files: remote-compute-nvidia/provider.py:11 remote-compute-nvidia/SKILL.md:22 bioprobench/kernel.py:743 and 38 more
- No requests to vote in Hot
Show all 5,200 signals by file
skills/admet_genetic/README.md
- Network accessline 31
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 36
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 37
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/admet_genetic/README_zh.md
- Network accessline 20
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 25
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/admet_genetic/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/admet_genetic/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何东西需要安装——wh
skills/admet_genetic/SKILL.md
- Network accessline 18
provider: Greenstone Biosciences license: MIT terms_url: https://github.com/swansonk14/admet_ai/blob/main/LICENSE.txt - Network accessline 19
license: MIT terms_url: https://github.com/swansonk14/admet_ai/blob/main/LICENSE.txt --- - Network accessline 20
terms_url: https://github.com/swansonk14/admet_ai/blob/main/LICENSE.txt
skills/admet_genetic/examples/build_example.py
- Executable scriptline 1
#!/usr/bin/env python
skills/admet_genetic/kernel.py
- Executable scriptline 1
#!/usr/bin/env python - Access to secretsline 56
[Credential-related source omitted] - Access to secretsline 57
[Credential-related source omitted] - Access to secretsline 58
[Credential-related source omitted] - Access to secretsline 59
[Credential-related source omitted] - Network accessline 268
return ( f'<svg viewBox="0 0 {width} {height}" role="img" ' 'aria-label="Structure unavailable" xmlns="http://www.w3.org/2000/svg">' - Network accessline 269
f'<svg viewBox="0 0 {width} {height}" role="img" ' 'aria-label="Structure unavailable" xmlns="http://www.w3.org/2000/svg">' f'<rect width="{width}" height="{height}" fill="#f8fafc"/>' - Network accessline 270
'aria-label="Structure unavailable" xmlns="http://www.w3.org/2000/svg">'
skills/alphafold2/README.md
- Network accessline 33
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 38
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 39
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/alphafold2/README_zh.md
- Network accessline 22
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 27
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/alphafold2/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/alphafold2/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何东西需要安装——wheel 自带
skills/alphafold2/SKILL.md
- Network accessline 26
provider: Google DeepMind license: CC-BY-4.0 terms_url: https://github.com/google-deepmind/alphafold#model-parameters-license - Network accessline 27
license: CC-BY-4.0 terms_url: https://github.com/google-deepmind/alphafold#model-parameters-license # api.colabfold.com has no published ToS or privacy policy. The GitHub - Network accessline 28
terms_url: https://github.com/google-deepmind/alphafold#model-parameters-license - Network accessline 33
name: ColabFold MSA server (api.colabfold.com) provider: Steinegger Lab info_url: https://github.com/sokrypton/ColabFold/wiki - Network accessline 34
provider: Steinegger Lab info_url: https://github.com/sokrypton/ColabFold/wiki --- - Network accessline 35
info_url: https://github.com/sokrypton/ColabFold/wiki
skills/audit-dataset/README.md
- Network accessline 29
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 34
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 35
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/audit-dataset/README_zh.md
- Network accessline 18
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 23
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/audit-dataset/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/audit-dataset/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何东西需要安装——wh
skills/audit-dataset/kernel.py
- Executable scriptline 1
Executable file
skills/bioprobench/README.md
- Network accessline 31
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 36
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 37
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/bioprobench/README_zh.md
- Network accessline 20
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 25
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/bioprobench/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/bioprobench/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何东西需要安装——wheel
skills/bioprobench/SKILL.md
- Network accessline 13
metadata: display-name: BioProBench upstream: https://github.com/YuyangSunshine/bioprotocolbench - Network accessline 14
display-name: BioProBench upstream: https://github.com/YuyangSunshine/bioprotocolbench --- - Network accessline 15
upstream: https://github.com/YuyangSunshine/bioprotocolbench - Network accessline 80
Only the two-record sample above ships here. Download the full benchmark from Hugging Face (<https://huggingface.co/BioProBench>) into your working directory - Network accessline 81
Hugging Face (<https://huggingface.co/BioProBench>) into your working directory - Access to secretsline 156
[Credential-related source omitted] - Network accessline 177
[Credential-related source omitted] - Network accessline 178
[Credential-related source omitted] - Access to secretsline 178
[Credential-related source omitted] - Network accessline 179
base_url="https://api.deepseek.com", - Network accessline 211
Adapted from the upstream BioProBench project (<https://github.com/YuyangSunshine/bioprotocolbench>). If you use this - Network accessline 212
(<https://github.com/YuyangSunshine/bioprotocolbench>). If you use this - Network accessline 218
author={Liu, Yuyang and Lv, Liuzhenghao and Zhang, Xiancheng and Yuan, Li and Tian, Yonghong}, journal={ICML}, url={https://github.com/YuyangSunshine/bioprotocolbench/tree/main}, - Network accessline 219
journal={ICML}, url={https://github.com/YuyangSunshine/bioprotocolbench/tree/main}, year={2026} - Network accessline 220
url={https://github.com/YuyangSunshine/bioprotocolbench/tree/main},
skills/bioprobench/Scripts/LLM-as-a-judge_for_REA-ERR.py
- Network accessline 1
# Adapted from the upstream BioProBench project: https://github.com/YuyangSunshine/bioprotocolbench - Executable scriptline 1
Executable file - Network accessline 14
[Credential-related source omitted] - Access to secretsline 14
[Credential-related source omitted] - Network accessline 15
BASE_URL = "https://api.deepseek.com" - Access to secretsline 29
[Credential-related source omitted]
skills/bioprobench/Scripts/README.md
- Network accessline 3
The upstream inference and LLM-judge scripts from the BioProBench project (<https://github.com/YuyangSunshine/bioprotocolbench>), adapted here with only a one-line provenance comment added at the top of each file.
skills/bioprobench/Scripts/README_zh.md
- Network accessline 3
来自 BioProBench 上游项目（<https://github.com/YuyangSunshine/bioprotocolbench>）的推理与 LLM 裁判脚本，此处仅在每个文件顶部加了一行来源说明注释，其余原样沿用。
skills/bioprobench/Scripts/generate_response.py
- Network accessline 1
# Adapted from the upstream BioProBench project: https://github.com/YuyangSunshine/bioprotocolbench - Executable scriptline 1
Executable file - Network accessline 14
[Credential-related source omitted] - Access to secretsline 14
[Credential-related source omitted] - Network accessline 15
BASE_URL = "https://api.openai.com/v1" # Replace with your base URL if different - Access to secretsline 27
[Credential-related source omitted]
skills/bioprobench/Scripts/generate_response_local.py
skills/bioprobench/Scripts/prompt_format.py
skills/bioprobench/data/README.md
- Network accessline 3
The full BioProBench test set is roughly 5,000 structured instances across five tasks. To keep this repository lightweight it is not hosted here — download it from Hugging Face (<https://huggingface.co/BioProBench>) into your working direct
skills/bioprobench/data/README_zh.md
- Network accessline 3
完整的 BioProBench 测试集约有 5,000 条结构化实例，覆盖五个任务。为了保持仓库轻量，这里不托管完整数据——请先从 Hugging Face（<https://huggingface.co/BioProBench>）下载到工作目录，再开始正式评测。
skills/bioprobench/kernel.py
- Executable scriptline 1
Executable file - Network accessline 742
[Credential-related source omitted] - Network accessline 743
[Credential-related source omitted] - Access to secretsline 743
[Credential-related source omitted] - Network accessline 744
base_url: str = "https://api.deepseek.com", - Access to secretsline 754
[Credential-related source omitted] - Access to secretsline 770
[Credential-related source omitted] - Access to secretsline 787
[Credential-related source omitted] - Access to secretsline 789
[Credential-related source omitted] - Access to secretsline 800
[Credential-related source omitted] - Access to secretsline 807
[Credential-related source omitted]
skills/bioskills/bio-alignment-alignment-io/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-alignment-alignment-io/scripts/batch_convert.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-alignment-alignment-io/scripts/convert_formats.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-alignment-alignment-io/scripts/read_alignment.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-alignment-alignment-io/scripts/slice_alignment.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-alignment-alignment-trimming/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-alignment-alignment-trimming/references/usage-guide.md
- Network accessline 12
# OR pip: pip install clipkit # trimAl: https://github.com/inab/trimal - Network accessline 14
# trimAl: https://github.com/inab/trimal - Network accessline 16
# BMGE (Java) # https://gitlab.pasteur.fr/GIPhy/BMGE - Network accessline 17
# https://gitlab.pasteur.fr/GIPhy/BMGE - Network accessline 19
# Divvier (compile from source) # https://github.com/simonwhelan/Divvier - Network accessline 20
# https://github.com/simonwhelan/Divvier
skills/bioskills/bio-alignment-alignment-trimming/scripts/bmge_trim.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-alignment-alignment-trimming/scripts/clipkit_trim.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-alignment-alignment-trimming/scripts/divvier_split.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-alignment-alignment-trimming/scripts/trimal_modes.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-alignment-files-alignment-amplicon-clipping/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-alignment-files-alignment-amplicon-clipping/scripts/ampliconclip_workflow.sh
skills/bioskills/bio-alignment-files-alignment-filtering/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 325
with pysam.AlignmentFile('input.bam', 'rb') as infile: with pysam.AlignmentFile('region.bam', 'wb', header=infile.header) as outfile: for read in infile.fetch('chr1', 1000000, 2000000): - Network accessline 326
with pysam.AlignmentFile('region.bam', 'wb', header=infile.header) as outfile: for read in infile.fetch('chr1', 1000000, 2000000): outfile.write(read) - Network accessline 327
for read in infile.fetch('chr1', 1000000, 2000000): - Network accessline 354
with pysam.AlignmentFile('targets.bam', 'wb', header=infile.header) as outfile: for chrom, start, end in regions: for read in infile.fetch(chrom, start, end): - Network accessline 355
for chrom, start, end in regions: for read in infile.fetch(chrom, start, end): outfile.write(read) - Network accessline 356
for read in infile.fetch(chrom, start, end):
skills/bioskills/bio-alignment-files-alignment-filtering/scripts/filter_bam.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 14
chrom, coords = region.split(':') start, end = map(int, coords.split('-')) iterator = infile.fetch(chrom, start, end) - Network accessline 15
start, end = map(int, coords.split('-')) iterator = infile.fetch(chrom, start, end) else: - Network accessline 16
iterator = infile.fetch(chrom, start, end)
skills/bioskills/bio-alignment-files-alignment-indexing/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 113
**Approach:** With the index present, `samtools view` or `pysam.fetch()` can jump directly to the relevant file offset instead of scanning the entire file. - Network accessline 150
with pysam.AlignmentFile('input.bam', 'rb') as bam: # fetch() requires index - Network accessline 151
# fetch() requires index - Network accessline 152
for read in bam.fetch('chr1', 1000000, 2000000): - Network accessline 175
with pysam.AlignmentFile('input.bam', 'rb') as bam: for chrom, start, end in regions: count = sum(1 for _ in bam.fetch(chrom, start, end)) - Network accessline 176
for chrom, start, end in regions: count = sum(1 for _ in bam.fetch(chrom, start, end)) print(f'{chrom}:{start}-{end}: {count} reads') - Network accessline 177
count = sum(1 for _ in bam.fetch(chrom, start, end)) - Network accessline 190
with pysam.AlignmentFile('input.bam', 'rb') as bam: for read in bam.fetch('chr1', 1000000, 1000001): - Network accessline 191
for read in bam.fetch('chr1', 1000000, 1000001): - Network accessline 266
with pysam.FastaFile('reference.fa') as ref: seq = ref.fetch('chr1', 1000, 2000) - Network accessline 267
seq = ref.fetch('chr1', 1000, 2000) - Network accessline 275
| Create BAI | `samtools index file.bam` | `pysam.index('file.bam')` | | Create CSI | `samtools index -c file.bam` | `pysam.index('-c', 'file.bam')` | | Fetch region | `samtools view file.bam chr1:1-1000` | `bam.fetch('chr1', 0, 1000)` | - Network accessline 276
| Create CSI | `samtools index -c file.bam` | `pysam.index('-c', 'file.bam')` | | Fetch region | `samtools view file.bam chr1:1-1000` | `bam.fetch('chr1', 0, 1000)` | | Count in region | `samtools view -c file.bam chr1:1-1000` | `bam.count( - Network accessline 277
| Fetch region | `samtools view file.bam chr1:1-1000` | `bam.fetch('chr1', 0, 1000)` |
skills/bioskills/bio-alignment-files-alignment-indexing/references/usage-guide.md
- Network accessline 127
with pysam.AlignmentFile('input.bam', 'rb') as bam: for read in bam.fetch('chr1', 1000000, 2000000): - Network accessline 128
for read in bam.fetch('chr1', 1000000, 2000000): - Network accessline 150
with pysam.FastaFile('reference.fa') as ref: seq = ref.fetch('chr1', 999, 2000) # 0-based coordinates - Network accessline 151
seq = ref.fetch('chr1', 999, 2000) # 0-based coordinates - Network accessline 186
- Rebuild the index after any BAM modification - Use CSI format for genomes with very large chromosomes - Check that index exists before using fetch() in pysam - Network accessline 187
- Use CSI format for genomes with very large chromosomes - Check that index exists before using fetch() in pysam - Multi-threading (`-@ 8`) significantly speeds up large file indexing - Network accessline 188
- Check that index exists before using fetch() in pysam
skills/bioskills/bio-alignment-files-alignment-indexing/scripts/fetch_regions.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 22
with pysam.AlignmentFile(bam_path, 'rb') as bam: count = 0 for read in bam.fetch(chrom, start - 1, end): # Convert to 0-based - Network accessline 23
count = 0 for read in bam.fetch(chrom, start - 1, end): # Convert to 0-based count += 1 - Network accessline 24
for read in bam.fetch(chrom, start - 1, end): # Convert to 0-based
skills/bioskills/bio-alignment-files-alignment-sorting/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-alignment-files-alignment-sorting/scripts/sort_pipeline.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-alignment-files-alignment-validation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 153
sizes = [] bam = pysam.AlignmentFile(bam_file, 'rb') for i, read in enumerate(bam.fetch()): - Network accessline 154
bam = pysam.AlignmentFile(bam_file, 'rb') for i, read in enumerate(bam.fetch()): if i >= max_reads: - Network accessline 155
for i, read in enumerate(bam.fetch()):
skills/bioskills/bio-alignment-files-alignment-validation/references/usage-guide.md
- Network accessline 157
sizes = [] with pysam.AlignmentFile(bam_file, 'rb') as bam: for i, read in enumerate(bam.fetch()): - Network accessline 158
with pysam.AlignmentFile(bam_file, 'rb') as bam: for i, read in enumerate(bam.fetch()): if i >= max_reads: - Network accessline 159
for i, read in enumerate(bam.fetch()): - Network accessline 187
def proper_pair_rate(self, sample_size=100000): proper, paired = 0, 0 for i, read in enumerate(self.bam.fetch()): - Network accessline 188
proper, paired = 0, 0 for i, read in enumerate(self.bam.fetch()): if i >= sample_size: - Network accessline 189
for i, read in enumerate(self.bam.fetch()): - Network accessline 198
def strand_balance(self, sample_size=100000): forward, reverse = 0, 0 for i, read in enumerate(self.bam.fetch()): - Network accessline 199
forward, reverse = 0, 0 for i, read in enumerate(self.bam.fetch()): if i >= sample_size: - Network accessline 200
for i, read in enumerate(self.bam.fetch()):
skills/bioskills/bio-alignment-files-alignment-validation/scripts/validate_alignment.py
skills/bioskills/bio-alignment-files-alignment-validation/scripts/validate_alignment.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-alignment-files-bam-statistics/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-alignment-files-bam-statistics/scripts/qc_report.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-alignment-files-duplicate-handling/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-alignment-files-duplicate-handling/scripts/markdup_pipeline.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-alignment-files-pileup-generation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 317
for pileup_column in bam.pileup(chrom, start, end, truncate=True): pos = pileup_column.pos ref_base = ref.fetch(chrom, pos, pos + 1) - Network accessline 318
pos = pileup_column.pos ref_base = ref.fetch(chrom, pos, pos + 1) depth = pileup_column.n - Network accessline 319
ref_base = ref.fetch(chrom, pos, pos + 1)
skills/bioskills/bio-alignment-files-pileup-generation/references/usage-guide.md
- Network accessline 187
for pileup_column in bam.pileup(chrom, start, end, truncate=True, min_base_quality=20): pos = pileup_column.pos ref_base = ref.fetch(chrom, pos, pos + 1).upper() - Network accessline 188
pos = pileup_column.pos ref_base = ref.fetch(chrom, pos, pos + 1).upper() alleles = Counter() - Network accessline 189
ref_base = ref.fetch(chrom, pos, pos + 1).upper()
skills/bioskills/bio-alignment-files-pileup-generation/scripts/allele_counts.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-alignment-files-reference-operations/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 216
with pysam.FastaFile('reference.fa') as ref: seq = ref.fetch('chr1', 999, 2000) # 0-based - Network accessline 217
seq = ref.fetch('chr1', 999, 2000) # 0-based - Network accessline 231
with pysam.FastaFile('reference.fa') as ref: for chrom in ref.references: seq = ref.fetch(chrom) - Network accessline 232
for chrom in ref.references: seq = ref.fetch(chrom) print(f'>{chrom}') - Network accessline 233
seq = ref.fetch(chrom)
skills/bioskills/bio-alignment-files-reference-operations/references/usage-guide.md
- Network accessline 129
with pysam.FastaFile('reference.fa') as ref: seq = ref.fetch('chr1', 999, 2000) # 0-based, half-open - Network accessline 130
seq = ref.fetch('chr1', 999, 2000) # 0-based, half-open - Network accessline 150
with pysam.FastaFile('reference.fa') as ref: for chrom, start, end in regions: seq = ref.fetch(chrom, start, end) - Network accessline 151
for chrom, start, end in regions: seq = ref.fetch(chrom, start, end) print(f'>{chrom}:{start+1}-{end}') - Network accessline 152
seq = ref.fetch(chrom, start, end) - Network accessline 188
consensus = simple_consensus(bam_path, chrom, start, end) with pysam.FastaFile(ref_path) as ref: reference = ref.fetch(chrom, start, end) - Network accessline 189
with pysam.FastaFile(ref_path) as ref: reference = ref.fetch(chrom, start, end) differences = [] - Network accessline 190
reference = ref.fetch(chrom, start, end)
skills/bioskills/bio-alignment-files-reference-operations/scripts/prepare_reference.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-alignment-files-sam-bam-basics/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 197
| BAM binary internal | 0-based, half-open | | `pysam read.reference_start` | 0-based | | `bam.fetch('chr1', 100, 200)` | 0-based, half-open | - Network accessline 198
| `pysam read.reference_start` | 0-based | | `bam.fetch('chr1', 100, 200)` | 0-based, half-open | | BED files | 0-based, half-open | - Network accessline 199
| `bam.fetch('chr1', 100, 200)` | 0-based, half-open | - Network accessline 204
`samtools view bam chr1:100-200` and `bam.fetch('chr1', 100, 200)` return different read sets at boundaries. - Network accessline 339
with pysam.AlignmentFile('input.bam', 'rb') as bam: for read in bam.fetch('chr1', 1000, 2000): - Network accessline 340
for read in bam.fetch('chr1', 1000, 2000): - Network accessline 365
| View header | `samtools view -H file.bam` | `bam.header` | | Count reads | `samtools view -c file.bam` | `sum(1 for _ in bam)` | | Get region | `samtools view file.bam chr1:1-1000` | `bam.fetch('chr1', 0, 1000)` | - Network accessline 366
| Count reads | `samtools view -c file.bam` | `sum(1 for _ in bam)` | | Get region | `samtools view file.bam chr1:1-1000` | `bam.fetch('chr1', 0, 1000)` | | BAM to SAM | `samtools view -h -o out.sam in.bam` | Open with 'w' mode | - Network accessline 367
| Get region | `samtools view file.bam chr1:1-1000` | `bam.fetch('chr1', 0, 1000)` |
skills/bioskills/bio-alignment-files-sam-bam-basics/references/usage-guide.md
skills/bioskills/bio-alignment-files-sam-bam-basics/scripts/convert_formats.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-alignment-files-sam-bam-basics/scripts/view_bam.py
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#!/usr/bin/env python3
skills/bioskills/bio-alignment-msa-parsing/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-alignment-msa-parsing/scripts/a2m_a3m_io.py
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skills/bioskills/bio-alignment-msa-parsing/scripts/analyze_alignment.py
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skills/bioskills/bio-alignment-msa-parsing/scripts/clean_alignment.py
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skills/bioskills/bio-alignment-msa-parsing/scripts/consensus_sequence.py
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skills/bioskills/bio-alignment-msa-parsing/scripts/find_conserved.py
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skills/bioskills/bio-alignment-msa-parsing/scripts/gap_analysis.py
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skills/bioskills/bio-alignment-msa-parsing/scripts/henikoff_weights.py
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skills/bioskills/bio-alignment-msa-parsing/scripts/mi_apc.py
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skills/bioskills/bio-alignment-msa-parsing/scripts/neff.py
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skills/bioskills/bio-alignment-msa-statistics/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-alignment-msa-statistics/scripts/capra_singh_jsd.py
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skills/bioskills/bio-alignment-msa-statistics/scripts/conservation_profile.py
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skills/bioskills/bio-alignment-msa-statistics/scripts/entropy_analysis.py
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skills/bioskills/bio-alignment-msa-statistics/scripts/gap_statistics.py
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skills/bioskills/bio-alignment-msa-statistics/scripts/identity_matrix.py
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skills/bioskills/bio-alignment-msa-statistics/scripts/kimura_protein_distance.py
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skills/bioskills/bio-alignment-msa-statistics/scripts/pssm.py
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skills/bioskills/bio-alignment-msa-statistics/scripts/substitution_counts.py
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skills/bioskills/bio-alignment-multiple-alignment/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-alignment-multiple-alignment/scripts/codon_alignment.py
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skills/bioskills/bio-alignment-multiple-alignment/scripts/run_msa.py
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skills/bioskills/bio-alignment-pairwise-alignment/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-alignment-pairwise-alignment/scripts/alignment_from_file.py
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skills/bioskills/bio-alignment-pairwise-alignment/scripts/empirical_pvalue.py
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skills/bioskills/bio-alignment-pairwise-alignment/scripts/global_alignment.py
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skills/bioskills/bio-alignment-pairwise-alignment/scripts/local_alignment.py
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skills/bioskills/bio-alignment-pairwise-alignment/scripts/protein_alignment.py
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skills/bioskills/bio-alignment-structural-alignment/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-alignment-structural-alignment/references/usage-guide.md
- Network accessline 11
conda install -c bioconda foldseek tmalign usalign foldmason # OR direct downloads # Foldseek: https://github.com/steineggerlab/foldseek - Network accessline 12
# OR direct downloads # Foldseek: https://github.com/steineggerlab/foldseek # TM-align: https://zhanggroup.org/TM-align/ - Network accessline 13
# Foldseek: https://github.com/steineggerlab/foldseek - Network accessline 14
# TM-align: https://zhanggroup.org/TM-align/ - Network accessline 15
# US-align: https://zhanggroup.org/US-align/ - Network accessline 16
# Foldmason: https://github.com/steineggerlab/foldmason - Network accessline 21
# Optional: pLM aligners (heavier installs; GPU strongly recommended) pip install fair-esm # ESM-2 embeddings # vcMSA: https://github.com/clairemcwhite/vcmsa - Network accessline 22
pip install fair-esm # ESM-2 embeddings # vcMSA: https://github.com/clairemcwhite/vcmsa # DEDAL: https://github.com/google-research/google-research/tree/master/dedal - Network accessline 23
# vcMSA: https://github.com/clairemcwhite/vcmsa - Network accessline 24
# DEDAL: https://github.com/google-research/google-research/tree/master/dedal - Network accessline 25
# TM-Vec: https://github.com/valentynbez/tmvec (active fork; original at tymor22/tm-vec is in limited maintenance)
skills/bioskills/bio-alignment-structural-alignment/scripts/biopython_superimposer.py
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skills/bioskills/bio-alignment-structural-alignment/scripts/foldmason_msa.py
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skills/bioskills/bio-alignment-structural-alignment/scripts/foldseek_search.py
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skills/bioskills/bio-alignment-structural-alignment/scripts/tm_align_pairwise.py
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skills/bioskills/bio-alternative-splicing-differential-splicing/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 222
Shiba (Kubota 2025 *NAR*) reportedly outperforms rMATS at n=2 vs n=2 by correcting differential mappability between inclusion and skipping junctions; community calibration still emerging. See https://sika-zheng-lab.github.io/Shiba/ for the
skills/bioskills/bio-alternative-splicing-differential-splicing/scripts/diff_splicing_leafcutter.R
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#!/usr/bin/env Rscript
skills/bioskills/bio-alternative-splicing-differential-splicing/scripts/diff_splicing_rmats.sh
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#!/bin/bash
skills/bioskills/bio-alternative-splicing-isoform-switching/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-alternative-splicing-isoform-switching/scripts/isoform_switch_analysis.R
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#!/usr/bin/env Rscript - Network accessline 59
cat(sprintf('Sequences written to: %s\n', output_dir)) cat('Run external tools:\n') cat(' - CPC2: http://cpc2.gao-lab.org/\n') - Network accessline 60
cat('Run external tools:\n') cat(' - CPC2: http://cpc2.gao-lab.org/\n') cat(' - Pfam: https://www.ebi.ac.uk/Tools/hmmer/\n') - Network accessline 61
cat(' - CPC2: http://cpc2.gao-lab.org/\n') - Network accessline 62
cat(' - Pfam: https://www.ebi.ac.uk/Tools/hmmer/\n') - Network accessline 63
cat(' - SignalP: https://services.healthtech.dtu.dk/service.php?SignalP\n') - Network accessline 64
cat(' - IUPred2A: https://iupred2a.elte.hu/\n')
skills/bioskills/bio-alternative-splicing-long-read-splicing/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-alternative-splicing-long-read-splicing/references/usage-guide.md
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# SQANTI3 git clone https://github.com/ConesaLab/SQANTI3 - Network accessline 16
git clone https://github.com/ConesaLab/SQANTI3 - Network accessline 19
# rMATS-long (separate from rMATS-turbo) git clone https://github.com/Xinglab/rMATS-long - Network accessline 20
git clone https://github.com/Xinglab/rMATS-long
skills/bioskills/bio-alternative-splicing-long-read-splicing/scripts/longread_splicing_pipeline.sh
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#!/bin/bash
skills/bioskills/bio-alternative-splicing-outlier-splicing-detection/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-alternative-splicing-outlier-splicing-detection/scripts/fraser2_rare_disease.R
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#!/usr/bin/env Rscript
skills/bioskills/bio-alternative-splicing-sashimi-plots/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-alternative-splicing-sashimi-plots/scripts/plot_sashimi.py
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#!/usr/bin/env python3
skills/bioskills/bio-alternative-splicing-single-cell-splicing/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-alternative-splicing-single-cell-splicing/references/usage-guide.md
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# scQuint, SpliZ, Psix from GitHub pip install git+https://github.com/songlab-cal/scquint - Network accessline 16
pip install git+https://github.com/songlab-cal/scquint - Network accessline 17
pip install git+https://github.com/salzman-lab/SpliZ - Network accessline 18
pip install git+https://github.com/lareaulab/psix
skills/bioskills/bio-alternative-splicing-single-cell-splicing/scripts/sc_splicing_brie2.py
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#!/usr/bin/env python3
skills/bioskills/bio-alternative-splicing-splice-variant-prediction/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 181
# Web API: https://kidsneuro.shinyapps.io/splicevault/
skills/bioskills/bio-alternative-splicing-splice-variant-prediction/references/usage-guide.md
skills/bioskills/bio-alternative-splicing-splice-variant-prediction/scripts/spliceai_clingen_classify.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-alternative-splicing-splicing-qc/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 214
for read in bam.fetch():
skills/bioskills/bio-alternative-splicing-splicing-qc/scripts/splicing_qc.py
skills/bioskills/bio-alternative-splicing-splicing-quantification/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-alternative-splicing-splicing-quantification/scripts/quantify_splicing.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-atac-seq-allele-specific-accessibility/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-atac-seq-allele-specific-accessibility/references/usage-guide.md
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# WASP for mapping bias correction (mandatory) git clone https://github.com/bmvdgeijn/WASP - Network accessline 11
git clone https://github.com/bmvdgeijn/WASP - Network accessline 16
# RASQUAL for joint caQTL git clone https://github.com/natsuhiko/rasqual - Network accessline 17
git clone https://github.com/natsuhiko/rasqual
skills/bioskills/bio-atac-seq-allele-specific-accessibility/scripts/wasp_ase_pipeline.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-atac-seq-atac-peak-calling/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 247
# ENCODE blacklist (Amemiya 2019) -- always remove wget https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz - Network accessline 248
wget https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz
skills/bioskills/bio-atac-seq-atac-peak-calling/references/usage-guide.md
skills/bioskills/bio-atac-seq-atac-peak-calling/scripts/call_atac_peaks.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-atac-seq-atac-qc/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 170
total = 0 with pysam.AlignmentFile(bam, 'rb') as bf: for r in bf.fetch(): - Network accessline 171
with pysam.AlignmentFile(bam, 'rb') as bf: for r in bf.fetch(): if r.is_unmapped or r.is_secondary or r.is_supplementary: - Network accessline 172
for r in bf.fetch():
skills/bioskills/bio-atac-seq-atac-qc/scripts/atac_qc_metrics.R
- Executable scriptline 1
#!/usr/bin/env Rscript
skills/bioskills/bio-atac-seq-co-accessibility/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-atac-seq-co-accessibility/references/usage-guide.md
skills/bioskills/bio-atac-seq-co-accessibility/scripts/cicero_workflow.R
- Executable scriptline 1
#!/usr/bin/env Rscript
skills/bioskills/bio-atac-seq-consensus-peakset/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-atac-seq-consensus-peakset/scripts/iterative_overlap.sh
skills/bioskills/bio-atac-seq-deep-learning-atac/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 169
# 4. Variant effect prediction at GWAS SNPs uses the SEPARATE kundajelab/variant-scorer repo # (the `chrombpnet snp_score` subcommand is commented out in current chrombpnet/parsers.py) git clone https://github.com/kundajelab/variant-scorer - Network accessline 170
# (the `chrombpnet snp_score` subcommand is commented out in current chrombpnet/parsers.py) git clone https://github.com/kundajelab/variant-scorer python variant-scorer/src/variant_scoring.py \ - Network accessline 171
git clone https://github.com/kundajelab/variant-scorer
skills/bioskills/bio-atac-seq-deep-learning-atac/scripts/chrombpnet_pipeline.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-atac-seq-differential-accessibility/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-atac-seq-differential-accessibility/scripts/diff_accessibility.R
- Executable scriptline 1
#!/usr/bin/env Rscript
skills/bioskills/bio-atac-seq-enhancer-gene-linking/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 206
# Snakemake-based; clone the ENCODE-rE2G repo git clone https://github.com/EngreitzLab/ENCODE_rE2G - Network accessline 207
git clone https://github.com/EngreitzLab/ENCODE_rE2G - Network accessline 218
Pre-trained models are at https://github.com/EngreitzLab/ENCODE_rE2G/tree/main/models. Choose by tissue similarity if exact cell type not present.
skills/bioskills/bio-atac-seq-enhancer-gene-linking/references/usage-guide.md
- Network accessline 10
# ABC pipeline (Snakemake-based; install its conda env) git clone https://github.com/broadinstitute/ABC-Enhancer-Gene-Prediction - Network accessline 11
git clone https://github.com/broadinstitute/ABC-Enhancer-Gene-Prediction - Network accessline 15
# ENCODE-rE2G git clone https://github.com/EngreitzLab/ENCODE_rE2G - Network accessline 16
git clone https://github.com/EngreitzLab/ENCODE_rE2G
skills/bioskills/bio-atac-seq-enhancer-gene-linking/scripts/run_abc.sh
- Executable scriptline 1
#!/bin/bash - Network accessline 77
# Optional: cross-validation # Compare against published CRISPRi-FlowFISH catalog (Fulco 2019 K562) # wget https://www.engreitzlab.org/crispri-flowfish/K562_validated_pairs.txt - Network accessline 78
# Compare against published CRISPRi-FlowFISH catalog (Fulco 2019 K562) # wget https://www.engreitzlab.org/crispri-flowfish/K562_validated_pairs.txt # Compute sensitivity / specificity at threshold 0.02 - Network accessline 79
# wget https://www.engreitzlab.org/crispri-flowfish/K562_validated_pairs.txt
skills/bioskills/bio-atac-seq-footprinting/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-atac-seq-footprinting/references/usage-guide.md
- Network accessline 10
conda install -c bioconda tobias rgt pydnase samtools bedtools # HINT-ATAC ships in the `rgt` package (invoked as `rgt-hint`) pip install pyBigWig # scPrinter installs from source: git clone https://github.com/buenrostrolab/scPrinter & - Network accessline 11
pip install pyBigWig # scPrinter installs from source: git clone https://github.com/buenrostrolab/scPrinter && cd scPrinter && pip install ./ - Network accessline 22
wget https://jaspar.genereg.net/download/data/2024/CORE/JASPAR2024_CORE_vertebrates_non-redundant_pfms.txt
skills/bioskills/bio-atac-seq-footprinting/scripts/run_tobias.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-atac-seq-motif-deviation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-atac-seq-motif-deviation/scripts/chromvar_analysis.R
- Executable scriptline 1
#!/usr/bin/env Rscript
skills/bioskills/bio-atac-seq-nucleosome-positioning/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 186
bam = pysam.AlignmentFile('sample.bam', 'rb') frag_lengths = [abs(r.template_length) for r in bam.fetch() - Network accessline 187
frag_lengths = [abs(r.template_length) for r in bam.fetch() - Network accessline 212
chrom, start, *_ = line.strip().split('\t') center = int(start) for r in bam.fetch(chrom, max(0, center - flank), center + flank): - Network accessline 213
center = int(start) for r in bam.fetch(chrom, max(0, center - flank), center + flank): if not r.is_proper_pair or not r.is_read1: continue - Network accessline 214
for r in bam.fetch(chrom, max(0, center - flank), center + flank): - Network accessline 278
# Per-region fragment-size mean as H2A.Z indicator def region_frag_size(bam, region): sizes = [abs(r.template_length) for r in bam.fetch(*region) - Network accessline 279
def region_frag_size(bam, region): sizes = [abs(r.template_length) for r in bam.fetch(*region) if r.is_proper_pair and r.is_read1 and 100 < abs(r.template_length) < 300] - Network accessline 280
sizes = [abs(r.template_length) for r in bam.fetch(*region)
skills/bioskills/bio-atac-seq-nucleosome-positioning/references/usage-guide.md
- Network accessline 14
conda install -c bioconda samtools # DANPOS3 (python danpos.py) installs from github.com/sklasfeld/DANPOS3 (bioconda `danpos` is DANPOS2) # scPrinter installs from source: git clone https://github.com/buenrostrolab/scPrinter && cd scPrinter - Network accessline 15
# DANPOS3 (python danpos.py) installs from github.com/sklasfeld/DANPOS3 (bioconda `danpos` is DANPOS2) # scPrinter installs from source: git clone https://github.com/buenrostrolab/scPrinter && cd scPrinter && pip install ./ pip install pysa - Network accessline 16
# scPrinter installs from source: git clone https://github.com/buenrostrolab/scPrinter && cd scPrinter && pip install ./
skills/bioskills/bio-atac-seq-nucleosome-positioning/scripts/nucleosome_analysis.R
- Executable scriptline 1
#!/usr/bin/env Rscript
skills/bioskills/bio-atac-seq-single-cell-atac/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-atac-seq-single-cell-atac/scripts/signac_workflow.R
- Executable scriptline 1
#!/usr/bin/env Rscript
skills/bioskills/bio-causal-genomics-colocalization-analysis/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 414
- **eCAVIAR**: Compile from GitHub fhormoz/caviar; C++ source. CLI `eCAVIAR`. PAINTOR is the related multi-trait fine-mapping toolkit. - **PWCoCo**: GitHub jwr-git/pwcoco. Compiled C++ CLI; can also be invoked from R via wrapper scripts. - - Network accessline 415
- **PWCoCo**: GitHub jwr-git/pwcoco. Compiled C++ CLI; can also be invoked from R via wrapper scripts. - **SharePro_coloc**: GitHub only (no PyPI release). `git clone https://github.com/zhwm/SharePro_coloc` then `pip install -r requirements - Network accessline 416
- **SharePro_coloc**: GitHub only (no PyPI release). `git clone https://github.com/zhwm/SharePro_coloc` then `pip install -r requirements.txt`.
skills/bioskills/bio-causal-genomics-colocalization-analysis/references/usage-guide.md
- Network accessline 19
# CLI tools (outside R) # SMR: download binary from https://yanglab.westlake.edu.cn/software/smr/ - Network accessline 20
# SMR: download binary from https://yanglab.westlake.edu.cn/software/smr/ - Network accessline 21
# eCAVIAR: compile from https://github.com/fhormoz/caviar - Network accessline 22
# PWCoCo: compile from https://github.com/jwr-git/pwcoco - Network accessline 23
# SharePro_coloc: git clone https://github.com/zhwm/SharePro_coloc then pip install -r requirements.txt
skills/bioskills/bio-causal-genomics-effector-gene-prioritization/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 259
resp = requests.post('https://api.platform.opentargets.org/api/v4/graphql', - Network accessline 366
- **Open Targets Platform (current)**: GraphQL at `api.platform.opentargets.org/api/v4/graphql`. Query L2G via `credibleSet(studyLocusId)` -> `l2GPredictions { rows { target, score, features } }`. Recommended for new pipelines. - **Open Tar - Network accessline 367
- **Open Targets Genetics (legacy, deprecated)**: GraphQL at `api.genetics.opentargets.org/graphql`. Python `pip install gentropy` (official Open Targets) or `pip install otargenpy` (community GraphQL wrapper) OR direct GraphQL queries via - Network accessline 368
- **PoPS**: `git clone https://github.com/FinucaneLab/pops`. Python; ships with feature matrix download instructions. Pre-built feature matrix at the releases page. - Network accessline 369
- **cS2G**: Pre-computed gene scores downloadable from zenodo.org/records/7754032 (cS2G_UKBB.zip / cS2G_1000GEUR.zip). No install; lookup table. - **DEPICT**: `git clone https://github.com/perslab/depict`. Java + Python; legacy method, see - Network accessline 370
- **DEPICT**: `git clone https://github.com/perslab/depict`. Java + Python; legacy method, see Pers 2015. - Network accessline 371
- **FLAMES**: Recent (Schipper M et al 2025 Nat Genet 57:323); check the publication's GitHub for the current install path. - **INQUISIT**: Originally for breast cancer (Fachal 2020 Nat Genet 52:56); see the paper's supplementary methods fo - Network accessline 372
- **INQUISIT**: Originally for breast cancer (Fachal 2020 Nat Genet 52:56); see the paper's supplementary methods for adaptation to other traits. - **ABC**: `git clone https://github.com/broadinstitute/ABC-Enhancer-Gene-Prediction`. Python; - Network accessline 373
- **ABC**: `git clone https://github.com/broadinstitute/ABC-Enhancer-Gene-Prediction`. Python; see atac-seq/enhancer-gene-linking for the full pipeline. - Network accessline 374
- **ENCODE-rE2G**: `git clone https://github.com/EngreitzLab/ENCODE_rE2G`. Snakemake; see atac-seq/enhancer-gene-linking.
skills/bioskills/bio-causal-genomics-effector-gene-prioritization/references/usage-guide.md
- Network accessline 22
# MAGMA ships as a pre-compiled zip, not a git repo. Download it manually from the official CNCR page # (https://cncr.nl/research/magma/) -- the old ctg.cncr.nl and vu.data.surfsara.nl direct links now - Network accessline 23
# (https://cncr.nl/research/magma/) -- the old ctg.cncr.nl and vu.data.surfsara.nl direct links now # redirect to an HTML landing page, so a hard-coded wget would save that page AS the zip and unzip # would fail. Once the real zip is in the - Network accessline 24
# redirect to an HTML landing page, so a hard-coded wget would save that page AS the zip and unzip - Network accessline 28
# PoPS git clone https://github.com/FinucaneLab/pops - Network accessline 29
git clone https://github.com/FinucaneLab/pops - Network accessline 41
# cS2G (Gazal 2022; hosted on Zenodo record 7754032) wget https://zenodo.org/records/7754032/files/cS2G_UKBB.zip - Network accessline 42
wget https://zenodo.org/records/7754032/files/cS2G_UKBB.zip
skills/bioskills/bio-causal-genomics-effector-gene-prioritization/scripts/magma_genebased.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/bioskills/bio-causal-genomics-fine-mapping/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-causal-genomics-fine-mapping/references/usage-guide.md
- Network accessline 16
# FINEMAP (CLI binary; not an R package) # Download from http://www.christianbenner.com/ - Network accessline 17
# Download from http://www.christianbenner.com/ - Network accessline 19
# PolyFun (Python) git clone https://github.com/omerwe/polyfun - Network accessline 20
git clone https://github.com/omerwe/polyfun - Network accessline 21
pip install -r polyfun/requirements.txt # Pre-baked baseline-LF priors: # https://data.broadinstitute.org/alkesgroup/UKBB_LD/baselineLF2.2.UKB.tar.gz - Network accessline 23
# https://data.broadinstitute.org/alkesgroup/UKBB_LD/baselineLF2.2.UKB.tar.gz - Network accessline 25
# PAINTOR (C++ CLI) git clone https://github.com/bogdanlab/PAINTOR_V3.0 - Network accessline 26
git clone https://github.com/bogdanlab/PAINTOR_V3.0 - Network accessline 29
# SuSiEx (C++ CLI) git clone https://github.com/getian107/SuSiEx - Network accessline 30
git clone https://github.com/getian107/SuSiEx - Network accessline 33
# DAP-G (CLI) git clone https://github.com/xqwen/dap - Network accessline 34
git clone https://github.com/xqwen/dap
skills/bioskills/bio-causal-genomics-fine-mapping/scripts/finemap_pipeline.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/bioskills/bio-causal-genomics-fine-mapping/scripts/susiex_multiancestry.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/bioskills/bio-causal-genomics-genetic-correlation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 437
# abdenlab/ldsc-python3 (v2.0.0) retains the working CLI. Docker # `jtb114/ldsc:latest` is the recommended belowlab fallback. git clone https://github.com/abdenlab/ldsc-python3.git - Network accessline 438
# `jtb114/ldsc:latest` is the recommended belowlab fallback. git clone https://github.com/abdenlab/ldsc-python3.git cd ldsc-python3 && pip install . # Poetry project (pyproject.toml); no environment.yml - Network accessline 439
git clone https://github.com/abdenlab/ldsc-python3.git - Network accessline 443
# HESS git clone https://github.com/huwenboshi/hess.git - Network accessline 444
git clone https://github.com/huwenboshi/hess.git - Network accessline 447
# Popcorn git clone https://github.com/brielin/Popcorn.git - Network accessline 448
git clone https://github.com/brielin/Popcorn.git
skills/bioskills/bio-causal-genomics-genetic-correlation/scripts/ldsc_crosstrait_rg.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/bioskills/bio-causal-genomics-genomic-sem/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 460
# --h2/--rg/--h2-cts CLI per its README; use abdenlab/ldsc-python3 (v2.0.0) # for a working CLI. Docker jtb114/ldsc:latest is the belowlab fallback. git clone https://github.com/abdenlab/ldsc-python3.git - Network accessline 461
# for a working CLI. Docker jtb114/ldsc:latest is the belowlab fallback. git clone https://github.com/abdenlab/ldsc-python3.git cd ldsc-python3 && pip install . # Poetry project (pyproject.toml); no requirements.txt - Network accessline 462
git clone https://github.com/abdenlab/ldsc-python3.git - Network accessline 465
# MTAG git clone https://github.com/JonJala/mtag.git - Network accessline 466
git clone https://github.com/JonJala/mtag.git
skills/bioskills/bio-causal-genomics-genomic-sem/scripts/mtag_pipeline.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/bioskills/bio-causal-genomics-heritability-partitioning/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 328
LDetect partition files (Berisa & Pickrell 2016) are available pre-computed for EUR/EAS/AFR at https://bitbucket.org/nygcresearch/ldetect-data. HESS detects high-h2 loci suitable for fine-mapping prioritization. - Network accessline 379
# For a working CLI matching the flags below, use abdenlab/ldsc-python3 (v2.0.0) # OR run belowlab/ldsc via Docker: `docker pull jtb114/ldsc:latest`. git clone https://github.com/abdenlab/ldsc-python3.git # working CLI - Network accessline 380
# OR run belowlab/ldsc via Docker: `docker pull jtb114/ldsc:latest`. git clone https://github.com/abdenlab/ldsc-python3.git # working CLI cd ldsc-python3 - Network accessline 381
git clone https://github.com/abdenlab/ldsc-python3.git # working CLI - Network accessline 385
# LDAK 6+ wget https://raw.githubusercontent.com/dougspeed/LDAK/main/ldak6.3.linux - Network accessline 386
wget https://raw.githubusercontent.com/dougspeed/LDAK/main/ldak6.3.linux - Network accessline 389
# Pre-computed reference resources (one-time download) # EUR LD scores (HapMap3 SNPs) wget https://alkesgroup.broadinstitute.org/LDSCORE/eur_w_ld_chr.tar.bz2 - Network accessline 390
# EUR LD scores (HapMap3 SNPs) wget https://alkesgroup.broadinstitute.org/LDSCORE/eur_w_ld_chr.tar.bz2 # Baseline-LD v2.2 (Gazal 2017) - Network accessline 391
wget https://alkesgroup.broadinstitute.org/LDSCORE/eur_w_ld_chr.tar.bz2 - Network accessline 392
# Baseline-LD v2.2 (Gazal 2017) wget https://alkesgroup.broadinstitute.org/LDSCORE/1000G_Phase3_baselineLD_v2.2_ldscores.tgz # 1000G EUR frequency files - Network accessline 393
wget https://alkesgroup.broadinstitute.org/LDSCORE/1000G_Phase3_baselineLD_v2.2_ldscores.tgz - Network accessline 394
# 1000G EUR frequency files wget https://alkesgroup.broadinstitute.org/LDSCORE/1000G_Phase3_frq.tgz # Weights - Network accessline 395
wget https://alkesgroup.broadinstitute.org/LDSCORE/1000G_Phase3_frq.tgz - Network accessline 396
# Weights wget https://alkesgroup.broadinstitute.org/LDSCORE/1000G_Phase3_weights_hm3_no_MHC.tgz # Multi-tissue chromatin ldcts (Finucane 2018) - Network accessline 397
wget https://alkesgroup.broadinstitute.org/LDSCORE/1000G_Phase3_weights_hm3_no_MHC.tgz - Network accessline 399
wget https://alkesgroup.broadinstitute.org/LDSCORE/Multi_tissue_chromatin_1000Gv3_ldscores.tgz - Network accessline 404
remotes::install_github('zhenin/HDL/HDL') # HDL UKB reference (downloads ~5 GB) # https://github.com/zhenin/HDL/wiki/Reference-panels - Network accessline 406
# https://github.com/zhenin/HDL/wiki/Reference-panels - Network accessline 410
# HESS (Python 2 by default; Python 3 fork: huwenboshi/hess Python 3 branch) git clone https://github.com/huwenboshi/hess.git - Network accessline 411
git clone https://github.com/huwenboshi/hess.git - Network accessline 414
# BOLT-LMM / BOLT-REML wget https://alkesgroup.broadinstitute.org/BOLT-LMM/downloads/BOLT-LMM_v2.4.1.tar.gz - Network accessline 415
wget https://alkesgroup.broadinstitute.org/BOLT-LMM/downloads/BOLT-LMM_v2.4.1.tar.gz - Network accessline 417
# GCTA wget https://yanglab.westlake.edu.cn/software/gcta/bin/gcta-1.94.1-linux-kernel-3-x86_64.zip - Network accessline 418
wget https://yanglab.westlake.edu.cn/software/gcta/bin/gcta-1.94.1-linux-kernel-3-x86_64.zip - Network accessline 420
# Popcorn git clone https://github.com/brielin/Popcorn.git && cd Popcorn && pip install . - Network accessline 421
git clone https://github.com/brielin/Popcorn.git && cd Popcorn && pip install .
skills/bioskills/bio-causal-genomics-heritability-partitioning/references/usage-guide.md
- Network accessline 12
# working CLI use abdenlab/ldsc-python3 (v2.0.0) or run belowlab via Docker # (`docker pull jtb114/ldsc:latest`). git clone https://github.com/abdenlab/ldsc-python3.git - Network accessline 13
# (`docker pull jtb114/ldsc:latest`). git clone https://github.com/abdenlab/ldsc-python3.git cd ldsc-python3 && pip install . # Poetry project (pyproject.toml); no environment.yml - Network accessline 14
git clone https://github.com/abdenlab/ldsc-python3.git - Network accessline 17
# LDAK 6+ (bare executable distributed via GitHub; no zip) wget https://raw.githubusercontent.com/dougspeed/LDAK/main/ldak6.3.linux && chmod +x ldak6.3.linux - Network accessline 18
wget https://raw.githubusercontent.com/dougspeed/LDAK/main/ldak6.3.linux && chmod +x ldak6.3.linux - Network accessline 20
# Pre-computed reference resources (EUR; ~3-5 GB total one-time) # All from https://alkesgroup.broadinstitute.org/LDSCORE/ - Network accessline 21
# All from https://alkesgroup.broadinstitute.org/LDSCORE/
skills/bioskills/bio-causal-genomics-heritability-partitioning/scripts/ldak_sumher.sh
skills/bioskills/bio-causal-genomics-heritability-partitioning/scripts/ldsc_partitioned_h2.sh
- Executable scriptline 1
#!/bin/bash - Network accessline 16
# belowlab/ldsc v3.0.1 README states the --h2/--rg/--h2-cts CLI is broken (use # Docker jtb114/ldsc:latest for the belowlab fallback). See SKILL.md. # Reference resources downloaded from https://alkesgroup.broadinstitute.org/LDSCORE/ - Network accessline 17
# Docker jtb114/ldsc:latest for the belowlab fallback). See SKILL.md. # Reference resources downloaded from https://alkesgroup.broadinstitute.org/LDSCORE/ # - eur_w_ld_chr/ (univariate h2) - Network accessline 18
# Reference resources downloaded from https://alkesgroup.broadinstitute.org/LDSCORE/
skills/bioskills/bio-causal-genomics-mediation-analysis/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Dangerous shell operationline 454
- Vansteelandt S, Daniel RM 2017 Epidemiology 28:258 (interventional / randomized indirect effects)
skills/bioskills/bio-causal-genomics-mediation-analysis/references/usage-guide.md
- Network accessline 18
# r-universe (see causal-genomics/mendelian-randomization) install.packages('TwoSampleMR', repos=c('https://mrcieu.r-universe.dev','https://cloud.r-project.org')) - Network accessline 19
install.packages('TwoSampleMR', repos=c('https://mrcieu.r-universe.dev','https://cloud.r-project.org'))
skills/bioskills/bio-causal-genomics-mediation-analysis/scripts/mvmr_mediation.R
- Network accessline 11
## Install: ## install.packages('TwoSampleMR', ## repos=c('https://mrcieu.r-universe.dev','https://cloud.r-project.org')) - Network accessline 12
## install.packages('TwoSampleMR', ## repos=c('https://mrcieu.r-universe.dev','https://cloud.r-project.org')) ## remotes::install_github('WSpiller/MVMR') - Network accessline 13
## repos=c('https://mrcieu.r-universe.dev','https://cloud.r-project.org'))
skills/bioskills/bio-causal-genomics-mendelian-randomization/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-causal-genomics-mendelian-randomization/references/usage-guide.md
skills/bioskills/bio-causal-genomics-pleiotropy-detection/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-causal-genomics-pleiotropy-detection/references/usage-guide.md
- Network accessline 41
remotes::install_github('qingyuanzhao/mr.raps') # CRAN-archived 2025-03; install from GitHub remotes::install_github('LizaDarrous/lhcMR') # Heritable-confounder MR # LCV: git clone https://github.com/lukejoconnor/LCV (R scr - Network accessline 43
# LCV: git clone https://github.com/lukejoconnor/LCV (R scripts, no package)
skills/bioskills/bio-causal-genomics-proteome-mr-drug-target/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 432
# 1000 Genomes EUR plink reference for local clumping / LD matrix # Prebuilt at https://mrcieu.github.io/ieugwasr/ - Network accessline 433
# Prebuilt at https://mrcieu.github.io/ieugwasr/
skills/bioskills/bio-causal-genomics-proteome-mr-drug-target/references/usage-guide.md
- Network accessline 24
# 1000 Genomes EUR plink reference (for local clumping and LD matrix) # https://mrcieu.github.io/ieugwasr/articles/local_ld.html - Network accessline 25
# https://mrcieu.github.io/ieugwasr/articles/local_ld.html - Network accessline 28
pQTL summary statistics: - UKB-PPP Olink (Sun 2023): ukb-ppp.gwas.eu portal (download by protein UniProt ID) - deCODE SomaScan (Ferkingstad 2021): https://www.decode.com/summarydata/ - Network accessline 29
- UKB-PPP Olink (Sun 2023): ukb-ppp.gwas.eu portal (download by protein UniProt ID) - deCODE SomaScan (Ferkingstad 2021): https://www.decode.com/summarydata/ - Fenland SomaScan (Pietzner 2021): EBI GWAS catalog - Network accessline 30
- deCODE SomaScan (Ferkingstad 2021): https://www.decode.com/summarydata/
skills/bioskills/bio-causal-genomics-transcriptome-wide-association/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 238
# Pre-computed FUSION weights live at http://gusevlab.org/projects/fusion/ # Example: GTEx v8 Whole_Blood; download .pos summary + per-gene RData files into wgt_dir/ - Network accessline 426
- **FUSION**: gusevlab.org/projects/fusion or `git clone https://github.com/gusevlab/fusion_twas`. R scripts; needs plink (PLINK 1.9), Rscript, and the GBJ R package for omnibus. Pre-computed weights for GTEx v7/v8, CMC, YFS, METSIM, NTR, M - Network accessline 427
- **MetaXcan / S-PrediXcan / S-MultiXcan**: `git clone https://github.com/hakyimlab/MetaXcan` (not on PyPI). Python scripts in `software/`. Compatible with Python 3.9-3.11. - Network accessline 428
- **PredictDB models**: predictdb.org. GTEx v8 elastic-net (single-tissue) and MASHR (cross-tissue posterior) databases for EUR; multi-ethnic panels emerging. - **UTMOST**: `git clone https://github.com/Joker-Jerome/UTMOST`. Python. Needs p - Network accessline 429
- **UTMOST**: `git clone https://github.com/Joker-Jerome/UTMOST`. Python. Needs precomputed cross-tissue weights or training pipeline. - Network accessline 430
- **FOCUS**: `pip install pyfocus`. CLI `focus`. Pre-built DBs for GTEx v7/v8 panels at github.com/bogdanlab/focus. - **MA-FOCUS**: `git clone https://github.com/mancusolab/ma-focus && cd ma-focus && pip install .` (no PyPI release; install - Network accessline 431
- **MA-FOCUS**: `git clone https://github.com/mancusolab/ma-focus && cd ma-focus && pip install .` (no PyPI release; install from source). Same CLI as single-ancestry FOCUS -- `focus finemap` -- with colon-separated per-ancestry sumstats / - Network accessline 432
- **TIGAR-V2**: `git clone https://github.com/yanglab-emory/TIGAR`. Python + R hybrid; ships with example data. - Network accessline 433
- **MOSTWAS**: `git clone https://github.com/bhattacharya-a-bt/MOSTWAS`. R package; install via `devtools::install_github`.
skills/bioskills/bio-causal-genomics-transcriptome-wide-association/references/usage-guide.md
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pip install pyfocus git clone https://github.com/hakyimlab/MetaXcan # S-PrediXcan / S-MultiXcan (not on PyPI) - Network accessline 21
git clone https://github.com/hakyimlab/MetaXcan # S-PrediXcan / S-MultiXcan (not on PyPI) - Network accessline 27
git clone https://github.com/gusevlab/fusion_twas
skills/bioskills/bio-causal-genomics-transcriptome-wide-association/scripts/focus_finemap.sh
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#!/usr/bin/env bash
skills/bioskills/bio-causal-genomics-transcriptome-wide-association/scripts/s_predixcan_pipeline.sh
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#!/usr/bin/env bash
skills/bioskills/bio-chemoinformatics-admet-prediction/SKILL.md
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- Heid E et al., *J. Chem. Inf. Model.* 64:9-17 (2024) -- Chemprop redesign (DOI 10.1021/acs.jcim.3c01250). - Chemprop documentation, training, descriptors, and uncertainty: https://chemprop.readthedocs.io/ - ADMET-AI official repository an - Network accessline 290
- Chemprop documentation, training, descriptors, and uncertainty: https://chemprop.readthedocs.io/ - Network accessline 291
- ADMET-AI official repository and version notes: https://github.com/swansonk14/admet_ai - Network accessline 292
- SwissADME Terms of Use: https://www.swissadme.ch/termsofuse.php
skills/bioskills/bio-chemoinformatics-admet-prediction/scripts/predict_admet.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-chemoinformatics-conformer-generation/SKILL.md
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repository: https://github.com/GPTomics/bioSkills - Network accessline 388
- Pracht P et al., *J. Chem. Phys.* 160:114110 (2024) -- CREST 3.0 (DOI 10.1063/5.0197592). - Bannwarth C, Ehlert S, Grimme S. *J. Chem. Theory Comput.* 15:1652-1671 (2019) -- GFN2-xTB (DOI 10.1021/acs.jctc.8b01176). - RDKit force-field API - Network accessline 389
- Bannwarth C, Ehlert S, Grimme S. *J. Chem. Theory Comput.* 15:1652-1671 (2019) -- GFN2-xTB (DOI 10.1021/acs.jctc.8b01176). - RDKit force-field API: https://www.rdkit.org/docs/source/rdkit.Chem.rdForceFieldHelpers.html - CREST command-line - Network accessline 390
- RDKit force-field API: https://www.rdkit.org/docs/source/rdkit.Chem.rdForceFieldHelpers.html - Network accessline 391
- CREST command-line documentation: https://crest-lab.github.io/crest-docs/page/documentation/keywords.html - Network accessline 392
- xTB documentation: https://xtb-docs.readthedocs.io/
skills/bioskills/bio-chemoinformatics-conformer-generation/scripts/gen_conformers.py
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skills/bioskills/bio-chemoinformatics-covalent-design/SKILL.md
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name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-chemoinformatics-covalent-design/scripts/warhead_classifier.py
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skills/bioskills/bio-chemoinformatics-free-energy-calculations/SKILL.md
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repository: https://github.com/GPTomics/bioSkills - Network accessline 297
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- OpenFE 1.7 documentation: https://docs.openfree.energy/en/v1.7.0/ - Network accessline 300
- alchemlyb documentation: https://alchemlyb.readthedocs.io/
skills/bioskills/bio-chemoinformatics-free-energy-calculations/scripts/openfe_rbfe.py
skills/bioskills/bio-chemoinformatics-generative-design/SKILL.md
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repository: https://github.com/GPTomics/bioSkills - Network accessline 314
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- Ertl P, Schuffenhauer A. *J. Cheminformatics* 1:8 (2009) -- SA score (DOI 10.1186/1758-2946-1-8). - REINVENT 4 official repository and release-matched configs: https://github.com/MolecularAI/REINVENT4 - RDKit SA-score implementation: http - Network accessline 316
- REINVENT 4 official repository and release-matched configs: https://github.com/MolecularAI/REINVENT4 - Network accessline 317
- RDKit SA-score implementation: https://github.com/rdkit/rdkit/tree/master/Contrib/SA_Score
skills/bioskills/bio-chemoinformatics-generative-design/references/usage-guide.md
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git clone https://github.com/MolecularAI/REINVENT4.git
skills/bioskills/bio-chemoinformatics-ml-docking-rescoring/SKILL.md
- Network accessline 9
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repository: https://github.com/GPTomics/bioSkills - Network accessline 260
- Corso et al., *ICLR* (2023) -- original DiffDock. https://arxiv.org/abs/2210.01776 - Network accessline 261
- Corso et al., *ICLR* (2024) -- DiffDock-L. https://proceedings.iclr.cc/paper_files/paper/2024/file/db334db287337b2a365120b524300ef3-Paper-Conference.pdf - Network accessline 262
- Buttenschoen et al., *Chem. Sci.* 15:3130-3139 (2024) -- PoseBusters benchmark. https://doi.org/10.1039/D3SC04185A - Network accessline 263
- Wohlwend et al., bioRxiv (2024) -- Boltz-1. https://doi.org/10.1101/2024.11.19.624167 - Network accessline 264
- Passaro et al., bioRxiv (2025) -- Boltz-2 with affinity module. https://doi.org/10.1101/2025.06.14.659707 - Network accessline 265
- Boltz, official repository -- affinity-output semantics and current prediction interface. https://github.com/jwohlwend/boltz - Network accessline 266
- Chai Discovery, *Chai-1 Technical Report* (2024). https://doi.org/10.1101/2024.10.10.615955 - Network accessline 267
- Chai Discovery, Chai-1 official repository -- code and model-weight licensing. https://github.com/chaidiscovery/chai-lab - Network accessline 268
- Abramson et al., *Nature* 630:493-500 (2024) -- AlphaFold 3. https://doi.org/10.1038/s41586-024-07487-w - Network accessline 269
- Google DeepMind, AlphaFold 3 official repository -- local code and model-parameter access. https://github.com/google-deepmind/alphafold3 - Network accessline 270
- McNutt et al., *J. Cheminformatics* 13:43 (2021) -- GNINA 1.0. https://doi.org/10.1186/s13321-021-00522-2 - Network accessline 271
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- Lu et al., *NeurIPS* 35 (2022) -- TANKBind. https://proceedings.neurips.cc/paper_files/paper/2022/hash/2f89a23a19d1617e7fb16d4f7a049ce2-Abstract-Conference.html - Network accessline 273
- Qiao et al., *Nat. Mach. Intell.* 6:195-208 (2024) -- NeuralPLexer. https://doi.org/10.1038/s42256-024-00792-z
skills/bioskills/bio-chemoinformatics-ml-docking-rescoring/scripts/ml_hybrid_dock.sh
skills/bioskills/bio-chemoinformatics-molecular-descriptors/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
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repository: https://github.com/GPTomics/bioSkills - Network accessline 234
- Rogers & Hahn, *J. Chem. Inf. Model.* 50:742-754 (2010) -- ECFP fingerprints. https://doi.org/10.1021/ci100050t - Network accessline 235
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- Capecchi et al., *J. Cheminformatics* 12:43 (2020) -- MAP4 fingerprint. https://doi.org/10.1186/s13321-020-00445-4 - Network accessline 237
- MAP4, official package -- installation and current Python interface. https://pypi.org/project/map4/ - Network accessline 238
- OpenFF Recharge, official documentation -- supported charge-model generation and fitting workflows. https://docs.openforcefield.org/projects/recharge/en/stable/ - Network accessline 239
- AutoDock Vina, official documentation -- Vina/Vinardo charge semantics. https://autodock-vina.readthedocs.io/en/stable/ - Network accessline 240
- Gardiner et al., *Future Med. Chem.* 3:405-414 (2011) -- scaffold-hopping fingerprint comparison. https://doi.org/10.4155/fmc.11.4 - Network accessline 241
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- Wu et al., *Chem. Sci.* 9:513-530 (2018) -- MoleculeNet benchmarks. https://doi.org/10.1039/C7SC02664A - Network accessline 244
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- Congreve et al., *Drug Discov. Today* 8:876-877 (2003) -- Rule of 3. https://doi.org/10.1016/S1359-6446(03)02831-9 - Network accessline 252
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- Ritchie & Macdonald, *Drug Discov. Today* 14:1011-1020 (2009) -- aromatic ring count and developability. https://doi.org/10.1016/j.drudis.2009.07.014 - Network accessline 254
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skills/bioskills/bio-chemoinformatics-molecular-descriptors/scripts/calculate_descriptors.py
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#!/usr/bin/env python3
skills/bioskills/bio-chemoinformatics-molecular-io/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 264
- Heller et al., *J. Cheminformatics* 7:23 (2015) -- InChI design, layout, and algorithms; software version 1.04. https://doi.org/10.1186/s13321-015-0068-4 - Network accessline 265
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- InChI Trust, "Technical FAQ" -- InChIKey layout and collision estimates. https://www.inchi-trust.org/technical-faq/ - Network accessline 267
- Daylight Chemical Information Systems, SMILES theory -- atom-local formal-charge and stereochemical syntax. https://www.daylight.com/dayhtml/doc/theory/theory.smiles.html - Network accessline 268
- RDKit Book, "Aromaticity" -- RDKit and MDL aromaticity-model rules. https://www.rdkit.org/docs/RDKit_Book.html#aromaticity - Network accessline 269
- RCSB PDB (2024), "Removal of MMTF files from RCSB PDB." https://www.rcsb.org/news/6661c73362451e4e35915f7b
skills/bioskills/bio-chemoinformatics-molecular-io/scripts/molecule_io.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-chemoinformatics-molecular-standardization/SKILL.md
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- Bento et al., *J. Cheminformatics* 12:51 (2020) -- ChEMBL structure pipeline. https://doi.org/10.1186/s13321-020-00456-1 - Network accessline 303
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- RDKit, `rdkit.Chem.MolStandardize.rdMolStandardize` API documentation. https://www.rdkit.org/docs/source/rdkit.Chem.MolStandardize.rdMolStandardize.html - Network accessline 306
- Open Babel, official `obabel` documentation -- pH-dependent hydrogen-addition CLI. https://openbabel.org/docs/Command-line_tools/babel.html
skills/bioskills/bio-chemoinformatics-molecular-standardization/scripts/standardize_library.py
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skills/bioskills/bio-chemoinformatics-pharmacophore-modeling/SKILL.md
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- RDKit, `Chem.Pharm3D` API documentation. https://www.rdkit.org/docs/source/rdkit.Chem.Pharm3D.html - Network accessline 288
- RDKit, `EmbedPharmacophore` API documentation -- embedding molecules against an existing pharmacophore. https://www.rdkit.org/docs/source/rdkit.Chem.Pharm3D.EmbedLib.html#rdkit.Chem.Pharm3D.EmbedLib.EmbedPharmacophore - Network accessline 289
- COCONUT, official resource -- open natural-products collection. https://coconut.naturalproducts.net/ - Network accessline 290
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- Sunseri & Koes, *Nucleic Acids Res.* 44:W442-W448 (2016) -- Pharmit interactive search. https://doi.org/10.1093/nar/gkw287
skills/bioskills/bio-chemoinformatics-pharmacophore-modeling/scripts/pharmacophore.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-chemoinformatics-pose-validation/SKILL.md
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name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 285
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skills/bioskills/bio-chemoinformatics-pose-validation/scripts/validate_poses.py
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skills/bioskills/bio-chemoinformatics-protac-degraders/SKILL.md
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- King EA et al. "Chemoproteomics-enabled discovery of a covalent molecular glue degrader targeting NF-kappaB." *Cell Chem. Biol.* 30:394–402.e9 (2023). DOI: 10.1016/j.chembiol.2023.02.008. - Abramson J et al. "Accurate structure prediction - Network accessline 264
- Abramson J et al. "Accurate structure prediction of biomolecular interactions with AlphaFold 3." *Nature* 630:493–500 (2024). DOI: 10.1038/s41586-024-07487-w. - REINVENT 4 official repository and installation documentation: https://github - Network accessline 265
- REINVENT 4 official repository and installation documentation: https://github.com/MolecularAI/REINVENT4. - Network accessline 266
- HADDOCK3 official documentation: https://www.bonvinlab.org/haddock3/. - Network accessline 267
- Boltz official repository and documentation: https://github.com/jwohlwend/boltz.
skills/bioskills/bio-chemoinformatics-protac-degraders/scripts/protac_enumerate.py
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#!/usr/bin/env python3
skills/bioskills/bio-chemoinformatics-qsar-modeling/SKILL.md
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repository: https://github.com/GPTomics/bioSkills - Network accessline 341
- Wu Z et al. "MoleculeNet: a benchmark for molecular machine learning." *Chem. Sci.* 9:513–530 (2018). DOI: 10.1039/C7SC02664A. - Ross J, Belgodere B, Chenthamarakshan V, Padhi I, Mroueh Y, Das P. "Large-scale chemical language representat - Network accessline 342
- Ross J, Belgodere B, Chenthamarakshan V, Padhi I, Mroueh Y, Das P. "Large-scale chemical language representations capture molecular structure and properties." *Nat. Mach. Intell.* 4:1256–1264 (2022). DOI: 10.1038/s42256-022-00580-7. - Zho - Network accessline 343
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- Svensson F et al. "Conformal Regression for Quantitative Structure–Activity Relationship Modeling—Quantifying Prediction Uncertainty." *J. Chem. Inf. Model.* 58:1132–1140 (2018). DOI: 10.1021/acs.jcim.8b00054. - Chemprop 2.x CLI documenta - Network accessline 348
- Chemprop 2.x CLI documentation, training and prediction: https://chemprop.readthedocs.io/en/latest/tutorial/cli/. - Network accessline 349
- MAPIE 0.8 documentation for the version-bounded `MapieRegressor` interface: https://mapie.readthedocs.io/en/v0.8.6/.
skills/bioskills/bio-chemoinformatics-qsar-modeling/scripts/chemprop_pipeline.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/bioskills/bio-chemoinformatics-reaction-enumeration/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 356
- Coley CW, Green WH, Jensen KF. "RDChiral: An RDKit Wrapper for Handling Stereochemistry in Retrosynthetic Template Extraction and Application." *J. Chem. Inf. Model.* 59:2529–2537 (2019). DOI: 10.1021/acs.jcim.9b00286. - Dalke A, Hert J, - Network accessline 357
- Dalke A, Hert J, Kramer C. "mmpdb: An Open-Source Matched Molecular Pair Platform for Large Multiproperty Data Sets." *J. Chem. Inf. Model.* 58:902–910 (2018). DOI: 10.1021/acs.jcim.8b00173. - RDKit Book, reaction SMARTS and reaction hand - Network accessline 358
- RDKit Book, reaction SMARTS and reaction handling: https://www.rdkit.org/docs/RDKit_Book.html. - Network accessline 359
- RDKit R-group decomposition API: https://www.rdkit.org/docs/source/rdkit.Chem.rdRGroupDecomposition.html.
skills/bioskills/bio-chemoinformatics-reaction-enumeration/scripts/enumerate_reactions.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-chemoinformatics-retrosynthesis/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 253
- Tu Z et al. "ASKCOS: Open-Source, Data-Driven Synthesis Planning." *Acc. Chem. Res.* 58:1764–1775 (2025). DOI: 10.1021/acs.accounts.5c00155. - Granqvist E, Mercado R, Genheden S. "Retrosynformer: planning multi-step chemical synthesis rou - Network accessline 254
- Granqvist E, Mercado R, Genheden S. "Retrosynformer: planning multi-step chemical synthesis routes via a decision transformer." *Digital Discovery* 5:348–362 (2026). DOI: 10.1039/D5DD00153F. - AiZynthFinder 4.4 Python interface and policy - Network accessline 255
- AiZynthFinder 4.4 Python interface and policy/stock selection: https://molecularai.github.io/aizynthfinder/python_interface.html. - Network accessline 256
- AiZynthFinder configuration documentation: https://molecularai.github.io/aizynthfinder/configuration.html. - Network accessline 257
- Chemformer official archived repository: https://github.com/MolecularAI/Chemformer.
skills/bioskills/bio-chemoinformatics-retrosynthesis/scripts/aizynth_batch.py
skills/bioskills/bio-chemoinformatics-scaffold-analysis/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 309
- Zheng et al., *J. Cheminformatics* 13:87 (2021), DOI 10.1186/s13321-021-00565-5 -- DeepHop conditional scaffold hopping. - Yang K et al., *J. Chem. Inf. Model.* 59:3370-3388 (2019) -- Chemprop molecular-property prediction (DOI 10.1021/ac - Network accessline 310
- Yang K et al., *J. Chem. Inf. Model.* 59:3370-3388 (2019) -- Chemprop molecular-property prediction (DOI 10.1021/acs.jcim.9b00237). - RDKit R-group decomposition API: https://www.rdkit.org/docs/source/rdkit.Chem.rdRGroupDecomposition.html - Network accessline 311
- RDKit R-group decomposition API: https://www.rdkit.org/docs/source/rdkit.Chem.rdRGroupDecomposition.html - Network accessline 312
- Chemprop splitting documentation: https://chemprop.readthedocs.io/en/main/tutorial/python/data/splitting.html
skills/bioskills/bio-chemoinformatics-scaffold-analysis/scripts/scaffold_split.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-chemoinformatics-shape-similarity/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 294
- Vainio, Puranen & Johnson, *J. Chem. Inf. Model.* 49:492-502 (2009), DOI 10.1021/ci800315d -- ShaEP. - Tosco, Balle & Shiri, *J. Comput. Aided Mol. Des.* 25:777-783 (2011), DOI 10.1007/s10822-011-9462-9 -- Open3DALIGN. - RDKit O3A API: ht - Network accessline 295
- Tosco, Balle & Shiri, *J. Comput. Aided Mol. Des.* 25:777-783 (2011), DOI 10.1007/s10822-011-9462-9 -- Open3DALIGN. - RDKit O3A API: https://www.rdkit.org/docs/source/rdkit.Chem.rdMolAlign.html - RDKit shape API: https://www.rdkit.org/doc - Network accessline 296
- RDKit O3A API: https://www.rdkit.org/docs/source/rdkit.Chem.rdMolAlign.html - Network accessline 297
- RDKit shape API: https://www.rdkit.org/docs/source/rdkit.Chem.rdShapeHelpers.html - Network accessline 298
- ShaEP official documentation/examples: https://cheminformatics.fi/ - Network accessline 299
- OpenEye ROCS X product documentation: https://www.eyesopen.com/rocsx
skills/bioskills/bio-chemoinformatics-shape-similarity/scripts/shape_search.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-chemoinformatics-similarity-searching/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 321
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- Tversky, *Psychol. Rev.* 84:327-352 (1977) -- features of similarity (Tversky coefficient). https://doi.org/10.1037/0033-295X.84.4.327 - Network accessline 325
- Probst & Reymond, *J. Cheminformatics* 10:66 (2018) -- MHFP6 MinHash fingerprint. https://doi.org/10.1186/s13321-018-0321-8 - Network accessline 326
- O'Boyle & Sayle, *J. Cheminformatics* 8:36 (2016) -- fingerprint similarity benchmark. https://doi.org/10.1186/s13321-016-0148-0 - Network accessline 327
- Butina, *J. Chem. Inf. Comput. Sci.* 39:747-750 (1999) -- Taylor-Butina clustering. https://doi.org/10.1021/ci9803381 - Network accessline 328
- RDKit, `rdFMCS` API documentation -- MCS ring-comparison parameter names and semantics. https://www.rdkit.org/docs/source/rdkit.Chem.rdFMCS.html
skills/bioskills/bio-chemoinformatics-similarity-searching/scripts/similarity_search.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-chemoinformatics-substructure-search/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 326
- Baell & Holloway, *J. Med. Chem.* 53:2719-2740 (2010) -- original PAINS filter and tier evidence. https://doi.org/10.1021/jm901137j - Network accessline 327
- Capuzzi et al., *J. Chem. Inf. Model.* 57:417-427 (2017) -- PAINS reality check (FDA drug overlap). https://doi.org/10.1021/acs.jcim.6b00465 - Network accessline 328
- Brenk et al., *ChemMedChem* 3:435-444 (2008) -- structural alerts (BRENK filter). https://doi.org/10.1002/cmdc.200700139 - Network accessline 329
- Walters & Murcko, *Adv. Drug Deliv. Rev.* 54:255-271 (2002) -- drug-likeness filtering, including REOS. https://doi.org/10.1016/S0169-409X(02)00003-0 - Network accessline 330
- Bruns & Watson, *J. Med. Chem.* 55:9763-9772 (2012) -- Eli Lilly medchem rules. https://doi.org/10.1021/jm301008n - Network accessline 331
- Daylight Chemical Information Systems, SMARTS theory documentation -- complete grammar reference. https://www.daylight.com/dayhtml/doc/theory/theory.smarts.html
skills/bioskills/bio-chemoinformatics-substructure-search/scripts/substructure_search.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-chemoinformatics-virtual-screening/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 342
- Trott & Olson, *J. Comput. Chem.* 31:455-461 (2010) -- AutoDock Vina. https://doi.org/10.1002/jcc.21334 - Network accessline 343
- Eberhardt et al., *J. Chem. Inf. Model.* 61:3891-3898 (2021) -- Vina 1.2 features. https://doi.org/10.1021/acs.jcim.1c00203 - Network accessline 344
- AutoDock Vina, official manual -- result-field and CLI semantics. https://vina.scripps.edu/manual/ - Network accessline 345
- AutoDock Vina, official zinc-metalloprotein tutorial -- AutoDock4Zn maps through the Vina executable. https://autodock-vina.readthedocs.io/en/latest/docking_zinc.html - Network accessline 346
- Quiroga & Villarreal, *PLoS ONE* 11:e0155183 (2016) -- Vinardo scoring. https://doi.org/10.1371/journal.pone.0155183 - Network accessline 347
- McNutt et al., *J. Cheminformatics* 13:43 (2021) -- GNINA 1.0 CNN docking. https://doi.org/10.1186/s13321-021-00522-2 - Network accessline 348
- GNINA, official repository -- current CLI modes and named CNN ensembles. https://github.com/gnina/gnina - Network accessline 349
- Buttenschoen et al., *Chem. Sci.* 15:3130-3139 (2024) -- PoseBusters benchmark. https://doi.org/10.1039/D3SC04185A - Network accessline 350
- Lyu et al., *Nature* 566:224-229 (2019) -- ultralarge virtual-screening proof of concept. https://doi.org/10.1038/s41586-019-0917-9 - Network accessline 351
- Krivak & Hoksza, *J. Cheminformatics* 10:39 (2018) -- P2Rank. https://doi.org/10.1186/s13321-018-0285-8 - Network accessline 352
- Forli et al., *Nat. Protoc.* 11:905-919 (2016) -- AutoDock suite and AutoDockTools. https://doi.org/10.1038/nprot.2016.051 - Network accessline 353
- Le Guilloux, Schmidtke & Tuffery, *BMC Bioinformatics* 10:168 (2009) -- fpocket. https://doi.org/10.1186/1471-2105-10-168 - Network accessline 354
- Meeko, official documentation -- ligand/receptor PDBQT preparation and export interfaces. https://meeko.readthedocs.io/ - Network accessline 355
- Dolinsky et al., *Nucleic Acids Res.* 35:W522-W525 (2007) -- PDB2PQR. https://doi.org/10.1093/nar/gkm276 - Network accessline 356
- Olsson et al., *J. Chem. Theory Comput.* 7:525-537 (2011) -- PROPKA 3. https://doi.org/10.1021/ct100578z - Network accessline 357
- PDBFixer, official repository -- missing-residue/atom repair interface. https://github.com/openmm/pdbfixer - Network accessline 358
- Corso et al., *ICLR* (2024) -- DiffDock-L. https://proceedings.iclr.cc/paper_files/paper/2024/file/db334db287337b2a365120b524300ef3-Paper-Conference.pdf - Network accessline 359
- Stärk et al., *ICML* (2022) -- EquiBind. https://proceedings.mlr.press/v162/stark22b.html - Network accessline 360
- Qiao et al., *Nat. Mach. Intell.* 6:195-208 (2024) -- NeuralPLexer. https://doi.org/10.1038/s42256-024-00792-z - Network accessline 361
- Passaro et al., bioRxiv (2025) -- Boltz-2. https://doi.org/10.1101/2025.06.14.659707 - Network accessline 362
- Abramson et al., *Nature* 630:493-500 (2024) -- AlphaFold 3. https://doi.org/10.1038/s41586-024-07487-w - Network accessline 363
- ZINC22, official resource. https://zinc22.docking.org/ - Network accessline 364
- Enamine REAL, official resource. https://enamine.net/compound-collections/real-compounds - Network accessline 365
- ChEMBL, official versioned database. https://www.ebi.ac.uk/chembl/
skills/bioskills/bio-chemoinformatics-virtual-screening/scripts/virtual_screen.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-chip-seq-allele-specific-binding/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-chip-seq-allele-specific-binding/references/usage-guide.md
- Network accessline 10
# WASP (mandatory upstream) git clone https://github.com/bmvdgeijn/WASP.git - Network accessline 11
git clone https://github.com/bmvdgeijn/WASP.git - Network accessline 26
# RASQUAL git clone https://github.com/dg13/rasqual.git - Network accessline 27
git clone https://github.com/dg13/rasqual.git - Network accessline 30
# AlleleSeq # https://github.com/gerstein-lab/AlleleSeq - Network accessline 31
# https://github.com/gerstein-lab/AlleleSeq
skills/bioskills/bio-chip-seq-allele-specific-binding/scripts/baalchip_workflow.R
- Executable scriptline 1
#!/usr/bin/env Rscript
skills/bioskills/bio-chip-seq-chip-deep-learning/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-chip-seq-chip-deep-learning/scripts/chrombpnet_variant_effect.py
- Executable scriptline 1
#!/usr/bin/env python
skills/bioskills/bio-chip-seq-chipseq-qc/SKILL.md
- Network accessline 10
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 11
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 12
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-chip-seq-chipseq-qc/scripts/calculate_frip.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-chip-seq-chipseq-qc/scripts/chipseq_qc.py
- Executable scriptline 1
#!/usr/bin/env python - Network accessline 36
position_counts = {} total = 0 for read in bam.fetch(): - Network accessline 37
total = 0 for read in bam.fetch(): if read.is_unmapped or read.is_secondary or read.is_supplementary: - Network accessline 38
for read in bam.fetch(): - Network accessline 63
bam = pysam.AlignmentFile(bam_file, 'rb') sizes = [] for read in bam.fetch(): - Network accessline 64
sizes = [] for read in bam.fetch(): if read.is_proper_pair and read.template_length > 0: - Network accessline 65
for read in bam.fetch():
skills/bioskills/bio-chip-seq-chipseq-qc/scripts/run_idr.sh
skills/bioskills/bio-chip-seq-chipseq-visualization/SKILL.md
- Network accessline 10
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 11
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 12
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-chip-seq-chipseq-visualization/references/usage-guide.md
skills/bioskills/bio-chip-seq-chipseq-visualization/scripts/deeptools_heatmap.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-chip-seq-chromatin-state-segmentation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 181
# Use precomputed ChromHMM segmentations across multiple cell types # Web interface: https://epilogos.altius.org/ - Network accessline 182
# Web interface: https://epilogos.altius.org/
skills/bioskills/bio-chip-seq-chromatin-state-segmentation/references/usage-guide.md
skills/bioskills/bio-chip-seq-chromatin-state-segmentation/scripts/chromhmm_pipeline.sh
- Executable scriptline 1
#!/bin/bash - Network accessline 81
# Use for cross-cell-type compatibility with Roadmap Epigenomics annotations. # Note: the 25-state model is the imputed 12-mark model; provide those 12 marks. # wget https://egg2.wustl.edu/roadmap/data/byFileType/chromhmmSegmentations/ChmmM - Network accessline 82
# Note: the 25-state model is the imputed 12-mark model; provide those 12 marks. # wget https://egg2.wustl.edu/roadmap/data/byFileType/chromhmmSegmentations/ChmmModels/imputed12marks/jointModel/final/model_25_imputed12marks.txt # java -mx16 - Network accessline 83
# wget https://egg2.wustl.edu/roadmap/data/byFileType/chromhmmSegmentations/ChmmModels/imputed12marks/jointModel/final/model_25_imputed12marks.txt
skills/bioskills/bio-chip-seq-cut-and-run-tag/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-chip-seq-cut-and-run-tag/references/usage-guide.md
skills/bioskills/bio-chip-seq-cut-and-run-tag/scripts/cutandrun_pipeline.sh
skills/bioskills/bio-chip-seq-differential-binding/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-chip-seq-differential-binding/scripts/csaw_windows_diff.R
- Executable scriptline 1
#!/usr/bin/env Rscript
skills/bioskills/bio-chip-seq-differential-binding/scripts/pydeseq2_from_counts.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-chip-seq-motif-analysis/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-chip-seq-motif-analysis/references/usage-guide.md
- Network accessline 17
# Motif databases wget https://jaspar.genereg.net/download/data/2024/CORE/JASPAR2024_CORE_vertebrates_non-redundant_pfms_meme.txt - Network accessline 18
wget https://jaspar.genereg.net/download/data/2024/CORE/JASPAR2024_CORE_vertebrates_non-redundant_pfms_meme.txt - Network accessline 19
wget https://hocomoco12.autosome.org/final_bundle/hocomoco12/H12CORE/formatted_motifs/H12CORE_meme_format.meme
skills/bioskills/bio-chip-seq-motif-analysis/scripts/motif_analysis.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-chip-seq-peak-annotation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 176
# Download ENCODE cCRE BED from SCREEN (GRCh38, expanded Registry-V4, uncompressed) wget https://downloads.wenglab.org/Registry-V4/GRCh38-cCREs.bed - Network accessline 177
wget https://downloads.wenglab.org/Registry-V4/GRCh38-cCREs.bed
skills/bioskills/bio-chip-seq-peak-annotation/scripts/annotate_peaks.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-chip-seq-peak-calling/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-chip-seq-peak-calling/scripts/macs3_peak_calling.sh
- Executable scriptline 1
#!/bin/bash - Network accessline 151
BLACKLIST_URL="https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz" wget -nc "$BLACKLIST_URL" || { echo "ERROR: blacklist download failed; check network or URL" >&2; exit 1; } - Network accessline 152
wget -nc "$BLACKLIST_URL" || { echo "ERROR: blacklist download failed; check network or URL" >&2; exit 1; }
skills/bioskills/bio-chip-seq-spike-in-normalization/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-chip-seq-spike-in-normalization/references/usage-guide.md
skills/bioskills/bio-chip-seq-spike-in-normalization/scripts/spikein_chiprx_diffbind.R
- Executable scriptline 1
#!/usr/bin/env Rscript
skills/bioskills/bio-chip-seq-super-enhancers/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 76
# Install ROSE2 (Python 3 port; unmaintained ROSE Py2 not recommended) git clone https://github.com/linlabbcm/rose2.git - Network accessline 77
git clone https://github.com/linlabbcm/rose2.git - Network accessline 133
# Install CRC pipeline (console command is `crc`; -g is the genome BUILD, not a GTF) pip install git+https://github.com/linlabcode/CRC.git - Network accessline 134
pip install git+https://github.com/linlabcode/CRC.git - Network accessline 151
wget https://downloads.wenglab.org/Registry-V4/GRCh38-cCREs.bed
skills/bioskills/bio-chip-seq-super-enhancers/references/usage-guide.md
- Network accessline 10
# ROSE (Python 3). For hg38 data use the stjude/ROSE fork (its genomeDict includes HG38); # run ROSE_main.py from the cloned repo dir so it finds its annotation/ files. git clone https://github.com/stjude/ROSE.git - Network accessline 11
# run ROSE_main.py from the cloned repo dir so it finds its annotation/ files. git clone https://github.com/stjude/ROSE.git # rose2 is pip-installable but its released genomeDict has no HG38 (hg18/hg19/mm*/rn* only): - Network accessline 12
git clone https://github.com/stjude/ROSE.git - Network accessline 22
# LILY (input-subtraction variant; optional) # https://github.com/BoevaLab/LILY - Network accessline 23
# https://github.com/BoevaLab/LILY
skills/bioskills/bio-chip-seq-super-enhancers/scripts/analyze_super_enhancers.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-chip-seq-super-enhancers/scripts/run_rose.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-clinical-biostatistics-adaptive-designs/SKILL.md
- Network accessline 10
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 11
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 12
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-clinical-biostatistics-bayesian-trials/SKILL.md
- Network accessline 10
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 11
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 12
repository: https://github.com/GPTomics/bioSkills - Network accessline 190
# Approximate posterior with mixture for downstream computation map_mix <- automixfit(map_prior, Nc = 2) - Network accessline 191
map_mix <- automixfit(map_prior, Nc = 2)
skills/bioskills/bio-clinical-biostatistics-bayesian-trials/scripts/bayesian_trials.R
- Network accessline 93
# Approximate posterior with mixture for analytical use map_mix <- automixfit(map_fit, Nc = 2) - Network accessline 94
map_mix <- automixfit(map_fit, Nc = 2) - Network accessline 119
beta.prior = cbind(0, 2) ) ess_sens <- ess(automixfit(map_sens, Nc = 2)) - Network accessline 120
) ess_sens <- ess(automixfit(map_sens, Nc = 2)) cat(sprintf('tau_prior = %.2f: ESS = %.1f\n', tau_prior, ess_sens)) - Network accessline 121
ess_sens <- ess(automixfit(map_sens, Nc = 2))
skills/bioskills/bio-clinical-biostatistics-categorical-tests/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-clinical-biostatistics-categorical-tests/scripts/categorical_tests_clinical.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-clinical-biostatistics-cdisc-data-handling/SKILL.md
- Network accessline 10
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 11
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 12
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-clinical-biostatistics-cdisc-data-handling/scripts/cdisc_data_preparation.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-clinical-biostatistics-effect-measures/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-clinical-biostatistics-effect-measures/scripts/effect_measures_clinical.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-clinical-biostatistics-logistic-regression/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-clinical-biostatistics-logistic-regression/scripts/logistic_regression_clinical.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-clinical-biostatistics-missing-data-sensitivity/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-clinical-biostatistics-missing-data-sensitivity/scripts/missing_data_sensitivity.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-clinical-biostatistics-multiplicity-graphical/SKILL.md
- Network accessline 10
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 11
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 12
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-clinical-biostatistics-power-and-sample-size/SKILL.md
- Network accessline 10
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 11
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 12
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-clinical-biostatistics-power-and-sample-size/scripts/power_sample_size.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-clinical-biostatistics-subgroup-analysis/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-clinical-biostatistics-subgroup-analysis/scripts/subgroup_analysis_clinical.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-clinical-biostatistics-survival-analysis/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-clinical-biostatistics-survival-analysis/scripts/survival_clinical.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-clinical-biostatistics-trial-reporting/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-clinical-biostatistics-trial-reporting/scripts/trial_reporting_clinical.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-clinical-databases-acmg-classification/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 24
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. VCEP-specific CSpec rules override default ACMG application; the authoritative di - Network accessline 30
- Python (automated): GeneBe API `https://api.genebe.net/cloud/api-public/v1/variant` - Network accessline 156
**ClinGen VCEP CSpec authoritative registry:** `https://cspec.genome.network/cspec/ui/svi/all`. ~80-90 VCEPs as of 2025. Examples: - Network accessline 302
VarSome (which is commercial, 82% ACMG criteria auto-application rate). ''' r = requests.get(f'https://api.genebe.net/cloud/api-public/v1/variant', - Network accessline 303
''' r = requests.get(f'https://api.genebe.net/cloud/api-public/v1/variant', params={'variant': hgvs, 'genome': 'hg38'}, - Network accessline 304
r = requests.get(f'https://api.genebe.net/cloud/api-public/v1/variant', - Network accessline 354
- Mechanism: VCEP CSpec overrides for the gene; e.g., Hearing Loss VCEP PM2 default = supporting, BA1 = 0.5%. - Symptom: Wrong strength applied; misclassification. - Fix: Check `https://cspec.genome.network/cspec/ui/svi/all` for active VCEP - Network accessline 356
- Fix: Check `https://cspec.genome.network/cspec/ui/svi/all` for active VCEP; apply gene-specific CSpec. - Network accessline 464
- Li Q, Wang K. 2017. InterVar: clinical interpretation of genetic variants. *Am J Hum Genet* 100:267. - Xiang J et al. 2020. AutoPVS1 -- automated PVS1 decision-tree implementation (verify exact venue/year against the published code/releas - Network accessline 465
- Xiang J et al. 2020. AutoPVS1 -- automated PVS1 decision-tree implementation (verify exact venue/year against the published code/release). - ClinGen CSpec Registry: `https://cspec.genome.network/cspec/ui/svi/all` - ClinGen VCI (Variant Cu - Network accessline 466
- ClinGen CSpec Registry: `https://cspec.genome.network/cspec/ui/svi/all` - Network accessline 467
- ClinGen VCI (Variant Curation Interface): `https://curation.clinicalgenome.org/`
skills/bioskills/bio-clinical-databases-acmg-classification/references/usage-guide.md
- Network accessline 12
# Automated ACMG classifiers # GeneBe (open-source): https://genebe.net (API key optional) - Network accessline 13
# GeneBe (open-source): https://genebe.net (API key optional) - Network accessline 14
# InterVar: git clone https://github.com/WGLab/InterVar.git - Network accessline 15
# VarSome (commercial, hosted) # Franklin (Genoox, commercial, hosted) # ClinGen VCI (gold standard for expert curation): https://curation.clinicalgenome.org/ - Network accessline 17
# ClinGen VCI (gold standard for expert curation): https://curation.clinicalgenome.org/ - Network accessline 19
# AutoPVS1 for VCEP-specific PVS1 decision trees # https://autopvs1.bgi.com/ - Network accessline 20
# https://autopvs1.bgi.com/
skills/bioskills/bio-clinical-databases-acmg-classification/scripts/acmg_classify.py
skills/bioskills/bio-clinical-databases-clinvar-lookup/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 30
- Python (REST): `requests.get()` against the E-utilities `clinvar` database - Network accessline 31
- Python (local VCF): `cyvcf2.VCF('clinvar.vcf.gz')` for batch queries against the weekly snapshot - CLI: `bcftools annotate -a clinvar.vcf.gz -c INFO/CLNSIG,INFO/CLNREVSTAT,INFO/CLNDN` - Cross-database: ClinGen Allele Registry CA ID via `h - Network accessline 33
- Cross-database: ClinGen Allele Registry CA ID via `https://reg.clinicalgenome.org/` - Network accessline 61
As of 2025, ~80-90 VCEPs are approved or in progress across RASopathies, hereditary cancer (ENIGMA BRCA1/2, InSiGHT MMR), cardiomyopathy (sarcomere genes), hearing loss, RPE65/IRD, inborn errors of metabolism, and FH. The current count is m - Network accessline 97
EUTILS = 'https://eutils.ncbi.nlm.nih.gov/entrez/eutils' - Network accessline 106
pinning these key paths in production. ''' r = requests.get(f'{EUTILS}/esummary.fcgi', - Network accessline 107
''' r = requests.get(f'{EUTILS}/esummary.fcgi', params={'db': 'clinvar', 'id': variation_id, 'retmode': 'json'}, - Network accessline 108
r = requests.get(f'{EUTILS}/esummary.fcgi', - Network accessline 125
if pathogenic_only: term += ' AND (clinsig_pathogenic[Properties] OR clinsig_likely_pathogenic[Properties])' r = requests.get(f'{EUTILS}/esearch.fcgi', - Network accessline 126
term += ' AND (clinsig_pathogenic[Properties] OR clinsig_likely_pathogenic[Properties])' r = requests.get(f'{EUTILS}/esearch.fcgi', params={'db': 'clinvar', 'term': term, 'retmax': retmax, 'retmode': 'json'}, - Network accessline 127
r = requests.get(f'{EUTILS}/esearch.fcgi', - Network accessline 140
mkdir -p clinvar/$(date +%Y%m); cd clinvar/$(date +%Y%m) wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz - Network accessline 141
wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz - Network accessline 142
wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz.tbi - Network accessline 180
ClinGen Allele Registry (`https://reg.clinicalgenome.org/`) computes a build-agnostic, transcript-agnostic CA ID (format `CA######`) for any allele projectable onto NCBI references (GRCh37, GRCh38, T2T-CHM13, any RefSeq transcript). The Reg - Network accessline 183
def car_id(hgvs_g): '''Resolve HGVS-g to canonical ClinGen Allele Registry CA ID.''' r = requests.put(f'https://reg.clinicalgenome.org/allele', - Network accessline 184
'''Resolve HGVS-g to canonical ClinGen Allele Registry CA ID.''' r = requests.put(f'https://reg.clinicalgenome.org/allele', headers={'Content-Type': 'text/plain'}, - Network accessline 185
r = requests.put(f'https://reg.clinicalgenome.org/allele', - Network accessline 306
- Brnich SE et al. 2020. Recommendations for application of the functional evidence PS3/BS3 criterion using the ACMG/AMP sequence variant interpretation framework. *Genome Med* 12:3. - Li MM et al. 2017. Standards and guidelines for the int - Network accessline 307
- Li MM et al. 2017. Standards and guidelines for the interpretation and reporting of sequence variants in cancer. *J Mol Diagn* 19:4. (AMP/ASCO/CAP somatic framework) - ClinGen Allele Registry: `https://reg.clinicalgenome.org/docs/cg-car/` - Network accessline 308
- ClinGen Allele Registry: `https://reg.clinicalgenome.org/docs/cg-car/` - Network accessline 309
- CSpec Registry: `https://cspec.genome.network/cspec/ui/svi/all`
skills/bioskills/bio-clinical-databases-clinvar-lookup/references/usage-guide.md
- Network accessline 12
# Local VCF (weekly snapshot; pin to monthly archive for reproducibility) mkdir -p clinvar/$(date +%Y%m); cd clinvar/$(date +%Y%m) wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz - Network accessline 13
mkdir -p clinvar/$(date +%Y%m); cd clinvar/$(date +%Y%m) wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz.tbi - Network accessline 14
wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz - Network accessline 15
wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz.tbi - Network accessline 17
# For reproducibility, use the monthly archive wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/archive_2.0/2025/clinvar_20251101.vcf.gz - Network accessline 18
wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/archive_2.0/2025/clinvar_20251101.vcf.gz - Network accessline 77
- Star rating 3 (VCEP) supersedes lower-star records per ClinGen FDA Recognition 2018; do not auto-aggregate by date. - `Conflicting interpretations` is 1-star (not 2-star as sometimes reported); inspect `CLNSIGCONF` to see whether the conf - Network accessline 78
- `Conflicting interpretations` is 1-star (not 2-star as sometimes reported); inspect `CLNSIGCONF` to see whether the conflict is clinically meaningful. - Use ClinGen Allele Registry CA ID (`https://reg.clinicalgenome.org/`) for any cross-d - Network accessline 79
- Use ClinGen Allele Registry CA ID (`https://reg.clinicalgenome.org/`) for any cross-database join; ClinVar VariationID was renumbered during the 2017 schema redesign.
skills/bioskills/bio-clinical-databases-clinvar-lookup/scripts/clinvar_query.py
- Executable scriptline 1
Executable file - Network accessline 11
EUTILS = 'https://eutils.ncbi.nlm.nih.gov/entrez/eutils' - Network accessline 12
ALLELE_REG = 'https://reg.clinicalgenome.org' - Network accessline 27
r = requests.get(f'{EUTILS}/esummary.fcgi', - Network accessline 47
r = requests.put(f'{ALLELE_REG}/allele', - Network accessline 91
continue time.sleep(sleep) car_payload = requests.get(f'{ALLELE_REG}/allele/{ca}', timeout=30).json() - Network accessline 92
time.sleep(sleep) car_payload = requests.get(f'{ALLELE_REG}/allele/{ca}', timeout=30).json() externals = car_payload.get('externalRecords', {}) - Network accessline 93
car_payload = requests.get(f'{ALLELE_REG}/allele/{ca}', timeout=30).json()
skills/bioskills/bio-clinical-databases-dbsnp-queries/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 30
- Python (aggregator): `myvariant.MyVariantInfo().getvariant(rsid, fields=['dbsnp', 'clinvar', 'gnomad_exome'])` - Python (direct): `requests.get(f'https://api.ncbi.nlm.nih.gov/variation/v0/refsnp/{rsid_int}')` - Network accessline 31
- Python (direct): `requests.get(f'https://api.ncbi.nlm.nih.gov/variation/v0/refsnp/{rsid_int}')` - Network accessline 105
VARSVC = 'https://api.ncbi.nlm.nih.gov/variation/v0' - Network accessline 108
'''Fetch full Build 156 RefSNP JSON. rsid can be 'rs121913529' or 121913529.''' rs_int = str(rsid).lstrip('rs') r = requests.get(f'{VARSVC}/refsnp/{rs_int}', timeout=30) - Network accessline 109
rs_int = str(rsid).lstrip('rs') r = requests.get(f'{VARSVC}/refsnp/{rs_int}', timeout=30) if r.status_code == 404: - Network accessline 110
r = requests.get(f'{VARSVC}/refsnp/{rs_int}', timeout=30) - Network accessline 191
def hgvs_to_spdi_canonical(hgvs): '''Resolve HGVS to canonical SPDI via the Variant Overprecision Correction Algorithm.''' r = requests.get(f'{VARSVC}/hgvs/{hgvs}/contextuals', timeout=30) - Network accessline 192
'''Resolve HGVS to canonical SPDI via the Variant Overprecision Correction Algorithm.''' r = requests.get(f'{VARSVC}/hgvs/{hgvs}/contextuals', timeout=30) if not r.ok: - Network accessline 193
r = requests.get(f'{VARSVC}/hgvs/{hgvs}/contextuals', timeout=30) - Network accessline 199
def spdi_to_rsid(spdi_str): '''SPDI 'NC_000017.11:43044294:G:A' -> rsID if a cluster exists.''' r = requests.get(f'{VARSVC}/spdi/{spdi_str}/rsids', timeout=30) - Network accessline 200
'''SPDI 'NC_000017.11:43044294:G:A' -> rsID if a cluster exists.''' r = requests.get(f'{VARSVC}/spdi/{spdi_str}/rsids', timeout=30) if not r.ok: - Network accessline 201
r = requests.get(f'{VARSVC}/spdi/{spdi_str}/rsids', timeout=30) - Network accessline 212
return None raw_spdi = f'{refseq}:{pos - 1}:{ref}:{alt}' r = requests.get(f'{VARSVC}/spdi/{raw_spdi}/canonical_representative', timeout=30) - Network accessline 213
raw_spdi = f'{refseq}:{pos - 1}:{ref}:{alt}' r = requests.get(f'{VARSVC}/spdi/{raw_spdi}/canonical_representative', timeout=30) return r.json().get('data', {}).get('spdi') if r.ok else None - Network accessline 214
r = requests.get(f'{VARSVC}/spdi/{raw_spdi}/canonical_representative', timeout=30) - Network accessline 331
- Sayers EW et al. 2024. Database resources of the National Center for Biotechnology Information. *Nucleic Acids Res* 52:D33. - Holmes JB et al. 2020. SPDI: data model for variants and applications at NCBI. *Bioinformatics* 36:1902. - NCBI - Network accessline 332
- Holmes JB et al. 2020. SPDI: data model for variants and applications at NCBI. *Bioinformatics* 36:1902. - NCBI Variation Services API: `https://api.ncbi.nlm.nih.gov/variation/v0/` - dbSNP FTP layout: `ftp.ncbi.nlm.nih.gov/snp/latest_rele - Network accessline 333
- NCBI Variation Services API: `https://api.ncbi.nlm.nih.gov/variation/v0/` - Network accessline 334
- dbSNP FTP layout: `ftp.ncbi.nlm.nih.gov/snp/latest_release/JSON/` - ALFA release notes: `https://www.ncbi.nlm.nih.gov/snp/docs/gsr/alfa/` - ClinGen Allele Registry: `https://reg.clinicalgenome.org/docs/cg-car/` - Network accessline 335
- ALFA release notes: `https://www.ncbi.nlm.nih.gov/snp/docs/gsr/alfa/` - Network accessline 336
- ClinGen Allele Registry: `https://reg.clinicalgenome.org/docs/cg-car/`
skills/bioskills/bio-clinical-databases-dbsnp-queries/references/usage-guide.md
- Network accessline 12
# Optional: bulk download for genome-wide rsID lookup (each ~5-20GB compressed) wget ftp://ftp.ncbi.nlm.nih.gov/snp/latest_release/JSON/refsnp-chr17.json.bz2 - Network accessline 13
wget ftp://ftp.ncbi.nlm.nih.gov/snp/latest_release/JSON/refsnp-chr17.json.bz2 - Network accessline 14
wget ftp://ftp.ncbi.nlm.nih.gov/snp/latest_release/JSON/refsnp-merged.json.bz2 - Network accessline 15
wget ftp://ftp.ncbi.nlm.nih.gov/snp/latest_release/JSON/refsnp-withdrawn.json.bz2
skills/bioskills/bio-clinical-databases-dbsnp-queries/scripts/dbsnp_lookup.py
- Executable scriptline 1
Executable file - Network accessline 11
VARSVC = 'https://api.ncbi.nlm.nih.gov/variation/v0' - Network accessline 25
rs_int = str(rsid).lstrip('rs') r = requests.get(f'{VARSVC}/refsnp/{rs_int}', timeout=30) - Network accessline 26
r = requests.get(f'{VARSVC}/refsnp/{rs_int}', timeout=30) - Network accessline 98
return None raw_spdi = f'{refseq}:{pos - 1}:{ref}:{alt}' r = requests.get(f'{VARSVC}/spdi/{raw_spdi}/canonical_representative', timeout=30) - Network accessline 99
raw_spdi = f'{refseq}:{pos - 1}:{ref}:{alt}' r = requests.get(f'{VARSVC}/spdi/{raw_spdi}/canonical_representative', timeout=30) time.sleep(0.34) - Network accessline 100
r = requests.get(f'{VARSVC}/spdi/{raw_spdi}/canonical_representative', timeout=30) - Network accessline 109
r = requests.get(f'{VARSVC}/spdi/{spdi_str}/rsids', timeout=30) - Network accessline 119
r = requests.get(f'{VARSVC}/hgvs/{hgvs}/contextuals', timeout=30)
skills/bioskills/bio-clinical-databases-gnomad-frequencies/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 24
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. The gnomAD browser GraphQL API at `https://gnomad.broadinstitute.org/api` is the - Network accessline 30
- Python (single variant): GraphQL via `requests.post('https://gnomad.broadinstitute.org/api', json={'query': ..., 'variables': ...})` - Network accessline 147
GNOMAD_API = 'https://gnomad.broadinstitute.org/api' - Network accessline 179
''' variant_id = f'{chrom}-{pos}-{ref}-{alt}' r = requests.post(GNOMAD_API, - Network accessline 180
variant_id = f'{chrom}-{pos}-{ref}-{alt}' r = requests.post(GNOMAD_API, json={'query': query, 'variables': {'variantId': variant_id, 'dataset': dataset}}, - Network accessline 181
r = requests.post(GNOMAD_API, - Network accessline 269
} ''' r = requests.post(GNOMAD_API, - Network accessline 270
''' r = requests.post(GNOMAD_API, json={'query': query, 'variables': {'symbol': gene_symbol}}, - Network accessline 271
r = requests.post(GNOMAD_API, - Network accessline 418
- Laricchia KM et al. 2022. Mitochondrial DNA variation across 56,434 individuals in gnomAD. *Genome Res* 32:569. - Whiffin N et al. 2017. Using high-resolution variant frequencies to empower clinical genome interpretation. *Genet Med* 19:1 - Network accessline 419
- Whiffin N et al. 2017. Using high-resolution variant frequencies to empower clinical genome interpretation. *Genet Med* 19:1151. - ClinGen guidance on gnomAD v4 (March 2024): `https://clinicalgenome.org/site/assets/files/9445/clingen_guid - Network accessline 420
- ClinGen guidance on gnomAD v4 (March 2024): `https://clinicalgenome.org/site/assets/files/9445/clingen_guidance_to_vceps_regarding_the_use_of_gnomad_v4_march_2024.pdf` - Network accessline 421
- gnomAD v4 release notes: `https://gnomad.broadinstitute.org/news/2023-11-gnomad-v4-0/` - Network accessline 422
- gnomAD v4.1 updates: `https://gnomad.broadinstitute.org/news/2024-05-gnomad-v4-1-updates/`
skills/bioskills/bio-clinical-databases-gnomad-frequencies/scripts/gnomad_query.py
- Executable scriptline 1
Executable file - Network accessline 10
GNOMAD_API = 'https://gnomad.broadinstitute.org/api' - Network accessline 45
''' variant_id = f'{chrom}-{pos}-{ref}-{alt}' r = requests.post(GNOMAD_API, json={'query': query, 'variables': {'variantId': variant_id}}, timeout=30) - Network accessline 46
variant_id = f'{chrom}-{pos}-{ref}-{alt}' r = requests.post(GNOMAD_API, json={'query': query, 'variables': {'variantId': variant_id}}, timeout=30) r.raise_for_status() - Network accessline 47
r = requests.post(GNOMAD_API, json={'query': query, 'variables': {'variantId': variant_id}}, timeout=30) - Network accessline 130
} ''' r = requests.post(GNOMAD_API, json={'query': query, 'variables': {'symbol': gene_symbol}}, timeout=30) - Network accessline 131
''' r = requests.post(GNOMAD_API, json={'query': query, 'variables': {'symbol': gene_symbol}}, timeout=30) r.raise_for_status() - Network accessline 132
r = requests.post(GNOMAD_API, json={'query': query, 'variables': {'symbol': gene_symbol}}, timeout=30)
skills/bioskills/bio-clinical-databases-hla-typing/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-clinical-databases-hla-typing/references/usage-guide.md
skills/bioskills/bio-clinical-databases-hla-typing/scripts/optitype_workflow.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-clinical-databases-hla-typing/scripts/t1k_workflow.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-clinical-databases-msi-detection/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-clinical-databases-msi-detection/references/usage-guide.md
- Network accessline 16
# MSIsensor-ct (liquid biopsy) # git clone https://github.com/niu-lab/msisensor-ct - Network accessline 17
# git clone https://github.com/niu-lab/msisensor-ct - Network accessline 19
# MANTIS (alternative paired WES) # git clone https://github.com/OSU-SRLab/MANTIS - Network accessline 20
# git clone https://github.com/OSU-SRLab/MANTIS - Network accessline 22
# mSINGS (background-panel tumor-only) # https://bitbucket.org/uwlabmed/msings/ - Network accessline 23
# https://bitbucket.org/uwlabmed/msings/
skills/bioskills/bio-clinical-databases-msi-detection/scripts/msi_detection.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-clinical-databases-myvariant-queries/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 30
- Python (batch): `mv.getvariants(ids_list, fields=...)`; up to 1000 IDs per request - Python (search): `mv.query('clinvar.gene.symbol:BRCA1 AND clinvar.clinical_significance:Pathogenic')` - REST: `GET https://myvariant.info/v1/variant/{hgv - Network accessline 31
- Python (search): `mv.query('clinvar.gene.symbol:BRCA1 AND clinvar.clinical_significance:Pathogenic')` - REST: `GET https://myvariant.info/v1/variant/{hgvs_or_id}?fields=...` - Bulk: `POST https://myvariant.info/v1/variant` with comma-sepa - Network accessline 32
- REST: `GET https://myvariant.info/v1/variant/{hgvs_or_id}?fields=...` - Network accessline 33
- Bulk: `POST https://myvariant.info/v1/variant` with comma-separated IDs - Network accessline 314
- Kopanos C et al. 2019. VarSome: the human genomic variant search engine. *Bioinformatics* 35:1978. - Stawiński P, Płoski R. 2024. Genebe.net: implementation and validation of an automatic ACMG variant pathogenicity criteria assignment. *C - Network accessline 315
- Stawiński P, Płoski R. 2024. Genebe.net: implementation and validation of an automatic ACMG variant pathogenicity criteria assignment. *Clin Genet* 106:119. - myvariant.info docs: `https://docs.myvariant.info/en/latest/` - BioThings field - Network accessline 316
- myvariant.info docs: `https://docs.myvariant.info/en/latest/` - Network accessline 317
- BioThings field metadata: `https://myvariant.info/v1/metadata/fields`
skills/bioskills/bio-clinical-databases-myvariant-queries/scripts/query_myvariant.py
skills/bioskills/bio-clinical-databases-pharmacogenomics/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 24
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. PharmVar is the authoritative star-allele source (`https://www.pharmvar.org`); th - Network accessline 33
- CLI (long-read 8-field): PacBio HiFi `starphase`; transplant-grade including HLA - R (SNP-array): HIBAG for HLA-B*57:01/B*15:02/B*58:01/A*31:01 imputation - API: `requests.get('https://api.pharmgkb.org/v1/data/clinicalAnnotation', ...)` - Network accessline 35
- API: `requests.get('https://api.pharmgkb.org/v1/data/clinicalAnnotation', ...)` - Network accessline 71
PharmVar (`https://www.pharmvar.org`) is authoritative for: CYP1A1, CYP1A2, CYP1B1, CYP2A6, CYP2A13, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP2F1, CYP2J2, CYP2R1, CYP2S1, CYP2W1, CYP3A4, CYP3A5, CYP3A7, CYP3A43, CYP4A11, CYP4F2, - Network accessline 354
PHARMGKB = 'https://api.pharmgkb.org/v1' - Network accessline 357
def clinical_annotation(gene_symbol): '''Query PharmGKB clinical annotations by gene.''' r = requests.get(f'{PHARMGKB}/data/clinicalAnnotation', - Network accessline 358
'''Query PharmGKB clinical annotations by gene.''' r = requests.get(f'{PHARMGKB}/data/clinicalAnnotation', params={'view': 'base', 'location.genes.symbol': gene_symbol}, - Network accessline 359
r = requests.get(f'{PHARMGKB}/data/clinicalAnnotation', - Network accessline 365
def cpic_guideline(gene_symbol): '''Query CPIC guidelines via PharmGKB.''' r = requests.get(f'{PHARMGKB}/data/guideline', - Network accessline 366
'''Query CPIC guidelines via PharmGKB.''' r = requests.get(f'{PHARMGKB}/data/guideline', params={'view': 'base', 'relatedGenes.symbol': gene_symbol, 'source': 'CPIC'}, - Network accessline 367
r = requests.get(f'{PHARMGKB}/data/guideline', - Network accessline 493
- Yang JJ et al. 2015. Inherited NUDT15 variant is a genetic determinant of mercaptopurine intolerance. *J Clin Oncol* 33:1235. - Relling MV et al. 2019. CPIC guideline for thiopurine dosing based on TPMT and NUDT15 genotypes: 2018 update. - Network accessline 494
- Relling MV et al. 2019. CPIC guideline for thiopurine dosing based on TPMT and NUDT15 genotypes: 2018 update. *Clin Pharmacol Ther* 105:1095. - PharmCAT documentation: `https://pharmcat.org` - PharmVar: `https://www.pharmvar.org` - Network accessline 495
- PharmCAT documentation: `https://pharmcat.org` - Network accessline 496
- PharmVar: `https://www.pharmvar.org` - Network accessline 497
- CPIC: `https://cpicpgx.org` - Network accessline 498
- DPWG: `https://www.knmp.nl/dpwg`
skills/bioskills/bio-clinical-databases-pharmacogenomics/references/usage-guide.md
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# PharmCAT (CPIC-recommended multi-gene) java -jar pharmcat.jar --help # Download from https://pharmcat.org - Network accessline 12
# Download from https://pharmcat.org - Network accessline 21
# HLA imputation from SNP arrays # R: install.packages('HIBAG') # Download ancestry-matched reference panel from https://hibag.s3.amazonaws.com/ - Network accessline 23
# Download ancestry-matched reference panel from https://hibag.s3.amazonaws.com/
skills/bioskills/bio-clinical-databases-pharmacogenomics/scripts/pharmgkb_query.py
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PHARMGKB = 'https://api.pharmgkb.org/v1' - Dangerous shell operationline 82
'''CYP2C19 diplotype -> phenotype. Note RM phenotype bin between NM and UM.''' - Network accessline 117
r = requests.get(f'{PHARMGKB}/data/clinicalAnnotation', - Network accessline 126
r = requests.get(f'{PHARMGKB}/data/guideline',
skills/bioskills/bio-clinical-databases-polygenic-risk/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 448
- Mahajan A et al. 2022. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations. *Nat Genet* 54:560. (DIAMANTE) - Suzuki K et al. 2024. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology - Network accessline 449
- Suzuki K et al. 2024. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology. *Nature* 627:347. (Largest T2D multi-ancestry GWAS) - PGS Catalog: `https://www.pgscatalog.org` - PGS Catalog Calculator: `https://github.com/PGSca - Network accessline 450
- PGS Catalog: `https://www.pgscatalog.org` - Network accessline 451
- PGS Catalog Calculator: `https://github.com/PGScatalog/pgsc_calc` - Network accessline 452
- Federal Register August 2025 Cancer Predisposition Risk Assessment System: `https://www.federalregister.gov/documents/2025/08/21/2025-16035/`
skills/bioskills/bio-clinical-databases-polygenic-risk/references/usage-guide.md
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# PRS-CS / PRS-CSx git clone https://github.com/getian107/PRScs.git - Network accessline 17
git clone https://github.com/getian107/PRScs.git - Network accessline 18
git clone https://github.com/getian107/PRScsx.git - Network accessline 21
# SBayesRC (GCTB) # Download from https://cnsgenomics.com/software/gctb/ - Network accessline 22
# Download from https://cnsgenomics.com/software/gctb/ - Network accessline 25
# Multi-ancestry # PROSPER: https://github.com/Jingning-Zhang/PROSPER - Network accessline 26
# PROSPER: https://github.com/Jingning-Zhang/PROSPER - Network accessline 27
# MUSSEL: https://github.com/Jin93/MUSSEL - Network accessline 28
# BridgePRS: https://github.com/clivehoggart/BridgePRS - Network accessline 29
# JointPRS: https://github.com/LeqiXu/JointPRS - Network accessline 31
# Sample overlap # Choi 2023 EraSOR: https://github.com/choishingwan/EraSOR - Network accessline 32
# Choi 2023 EraSOR: https://github.com/choishingwan/EraSOR
skills/bioskills/bio-clinical-databases-polygenic-risk/scripts/ldpred2_analysis.R
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# 3. Restrict to HapMap3 if using PRS-CS/LDpred2 default reference (recommended for stability) if (hapmap3_only) { hm3 <- fread("https://github.com/privefl/bigsnpr/raw/master/data-raw/hm3.csv") - Network accessline 22
if (hapmap3_only) { hm3 <- fread("https://github.com/privefl/bigsnpr/raw/master/data-raw/hm3.csv") sumstats <- sumstats[rsid %in% hm3$rsid] - Network accessline 23
hm3 <- fread("https://github.com/privefl/bigsnpr/raw/master/data-raw/hm3.csv")
skills/bioskills/bio-clinical-databases-polygenic-risk/scripts/prsice2_workflow.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-clinical-databases-somatic-signatures/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 387
- Hodel KP et al. 2020. POLE mutation spectra are shaped by the mutant allele identity, its abundance, and mismatch repair status. *Mol Cell* 78:1166. - (FFPE-induced C>T mutational artifact: the earlier "Guyard 2022 Nat Commun" attribution - Network accessline 389
- COSMIC Signatures: `https://cancer.sanger.ac.uk/signatures/`
skills/bioskills/bio-clinical-databases-somatic-signatures/references/usage-guide.md
skills/bioskills/bio-clinical-databases-somatic-signatures/scripts/mutsig_fitting.R
skills/bioskills/bio-clinical-databases-somatic-signatures/scripts/sigprofiler_analysis.py
- Executable scriptline 1
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skills/bioskills/bio-clinical-databases-tumor-mutational-burden/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Dangerous shell operationline 423
- Samstein RM et al. 2019. TMB and survival after immunotherapy across cancer types. *Nat Genet* 51:202. - Network accessline 433
- Montesion M et al. 2021. Somatic HLA class I loss is a widespread mechanism of immune evasion which refines the use of TMB as a biomarker. *Cancer Discov* 11:282. - Pyke RM et al. 2022. A machine learning algorithm with subclonal sensitiv - Dangerous shell operationline 434
- Pyke RM et al. 2022. A machine learning algorithm with subclonal sensitivity reveals widespread pan-cancer HLA loss of heterozygosity. *Nat Commun* 13:1925. (DASH) - Network accessline 435
- Friends of Cancer Research TMB harmonization resources: `https://friendsofcancerresearch.org/tmb/`
skills/bioskills/bio-clinical-databases-tumor-mutational-burden/references/usage-guide.md
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# HLA-LOH # LOHHLA: https://github.com/mskcc/lohhla - Network accessline 17
# LOHHLA: https://github.com/mskcc/lohhla - Network accessline 18
# DASH: https://github.com/RamratnamLab/DASH - Network accessline 20
# Friends of Cancer Research TMB harmonization tool # https://friendsofcancerresearch.org/tmb/ - Network accessline 21
# https://friendsofcancerresearch.org/tmb/
skills/bioskills/bio-clinical-databases-tumor-mutational-burden/scripts/tmb_calculation.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-clinical-databases-variant-prioritization/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 33
- CLI (compound het phasing): `whatshap phase --indels` for singletons; trio-based for families - Python (HPO concordance): Phen2Gene / AMELIE / Phenolyzer - VCEP curations: `https://cspec.genome.network/cspec/ui/svi/all` - Network accessline 35
- VCEP curations: `https://cspec.genome.network/cspec/ui/svi/all` - Network accessline 109
**Many commercial panels include genes with only Limited validity.** ClinGen-curated `https://search.clinicalgenome.org/kb/gene-validity` is the authoritative directory. - Network accessline 250
validity_table: DataFrame from `https://search.clinicalgenome.org/kb/gene-validity` - Network accessline 393
- Lunke S et al. 2025. Feasibility, acceptability and clinical outcomes of the BabyScreen+ genomic newborn screening study. *Nat Med* 31:4236. - Yauy K et al. 2022. Genome Alert! *Genet Med* 24:1316. (VUS reclassification monitoring) - Clin - Network accessline 394
- Yauy K et al. 2022. Genome Alert! *Genet Med* 24:1316. (VUS reclassification monitoring) - ClinGen gene-disease validity: `https://search.clinicalgenome.org/kb/gene-validity` - HPO: `https://hpo.jax.org/` - Network accessline 395
- ClinGen gene-disease validity: `https://search.clinicalgenome.org/kb/gene-validity` - Network accessline 396
- HPO: `https://hpo.jax.org/` - Network accessline 397
- ACMG SF v3.2 supplement: `https://www.gimjournal.org/article/S1098-3600(23)00879-1/fulltext`
skills/bioskills/bio-clinical-databases-variant-prioritization/references/usage-guide.md
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# De novo callers conda install -c bioconda denovogear # Triodenovo: https://genome.sph.umich.edu/wiki/Triodenovo - Network accessline 13
conda install -c bioconda denovogear # Triodenovo: https://genome.sph.umich.edu/wiki/Triodenovo # DeNovoCNN: https://github.com/Genomics-CISDC/DeNovoCNN - Network accessline 14
# Triodenovo: https://genome.sph.umich.edu/wiki/Triodenovo - Network accessline 15
# DeNovoCNN: https://github.com/Genomics-CISDC/DeNovoCNN - Network accessline 17
# Phenotype-driven # Exomiser: https://www.sanger.ac.uk/tool/exomiser/ - Network accessline 18
# Exomiser: https://www.sanger.ac.uk/tool/exomiser/ - Network accessline 20
# AMELIE: https://amelie.stanford.edu/ - Network accessline 25
# HPO + ClinGen # Download HPO: wget http://purl.obolibrary.org/obo/hp.obo - Network accessline 26
# Download HPO: wget http://purl.obolibrary.org/obo/hp.obo - Network accessline 27
# ClinGen validity: https://search.clinicalgenome.org/kb/gene-validity
skills/bioskills/bio-clinical-databases-variant-prioritization/scripts/prioritize_variants.py
- Executable scriptline 1
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skills/bioskills/bio-clip-seq-ago-clip-mirna-targets/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-clip-seq-ago-clip-mirna-targets/references/usage-guide.md
- Network accessline 10
conda install -c bioconda samtools bedtools # Hyb pipeline for chimeras git clone https://github.com/gkudla/hyb - Network accessline 11
# Hyb pipeline for chimeras git clone https://github.com/gkudla/hyb # miR-eCLIP analysis from Yeo lab - Network accessline 12
git clone https://github.com/gkudla/hyb
skills/bioskills/bio-clip-seq-ago-clip-mirna-targets/scripts/run_chimeric_eclip.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-clip-seq-binding-site-annotation/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 126
# Yeo lab rbp-maps Snakemake workflow git clone https://github.com/YeoLab/rbp-maps - Network accessline 127
git clone https://github.com/YeoLab/rbp-maps
skills/bioskills/bio-clip-seq-binding-site-annotation/references/usage-guide.md
skills/bioskills/bio-clip-seq-clip-alignment/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-clip-seq-clip-alignment/scripts/align_clip.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-clip-seq-clip-deep-learning/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-clip-seq-clip-deep-learning/scripts/train_rnaprot.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-clip-seq-clip-motif-analysis/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-clip-seq-clip-motif-analysis/references/usage-guide.md
- Network accessline 10
conda install -c bioconda homer meme bedtools # CL-registered tools git clone https://github.com/chaolinzhanglab/mCross - Network accessline 11
# CL-registered tools git clone https://github.com/chaolinzhanglab/mCross pip install peka - Network accessline 12
git clone https://github.com/chaolinzhanglab/mCross
skills/bioskills/bio-clip-seq-clip-motif-analysis/scripts/find_motifs.sh
- Executable scriptline 1
#!/bin/bash - Network accessline 81
echo "" echo "Cross-validate top motif against:" echo " - CISBP-RNA database (https://cisbp-rna.ccbr.utoronto.ca/)" - Network accessline 82
echo "Cross-validate top motif against:" echo " - CISBP-RNA database (https://cisbp-rna.ccbr.utoronto.ca/)" echo " - ATtRACT (https://attract.cnic.es/)" - Network accessline 83
echo " - CISBP-RNA database (https://cisbp-rna.ccbr.utoronto.ca/)" - Network accessline 84
echo " - ATtRACT (https://attract.cnic.es/)"
skills/bioskills/bio-clip-seq-clip-peak-calling/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-clip-seq-clip-peak-calling/references/usage-guide.md
- Network accessline 10
conda install -c bioconda clipper pureclip piranha bedtools samtools idr # Skipper - Snakemake workflow git clone https://github.com/algaebrown/skipper && cd skipper && conda env create -f environment.yaml - Network accessline 11
# Skipper - Snakemake workflow git clone https://github.com/algaebrown/skipper && cd skipper && conda env create -f environment.yaml # CTK - Network accessline 12
git clone https://github.com/algaebrown/skipper && cd skipper && conda env create -f environment.yaml - Network accessline 13
# CTK git clone https://github.com/chaolinzhanglab/ctk # CLAM - Network accessline 14
git clone https://github.com/chaolinzhanglab/ctk
skills/bioskills/bio-clip-seq-clip-peak-calling/scripts/call_peaks.sh
skills/bioskills/bio-clip-seq-clip-preprocessing/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-clip-seq-clip-preprocessing/scripts/preprocess_clip.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-clip-seq-clip-qc/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-clip-seq-clip-qc/scripts/clip_qc.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-clip-seq-crosslink-site-detection/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-clip-seq-crosslink-site-detection/references/usage-guide.md
- Network accessline 10
conda install -c bioconda pureclip samtools bedtools # CTK git clone https://github.com/chaolinzhanglab/ctk - Network accessline 11
# CTK git clone https://github.com/chaolinzhanglab/ctk # PARalyzer - Network accessline 12
git clone https://github.com/chaolinzhanglab/ctk - Network accessline 13
# PARalyzer git clone https://github.com/ohlerlab/PARalyzer # wavClusteR - Network accessline 14
git clone https://github.com/ohlerlab/PARalyzer
skills/bioskills/bio-clip-seq-crosslink-site-detection/scripts/detect_crosslinks.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-clip-seq-differential-clip/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-clip-seq-differential-clip/references/usage-guide.md
skills/bioskills/bio-clip-seq-m6a-clip/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-clip-seq-m6a-clip/references/usage-guide.md
- Network accessline 10
conda install -c bioconda samtools bedtools pip install m6anet nanopolish-eventalign # miCLIP2 m6Aboost: https://github.com/ZarnackGroup/m6Aboost - Network accessline 11
pip install m6anet nanopolish-eventalign # miCLIP2 m6Aboost: https://github.com/ZarnackGroup/m6Aboost # GLORI: https://github.com/liucongcas/GLORI-tools - Network accessline 12
# miCLIP2 m6Aboost: https://github.com/ZarnackGroup/m6Aboost - Network accessline 13
# GLORI: https://github.com/liucongcas/GLORI-tools - Network accessline 14
# Bullseye DART: https://github.com/mekoulnik/Bullseye
skills/bioskills/bio-clip-seq-m6a-clip/scripts/run_miclip2.sh
- Executable scriptline 1
#!/bin/bash - Network accessline 78
echo "High-confidence m6A sites (m6Aboost >= 0.5): $(wc -l < ${OUT_PREFIX}_m6a_high.bed)" else echo "m6Aboost not installed; install from https://github.com/ZarnackGroup/m6Aboost" - Network accessline 79
else echo "m6Aboost not installed; install from https://github.com/ZarnackGroup/m6Aboost" echo "Without ML filtering, expect 30-50% false-positive rate" - Network accessline 80
echo "m6Aboost not installed; install from https://github.com/ZarnackGroup/m6Aboost"
skills/bioskills/bio-clip-seq-stamp-antibody-free/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-clip-seq-stamp-antibody-free/scripts/run_stamp.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-comparative-genomics-ancestral-reconstruction/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 419
# CLI conda install -c bioconda paml iqtree # RevBayes via source build (https://revbayes.github.io/download) or homebrew on macOS - Network accessline 420
conda install -c bioconda paml iqtree # RevBayes via source build (https://revbayes.github.io/download) or homebrew on macOS # GRASP from https://github.com/bodenlab/GRASP (Java) - Network accessline 421
# RevBayes via source build (https://revbayes.github.io/download) or homebrew on macOS - Network accessline 422
# GRASP from https://github.com/bodenlab/GRASP (Java)
skills/bioskills/bio-comparative-genomics-ancestral-reconstruction/references/usage-guide.md
skills/bioskills/bio-comparative-genomics-ancestral-reconstruction/scripts/ancestral_reconstruction.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-comparative-genomics-comparative-annotation-projection/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 293
# Setup git clone https://github.com/ComparativeGenomicsToolkit/Comparative-Annotation-Toolkit - Network accessline 294
git clone https://github.com/ComparativeGenomicsToolkit/Comparative-Annotation-Toolkit - Network accessline 371
# TOGA conda env create -f https://raw.githubusercontent.com/hillerlab/TOGA/master/toga_env.yml - Network accessline 372
conda env create -f https://raw.githubusercontent.com/hillerlab/TOGA/master/toga_env.yml - Network accessline 375
# CESAR 2.0 (bundled with TOGA) git clone https://github.com/hillerlab/CESAR2.0 - Network accessline 376
git clone https://github.com/hillerlab/CESAR2.0 - Network accessline 384
# Comparative Annotation Toolkit (CAT) git clone https://github.com/ComparativeGenomicsToolkit/Comparative-Annotation-Toolkit - Network accessline 385
git clone https://github.com/ComparativeGenomicsToolkit/Comparative-Annotation-Toolkit - Network accessline 388
# GeMoMa wget https://gemoma.de/jcag/gemoma.zip && unzip gemoma.zip - Network accessline 389
wget https://gemoma.de/jcag/gemoma.zip && unzip gemoma.zip
skills/bioskills/bio-comparative-genomics-comparative-annotation-projection/references/usage-guide.md
- Network accessline 12
# TOGA (WGA-based projection) conda env create -f https://raw.githubusercontent.com/hillerlab/TOGA/master/toga_env.yml - Network accessline 13
conda env create -f https://raw.githubusercontent.com/hillerlab/TOGA/master/toga_env.yml - Network accessline 16
# CESAR 2.0 (bundled with TOGA) git clone https://github.com/hillerlab/CESAR2.0 - Network accessline 17
git clone https://github.com/hillerlab/CESAR2.0 - Network accessline 25
# Comparative Annotation Toolkit (CAT) git clone https://github.com/ComparativeGenomicsToolkit/Comparative-Annotation-Toolkit - Network accessline 26
git clone https://github.com/ComparativeGenomicsToolkit/Comparative-Annotation-Toolkit
skills/bioskills/bio-comparative-genomics-comparative-annotation-projection/scripts/toga_annotation_projection.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/bioskills/bio-comparative-genomics-gene-family-evolution/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 383
# CAFE5 conda install -c bioconda cafe # Or: git clone https://github.com/hahnlab/CAFE5 - Network accessline 385
# Or: git clone https://github.com/hahnlab/CAFE5 - Network accessline 387
# Count wget http://www.iro.umontreal.ca/~csuros/gene_content/count.tar.gz - Network accessline 388
wget http://www.iro.umontreal.ca/~csuros/gene_content/count.tar.gz - Network accessline 391
# BadiRate git clone https://github.com/PauloRoldan/badirate - Network accessline 392
git clone https://github.com/PauloRoldan/badirate - Dangerous shell operationline 422
- Ames RM et al 2012 Bioinformatics 28:48 (DupliPHY-Family) - Network accessline 433
- Han MV et al 2013 MBE 30:1987 (CAFE 3) - Otto SP & Whitton J 2000 Annu Rev Genet 34:401 (polyploidy mechanisms) - TimeTree (database, http://www.timetree.org) - Network accessline 435
- TimeTree (database, http://www.timetree.org)
skills/bioskills/bio-comparative-genomics-gene-family-evolution/references/usage-guide.md
- Network accessline 15
# Alternative methods # Count (Csurös 2010; Java) wget http://www.iro.umontreal.ca/~csuros/gene_content/count.tar.gz && tar xf count.tar.gz - Network accessline 17
wget http://www.iro.umontreal.ca/~csuros/gene_content/count.tar.gz && tar xf count.tar.gz - Network accessline 19
# BadiRate (Librado 2012) git clone https://github.com/PauloRoldan/badirate - Network accessline 20
git clone https://github.com/PauloRoldan/badirate - Network accessline 26
# Time-source database # TimeTree (web): http://www.timetree.org - Network accessline 27
# TimeTree (web): http://www.timetree.org
skills/bioskills/bio-comparative-genomics-gene-family-evolution/scripts/cafe5_birth_death_analysis.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/bioskills/bio-comparative-genomics-gene-tree-species-tree-reconciliation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 351
# IMPORTANT: Whale.jl public API evolves; verify against current docs at # https://github.com/arzwa/Whale.jl before scripting. The sketch below is - Network accessline 352
# https://github.com/arzwa/Whale.jl before scripting. The sketch below is - Network accessline 434
# ALE (C++) git clone https://github.com/ssolo/ALE && cd ALE && mkdir build && cd build && cmake .. && make - Network accessline 435
git clone https://github.com/ssolo/ALE && cd ALE && mkdir build && cd build && cmake .. && make - Network accessline 439
# GeneRax (with MPI) git clone --recursive https://github.com/BenoitMorel/GeneRax && cd GeneRax && ./install.sh - Network accessline 440
git clone --recursive https://github.com/BenoitMorel/GeneRax && cd GeneRax && ./install.sh - Network accessline 442
# AleRax git clone --recursive https://github.com/BenoitMorel/AleRax && cd AleRax && ./install.sh - Network accessline 443
git clone --recursive https://github.com/BenoitMorel/AleRax && cd AleRax && ./install.sh - Network accessline 448
# RANGER-DTL wget https://compbio.engr.uconn.edu/software/RANGER-DTL/RANGER-DTL-Linux.tar.gz - Network accessline 449
wget https://compbio.engr.uconn.edu/software/RANGER-DTL/RANGER-DTL-Linux.tar.gz - Network accessline 452
# NOTUNG wget https://www.cs.cmu.edu/~durand/Notung/download/Notung-2.9.1.5.tar.gz - Network accessline 453
wget https://www.cs.cmu.edu/~durand/Notung/download/Notung-2.9.1.5.tar.gz - Network accessline 455
# ecceTERA git clone https://github.com/cchauve/ecceTERA && cd ecceTERA && make - Network accessline 456
git clone https://github.com/cchauve/ecceTERA && cd ecceTERA && make
skills/bioskills/bio-comparative-genomics-gene-tree-species-tree-reconciliation/references/usage-guide.md
- Network accessline 12
# Probabilistic reconciliation conda install -c bioconda ale generax git clone --recursive https://github.com/BenoitMorel/AleRax && cd AleRax && ./install.sh - Network accessline 14
git clone --recursive https://github.com/BenoitMorel/AleRax && cd AleRax && ./install.sh - Network accessline 16
# Parsimony / alternative methods # RANGER-DTL: https://compbio.engr.uconn.edu/software/RANGER-DTL/ # NOTUNG: https://www.cs.cmu.edu/~durand/Notung/download - Network accessline 17
# RANGER-DTL: https://compbio.engr.uconn.edu/software/RANGER-DTL/ - Network accessline 18
# NOTUNG: https://www.cs.cmu.edu/~durand/Notung/download
skills/bioskills/bio-comparative-genomics-gene-tree-species-tree-reconciliation/scripts/ale_dtl_reconciliation.sh
skills/bioskills/bio-comparative-genomics-genome-distance-and-species-delineation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 32
- CLI: `gtdbtk classify_wf --genome_dir genomes/ --out_dir gtdbtk_out --cpus 32` -- GTDB classification - CLI: `mash dist *.fa` -- k-mer MinHash distance - Web: TYGS (https://tygs.dsmz.de/) and GGDC (https://ggdc.dsmz.de/) for dDDH - Network accessline 34
- Web: TYGS (https://tygs.dsmz.de/) and GGDC (https://ggdc.dsmz.de/) for dDDH - Network accessline 291
For publication-grade novel-species naming, dDDH is required. Use the TYGS web service (https://tygs.dsmz.de/) or GGDC (https://ggdc.dsmz.de/). Submit FASTA -> receive dDDH against type strains + phylogeny. - Network accessline 388
conda install -c bioconda gtdbtk # Download database wget https://data.gtdb.ecogenomic.org/releases/release220/220.0/auxillary_files/gtdbtk_r220_data.tar.gz - Network accessline 389
# Download database wget https://data.gtdb.ecogenomic.org/releases/release220/220.0/auxillary_files/gtdbtk_r220_data.tar.gz tar xf gtdbtk_r220_data.tar.gz - Network accessline 390
wget https://data.gtdb.ecogenomic.org/releases/release220/220.0/auxillary_files/gtdbtk_r220_data.tar.gz - Network accessline 404
# OrthoANI / OrthoANIu git clone https://github.com/EzbioCloud-Bioinformatics-Team/OrthoANIu - Network accessline 405
git clone https://github.com/EzbioCloud-Bioinformatics-Team/OrthoANIu
skills/bioskills/bio-comparative-genomics-genome-distance-and-species-delineation/references/usage-guide.md
- Network accessline 25
conda install -c bioconda gtdbtk # Download GTDB release (must match GTDB-Tk version) wget https://data.gtdb.ecogenomic.org/releases/release220/220.0/auxillary_files/gtdbtk_r220_data.tar.gz - Network accessline 26
# Download GTDB release (must match GTDB-Tk version) wget https://data.gtdb.ecogenomic.org/releases/release220/220.0/auxillary_files/gtdbtk_r220_data.tar.gz tar xf gtdbtk_r220_data.tar.gz - Network accessline 27
wget https://data.gtdb.ecogenomic.org/releases/release220/220.0/auxillary_files/gtdbtk_r220_data.tar.gz
skills/bioskills/bio-comparative-genomics-genome-distance-and-species-delineation/scripts/skani_ani_species_delineation.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/bioskills/bio-comparative-genomics-hgt-detection/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 351
# AvP git clone https://github.com/GDKO/AvP && cd AvP && pip install . - Network accessline 352
git clone https://github.com/GDKO/AvP && cd AvP && pip install . - Network accessline 355
# ALE + GeneRax + AleRax conda install -c bioconda ale generax # AleRax: build from source per https://github.com/BenoitMorel/AleRax - Network accessline 356
conda install -c bioconda ale generax # AleRax: build from source per https://github.com/BenoitMorel/AleRax git clone --recursive https://github.com/BenoitMorel/AleRax && cd AleRax && ./install.sh - Network accessline 357
# AleRax: build from source per https://github.com/BenoitMorel/AleRax - Network accessline 358
git clone --recursive https://github.com/BenoitMorel/AleRax && cd AleRax && ./install.sh - Network accessline 364
# Mobile elements git clone https://github.com/clb21565/mobileOG-db - Network accessline 365
git clone https://github.com/clb21565/mobileOG-db
skills/bioskills/bio-comparative-genomics-hgt-detection/references/usage-guide.md
- Network accessline 12
# Probabilistic DTL conda install -c bioconda ale generax git clone --recursive https://github.com/BenoitMorel/AleRax && cd AleRax && ./install.sh - Network accessline 14
git clone --recursive https://github.com/BenoitMorel/AleRax && cd AleRax && ./install.sh - Network accessline 19
# Eukaryotic HGT pipeline (AvP) git clone https://github.com/GDKO/AvP && cd AvP && pip install . - Network accessline 20
git clone https://github.com/GDKO/AvP && cd AvP && pip install . - Network accessline 22
# Mobile elements (bacterial) # IslandViewer 4 (web): https://www.pathogenomics.sfu.ca/islandviewer/ - Network accessline 23
# IslandViewer 4 (web): https://www.pathogenomics.sfu.ca/islandviewer/ - Network accessline 24
git clone https://github.com/clb21565/mobileOG-db
skills/bioskills/bio-comparative-genomics-hgt-detection/scripts/hgt_detection.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-comparative-genomics-introgression-detection/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 420
# Dsuite git clone https://github.com/millanek/Dsuite && cd Dsuite && make - Network accessline 421
git clone https://github.com/millanek/Dsuite && cd Dsuite && make - Network accessline 427
# AdmixTools (qpDstat, qpAdm, qpGraph) git clone https://github.com/DReichLab/AdmixTools && cd AdmixTools && make - Network accessline 428
git clone https://github.com/DReichLab/AdmixTools && cd AdmixTools && make - Network accessline 433
# HyDe git clone https://github.com/pblischak/HyDe && cd HyDe && pip install -e . - Network accessline 434
git clone https://github.com/pblischak/HyDe && cd HyDe && pip install -e . - Network accessline 436
# QuIBL git clone https://github.com/miriamtnzr/QuIBL - Network accessline 437
git clone https://github.com/miriamtnzr/QuIBL - Network accessline 439
# Twisst git clone https://github.com/simonhmartin/twisst && pip install -e . - Network accessline 440
git clone https://github.com/simonhmartin/twisst && pip install -e . - Network accessline 442
# PhyloNet git clone https://github.com/NakhlehLab/PhyloNet && cd PhyloNet && mvn package - Network accessline 443
git clone https://github.com/NakhlehLab/PhyloNet && cd PhyloNet && mvn package
skills/bioskills/bio-comparative-genomics-introgression-detection/references/usage-guide.md
- Network accessline 12
# Modern Dsuite git clone https://github.com/millanek/Dsuite && cd Dsuite && make - Network accessline 13
git clone https://github.com/millanek/Dsuite && cd Dsuite && make - Network accessline 16
# Population-genetics ABBA-BABA + qpAdm git clone https://github.com/DReichLab/AdmixTools && cd AdmixTools && make - Network accessline 17
git clone https://github.com/DReichLab/AdmixTools && cd AdmixTools && make - Network accessline 24
# Locus-level topology git clone https://github.com/miriamtnzr/QuIBL # QuIBL - Network accessline 25
git clone https://github.com/miriamtnzr/QuIBL # QuIBL - Network accessline 26
git clone https://github.com/simonhmartin/twisst # Twisst - Network accessline 28
# Network methods git clone https://github.com/NakhlehLab/PhyloNet && cd PhyloNet && mvn package - Network accessline 29
git clone https://github.com/NakhlehLab/PhyloNet && cd PhyloNet && mvn package - Network accessline 34
# Hybridization-specific git clone https://github.com/pblischak/HyDe && cd HyDe && pip install -e . - Network accessline 35
git clone https://github.com/pblischak/HyDe && cd HyDe && pip install -e .
skills/bioskills/bio-comparative-genomics-introgression-detection/scripts/dsuite_abba_baba_workflow.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/bioskills/bio-comparative-genomics-ortholog-inference/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 385
# Broccoli git clone https://github.com/rderelle/Broccoli && cd Broccoli && pip install . - Network accessline 386
git clone https://github.com/rderelle/Broccoli && cd Broccoli && pip install . - Network accessline 391
# OMA standalone (Linux) wget https://omabrowser.org/standalone/OMA.tgz && tar xf OMA.tgz && cd OMA && ./install.sh - Network accessline 392
wget https://omabrowser.org/standalone/OMA.tgz && tar xf OMA.tgz && cd OMA && ./install.sh - Network accessline 399
# JustOrthologs git clone https://github.com/ridgelab/justOrthologs && cd justOrthologs && python setup.py install - Network accessline 400
git clone https://github.com/ridgelab/justOrthologs && cd justOrthologs && python setup.py install - Network accessline 402
# TOGA conda env create -f https://raw.githubusercontent.com/hillerlab/TOGA/master/toga_env.yml - Network accessline 403
conda env create -f https://raw.githubusercontent.com/hillerlab/TOGA/master/toga_env.yml - Network accessline 410
For Quest-for-Orthologs benchmark submission, follow https://orthology.benchmarkservice.org/ instructions; the benchmark refreshes annually.
skills/bioskills/bio-comparative-genomics-ortholog-inference/references/usage-guide.md
- Network accessline 16
# OMA / FastOMA wget https://omabrowser.org/standalone/OMA.tgz && tar xf OMA.tgz - Network accessline 17
wget https://omabrowser.org/standalone/OMA.tgz && tar xf OMA.tgz - Network accessline 23
# JustOrthologs (close species, fast) git clone https://github.com/ridgelab/justOrthologs - Network accessline 24
git clone https://github.com/ridgelab/justOrthologs - Network accessline 26
# TOGA (WGA-anchored orthology + gene loss) conda env create -f https://raw.githubusercontent.com/hillerlab/TOGA/master/toga_env.yml - Network accessline 27
conda env create -f https://raw.githubusercontent.com/hillerlab/TOGA/master/toga_env.yml
skills/bioskills/bio-comparative-genomics-ortholog-inference/scripts/ortholog_analysis.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-comparative-genomics-pangenome-analysis/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 18
Reference examples tested with: Panaroo 1.5.1+ (Tonkin-Hill 2020 Genome Biol 21:180), PPanGGOLiN 2.2.0+ (Gautreau 2020 PLoS Comp Biol 16:e1007732), PEPPAN 1.0.5+ (Zhou 2020 GR 30:1667), GET_HOMOLOGUES 25102023+, anvi'o 8.0+ (Eren 2021 Nat M - Network accessline 460
# Mobile elements git clone https://github.com/clb21565/mobileOG-db - Network accessline 461
git clone https://github.com/clb21565/mobileOG-db
skills/bioskills/bio-comparative-genomics-pangenome-analysis/scripts/panaroo_bacterial_pangenome.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/bioskills/bio-comparative-genomics-positive-selection/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 350
# Asymptotic alpha via the Messer-Petrov 2013 web tool # (https://benhaller.com/messerlab/asymptoticMK.html) or the impMKT R package - Network accessline 351
# (https://benhaller.com/messerlab/asymptoticMK.html) or the impMKT R package - Network accessline 460
conda install -c bioconda paml hyphy gard prank prequal hmmcleaner # RDP5: http://web.cbio.uct.ac.za/~darren/rdp.html # MACSE V2: wget https://bioweb.supagro.inra.fr/macse/releases/macse_v2.07.jar - Network accessline 461
# RDP5: http://web.cbio.uct.ac.za/~darren/rdp.html # MACSE V2: wget https://bioweb.supagro.inra.fr/macse/releases/macse_v2.07.jar pip install ete4 pyhyphy csubst - Network accessline 462
# MACSE V2: wget https://bioweb.supagro.inra.fr/macse/releases/macse_v2.07.jar
skills/bioskills/bio-comparative-genomics-positive-selection/references/usage-guide.md
- Network accessline 12
# Codon model engines conda install -c bioconda paml hyphy gard # RDP5 (viral recombination): http://web.cbio.uct.ac.za/~darren/rdp.html - Network accessline 14
# RDP5 (viral recombination): http://web.cbio.uct.ac.za/~darren/rdp.html - Network accessline 22
# McDonald-Kreitman framework Rscript -e "install.packages('asymptoticMK')" # polyDFE: https://github.com/paula-tataru/polyDFE - Network accessline 23
Rscript -e "install.packages('asymptoticMK')" # polyDFE: https://github.com/paula-tataru/polyDFE # GRAPES: https://github.com/BioPP/grapes - Network accessline 24
# polyDFE: https://github.com/paula-tataru/polyDFE - Network accessline 25
# GRAPES: https://github.com/BioPP/grapes - Network accessline 28
Rscript -e "remotes::install_github('nclark-lab/RERconverge')" pip install csubst # PhyloAcc: https://phyloacc.github.io/ - Network accessline 30
# PhyloAcc: https://phyloacc.github.io/
skills/bioskills/bio-comparative-genomics-positive-selection/scripts/selection_analysis.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-comparative-genomics-synteny-analysis/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 430
# MCScanX git clone https://github.com/wyp1125/MCScanX && cd MCScanX && make - Network accessline 431
git clone https://github.com/wyp1125/MCScanX && cd MCScanX && make - Network accessline 439
conda install -c bioconda anchorwave # i-ADHoRe git clone https://github.com/VIB-PSB/i-ADHoRe && cd i-ADHoRe && mkdir build && cd build && cmake .. && make - Network accessline 440
# i-ADHoRe git clone https://github.com/VIB-PSB/i-ADHoRe && cd i-ADHoRe && mkdir build && cd build && cmake .. && make # ntSynt - Network accessline 441
git clone https://github.com/VIB-PSB/i-ADHoRe && cd i-ADHoRe && mkdir build && cd build && cmake .. && make
skills/bioskills/bio-comparative-genomics-synteny-analysis/references/usage-guide.md
- Network accessline 14
# MCScanX (compile from source) git clone https://github.com/wyp1125/MCScanX && cd MCScanX && make - Network accessline 15
git clone https://github.com/wyp1125/MCScanX && cd MCScanX && make - Network accessline 30
# i-ADHoRe 3.0 (deeply diverged species) git clone https://github.com/VIB-PSB/i-ADHoRe && cd i-ADHoRe && mkdir build && cd build && cmake .. && make - Network accessline 31
git clone https://github.com/VIB-PSB/i-ADHoRe && cd i-ADHoRe && mkdir build && cd build && cmake .. && make
skills/bioskills/bio-comparative-genomics-synteny-analysis/scripts/synteny_analysis.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-comparative-genomics-whole-genome-alignment/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 392
# HAL toolkit (usually bundled with Cactus) git clone https://github.com/ComparativeGenomicsToolkit/hal && cd hal && make - Network accessline 393
git clone https://github.com/ComparativeGenomicsToolkit/hal && cd hal && make - Network accessline 440
- Garrison E et al 2024 Nat Methods 21:2008 (PGGB) - Paten B et al 2011 Genome Res 21:1512 (Cactus algorithm) - ComparativeGenomicsToolkit Cactus + HAL toolkit documentation (https://github.com/ComparativeGenomicsToolkit) - Network accessline 442
- ComparativeGenomicsToolkit Cactus + HAL toolkit documentation (https://github.com/ComparativeGenomicsToolkit)
skills/bioskills/bio-comparative-genomics-whole-genome-alignment/references/usage-guide.md
skills/bioskills/bio-comparative-genomics-whole-genome-alignment/scripts/progressive_cactus_wga.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/bioskills/bio-comparative-genomics-whole-genome-duplication/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 292
# Input: MCScanX collinearity file + GFF git clone https://github.com/qiao-xin/DupGen_finder - Network accessline 293
git clone https://github.com/qiao-xin/DupGen_finder - Network accessline 326
MAPS is heavyweight; requires CRG database setup and significant compute. See https://bitbucket.org/barker-lab/maps/src for the current pipeline. - Network accessline 387
# wgd v2 pip install wgd # Or: git clone https://github.com/heche-psb/wgd && cd wgd && pip install . - Network accessline 389
# Or: git clone https://github.com/heche-psb/wgd && cd wgd && pip install . - Network accessline 394
# DupGen_finder (Perl + MCScanX dependency) git clone https://github.com/qiao-xin/DupGen_finder - Network accessline 395
git clone https://github.com/qiao-xin/DupGen_finder - Network accessline 396
# Install MCScanX (Wang 2012): git clone https://github.com/wyp1125/MCScanX && cd MCScanX && make - Network accessline 398
# MAPS (Python; requires CRG, ete3) git clone https://bitbucket.org/barker-lab/maps - Network accessline 399
git clone https://bitbucket.org/barker-lab/maps - Network accessline 404
# SLEDGe git clone https://github.com/SLEDGe-team/SLEDGe - Network accessline 405
git clone https://github.com/SLEDGe-team/SLEDGe - Network accessline 407
# POInT (C++) git clone https://github.com/gconant0/PoInT - Network accessline 408
git clone https://github.com/gconant0/PoInT
skills/bioskills/bio-comparative-genomics-whole-genome-duplication/references/usage-guide.md
- Network accessline 15
# DupGen_finder (Perl) git clone https://github.com/qiao-xin/DupGen_finder - Network accessline 16
git clone https://github.com/qiao-xin/DupGen_finder - Network accessline 18
# MCScanX (dependency for wgd syn) git clone https://github.com/wyp1125/MCScanX && cd MCScanX && make - Network accessline 19
git clone https://github.com/wyp1125/MCScanX && cd MCScanX && make - Network accessline 21
# Phylogenomic WGD detection git clone https://bitbucket.org/barker-lab/maps # MAPS - Network accessline 22
git clone https://bitbucket.org/barker-lab/maps # MAPS
skills/bioskills/bio-comparative-genomics-whole-genome-duplication/scripts/wgd_v2_ks_pipeline.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/bioskills/bio-copy-number-allele-specific-copy-number/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-copy-number-allele-specific-copy-number/scripts/run_facets.R
- Executable scriptline 1
#!/usr/bin/env Rscript
skills/bioskills/bio-copy-number-cnv-annotation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-copy-number-cnv-annotation/scripts/annotate_cnvs.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-copy-number-cnv-visualization/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-copy-number-cnv-visualization/scripts/plot_cnv.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-copy-number-cnvkit-analysis/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-copy-number-cnvkit-analysis/scripts/run_cnvkit.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/bioskills/bio-copy-number-copy-ratio-segmentation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-copy-number-copy-ratio-segmentation/scripts/segment_cbs.R
- Executable scriptline 1
#!/usr/bin/env Rscript
skills/bioskills/bio-copy-number-focal-amplification-ecdna/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-copy-number-focal-amplification-ecdna/scripts/run_ampliconsuite.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/bioskills/bio-copy-number-gatk-cnv/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-copy-number-gatk-cnv/scripts/run_gatk_cnv.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/bioskills/bio-copy-number-germline-cnv-interpretation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-copy-number-germline-cnv-interpretation/references/usage-guide.md
- Network accessline 10
git clone https://github.com/Genotek/ClassifyCNV # then run its update_clingen.sh
skills/bioskills/bio-copy-number-germline-cnv-interpretation/scripts/classify_germline_cnv.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-copy-number-hrd-scoring/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-copy-number-hrd-scoring/scripts/compute_hrd.R
- Executable scriptline 1
#!/usr/bin/env Rscript
skills/bioskills/bio-copy-number-recurrent-cnv/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Dangerous shell operationline 182
- Drews RM et al 2022. A pan-cancer compendium of chromosomal instability. Nature 606:976
skills/bioskills/bio-copy-number-recurrent-cnv/scripts/run_gistic2.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/bioskills/bio-copy-number-subclonal-copy-number/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-copy-number-subclonal-copy-number/scripts/run_titan.R
- Executable scriptline 1
#!/usr/bin/env Rscript
skills/bioskills/bio-crispr-screens-bagel-essentiality/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 31
- CLI: `BAGEL.py bf` to compute Bayes Factors - CLI: `BAGEL.py pr` for precision-recall curves - Reference sets: CEGv2 (essentials) and NEGv1 (non-essentials); both at https://github.com/hart-lab/bagel - Network accessline 33
- Reference sets: CEGv2 (essentials) and NEGv1 (non-essentials); both at https://github.com/hart-lab/bagel - Network accessline 68
# BAGEL2 installation: distributed via git clone (no canonical PyPI release). git clone https://github.com/hart-lab/bagel - Network accessline 69
git clone https://github.com/hart-lab/bagel - Network accessline 190
**Mechanism:** BAGEL2 trains KDEs on the reference; if references are not representative, KDE separation is poor and no gene has BF >6. **Symptom:** Median BF near zero; no genes >6 even at low FDR. **Fix:** Re-download CEGv2 / NEGv1 from h - Network accessline 192
**Fix:** Re-download CEGv2 / NEGv1 from https://github.com/hart-lab/bagel. Verify gene symbols match the screen's annotation.
skills/bioskills/bio-crispr-screens-bagel-essentiality/references/usage-guide.md
- Network accessline 10
# BAGEL2 (Python) git clone https://github.com/hart-lab/bagel - Network accessline 11
git clone https://github.com/hart-lab/bagel - Network accessline 14
# Reference gene sets wget https://raw.githubusercontent.com/hart-lab/bagel/master/CEGv2.txt - Network accessline 15
wget https://raw.githubusercontent.com/hart-lab/bagel/master/CEGv2.txt - Network accessline 16
wget https://raw.githubusercontent.com/hart-lab/bagel/master/NEGv1.txt
skills/bioskills/bio-crispr-screens-bagel-essentiality/scripts/run_bagel2.sh
- Executable scriptline 1
#!/bin/bash - Network accessline 22
[ ! -f "$CEG" ] && curl -L -o "$CEG" \ - Network accessline 23
https://raw.githubusercontent.com/hart-lab/bagel/master/CEGv2.txt [ ! -f "$NEG" ] && curl -L -o "$NEG" \ https://raw.githubusercontent.com/hart-lab/bagel/master/NEGv1.txt - Network accessline 24
[ ! -f "$NEG" ] && curl -L -o "$NEG" \ - Network accessline 25
https://raw.githubusercontent.com/hart-lab/bagel/master/NEGv1.txt
skills/bioskills/bio-crispr-screens-base-editing-analysis/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 258
The Broad Institute's `be-validation-pipeline` (https://broadinstitute.github.io/be-validation-pipeline/) is a CRISPResso2 post-processing and validation toolkit for BE amplicon data -- a set of Jupyter notebooks, not a workflow-engine pipe - Network accessline 261
git clone https://github.com/broadinstitute/be-validation-pipeline
skills/bioskills/bio-crispr-screens-base-editing-analysis/references/usage-guide.md
- Network accessline 10
conda install -c bioconda crispresso2 # not on PyPI pandas biopython numpy scipy # BE-Hive for editing efficiency prediction git clone https://github.com/maxwshen/be_predict_bystander # BE-Hive is not on PyPI - Network accessline 11
# BE-Hive for editing efficiency prediction git clone https://github.com/maxwshen/be_predict_bystander # BE-Hive is not on PyPI # Broad end-to-end pipeline - Network accessline 12
git clone https://github.com/maxwshen/be_predict_bystander # BE-Hive is not on PyPI - Network accessline 13
# Broad end-to-end pipeline git clone https://github.com/broadinstitute/be-validation-pipeline cd be-validation-pipeline && pip install -r requirements.txt - Network accessline 14
git clone https://github.com/broadinstitute/be-validation-pipeline
skills/bioskills/bio-crispr-screens-base-editing-analysis/scripts/base_editing_analysis.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-crispr-screens-batch-correction/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-crispr-screens-batch-correction/scripts/batch_correct.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-crispr-screens-combinatorial-screens/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-crispr-screens-combinatorial-screens/scripts/gi_scoring.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-crispr-screens-copy-number-correction/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 18
Reference examples tested with: CRISPRcleanR 3.0+ (R; github.com/francescojm/CRISPRcleanR), Chronos 2.0+ (https://github.com/broadinstitute/chronos), CERES (legacy, superseded by Chronos), pandas 2.2+, numpy 1.26+, scipy 1.12+. - Network accessline 116
# Chronos (pip install crispr_chronos, or pip install git+https://github.com/broadinstitute/chronos) - Dangerous shell operationline 146
**Meyers RM et al 2017 *Nat Genet* 49:1779** introduced the first formal CN-correction method at DepMap scale. CERES decomposes per-sgRNA LFC as `sgRNA_efficacy * gene_effect - CN_term(copy_number)`, fitting jointly. Superseded by Chronos a - Dangerous shell operationline 289
- Meyers RM et al. 2017. *Nat Genet* 49:1779. CERES; first formal CN correction at DepMap scale. - Network accessline 292
- Behan FM et al. 2019. *Nature* 568:511. Project Score with CRISPRcleanR-corrected data. - Pacini C et al. 2021. *Nat Commun* 12:1661. Integrated cross-study dependencies; DepMap quality scoring. - DepMap Q4 2024+ data releases. https://de - Network accessline 294
- DepMap Q4 2024+ data releases. https://depmap.org/portal/
skills/bioskills/bio-crispr-screens-copy-number-correction/references/usage-guide.md
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# Chronos (Python) pip install crispr_chronos # or from source: git clone https://github.com/broadinstitute/chronos - Network accessline 13
pip install crispr_chronos # or from source: git clone https://github.com/broadinstitute/chronos # Helpers - Network accessline 14
# or from source: git clone https://github.com/broadinstitute/chronos
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#!/bin/bash
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Before using code patterns, verify installed versions match. If versions differ: - CLI: `python drugz.py --help` (the repo has no setup.py, so there is no `drugz` console script) - GitHub: install via `git clone https://github.com/hart-lab/ - Network accessline 22
- GitHub: install via `git clone https://github.com/hart-lab/drugz` - Network accessline 67
git clone https://github.com/hart-lab/drugz
skills/bioskills/bio-crispr-screens-drugz-chemogenomic/references/usage-guide.md
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ceg_file = Path('CEGv2.txt') if not ceg_file.exists(): subprocess.run(['curl', '-L', '-o', str(ceg_file), - Network accessline 20
if not ceg_file.exists(): subprocess.run(['curl', '-L', '-o', str(ceg_file), 'https://raw.githubusercontent.com/hart-lab/bagel/master/CEGv2.txt'], - Network accessline 21
subprocess.run(['curl', '-L', '-o', str(ceg_file), - Network accessline 22
'https://raw.githubusercontent.com/hart-lab/bagel/master/CEGv2.txt'],
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# All five primary tools conda install -c bioconda mageck # RRA + MLE (not on PyPI) git clone https://github.com/hart-lab/bagel # BAGEL2 (PyPI 'bagel' is an unrelated package) - Network accessline 11
conda install -c bioconda mageck # RRA + MLE (not on PyPI) git clone https://github.com/hart-lab/bagel # BAGEL2 (PyPI 'bagel' is an unrelated package) git clone https://github.com/felicityallen/JACKS # JACKS - Network accessline 12
git clone https://github.com/hart-lab/bagel # BAGEL2 (PyPI 'bagel' is an unrelated package) - Network accessline 13
git clone https://github.com/felicityallen/JACKS # JACKS (PyPI 'jacks' is an unrelated package) - Network accessline 14
# drugZ via PyPI or GitHub git clone https://github.com/hart-lab/drugz # drugZ is not on PyPI # or - Network accessline 15
git clone https://github.com/hart-lab/drugz # drugZ is not on PyPI - Network accessline 16
# or git clone https://github.com/hart-lab/drugz # Chronos (DepMap) - Network accessline 17
git clone https://github.com/hart-lab/drugz
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- CLI: `python run_JACKS.py --help` (run_JACKS.py at the JACKS repo root after clone) - Python: from jacks.jacks_io import runJACKS; help(runJACKS) - GitHub: install via `git clone https://github.com/felicityallen/JACKS && cd JACKS && pip i - Network accessline 23
- GitHub: install via `git clone https://github.com/felicityallen/JACKS && cd JACKS && pip install .` - Network accessline 268
- Allen F et al. 2019. *Genome Research* 29:464. JACKS; original Bayesian joint analysis paper. - Allen F, Parts L (Wellcome Sanger Institute). https://github.com/felicityallen/JACKS. Official repository. - Network accessline 269
- Allen F, Parts L (Wellcome Sanger Institute). https://github.com/felicityallen/JACKS. Official repository. - Dangerous shell operationline 271
- Meyers RM et al. 2017. *Nat Genet* 49:1779. Avana CRISPR DepMap; reference panel for efficacy transfer.
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git clone https://github.com/felicityallen/JACKS && cd JACKS/jacks && pip install . # the PyPI 'jacks' is an unrelated package - Network accessline 11
# or latest from GitHub (run_JACKS.py is at the repo root) git clone https://github.com/felicityallen/JACKS && cd JACKS && pip install . # Optional: pre-computed reference efficacy priors - Network accessline 12
git clone https://github.com/felicityallen/JACKS && cd JACKS && pip install . # Optional: pre-computed reference efficacy priors # Download DepMap Brunello / Project Score efficacy posteriors for transfer learning
skills/bioskills/bio-crispr-screens-jacks-analysis/scripts/run_jacks.py
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git clone https://github.com/maximilianh/crisporWebsite # CRISPOR is not on PyPI biopython pandas numpy
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**Pertpy** (https://pertpy.readthedocs.io) integrates Mixscape, distance-based perturbation comparison, EdgeR/PyDESeq2/WilcoxonTest DE, and factor models in a single AnnData-based interface. For SCEPTRE specifically, invoke the R sceptre pa
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Reference examples tested with: PRIDICT2 v1.0+ (https://github.com/uzh-dqbm-cmi/PRIDICT2), CRISPResso2 2.2.14+, pandas 2.2+, biopython 1.83+, numpy 1.26+. - Network accessline 20
Before using code patterns, verify installed versions match. If versions differ: - CLI: `python pridict2_pegRNA_design.py single --help`; `python pridict2_pegRNA_design.py batch --help` - Web: PRIDICT2 web interface at https://pridict.it/ - Network accessline 22
- Web: PRIDICT2 web interface at https://pridict.it/
skills/bioskills/bio-crispr-screens-prime-editing-screens/references/usage-guide.md
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**Source / threshold:** Hart 2017 *G3* 7:2719 defines CEGv2 (~684 core essentials); NEGv1 (~927 non-essentials) comes from Hart 2014 *Mol Syst Biol* 10:733. Both lists at https://github.com/hart-lab/bagel/blob/master/CEGv2.txt and NEGv1.txt - Dangerous shell operationline 359
- Meyers RM et al. 2017. *Nat Genet* 49:1779. CERES; mechanism of CN bias.
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- Heer J, Bostock M. 2010. Crowdsourcing graphical perception: using Mechanical Turk to assess visualization design. *Proc CHI* 203-212. - Krzywinski M, Schein J, Birol I, et al. 2009. Circos: an information aesthetic for comparative genomi - Network accessline 278
- Shimoyama Y. 2024. pyCirclize: Circular visualization in Python. *GitHub* https://github.com/moshi4/pyCirclize
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- Borland D, Taylor RM II. 2007. Rainbow color map (still) considered harmful. *IEEE Comput Graph Appl* 27(2):14-17.
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- Sievert C. 2020. *Interactive Web-Based Data Visualization with R, plotly, and shiny.* Chapman and Hall/CRC. - Plotly Python — Static Image Generation Changes (2024-2025). https://plotly.com/python/static-image-generation-changes/ - Network accessline 262
- Plotly Python — Static Image Generation Changes (2024-2025). https://plotly.com/python/static-image-generation-changes/ - Network accessline 264
- Wickham H, Pedersen TL, Seidel D. 2022. gganimate (CRAN). https://gganimate.com
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- Jacomy M, Venturini T, Heymann S, Bastian M. 2014. ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software. *PLoS ONE* 9(6):e98679. - Krzywinski M, Birol I, Jones SJM, Marra MA. 201 - Network accessline 316
- Krzywinski M, Birol I, Jones SJM, Marra MA. 2012. Hive plots—rational approach to visualizing networks. *Brief Bioinform* 13(5):627-644. - Pedersen T. 2024. ggraph (CRAN). https://ggraph.data-imaginist.com - Shannon P, et al. 2003. Cytosc - Network accessline 317
- Pedersen T. 2024. ggraph (CRAN). https://ggraph.data-imaginist.com
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- Conway JR, Lex A, Gehlenborg N. 2017. UpSetR: an R package for the visualization of intersecting sets and their properties. *Bioinformatics* 33(18):2938-2940. - Krassowski M. 2020. ComplexUpset (R package). https://github.com/krassowski/c - Network accessline 246
- Krassowski M. 2020. ComplexUpset (R package). https://github.com/krassowski/complex-upset
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- Kans J. (2024) Entrez Direct: E-utilities on the Unix Command Line. NCBI Bookshelf NBK179288. - NCBI. EPost help and Usage Guidelines. NBK25499. - NCBI Datasets documentation: https://www.ncbi.nlm.nih.gov/datasets/docs/v2/ - Network accessline 310
- NCBI Datasets documentation: https://www.ncbi.nlm.nih.gov/datasets/docs/v2/
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skills/bioskills/bio-database-access-batch-downloads/scripts/robust_download.py
skills/bioskills/bio-database-access-biomart-queries/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 32
- Python: `pybiomart` (https://github.com/jrderuiter/pybiomart) is the lightest client - R: `biomaRt` Bioconductor (Durinck et al. 2009 *Nat Protoc* 4:1184) is the canonical client - CLI: `curl` against the XML endpoint works but is rarely - Network accessline 33
- R: `biomaRt` Bioconductor (Durinck et al. 2009 *Nat Protoc* 4:1184) is the canonical client - CLI: `curl` against the XML endpoint works but is rarely used directly - Web: `https://www.ensembl.org/biomart/martview` for interactive query d - Network accessline 35
- Web: `https://www.ensembl.org/biomart/martview` for interactive query design - Network accessline 58
from pybiomart import Server server = Server(host='http://www.ensembl.org') - Network accessline 59
server = Server(host='http://www.ensembl.org') - Network accessline 125
server = Server(host='http://www.ensembl.org') - Network accessline 194
# ensembl <- useMart('ENSEMBL_MART_ENSEMBL', # dataset='hsapiens_gene_ensembl', # host='https://nov2020.archive.ensembl.org') - Network accessline 196
# host='https://nov2020.archive.ensembl.org') - Network accessline 279
- Kinsella RJ, Kahari A, Haider S, et al. (2011) Ensembl BioMarts: a hub for data retrieval across taxonomic space. *Database* 2011:bar030. - Smedley D, Haider S, Durinck S, et al. (2015) The BioMart community portal: an innovative alternat - Network accessline 281
- pybiomart documentation: https://github.com/jrderuiter/pybiomart
skills/bioskills/bio-database-access-biomart-queries/references/usage-guide.md
- Network accessline 62
- Cross-reference joins multiply rows. A gene with 10 RefSeq mRNAs produces 10 rows in a query that includes refseq_mrna. Filter to canonical isoforms downstream when one-per-gene is needed. - HGNC symbols are unstable (MARCH1 -> MARCHF1 in - Network accessline 63
- HGNC symbols are unstable (MARCH1 -> MARCHF1 in 2020). Filter by `ensembl_gene_id` or `hgnc_id` for stability. - For Ensembl release pinning: R `useEnsembl(version=110)`, or use archive host URL (`https://nov2020.archive.ensembl.org`). - - Network accessline 64
- For Ensembl release pinning: R `useEnsembl(version=110)`, or use archive host URL (`https://nov2020.archive.ensembl.org`). - Network accessline 65
- pybiomart (Python) is light; R biomaRt is more mature and Bioconductor-supported. For R-based pipelines, biomaRt is the canonical choice. - For non-vertebrate species, switch to the Ensembl Genomes BioMart at `http://plants.ensembl.org/bi - Network accessline 66
- For non-vertebrate species, switch to the Ensembl Genomes BioMart at `http://plants.ensembl.org/biomart/martservice`.
skills/bioskills/bio-database-access-biomart-queries/scripts/bulk_id_mapping.py
skills/bioskills/bio-database-access-biomart-queries/scripts/coordinate_table.sh
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#!/bin/bash
skills/bioskills/bio-database-access-biomart-queries/scripts/ortholog_table.py
skills/bioskills/bio-database-access-blast-searches/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 33
- Python: `NCBIWWW.qblast(program, db, sequence)` + `NCBIXML.read(handle)` (BioPython) - CLI: `blastn -remote -db nt -query seq.fa -out hits.xml -outfmt 5` (BLAST+) - Web: https://blast.ncbi.nlm.nih.gov/Blast.cgi (RID lookup) - Network accessline 35
- Web: https://blast.ncbi.nlm.nih.gov/Blast.cgi (RID lookup) - Network accessline 137
`NCBIWWW.qblast()` handles polling internally with a fixed retry interval. For long-running searches (>5 min) or batches, submit and capture the RID, then poll independently to avoid blocking. The RID is visible at `https://blast.ncbi.nlm.n
skills/bioskills/bio-database-access-blast-searches/scripts/basic_blast.py
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skills/bioskills/bio-database-access-blast-searches/scripts/blastp_filtered.py
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skills/bioskills/bio-database-access-blast-searches/scripts/save_and_parse.py
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skills/bioskills/bio-database-access-ensembl-rest/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 20
Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show requests` - API surface: check release notes at https://rest.ensembl.org - Network accessline 22
- API surface: check release notes at https://rest.ensembl.org - Network accessline 24
Each Ensembl release has an archive REST endpoint (e.g. `https://e110.rest.ensembl.org`) for reproducibility. - Network accessline 30
Two facts dominate Ensembl REST work: (1) the **15 req/sec / 55,000 req/hour rate limit** — high enough for hundreds of queries, low enough that bulk work (>5,000) belongs in BioMart instead; (2) **versioned archive endpoints** — `https://e - Network accessline 32
- Python: `requests.get('https://rest.ensembl.org/...')` - Network accessline 33
- Web: https://rest.ensembl.org (interactive doc with try-it-now) - Network accessline 42
BASE = 'https://rest.ensembl.org' - Network accessline 53
| Division | Host | Scope | |---|---|---| | Vertebrates | https://rest.ensembl.org | Human, mouse, fish, etc. (the "main" Ensembl) | - Network accessline 54
|---|---|---| | Vertebrates | https://rest.ensembl.org | Human, mouse, fish, etc. (the "main" Ensembl) | | Plants | https://rest.ensembl.org (plants division also accessible) | Arabidopsis, rice, etc. via Ensembl Genomes | - Network accessline 55
| Vertebrates | https://rest.ensembl.org | Human, mouse, fish, etc. (the "main" Ensembl) | - Network accessline 56
| Plants | https://rest.ensembl.org (plants division also accessible) | Arabidopsis, rice, etc. via Ensembl Genomes | - Network accessline 57
| Fungi | https://rest.ensemblgenomes.org | Yeasts, Aspergillus, etc. | - Network accessline 58
| Metazoa | https://rest.ensemblgenomes.org | Insects, nematodes, etc. | - Network accessline 59
| Bacteria | https://rest.ensemblgenomes.org | Limited (most bacteria in NCBI) | - Network accessline 65
| URL | Behavior | |---|---| | `https://rest.ensembl.org` | Current release (rolling) | - Network accessline 66
|---|---| | `https://rest.ensembl.org` | Current release (rolling) | | `https://e110.rest.ensembl.org` | Pinned to release 110 | - Network accessline 67
| `https://rest.ensembl.org` | Current release (rolling) | - Network accessline 68
| `https://e110.rest.ensembl.org` | Pinned to release 110 | - Network accessline 69
| `https://e111.rest.ensembl.org` | Pinned to release 111 | - Network accessline 70
| `https://grch37.rest.ensembl.org` | Pinned to GRCh37 (legacy assembly) | - Network accessline 90
Full reference: https://rest.ensembl.org (interactive). - Network accessline 146
BASE = 'https://rest.ensembl.org' - Network accessline 150
def get_with_retry(url, params=None, max_retries=3): for attempt in range(max_retries): r = requests.get(url, params=params, headers=HEADERS) - Network accessline 151
for attempt in range(max_retries): r = requests.get(url, params=params, headers=HEADERS) if r.status_code == 429: - Network accessline 152
r = requests.get(url, params=params, headers=HEADERS) - Network accessline 214
# BRCA1 missense variant in GRCh38 coordinates (rest.ensembl.org defaults to GRCh38); # for GRCh37 coords use https://grch37.rest.ensembl.org instead. - Network accessline 215
# for GRCh37 coords use https://grch37.rest.ensembl.org instead. - Network accessline 256
# Pin to release 110 ARCHIVE = 'https://e110.rest.ensembl.org' r = requests.get(f'{ARCHIVE}/lookup/symbol/human/BRCA1', headers={'Accept': 'application/json'}) - Network accessline 257
ARCHIVE = 'https://e110.rest.ensembl.org' r = requests.get(f'{ARCHIVE}/lookup/symbol/human/BRCA1', headers={'Accept': 'application/json'}) print(r.json()['id']) - Network accessline 258
r = requests.get(f'{ARCHIVE}/lookup/symbol/human/BRCA1', headers={'Accept': 'application/json'}) - Network accessline 280
### No version pinning, results drift - **Trigger:** Re-running a pipeline against `https://rest.ensembl.org` a year later. - Network accessline 281
- **Trigger:** Re-running a pipeline against `https://rest.ensembl.org` a year later. - Network accessline 282
- **Mechanism:** Live endpoint follows current release; gene models update quarterly. - **Symptom:** Different transcript coordinates, exon counts, sometimes Gene ID changes. - **Fix:** Pin to an archive endpoint (`https://e110.rest.ensembl - Network accessline 284
- **Fix:** Pin to an archive endpoint (`https://e110.rest.ensembl.org`) for reproducibility. - Network accessline 300
- **Mechanism:** Ensembl species names are lowercase with underscores or common names; case matters. - **Symptom:** 404 or empty result. - **Fix:** Use `https://rest.ensembl.org/info/species` to enumerate valid names. - Network accessline 302
- **Fix:** Use `https://rest.ensembl.org/info/species` to enumerate valid names. - Network accessline 312
- **Mechanism:** Very old archives are retired ~5 years after release. - **Symptom:** DNS failure or 503. - **Fix:** Check the current archive list at https://www.ensembl.org/info/website/archives/index.html. - Network accessline 314
- **Fix:** Check the current archive list at https://www.ensembl.org/info/website/archives/index.html.
skills/bioskills/bio-database-access-ensembl-rest/references/usage-guide.md
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Query Ensembl's REST API for gene/transcript/protein lookup, sequence retrieval, Compara orthologs, VEP variant annotation, regulatory features, and LD. Encodes: the 15 req/sec rate limit (55K/hour); version-pinned archive endpoints (`https - Network accessline 19
- "What genes are in chr17:43000000-43200000? Use the overlap endpoint" - "VEP annotate a single missense variant by region/allele notation" - "Pin queries to release 110 archive (https://e110.rest.ensembl.org) for reproducibility" - Network accessline 21
- "Pin queries to release 110 archive (https://e110.rest.ensembl.org) for reproducibility" - Network accessline 31
> "I'm writing a paper. Pin all Ensembl REST calls to https://e110.rest.ensembl.org so re-running in 2030 returns the same gene models. Live https://rest.ensembl.org follows the current release and drifts." - Network accessline 68
- For HGNC-renamed genes (MARCH1, SEPT*), Ensembl mirrors the rename. Symbol lookup for the old name returns 404; use the new symbol or pre-resolve. - Some endpoints accept multiple IDs in POST body (e.g. `/lookup/id` POST with `{"ids": [.. - Network accessline 70
- For older assembly versions (GRCh37), use `https://grch37.rest.ensembl.org`.
skills/bioskills/bio-database-access-ensembl-rest/scripts/compara_homology.py
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BASE = 'https://rest.ensembl.org' - Network accessline 11
def get_with_retry(url, params=None, max_retries=3): for attempt in range(max_retries): r = requests.get(url, params=params, headers=HEADERS) - Network accessline 12
for attempt in range(max_retries): r = requests.get(url, params=params, headers=HEADERS) if r.status_code == 429: - Network accessline 13
r = requests.get(url, params=params, headers=HEADERS)
skills/bioskills/bio-database-access-ensembl-rest/scripts/lookup_and_overlap.py
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BASE = 'https://rest.ensembl.org' - Network accessline 7
ARCHIVE = 'https://e110.rest.ensembl.org' # pin for reproducibility - Network accessline 12
def get_with_retry(url, params=None, max_retries=3): for attempt in range(max_retries): r = requests.get(url, params=params, headers=HEADERS) - Network accessline 13
for attempt in range(max_retries): r = requests.get(url, params=params, headers=HEADERS) if r.status_code == 429: - Network accessline 14
r = requests.get(url, params=params, headers=HEADERS)
skills/bioskills/bio-database-access-ensembl-rest/scripts/vep_annotation.py
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BASE = 'https://rest.ensembl.org' - Network accessline 11
def get_with_retry(url, params=None, max_retries=3): for attempt in range(max_retries): r = requests.get(url, params=params, headers=HEADERS) - Network accessline 12
for attempt in range(max_retries): r = requests.get(url, params=params, headers=HEADERS) if r.status_code == 429: - Network accessline 13
r = requests.get(url, params=params, headers=HEADERS) - Network accessline 54
# rest.ensembl.org defaults to GRCh38; for GRCh37 swap base URL to https://grch37.rest.ensembl.org
skills/bioskills/bio-database-access-entrez-fetch/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Access to secretsline 42
[Credential-related source omitted] - Access to secretsline 168
[Credential-related source omitted] - Network accessline 208
### History-server fetch (post-ESearch) - Access to secretsline 226
[Credential-related source omitted]
skills/bioskills/bio-database-access-entrez-fetch/references/usage-guide.md
- Access to secretsline 16
[Credential-related source omitted]
skills/bioskills/bio-database-access-entrez-fetch/scripts/fetch_pubmed.py
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skills/bioskills/bio-database-access-entrez-fetch/scripts/fetch_sequences.py
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skills/bioskills/bio-database-access-entrez-fetch/scripts/fetch_summaries.py
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skills/bioskills/bio-database-access-entrez-link/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Access to secretsline 42
[Credential-related source omitted] - Access to secretsline 219
[Credential-related source omitted] - Access to secretsline 259
[Credential-related source omitted] - Access to secretsline 293
[Credential-related source omitted]
skills/bioskills/bio-database-access-entrez-link/references/usage-guide.md
skills/bioskills/bio-database-access-entrez-link/scripts/basic_linking.py
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skills/bioskills/bio-database-access-entrez-link/scripts/chain_links.py
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skills/bioskills/bio-database-access-entrez-link/scripts/discover_links.py
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skills/bioskills/bio-database-access-entrez-search/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Access to secretsline 44
[Credential-related source omitted] - Access to secretsline 177
[Credential-related source omitted]
skills/bioskills/bio-database-access-entrez-search/references/usage-guide.md
skills/bioskills/bio-database-access-entrez-search/scripts/basic_search.py
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skills/bioskills/bio-database-access-entrez-search/scripts/database_info.py
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skills/bioskills/bio-database-access-entrez-search/scripts/global_query.py
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skills/bioskills/bio-database-access-geo-data/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Access to secretsline 47
[Credential-related source omitted] - Network accessline 71
print(r.get('summary')) # may or may not flag SuperSeries # Definitive check: download SOFT and grep: # curl ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE122nnn/GSE122288/soft/GSE122288_family.soft.gz | zgrep Series_relation - Network accessline 73
# curl ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE122nnn/GSE122288/soft/GSE122288_family.soft.gz | zgrep Series_relation - Network accessline 86
| "I want raw RNA-seq FASTQ" | pysradb `gse_to_srp -> srp_to_srr` (Entrez gds->sra ELink unreliable) | Always raw; processed at submitter is rarely re-usable | | "I want submitter-provided counts (RNA-seq)" | Supplementary files (usually a - Network accessline 88
| "I want a curated subset across many studies" | Use ArchS4 (https://archs4.org) or recount3 | Curated re-processing | - Network accessline 129
| Old (pre-2020) | New (BioStudies) | |---|---| | `https://www.ebi.ac.uk/arrayexpress/...` | `https://www.ebi.ac.uk/biostudies/...` | - Network accessline 130
|---|---| | `https://www.ebi.ac.uk/arrayexpress/...` | `https://www.ebi.ac.uk/biostudies/...` | | ArrayExpress REST | BioStudies REST: `https://www.ebi.ac.uk/biostudies/api/v1/...` | - Network accessline 131
| `https://www.ebi.ac.uk/arrayexpress/...` | `https://www.ebi.ac.uk/biostudies/...` | - Network accessline 132
| ArrayExpress REST | BioStudies REST: `https://www.ebi.ac.uk/biostudies/api/v1/...` | - Network accessline 183
def check_super_or_sub_series(gse): prefix = gse[:-3] + 'nnn' url = f'https://ftp.ncbi.nlm.nih.gov/geo/series/{prefix}/{gse}/soft/{gse}_family.soft.gz' - Network accessline 184
prefix = gse[:-3] + 'nnn' url = f'https://ftp.ncbi.nlm.nih.gov/geo/series/{prefix}/{gse}/soft/{gse}_family.soft.gz' urllib.request.urlretrieve(url, f'{gse}.soft.gz') - Network accessline 185
url = f'https://ftp.ncbi.nlm.nih.gov/geo/series/{prefix}/{gse}/soft/{gse}_family.soft.gz' urllib.request.urlretrieve(url, f'{gse}.soft.gz') super_of = [] - Network accessline 186
urllib.request.urlretrieve(url, f'{gse}.soft.gz') - Network accessline 212
def download_series_matrix(gse): prefix = gse[:-3] + 'nnn' url = f'https://ftp.ncbi.nlm.nih.gov/geo/series/{prefix}/{gse}/matrix/{gse}_series_matrix.txt.gz' - Network accessline 213
prefix = gse[:-3] + 'nnn' url = f'https://ftp.ncbi.nlm.nih.gov/geo/series/{prefix}/{gse}/matrix/{gse}_series_matrix.txt.gz' urllib.request.urlretrieve(url, f'{gse}_matrix.txt.gz') - Network accessline 214
url = f'https://ftp.ncbi.nlm.nih.gov/geo/series/{prefix}/{gse}/matrix/{gse}_series_matrix.txt.gz' urllib.request.urlretrieve(url, f'{gse}_matrix.txt.gz') return f'{gse}_matrix.txt.gz' - Network accessline 215
urllib.request.urlretrieve(url, f'{gse}_matrix.txt.gz') - Network accessline 336
- **Mechanism:** Known issue with the GEOparse FTP enumeration since ~2022. - **Symptom:** Local cache missing CEL or counts files. - **Fix:** Use R GEOquery or direct FTP `wget -r` on the suppl/ subdirectory. - Network accessline 338
- **Fix:** Use R GEOquery or direct FTP `wget -r` on the suppl/ subdirectory. - Network accessline 340
### ArrayExpress URL rot - **Trigger:** Old paper links `https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-1234/`. - Network accessline 341
- **Trigger:** Old paper links `https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-1234/`. - Network accessline 342
- **Mechanism:** ArrayExpress migrated to BioStudies in 2020. - **Symptom:** 404 or redirect. - **Fix:** Use `https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-1234`. - Network accessline 344
- **Fix:** Use `https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-1234`. - Network accessline 366
- Barrett T, Wilhite SE, Ledoux P, et al. (2013) NCBI GEO: archive for functional genomics data sets - update. *Nucleic Acids Res* 41:D991-D995. - Davis S, Meltzer PS. (2007) GEOquery: a bridge between the Gene Expression Omnibus (GEO) and - Network accessline 367
- Davis S, Meltzer PS. (2007) GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. *Bioinformatics* 23:1846-1847. - Gumienny R. GEOparse: Python library to parse GEO databases. https://github.com/guma44/GEOparse (n - Network accessline 368
- Gumienny R. GEOparse: Python library to parse GEO databases. https://github.com/guma44/GEOparse (no journal publication).
skills/bioskills/bio-database-access-geo-data/references/usage-guide.md
- Access to secretsline 18
[Credential-related source omitted] - Network accessline 64
- The SuperSeries trap is the single biggest GEO mistake. ALWAYS check `!Series_relation` for `SuperSeries of: ...` before treating a GSE as one experiment. - Submitter-normalized series matrices vary widely. For Affymetrix, default to CEL - Network accessline 65
- Submitter-normalized series matrices vary widely. For Affymetrix, default to CEL + locally-run RMA. For RNA-seq, default to SRA FASTQ + locally-run quantification. - GEOparse (Python) is OK for SOFT parsing but flakey on supplementary-fil - Network accessline 66
- GEOparse (Python) is OK for SOFT parsing but flakey on supplementary-file download since ~2022. For Python pipelines that need suppl files, use direct FTP (`wget -r` on `suppl/`). - Network accessline 68
- `[Entry Type]` is case-sensitive: `gse[Entry Type]` works; `gse[entry_type]` returns empty. - GEOmetadb is unmaintained since 2020. Use pysradb for GSE<->SRA mapping; Entrez gds for full GEO search. - ArrayExpress was migrated into BioStu - Network accessline 69
- GEOmetadb is unmaintained since 2020. Use pysradb for GSE<->SRA mapping; Entrez gds for full GEO search. - ArrayExpress was migrated into BioStudies in 2020; old `E-MTAB-*` accessions still resolve via `https://www.ebi.ac.uk/biostudies/ar - Network accessline 70
- ArrayExpress was migrated into BioStudies in 2020; old `E-MTAB-*` accessions still resolve via `https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-XXXX`. - Network accessline 71
- For curated re-processed RNA-seq across thousands of GEO studies, ARCHS4 (https://archs4.org) and recount3 are alternatives with consistent pipelines.
skills/bioskills/bio-database-access-geo-data/scripts/geo_from_pubmed.py
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skills/bioskills/bio-database-access-geo-data/scripts/geo_to_sra.py
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skills/bioskills/bio-database-access-geo-data/scripts/search_geo.py
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prefix = gse[:-3] + 'nnn' url = f'https://ftp.ncbi.nlm.nih.gov/geo/series/{prefix}/{gse}/soft/{gse}_family.soft.gz' - Network accessline 30
url = f'https://ftp.ncbi.nlm.nih.gov/geo/series/{prefix}/{gse}/soft/{gse}_family.soft.gz' try: with urllib.request.urlopen(url, timeout=30) as resp: - Network accessline 31
try: with urllib.request.urlopen(url, timeout=30) as resp: data = resp.read() - Network accessline 32
with urllib.request.urlopen(url, timeout=30) as resp:
skills/bioskills/bio-database-access-interaction-databases/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 32
- Python: `requests.get()` against REST endpoints; `pandas` for parsing; `networkx` for graphs - Network accessline 47
API key requirements: - **BioGRID**: free key required (`https://webservice.thebiogrid.org/`) - Network accessline 48
- **BioGRID**: free key required (`https://webservice.thebiogrid.org/`) - Network accessline 93
**Version pinning**: STRING URL is `https://version-12-0.string-db.org/api/...` as of 2024. Older `version-11-5` URLs were deprecated 2023 — code using them silently fails. Use the unversioned `https://string-db.org/api/...` to follow the c - Network accessline 99
BioGRID (Oughtred et al. 2021 *Protein Sci* 30:187) covers physical and genetic interactions across the broadest organism set of any major resource. Requires a **free API key** (`https://webservice.thebiogrid.org/`). - Network accessline 136
Reactome ContentService REST: `https://reactome.org/ContentService/`. - Network accessline 143
Both available for download; HuRI also has a web portal (`http://www.interactome-atlas.org/`). - Network accessline 192
STRING = 'https://version-12-0.string-db.org/api' - Network accessline 202
'caller_identity': 'bioskills-2026', } r = requests.get(url, params=params); r.raise_for_status() - Network accessline 203
} r = requests.get(url, params=params); r.raise_for_status() df = pd.read_csv(StringIO(r.text), sep='\t') - Network accessline 204
r = requests.get(url, params=params); r.raise_for_status() - Network accessline 224
BIOGRID = 'https://webservice.thebiogrid.org/interactions/' - Access to secretsline 230
[Credential-related source omitted] - Access to secretsline 232
[Credential-related source omitted] - Network accessline 238
'max': 10000, } r = requests.get(BIOGRID, params=params); r.raise_for_status() - Network accessline 239
} r = requests.get(BIOGRID, params=params); r.raise_for_status() data = r.json() - Network accessline 240
r = requests.get(BIOGRID, params=params); r.raise_for_status() - Network accessline 257
SIGNOR = 'https://signor.uniroma2.it/getData.php' - Network accessline 261
'''Return signed, directed signaling interactions involving the gene.''' params = {'organism': 'human', 'entity': gene_symbol} r = requests.get(SIGNOR, params=params); r.raise_for_status() - Network accessline 262
params = {'organism': 'human', 'entity': gene_symbol} r = requests.get(SIGNOR, params=params); r.raise_for_status() rows = [] - Network accessline 263
r = requests.get(SIGNOR, params=params); r.raise_for_status() - Network accessline 278
OMNI = 'https://omnipathdb.org' - Network accessline 288
'license': 'academic', # or 'commercial' for permissive-only sources } r = requests.get(f'{OMNI}/interactions', params=params); r.raise_for_status() - Network accessline 289
} r = requests.get(f'{OMNI}/interactions', params=params); r.raise_for_status() df = pd.read_csv(StringIO(r.text), sep='\t') - Network accessline 290
r = requests.get(f'{OMNI}/interactions', params=params); r.raise_for_status()
skills/bioskills/bio-database-access-interaction-databases/references/usage-guide.md
- Network accessline 16
BioGRID requires a free API key from `https://webservice.thebiogrid.org/`. All other listed resources are key-free.
skills/bioskills/bio-database-access-interaction-databases/scripts/interaction_query.py
- Executable scriptline 1
Executable file - Network accessline 8
STRING = 'https://version-12-0.string-db.org/api' - Network accessline 9
OMNI = 'https://omnipathdb.org' - Network accessline 10
SIGNOR = 'https://signor.uniroma2.it/getData.php' - Network accessline 14
def string_interactions(genes, species=9606, threshold=700): r = requests.get(f'{STRING}/tsv/network', - Network accessline 15
r = requests.get(f'{STRING}/tsv/network', - Network accessline 22
def omnipath_interactions(genes, license='academic'): r = requests.get(f'{OMNI}/interactions', - Network accessline 23
r = requests.get(f'{OMNI}/interactions', - Network accessline 30
def signor_interactions(gene): r = requests.get(SIGNOR, params={'organism': 'human', 'entity': gene}) - Network accessline 31
r = requests.get(SIGNOR, params={'organism': 'human', 'entity': gene})
skills/bioskills/bio-database-access-interaction-databases/scripts/string_network.py
- Executable scriptline 1
Executable file - Network accessline 8
STRING = 'https://version-12-0.string-db.org/api' - Network accessline 18
'caller_identity': CALLER, } r = requests.get(url, params=params); r.raise_for_status() - Network accessline 19
} r = requests.get(url, params=params); r.raise_for_status() return pd.read_csv(StringIO(r.text), sep='\t') - Network accessline 20
r = requests.get(url, params=params); r.raise_for_status() - Network accessline 25
url = f'{STRING}/tsv/get_string_ids' params = {'identifiers': '%0d'.join(genes), 'species': species, 'caller_identity': CALLER} r = requests.get(url, params=params); r.raise_for_status() - Network accessline 26
params = {'identifiers': '%0d'.join(genes), 'species': species, 'caller_identity': CALLER} r = requests.get(url, params=params); r.raise_for_status() return pd.read_csv(StringIO(r.text), sep='\t') - Network accessline 27
r = requests.get(url, params=params); r.raise_for_status()
skills/bioskills/bio-database-access-local-blast/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Dangerous shell operationline 45
sudo apt install ncbi-blast+
skills/bioskills/bio-database-access-local-blast/scripts/blast_wrapper.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-database-access-local-blast/scripts/create_database.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-database-access-local-blast/scripts/reciprocal_best.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-database-access-local-blast/scripts/run_blast.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-database-access-ncbi-datasets-cli/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 42
# Or direct download (Linux, macOS, Windows binaries) curl -O https://ftp.ncbi.nlm.nih.gov/pub/datasets/command-line/v2/linux-amd64/datasets - Network accessline 43
curl -O https://ftp.ncbi.nlm.nih.gov/pub/datasets/command-line/v2/linux-amd64/datasets - Network accessline 275
### Old assembly_summary.txt-based scripts still in use - **Trigger:** Legacy pipeline scraping `https://ftp.ncbi.nlm.nih.gov/genomes/all/refseq/...`. - Network accessline 276
- **Trigger:** Legacy pipeline scraping `https://ftp.ncbi.nlm.nih.gov/genomes/all/refseq/...`. - Network accessline 283
- **Mechanism:** NCBI rate-limits unauthenticated bulk traffic. - **Symptom:** Throttling; slow downloads. - **Fix:** Pass `--api-key YOUR_KEY` to bulk commands; obtain from `https://www.ncbi.nlm.nih.gov/account/settings/`. - Network accessline 285
- **Fix:** Pass `--api-key YOUR_KEY` to bulk commands; obtain from `https://www.ncbi.nlm.nih.gov/account/settings/`. - Network accessline 307
- O'Leary NA, Cox E, Holmes JB, et al. (2024) Exploring and retrieving sequence and metadata for species across the tree of life with NCBI Datasets. *Sci Data* 11:732. - NCBI Datasets documentation: https://www.ncbi.nlm.nih.gov/datasets/doc - Network accessline 308
- NCBI Datasets documentation: https://www.ncbi.nlm.nih.gov/datasets/docs/v2/ - Network accessline 309
- NCBI. Datasets CLI usage. https://www.ncbi.nlm.nih.gov/datasets/docs/v2/reference-docs/command-line/datasets/
skills/bioskills/bio-database-access-ncbi-datasets-cli/references/usage-guide.md
- Network accessline 15
For high-volume bulk: get an NCBI API key from `https://www.ncbi.nlm.nih.gov/account/settings/` and pass `--api-key`.
skills/bioskills/bio-database-access-ncbi-datasets-cli/scripts/bulk_dehydrated.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-database-access-ncbi-datasets-cli/scripts/download_genome.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-database-access-ncbi-datasets-cli/scripts/gene_metadata.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-database-access-ortholog-inference/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 41
- Python: `requests.get()` against REST endpoints; `pandas` for parsing - Network accessline 73
Base URL: `https://data.orthodb.org/v12/` - Network accessline 84
def orthodb_search(symbol, species_taxid=9606): r = requests.get('https://data.orthodb.org/v12/search', - Network accessline 85
r = requests.get('https://data.orthodb.org/v12/search', - Network accessline 93
Base URL: `https://rest.ensembl.org/`. JSON: `Accept: application/json`. Rate limit: 15 req/sec, 55,000 req/hour. Respect `Retry-After` on 429. - Network accessline 103
def ensembl_orthologs(symbol, species='human', target=None): url = f'https://rest.ensembl.org/homology/symbol/{species}/{symbol}' - Network accessline 104
url = f'https://rest.ensembl.org/homology/symbol/{species}/{symbol}' - Network accessline 106
if target: params['target_species'] = target r = requests.get(url, params=params, headers={'Accept': 'application/json'}) - Network accessline 107
params['target_species'] = target r = requests.get(url, params=params, headers={'Accept': 'application/json'}) r.raise_for_status() - Network accessline 108
r = requests.get(url, params=params, headers={'Accept': 'application/json'}) - Network accessline 123
Base URL: `https://omabrowser.org/api/`. JSON returned. No rate-limit doc but be polite. - Network accessline 131
def oma_orthologs(uniprot_acc): r = requests.get(f'https://omabrowser.org/api/protein/{uniprot_acc}/orthologs/') - Network accessline 132
r = requests.get(f'https://omabrowser.org/api/protein/{uniprot_acc}/orthologs/') - Network accessline 139
Base URL: `http://eggnog6.embl.de/api/` (web API; lighter than running eggNOG-mapper). Most heavy lifting still uses **eggNOG-mapper** locally (Cantalapiedra et al. 2021 *Mol Biol Evol* 38:5825) — for batch protein-set annotation, mapper > - Network accessline 145
Base URL: `https://rest.kegg.jp/`. Returns plain text TSV by default (NOT JSON). - Network accessline 148
def kegg_ko_for_gene(species_code, gene): '''KEGG species codes: hsa=human, mmu=mouse, dme=fly, etc.''' r = requests.get(f'https://rest.kegg.jp/link/ko/{species_code}:{gene}') - Network accessline 149
'''KEGG species codes: hsa=human, mmu=mouse, dme=fly, etc.''' r = requests.get(f'https://rest.kegg.jp/link/ko/{species_code}:{gene}') r.raise_for_status() - Network accessline 150
r = requests.get(f'https://rest.kegg.jp/link/ko/{species_code}:{gene}') - Network accessline 155
def kegg_orthologs(ko_id): r = requests.get(f'https://rest.kegg.jp/link/genes/{ko_id}') - Network accessline 156
r = requests.get(f'https://rest.kegg.jp/link/genes/{ko_id}') - Network accessline 164
Base URL: `http://pantherdb.org/services/oai/pantherdb/` (note the unusual base). Has a curated, smaller scope than OrthoDB but with experimental evidence. - Network accessline 168
`https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=homologene&id=<id>&rettype=xml` — still works; data frozen since 2014. Useful only for backward-compatibility with old pipelines. - Network accessline 210
def compara_one2one(symbol, source='mouse', target='human'): url = f'https://rest.ensembl.org/homology/symbol/{source}/{symbol}' r = requests.get(url, params={'type': 'orthologues', 'target_species': target}, - Network accessline 211
url = f'https://rest.ensembl.org/homology/symbol/{source}/{symbol}' r = requests.get(url, params={'type': 'orthologues', 'target_species': target}, headers={'Accept': 'application/json'}) - Network accessline 212
r = requests.get(url, params={'type': 'orthologues', 'target_species': target}, - Network accessline 282
def oma_hog_for_protein(oma_or_uniprot_id): r = requests.get(f'https://omabrowser.org/api/protein/{oma_or_uniprot_id}/') - Network accessline 283
r = requests.get(f'https://omabrowser.org/api/protein/{oma_or_uniprot_id}/') - Network accessline 288
def oma_hog_members(hog_id, level=None): url = f'https://omabrowser.org/api/hog/{hog_id}/' - Network accessline 289
url = f'https://omabrowser.org/api/hog/{hog_id}/' params = {'level': level} if level else {} r = requests.get(url, params=params) - Network accessline 290
params = {'level': level} if level else {} r = requests.get(url, params=params) r.raise_for_status() - Network accessline 291
r = requests.get(url, params=params)
skills/bioskills/bio-database-access-ortholog-inference/scripts/compara_orthologs.py
- Executable scriptline 1
Executable file - Network accessline 7
BASE = 'https://rest.ensembl.org' - Network accessline 12
def get_with_retry(url, params=None, max_retries=3): for attempt in range(max_retries): r = requests.get(url, params=params, headers=HEADERS) - Network accessline 13
for attempt in range(max_retries): r = requests.get(url, params=params, headers=HEADERS) if r.status_code == 429: - Network accessline 14
r = requests.get(url, params=params, headers=HEADERS)
skills/bioskills/bio-database-access-ortholog-inference/scripts/cross_resource.py
- Executable scriptline 1
Executable file - Network accessline 6
ENSEMBL = 'https://rest.ensembl.org' - Network accessline 7
OMA = 'https://omabrowser.org/api' - Network accessline 8
ORTHODB = 'https://data.orthodb.org/v12' - Network accessline 12
def ensembl_orthologs(symbol, source='human', target='mouse'): r = requests.get(f'{ENSEMBL}/homology/symbol/{source}/{symbol}', - Network accessline 13
r = requests.get(f'{ENSEMBL}/homology/symbol/{source}/{symbol}', - Network accessline 23
def oma_orthologs(uniprot_acc): r = requests.get(f'{OMA}/protein/{uniprot_acc}/orthologs/') - Network accessline 24
r = requests.get(f'{OMA}/protein/{uniprot_acc}/orthologs/') - Network accessline 31
def orthodb_groups(symbol, species_taxid=9606): r = requests.get(f'{ORTHODB}/search', params={'query': symbol, 'species': species_taxid}) - Network accessline 32
r = requests.get(f'{ORTHODB}/search', params={'query': symbol, 'species': species_taxid}) - Network accessline 39
r = requests.get(f'{ORTHODB}/orthologs', params={'id': og_id, 'species': species_taxid})
skills/bioskills/bio-database-access-ortholog-inference/scripts/kegg_orthology.py
skills/bioskills/bio-database-access-remote-homology/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 99
# Build domain database once wget https://ftp.ebi.ac.uk/pub/databases/Pfam/current_release/Pfam-A.hmm.gz - Network accessline 100
wget https://ftp.ebi.ac.uk/pub/databases/Pfam/current_release/Pfam-A.hmm.gz
skills/bioskills/bio-database-access-remote-homology/scripts/foldseek_search.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-database-access-remote-homology/scripts/iterative_profile.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-database-access-remote-homology/scripts/pfam_annotation.sh
- Executable scriptline 1
#!/bin/bash - Network accessline 13
echo "=== One-time: download and press Pfam-A ===" cd "${PFAM_DIR}" wget -q https://ftp.ebi.ac.uk/pub/databases/Pfam/current_release/Pfam-A.hmm.gz - Network accessline 14
cd "${PFAM_DIR}" wget -q https://ftp.ebi.ac.uk/pub/databases/Pfam/current_release/Pfam-A.hmm.gz gunzip Pfam-A.hmm.gz - Network accessline 15
wget -q https://ftp.ebi.ac.uk/pub/databases/Pfam/current_release/Pfam-A.hmm.gz
skills/bioskills/bio-database-access-sra-data/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 32
- CLI: `prefetch SRR...`, `fasterq-dump SRR...`, `vdb-validate SRR...` (sra-tools) - CLI: `curl https://ftp.sra.ebi.ac.uk/...` (ENA mirror; direct FASTQ) - Network accessline 33
- CLI: `curl https://ftp.sra.ebi.ac.uk/...` (ENA mirror; direct FASTQ) - Network accessline 119
curl 'https://www.ebi.ac.uk/ena/portal/api/filereport?accession=SRR12345678&result=read_run&fields=fastq_ftp,fastq_md5,read_count&format=tsv' - Network accessline 126
curl -O 'https://ftp.sra.ebi.ac.uk/vol1/fastq/SRR123/078/SRR12345678/SRR12345678_1.fastq.gz' - Network accessline 192
# Get FASTQ URLs + md5 from ENA portal API META=$(curl -fsSL "https://www.ebi.ac.uk/ena/portal/api/filereport?accession=${SRR}&result=read_run&fields=fastq_ftp,fastq_md5&format=tsv" | tail -1) - Network accessline 193
META=$(curl -fsSL "https://www.ebi.ac.uk/ena/portal/api/filereport?accession=${SRR}&result=read_run&fields=fastq_ftp,fastq_md5&format=tsv" | tail -1) - Network accessline 200
expected_md5=$(echo "${MD5S}" | sed -n "$((i+1))p") echo "Downloading ${fname}" curl -fsSL -o "${fname}" "https://${url}" - Network accessline 201
echo "Downloading ${fname}" curl -fsSL -o "${fname}" "https://${url}" actual_md5=$(md5sum "${fname}" | awk '{print $1}') - Network accessline 202
curl -fsSL -o "${fname}" "https://${url}" - Dangerous shell operationline 235
# rm -rf ~/ncbi/sra/${SRR}.sra - Network accessline 373
- NCBI. SRA Toolkit documentation. https://github.com/ncbi/sra-tools/wiki - Network accessline 374
- NCBI. STRIDES program. https://datascience.nih.gov/strides
skills/bioskills/bio-database-access-sra-data/references/usage-guide.md
- Network accessline 29
> "Download SRR12345678 via the ENA mirror (https://ftp.sra.ebi.ac.uk/). It's typically faster than SRA-direct because the FASTQ is pre-compressed -- no SRA->FASTQ conversion needed. Get the md5 from the ENA portal API and verify."
skills/bioskills/bio-database-access-sra-data/scripts/download_batch.sh
- Executable scriptline 1
#!/bin/bash - Network accessline 31
META=$(curl -fsS "https://www.ebi.ac.uk/ena/portal/api/filereport?accession=${ACC}&result=read_run&fields=fastq_ftp,fastq_md5,read_count&format=tsv" | tail -1) || { - Network accessline 50
fname="${OUT}/$(basename ${url})" echo " Downloading $(basename ${url})" if ! curl -fsSL -o "${fname}" "https://${url}"; then - Network accessline 51
echo " Downloading $(basename ${url})" if ! curl -fsSL -o "${fname}" "https://${url}"; then echo " curl failed" - Network accessline 52
if ! curl -fsSL -o "${fname}" "https://${url}"; then - Network accessline 53
echo " curl failed"
skills/bioskills/bio-database-access-sra-data/scripts/download_single.sh
skills/bioskills/bio-database-access-sra-data/scripts/find_sra_runs.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-database-access-sra-data/scripts/prefetch_large.sh
skills/bioskills/bio-database-access-uniprot-access/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 20
Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show requests pandas` - API surface: confirm endpoint URLs match https://www.uniprot.org/help/api - Network accessline 22
- API surface: confirm endpoint URLs match https://www.uniprot.org/help/api - Network accessline 24
The REST API JSON schema is stable within a release; major schema changes are documented at https://www.uniprot.org/release-notes. The 2022 migration broke the legacy `https://www.uniprot.org/uniprot/...` endpoints. - Network accessline 28
**"Get protein information from UniProt"** -> Two facts dominate every UniProt workflow in 2026: (1) **the API endpoint migrated in 2022** from `https://www.uniprot.org/uniprot/...` to `https://rest.uniprot.org/uniprotkb/...` with a substan - Network accessline 37
- Python: `requests.get('https://rest.uniprot.org/uniprotkb/...')` (REST API) - Network accessline 39
- CLI: `curl https://rest.uniprot.org/uniprotkb/P04637.json` - Network accessline 53
Base: `https://rest.uniprot.org/` - Network accessline 99
fields = 'accession,id,gene_names,protein_name,length,sequence,xref_pdb,xref_alphafolddb' url = 'https://rest.uniprot.org/uniprotkb/search' - Network accessline 100
url = 'https://rest.uniprot.org/uniprotkb/search' - Network accessline 139
entry = requests.get('https://rest.uniprot.org/uniprotkb/P04637.json').json() - Network accessline 171
iso = requests.get('https://rest.uniprot.org/uniprotkb/P04637-2.fasta').text - Network accessline 196
Full from/to list at https://rest.uniprot.org/configure/idmapping/fields. - Network accessline 211
def fetch_uniprot_entry(accession): r = requests.get(f'https://rest.uniprot.org/uniprotkb/{accession}.json') - Network accessline 212
r = requests.get(f'https://rest.uniprot.org/uniprotkb/{accession}.json') - Network accessline 243
def search_uniprot_tsv(query, fields, size=500): url = 'https://rest.uniprot.org/uniprotkb/search' - Network accessline 244
url = 'https://rest.uniprot.org/uniprotkb/search' params = {'query': query, 'fields': ','.join(fields), 'format': 'tsv', 'size': size} r = requests.get(url, params=params) - Network accessline 245
params = {'query': query, 'fields': ','.join(fields), 'format': 'tsv', 'size': size} r = requests.get(url, params=params) r.raise_for_status() - Network accessline 246
r = requests.get(url, params=params) - Network accessline 267
def stream_uniprot(query, fields): url = 'https://rest.uniprot.org/uniprotkb/stream' - Network accessline 268
url = 'https://rest.uniprot.org/uniprotkb/stream' params = {'query': query, 'fields': ','.join(fields), 'format': 'tsv'} r = requests.get(url, params=params, stream=True) - Network accessline 269
params = {'query': query, 'fields': ','.join(fields), 'format': 'tsv'} r = requests.get(url, params=params, stream=True) r.raise_for_status() - Network accessline 270
r = requests.get(url, params=params, stream=True) - Network accessline 294
def map_ids(ids, from_db='Ensembl', to_db='UniProtKB', timeout=600, poll_interval=3): submit = requests.post('https://rest.uniprot.org/idmapping/run', - Network accessline 295
submit = requests.post('https://rest.uniprot.org/idmapping/run', - Network accessline 301
elapsed = 0 while elapsed < timeout: status = requests.get(f'https://rest.uniprot.org/idmapping/status/{job_id}') - Network accessline 302
while elapsed < timeout: status = requests.get(f'https://rest.uniprot.org/idmapping/status/{job_id}') status.raise_for_status() - Network accessline 303
status = requests.get(f'https://rest.uniprot.org/idmapping/status/{job_id}') - Network accessline 315
results = requests.get(f'https://rest.uniprot.org/idmapping/results/{job_id}') - Network accessline 341
def download_proteome(upid, out_path): '''upid: UniProt Proteome ID, e.g. UP000005640 (human reference).''' url = f'https://rest.uniprot.org/proteomes/{upid}.fasta.gz' - Network accessline 342
'''upid: UniProt Proteome ID, e.g. UP000005640 (human reference).''' url = f'https://rest.uniprot.org/proteomes/{upid}.fasta.gz' r = requests.get(url, stream=True) - Network accessline 343
url = f'https://rest.uniprot.org/proteomes/{upid}.fasta.gz' r = requests.get(url, stream=True) r.raise_for_status() - Network accessline 344
r = requests.get(url, stream=True) - Network accessline 358
def uniref_cluster(uniref_id): '''e.g. UniRef50_P04637 -- the UniRef50 cluster centered on P04637.''' r = requests.get(f'https://rest.uniprot.org/uniref/{uniref_id}.json') - Network accessline 359
'''e.g. UniRef50_P04637 -- the UniRef50 cluster centered on P04637.''' r = requests.get(f'https://rest.uniprot.org/uniref/{uniref_id}.json') r.raise_for_status() - Network accessline 360
r = requests.get(f'https://rest.uniprot.org/uniref/{uniref_id}.json') - Network accessline 373
### Legacy URL still in code (post-2022) - **Trigger:** Old code using `https://www.uniprot.org/uniprot/{acc}.json`. - Network accessline 374
- **Trigger:** Old code using `https://www.uniprot.org/uniprot/{acc}.json`. - Network accessline 375
- **Mechanism:** 2022 migration; old URLs redirect but JSON schema is the new one — old parsers break. - **Symptom:** Either 404 or `KeyError` from old field paths. - **Fix:** Use `https://rest.uniprot.org/uniprotkb/{acc}.json`; update fiel - Network accessline 377
- **Fix:** Use `https://rest.uniprot.org/uniprotkb/{acc}.json`; update field navigation to the new nested layout. - Network accessline 436
- The UniProt Consortium. (2024) UniProt: the Universal Protein Knowledgebase in 2025. *Nucleic Acids Res* 53:D609-D617. - Bursteinas B, Britto R, Bely B, et al. (2016) Minimizing proteome redundancy in the UniProt Knowledgebase. *Database* - Network accessline 437
- Bursteinas B, Britto R, Bely B, et al. (2016) Minimizing proteome redundancy in the UniProt Knowledgebase. *Database* 2016:baw139. - UniProt help: https://www.uniprot.org/help/api - UniProt REST: https://rest.uniprot.org - Network accessline 438
- UniProt help: https://www.uniprot.org/help/api - Network accessline 439
- UniProt REST: https://rest.uniprot.org
skills/bioskills/bio-database-access-uniprot-access/references/usage-guide.md
- Network accessline 5
Query UniProt's REST API (`https://rest.uniprot.org/`). Encodes the 2022 endpoint migration (legacy `https://www.uniprot.org/uniprot/...` is deprecated; JSON schema changed substantially), the four UniProt resources (UniProtKB / UniRef / Un - Network accessline 63
- The 2022 endpoint migration is the biggest gotcha for legacy code. Old `https://www.uniprot.org/uniprot/...` URLs may redirect but the JSON schema is the new one -- old field paths break.
skills/bioskills/bio-database-access-uniprot-access/scripts/isoforms_and_xrefs.py
- Executable scriptline 1
Executable file - Network accessline 11
r = requests.get(f'https://rest.uniprot.org/uniprotkb/{accession}.json') - Network accessline 24
def fetch_isoform_fasta(accession_with_suffix): r = requests.get(f'https://rest.uniprot.org/uniprotkb/{accession_with_suffix}.fasta') - Network accessline 25
r = requests.get(f'https://rest.uniprot.org/uniprotkb/{accession_with_suffix}.fasta') - Network accessline 30
def xref_summary(accession): r = requests.get(f'https://rest.uniprot.org/uniprotkb/{accession}.json') - Network accessline 31
r = requests.get(f'https://rest.uniprot.org/uniprotkb/{accession}.json') - Network accessline 42
url = f'https://rest.uniprot.org/proteomes/{upid}.fasta.gz' with requests.get(url, stream=True) as r: - Network accessline 43
with requests.get(url, stream=True) as r:
skills/bioskills/bio-database-access-uniprot-access/scripts/uniprot_query.py
- Executable scriptline 1
Executable file - Network accessline 9
def fetch_entry_json(accession): r = requests.get(f'https://rest.uniprot.org/uniprotkb/{accession}.json') - Network accessline 10
r = requests.get(f'https://rest.uniprot.org/uniprotkb/{accession}.json') - Network accessline 28
def search_tsv(query, fields, size=500): url = 'https://rest.uniprot.org/uniprotkb/search' - Network accessline 29
url = 'https://rest.uniprot.org/uniprotkb/search' params = {'query': query, 'format': 'tsv', 'fields': ','.join(fields), 'size': size} r = requests.get(url, params=params); r.raise_for_status() - Network accessline 30
params = {'query': query, 'format': 'tsv', 'fields': ','.join(fields), 'size': size} r = requests.get(url, params=params); r.raise_for_status() return pd.read_csv(StringIO(r.text), sep='\t') - Network accessline 31
r = requests.get(url, params=params); r.raise_for_status() - Network accessline 37
url = 'https://rest.uniprot.org/uniprotkb/stream' params = {'query': query, 'format': 'tsv', 'fields': ','.join(fields)} r = requests.get(url, params=params, stream=True); r.raise_for_status() - Network accessline 38
params = {'query': query, 'format': 'tsv', 'fields': ','.join(fields)} r = requests.get(url, params=params, stream=True); r.raise_for_status() return pd.read_csv(StringIO(r.text), sep='\t') - Network accessline 39
r = requests.get(url, params=params, stream=True); r.raise_for_status() - Network accessline 43
def map_ids(ids, from_db='Ensembl', to_db='UniProtKB', timeout=600, poll=3): submit = requests.post('https://rest.uniprot.org/idmapping/run', - Network accessline 44
submit = requests.post('https://rest.uniprot.org/idmapping/run', - Network accessline 48
elapsed = 0 while elapsed < timeout: s = requests.get(f'https://rest.uniprot.org/idmapping/status/{job_id}') - Network accessline 49
while elapsed < timeout: s = requests.get(f'https://rest.uniprot.org/idmapping/status/{job_id}') s.raise_for_status() - Network accessline 50
s = requests.get(f'https://rest.uniprot.org/idmapping/status/{job_id}') - Network accessline 57
else: raise TimeoutError(f'job {job_id} did not complete in {timeout}s') r = requests.get(f'https://rest.uniprot.org/idmapping/results/{job_id}') - Network accessline 58
raise TimeoutError(f'job {job_id} did not complete in {timeout}s') r = requests.get(f'https://rest.uniprot.org/idmapping/results/{job_id}') r.raise_for_status() - Network accessline 59
r = requests.get(f'https://rest.uniprot.org/idmapping/results/{job_id}')
skills/bioskills/bio-differential-expression-batch-correction/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-differential-expression-de-results/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-differential-expression-de-visualization/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-differential-expression-deseq2-basics/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-differential-expression-edger-basics/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-differential-expression-timeseries-de/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-differential-expression-timeseries-de/scripts/timeseries_limma.R
- Executable scriptline 1
#!/usr/bin/env Rscript
skills/bioskills/bio-ecological-genomics-biodiversity-metrics/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-ecological-genomics-community-ecology/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-ecological-genomics-conservation-genetics/SKILL.md
- Network accessline 1
--- name: bio-ecological-genomics-conservation-genetics description: Assesses genetic health of populations for conservation with Ne estimation across time horizons (LDNe NeEstimator V2 option-file API + SNeP physical-linkage correction; re - Network accessline 2
name: bio-ecological-genomics-conservation-genetics description: Assesses genetic health of populations for conservation with Ne estimation across time horizons (LDNe NeEstimator V2 option-file API + SNeP physical-linkage correction; recent - Network accessline 3
description: Assesses genetic health of populations for conservation with Ne estimation across time horizons (LDNe NeEstimator V2 option-file API + SNeP physical-linkage correction; recent trajectory via GONE/GONE2; deep history via Stairwa - Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 45
A second cornerstone: **Ne/Nc ratio is NOT 0.1 universally.** Frankham 1995 reported a median of 0.1 in vertebrates, but Hauser & Carvalho 2008 *Fish Fish* 9:333-362 documented Ne/Nc spanning 2-6 orders of magnitude smaller than census (i.e - Network accessline 80
| Define management unit (MU) or ESU | Moritz 1994 reciprocal monophyly + nuclear divergence | Do NOT use BPP/BFD* species-delimitation methods (Sukumaran-Knowles 2017 oversplitting) | | Forward simulation of selection + demography | SLiM 4 - Network accessline 81
| Forward simulation of selection + demography | SLiM 4 with tree-sequence recording + pyslim/tskit | Tree-sequence recording is 5-100x faster than mutation tracking | | Comparing Ne across species | Cite Ne/Nc taxonomic variation (Hauser & - Network accessline 82
| Comparing Ne across species | Cite Ne/Nc taxonomic variation (Hauser & Carvalho 2008) | The Ne/Nc = 0.1 default is wrong for many taxa | - Network accessline 163
# Example NeEstimator V2 option file (option_file.ne2) # Line order matters; defaults can cause silent failures # See NeEstimator V2 documentation: https://github.com/bunop/NeEstimator2.X - Network accessline 165
# See NeEstimator V2 documentation: https://github.com/bunop/NeEstimator2.X - Network accessline 307
# Reference: SLiM 4+, pyslim 1.0+, tskit 0.5+, msprime 1.3+ # Note: SLiM scripts (.slim files) use Eidos, not Python. # See https://messerlab.org/slim/ for full Eidos syntax. - Network accessline 309
# See https://messerlab.org/slim/ for full Eidos syntax. - Network accessline 361
**Fix:** Strictly follow the documented option-file format. The INFO line confirms which methods actually ran. Cross-reference with the official documentation (https://github.com/bunop/NeEstimator2.X). - Network accessline 373
### Ne/Nc = 0.1 assumption applied to fish or other high-fecundity taxon - Network accessline 375
**Trigger:** Converting Ne to Nc via ratio = 0.1 for a marine fish, broadcast spawner, or other species with high reproductive skew. - Network accessline 377
**Mechanism:** Frankham 1995 median Ne/Nc = 0.1 was derived from primarily terrestrial vertebrates. Hauser & Carvalho 2008 documented Ne/Nc spanning 2-6 orders of magnitude smaller than census (10^-2 to 10^-6) in marine fish with sex-biased - Network accessline 381
**Fix:** Use taxon-specific Ne/Nc ratios from the literature. For marine fish with sweepstakes recruitment, Hauser & Carvalho 2008 documented Ne/Nc spanning 2-6 orders of magnitude smaller than census (10^-2 to 10^-6). For long-lived mammal - Network accessline 394
| PSMC minimum coverage | >= 20x WGS | Single-genome SMC requires high coverage | | dadi / fsc2 optimization replicates | >= 50 | Local-optima trap below this | | Ne/Nc default (use cautiously) | 0.1 vertebrate median | Hauser & Carvalho 20 - Network accessline 395
| dadi / fsc2 optimization replicates | >= 50 | Local-optima trap below this | | Ne/Nc default (use cautiously) | 0.1 vertebrate median | Hauser & Carvalho 2008 documented 2-6 orders of magnitude variation in marine fish (10^-2 to 10^-6) | - Network accessline 396
| Ne/Nc default (use cautiously) | 0.1 vertebrate median | Hauser & Carvalho 2008 documented 2-6 orders of magnitude variation in marine fish (10^-2 to 10^-6) |
skills/bioskills/bio-ecological-genomics-conservation-genetics/references/usage-guide.md
- Network accessline 5
Assesses genetic health of populations for conservation management with effective-population-size estimation across multiple time horizons (LD-based contemporary Ne via NeEstimator V2 option-file API with physical-linkage correction via SNe - Network accessline 11
- detectRUNS for ROH from PLINK (`install.packages('detectRUNS')`) - poppr for private alleles (`install.packages('poppr')`) - NeEstimator V2 (Java; https://github.com/bunop/NeEstimator2.X) - Network accessline 12
- poppr for private alleles (`install.packages('poppr')`) - NeEstimator V2 (Java; https://github.com/bunop/NeEstimator2.X) - GONE2 (`remotes::install_github('esrud/GONE2')` or standalone) - Network accessline 13
- NeEstimator V2 (Java; https://github.com/bunop/NeEstimator2.X) - Network accessline 14
- GONE2 (`remotes::install_github('esrud/GONE2')` or standalone) - SNeP (https://sourceforge.net/projects/snepnetrends/) for physical-linkage-corrected LDNe - Stairway Plot 2 (Java; https://github.com/xiaoming-liu/stairway-plot-v2) - Network accessline 15
- SNeP (https://sourceforge.net/projects/snepnetrends/) for physical-linkage-corrected LDNe - Network accessline 16
- Stairway Plot 2 (Java; https://github.com/xiaoming-liu/stairway-plot-v2) - Network accessline 17
- PSMC (https://github.com/lh3/psmc) - Network accessline 19
- SLiM 4 (https://messerlab.org/slim/) with pyslim/tskit for forward simulations - Network accessline 61
### Ne/Nc Caveat - Network accessline 63
> "Convert my estimated Ne to census size N. Do NOT default to Ne/Nc = 0.1; cite Hauser & Carvalho 2008 for taxon-specific ratios (Ne/Nc 2-6 orders of magnitude smaller than census, i.e., 10^-2 to 10^-6, documented for marine fish with swee - Network accessline 81
- The 50/500 rule has been revised to 100/1000 by Frankham et al. 2014 (Ne >= 100 for short-term, Ne >= 1000 for long-term adaptive potential) - Ne/Nc varies dramatically by life history; the 0.1 default is for terrestrial vertebrates and i - Network accessline 82
- Ne/Nc varies dramatically by life history; the 0.1 default is for terrestrial vertebrates and is wrong for marine fish (Hauser & Carvalho 2008 documented Ne/Nc spanning 2-6 orders of magnitude smaller than census, i.e., 10^-2 to 10^-6, in
skills/bioskills/bio-ecological-genomics-conservation-genetics/scripts/ne_estimation.R
- Network accessline 123
# --- Ne/Nc conversion warning --- - Network accessline 124
# Frankham 1995 reported Ne/Nc = 0.1 median in terrestrial vertebrates - Network accessline 125
# Hauser & Carvalho 2008 documented Ne/Nc spanning 2-6 orders of magnitude (10^-2 to 10^-6) in marine fish - Network accessline 126
# Do NOT default to 0.1 for high-fecundity / sweepstakes-reproduction species cat('\n--- Ne/Nc conversion caveat ---\n') cat('For marine fish with sweepstakes recruitment, Hauser & Carvalho 2008\n', - Network accessline 127
cat('\n--- Ne/Nc conversion caveat ---\n') - Network accessline 128
cat('For marine fish with sweepstakes recruitment, Hauser & Carvalho 2008\n', 'documented Ne/Nc spanning 2-6 orders of magnitude smaller than census\n', '(i.e., Ne/Nc ratio from 10^-2 to 10^-6 across species).\n', - Network accessline 129
'documented Ne/Nc spanning 2-6 orders of magnitude smaller than census\n', - Network accessline 130
'(i.e., Ne/Nc ratio from 10^-2 to 10^-6 across species).\n',
skills/bioskills/bio-ecological-genomics-edna-metabarcoding/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-ecological-genomics-edna-metabarcoding/references/usage-guide.md
- Network accessline 12
- decontam (`BiocManager::install('decontam')`) - microDecon (`remotes::install_github('donaldtmcknight/microDecon')`) - occumb and JAGS for occupancy modeling (`install.packages('occumb')`; JAGS from https://mcmc-jags.sourceforge.io) - Network accessline 13
- microDecon (`remotes::install_github('donaldtmcknight/microDecon')`) - occumb and JAGS for occupancy modeling (`install.packages('occumb')`; JAGS from https://mcmc-jags.sourceforge.io) - metabaR for tag-jumping filtering (`install.package - Network accessline 14
- occumb and JAGS for occupancy modeling (`install.packages('occumb')`; JAGS from https://mcmc-jags.sourceforge.io)
skills/bioskills/bio-ecological-genomics-edna-metabarcoding/scripts/obitools3_edna_pipeline.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/bioskills/bio-ecological-genomics-landscape-genomics/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-ecological-genomics-landscape-genomics/references/usage-guide.md
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- OutFLANK (`devtools::install_github('whitlock/OutFLANK')`) - vegan for RDA / pRDA (`install.packages('vegan')`) - gradientForest from R-Forge (`install.packages('gradientForest', repos = 'http://R-Forge.R-project.org')`) - Network accessline 12
- vegan for RDA / pRDA (`install.packages('vegan')`) - gradientForest from R-Forge (`install.packages('gradientForest', repos = 'http://R-Forge.R-project.org')`) - terra for environmental raster extraction (`install.packages('terra')`) - Network accessline 13
- gradientForest from R-Forge (`install.packages('gradientForest', repos = 'http://R-Forge.R-project.org')`) - Network accessline 14
- terra for environmental raster extraction (`install.packages('terra')`) - qvalue for FDR (`BiocManager::install('qvalue')`) - BayPass CLI from https://forgemia.inra.fr/mathieu.gautier/baypass_public - Network accessline 15
- qvalue for FDR (`BiocManager::install('qvalue')`) - BayPass CLI from https://forgemia.inra.fr/mathieu.gautier/baypass_public - adespatial / Circuitscape (Julia version) / ResistanceGA as needed - Network accessline 16
- BayPass CLI from https://forgemia.inra.fr/mathieu.gautier/baypass_public
skills/bioskills/bio-ecological-genomics-species-delimitation/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 30
**"Delineate species boundaries from my DNA barcoding or genomic data"** -> Apply distance-based (ASAP) and tree-based (mPTP) methods for primary delimitation, multilocus coalescent (BPP) for genomic confirmation, DELINEATE for speciation-p - Network accessline 31
- CLI: `asap` (downloadable C binary) OR web (https://bioinfo.mnhn.fr/abi/public/asap/) for single-locus primary delimitation - Network accessline 32
- CLI: `mptp` (https://github.com/Pas-Kapli/mptp) for tree-based multi-rate PTP (faster than bPTP) - Network accessline 33
- Python: `bPTP` (https://github.com/zhangjiajie/PTP) for legacy tree-based delimitation — NOTE: Python not R - Network accessline 34
- R: `splits::gmyc()` for GMYC on ultrametric trees - CLI: `bpp` (https://bpp.github.io) for multilocus MSC delimitation; control-file driven - CLI: `Dsuite` for D-statistics / f4-ratio / f-branch introgression testing - Network accessline 35
- CLI: `bpp` (https://bpp.github.io) for multilocus MSC delimitation; control-file driven - Network accessline 88
**Approach:** Run ASAP via the web interface (https://bioinfo.mnhn.fr/abi/public/asap/) OR the downloadable C binary. ASAP ranks candidate partitions by asap-score (lower = better); always inspect the top 5-10 partitions, not just the best, - Network accessline 143
**Approach:** Run mPTP on a rooted ML tree (e.g., from RAxML or IQ-TREE). mPTP's multi-rate model is more flexible than bPTP's single-rate; for shallow phylogenies with similar rates, bPTP may be competitive. mPTP is C++ (5+ orders of magni - Network accessline 146
# mPTP installation: build from C source # git clone https://github.com/Pas-Kapli/mptp - Network accessline 147
# git clone https://github.com/Pas-Kapli/mptp - Network accessline 165
**Approach:** Run bPTP via the Python package from https://github.com/zhangjiajie/PTP (NOTE: this is a Python tool, NOT an R package; `install.packages('PTP')` does not exist). For heterogeneous-rate data, prefer mPTP. - Network accessline 168
# bPTP Python install (NOT R) pip install git+https://github.com/iTaxoTools/PTP-pyqt5 - Network accessline 169
pip install git+https://github.com/iTaxoTools/PTP-pyqt5 - Network accessline 276
# DELINEATE (https://github.com/jsukumaran/delineate) - Network accessline 299
# Dsuite (https://github.com/millanek/Dsuite) - Network accessline 385
|-------|-------|----------| | ABGD/ASAP returns no clear partition | Barcoding gap absent in data | Inspect distance histogram; do not force partition | | bPTP install error in R | bPTP is Python, NOT R | `pip install git+https://github.co - Network accessline 386
| ABGD/ASAP returns no clear partition | Barcoding gap absent in data | Inspect distance histogram; do not force partition | | bPTP install error in R | bPTP is Python, NOT R | `pip install git+https://github.com/iTaxoTools/PTP-pyqt5` | | G - Network accessline 387
| bPTP install error in R | bPTP is Python, NOT R | `pip install git+https://github.com/iTaxoTools/PTP-pyqt5` |
skills/bioskills/bio-ecological-genomics-species-delimitation/references/usage-guide.md
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- ASAP web interface (https://bioinfo.mnhn.fr/abi/public/asap/) OR downloadable C binary - Network accessline 10
- mPTP: build from C source at https://github.com/Pas-Kapli/mptp (faster successor to bPTP) - Network accessline 11
- bPTP (Python, NOT R): `pip install git+https://github.com/iTaxoTools/PTP-pyqt5` - Network accessline 12
- R with splits for GMYC (`install.packages('splits', repos = 'http://R-Forge.R-project.org')`) - Network accessline 13
- ape for tree manipulation (`install.packages('ape')`) - phytools for `force.ultrametric` and tree visualization (`install.packages('phytools')`) - BPP v4 (https://github.com/bpp/bpp; control-file driven) - Network accessline 14
- phytools for `force.ultrametric` and tree visualization (`install.packages('phytools')`) - BPP v4 (https://github.com/bpp/bpp; control-file driven) - SNAPP via BEAST 2 (https://www.beast2.org/snapp/) - Network accessline 15
- BPP v4 (https://github.com/bpp/bpp; control-file driven) - Network accessline 16
- SNAPP via BEAST 2 (https://www.beast2.org/snapp/) - Network accessline 17
- DELINEATE (https://github.com/jsukumaran/delineate; Python, current speciation-process modeling) - Network accessline 18
- Dsuite (https://github.com/millanek/Dsuite) for D-statistic and f-branch - Network accessline 75
- ASAP supersedes ABGD as the modern distance-based primary delimitation method; faster, less prior-dependent, ranks partitions by asap-score - mPTP supersedes bPTP for most uses (5+ orders of magnitude faster, multi-rate model); for shallo - Network accessline 76
- mPTP supersedes bPTP for most uses (5+ orders of magnitude faster, multi-rate model); for shallow phylogenies with similar intraspecific rates, bPTP may still be useful - bPTP is a PYTHON tool, not R; `install.packages('PTP')` does not ex - Network accessline 77
- bPTP is a PYTHON tool, not R; `install.packages('PTP')` does not exist; `pip install git+https://github.com/iTaxoTools/PTP-pyqt5` - Network accessline 78
- ASAP is a STANDALONE C binary OR web service (https://bioinfo.mnhn.fr/abi/public/asap/); `install.packages('ASAP')` does not exist for the species delimitation tool
skills/bioskills/bio-ecological-genomics-species-delimitation/scripts/asap_delimitation.py
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# NOTE: this is a barcoding-gap / threshold-scan INSPECTION tool, NOT a substitute # for ASAP. Run actual ASAP via the C binary or the web service at # https://bioinfo.mnhn.fr/abi/public/asap/ for primary species delimitation. - Network accessline 3
# for ASAP. Run actual ASAP via the C binary or the web service at # https://bioinfo.mnhn.fr/abi/public/asap/ for primary species delimitation. # This script helps you visualize the distance distribution and check for the - Network accessline 4
# https://bioinfo.mnhn.fr/abi/public/asap/ for primary species delimitation.
skills/bioskills/bio-epidemiological-genomics-amr-surveillance/SKILL.md
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-epidemiological-genomics-amr-surveillance/scripts/amr_surveillance.py
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skills/bioskills/bio-epidemiological-genomics-pathogen-typing/SKILL.md
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-epidemiological-genomics-pathogen-typing/scripts/mlst_typing.py
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skills/bioskills/bio-epidemiological-genomics-phylodynamics/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-epidemiological-genomics-phylodynamics/references/usage-guide.md
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R -e "install.packages(c('BactDating', 'bdskytools', 'coda', 'ape', 'ggplot2'), repos='https://cran.r-project.org')"
skills/bioskills/bio-epidemiological-genomics-phylodynamics/scripts/treetime_analysis.py
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skills/bioskills/bio-epidemiological-genomics-transmission-inference/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-epidemiological-genomics-transmission-inference/references/usage-guide.md
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R -e "install.packages(c('TransPhylo', 'outbreaker2', 'phybreak', 'BactDating', 'ape', 'igraph', 'transcluster'), repos='https://cran.r-project.org')"
skills/bioskills/bio-epidemiological-genomics-transmission-inference/scripts/transmission_inference.py
- Executable scriptline 1
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skills/bioskills/bio-epidemiological-genomics-variant-surveillance/SKILL.md
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name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-epidemiological-genomics-variant-surveillance/scripts/variant_surveillance.py
- Executable scriptline 1
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skills/bioskills/bio-epitranscriptomics-m6a-differential/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-epitranscriptomics-m6a-peak-calling/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-epitranscriptomics-m6a-peak-calling/scripts/macs3_peaks.sh
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#!/usr/bin/env bash
skills/bioskills/bio-epitranscriptomics-m6anet-analysis/SKILL.md
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name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-epitranscriptomics-m6anet-analysis/references/usage-guide.md
skills/bioskills/bio-epitranscriptomics-m6anet-analysis/scripts/m6anet_workflow.py
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#!/usr/bin/env python3
skills/bioskills/bio-epitranscriptomics-merip-preprocessing/SKILL.md
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name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-epitranscriptomics-merip-preprocessing/scripts/align_merip.sh
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#!/usr/bin/env bash
skills/bioskills/bio-epitranscriptomics-merip-preprocessing/scripts/merip_qc.py
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#!/usr/bin/env python3
skills/bioskills/bio-epitranscriptomics-modification-visualization/SKILL.md
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-epitranscriptomics-modification-visualization/scripts/deeptools_metagene.sh
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#!/usr/bin/env bash
skills/bioskills/bio-experimental-design-batch-design/SKILL.md
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-experimental-design-multiple-testing/SKILL.md
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-experimental-design-power-analysis/SKILL.md
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-experimental-design-randomization-blocking/SKILL.md
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-experimental-design-sample-size/SKILL.md
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-expression-matrix-counts-ingest/SKILL.md
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-expression-matrix-counts-ingest/scripts/load_counts.py
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#!/usr/bin/env python3
skills/bioskills/bio-expression-matrix-gene-id-mapping/SKILL.md
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| Ensembl REST | Direct REST API to Ensembl | Rate-limited (15 req/sec) | Low-level access to variant consequence, sequence, etc. | Specialized queries not covered by biomaRt | | pyensembl | Local Ensembl database (Python) | Fast, local, ve - Network accessline 59
| HGNC API direct | https://rest.genenames.org | REST | Authoritative source for HGNC | Symbol provenance, prev/alias detection |
skills/bioskills/bio-expression-matrix-gene-id-mapping/scripts/map_gene_ids.py
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#!/usr/bin/env python3
skills/bioskills/bio-expression-matrix-metadata-joins/SKILL.md
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-expression-matrix-metadata-joins/scripts/join_metadata.py
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#!/usr/bin/env python3
skills/bioskills/bio-expression-matrix-normalization/SKILL.md
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-expression-matrix-normalization/scripts/normalization_workflow.py
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skills/bioskills/bio-expression-matrix-sparse-handling/SKILL.md
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-expression-matrix-sparse-handling/scripts/sparse_operations.py
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#!/usr/bin/env python3
skills/bioskills/bio-flow-cytometry-bead-normalization/SKILL.md
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-flow-cytometry-clustering-phenotyping/SKILL.md
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-flow-cytometry-compensation-transformation/SKILL.md
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name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-flow-cytometry-compensation-transformation/scripts/compensate_transform.R
skills/bioskills/bio-flow-cytometry-cytometry-qc/SKILL.md
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-flow-cytometry-differential-analysis/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-flow-cytometry-doublet-detection/SKILL.md
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name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-flow-cytometry-fcs-handling/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-flow-cytometry-fcs-handling/scripts/load_fcs.R
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# Public flow cytometry data sources: # - FlowRepository: https://flowrepository.org (FR-FCM-ZZPH for CyTOF) - Network accessline 5
# - FlowRepository: https://flowrepository.org (FR-FCM-ZZPH for CyTOF) - Network accessline 6
# - ImmPort: https://www.immport.org (immunology FCS files) - Network accessline 7
# - Cytobank: https://community.cytobank.org (public experiments)
skills/bioskills/bio-flow-cytometry-fcs-handling/scripts/load_fcs.py
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skills/bioskills/bio-flow-cytometry-gating-analysis/SKILL.md
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-gene-regulatory-networks-coexpression-networks/SKILL.md
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skills/bioskills/bio-gene-regulatory-networks-differential-networks/SKILL.md
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-gene-regulatory-networks-differential-networks/scripts/differential_network.py
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skills/bioskills/bio-gene-regulatory-networks-grn-inference/SKILL.md
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-gene-regulatory-networks-grn-inference/references/usage-guide.md
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# ARACNe-AP is a Java tool built from source (https://github.com/califano-lab/ARACNe-AP)
skills/bioskills/bio-gene-regulatory-networks-multiomics-grn/SKILL.md
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-gene-regulatory-networks-multiomics-grn/references/usage-guide.md
skills/bioskills/bio-gene-regulatory-networks-multiomics-grn/scripts/scenicplus_multiome.py
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skills/bioskills/bio-gene-regulatory-networks-perturbation-simulation/SKILL.md
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skills/bioskills/bio-gene-regulatory-networks-perturbation-simulation/scripts/celloracle_simulation.py
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skills/bioskills/bio-gene-regulatory-networks-scenic-regulons/SKILL.md
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# Human hg38 gene-based rankings (~1.5 GB each). Run ctx with BOTH search-space DBs # (500bp+100bp around TSS, and TSS +/-10kb) so the leading-edge logic pools them. wget https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg38/ - Network accessline 67
# (500bp+100bp around TSS, and TSS +/-10kb) so the leading-edge logic pools them. wget https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg38/refseq_r80/mc9nr/gene_based/hg38__refseq-r80__10kb_up_and_down_tss.mc9nr.genes_vs_mo - Network accessline 68
wget https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg38/refseq_r80/mc9nr/gene_based/hg38__refseq-r80__10kb_up_and_down_tss.mc9nr.genes_vs_motifs.rankings.feather - Network accessline 69
wget https://resources.aertslab.org/cistarget/motif2tf/motifs-v9-nr.hgnc-m0.001-o0.0.tbl
skills/bioskills/bio-gene-regulatory-networks-scenic-regulons/references/usage-guide.md
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# Download required databases (human hg38 example) # Ranking databases (~1.5 GB each) wget https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg38/refseq_r80/mc9nr/gene_based/hg38__refseq-r80__10kb_up_and_down_tss.mc9nr.genes_v - Network accessline 17
wget https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg38/refseq_r80/mc9nr/gene_based/hg38__refseq-r80__10kb_up_and_down_tss.mc9nr.genes_vs_motifs.rankings.feather - Network accessline 19
# Motif annotations wget https://resources.aertslab.org/cistarget/motif2tf/motifs-v9-nr.hgnc-m0.001-o0.0.tbl - Network accessline 20
wget https://resources.aertslab.org/cistarget/motif2tf/motifs-v9-nr.hgnc-m0.001-o0.0.tbl - Network accessline 22
# TF list wget https://resources.aertslab.org/cistarget/tf_lists/allTFs_hg38.txt - Network accessline 23
wget https://resources.aertslab.org/cistarget/tf_lists/allTFs_hg38.txt
skills/bioskills/bio-gene-regulatory-networks-scenic-regulons/scripts/pyscenic_workflow.py
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skills/bioskills/bio-gene-regulatory-networks-scenic-regulons/scripts/scenic_visualization.py
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skills/bioskills/bio-genome-annotation-annotation-qc/SKILL.md
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-genome-annotation-annotation-qc/scripts/busco_genome_vs_proteome.sh
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#!/bin/bash
skills/bioskills/bio-genome-annotation-annotation-qc/scripts/gene_set_sanity.py
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#!/usr/bin/env python3
skills/bioskills/bio-genome-annotation-annotation-transfer/SKILL.md
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-genome-annotation-annotation-transfer/scripts/compare_annotations.py
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#!/usr/bin/env python3
skills/bioskills/bio-genome-annotation-annotation-transfer/scripts/liftoff_transfer.sh
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#!/bin/bash
skills/bioskills/bio-genome-annotation-annotation-transfer/scripts/miniprot_crossspecies.sh
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#!/bin/bash
skills/bioskills/bio-genome-annotation-eukaryotic-gene-prediction/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-genome-annotation-eukaryotic-gene-prediction/scripts/braker3_pipeline.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-genome-annotation-eukaryotic-gene-prediction/scripts/galba_protein_only.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-genome-annotation-functional-annotation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-genome-annotation-functional-annotation/scripts/functional_annotation.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-genome-annotation-functional-annotation/scripts/merge_annotations.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-genome-annotation-ncrna-annotation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-genome-annotation-ncrna-annotation/references/usage-guide.md
- Network accessline 10
# Infernal + Rfam (record the Rfam release) conda install -c bioconda infernal wget https://ftp.ebi.ac.uk/pub/databases/Rfam/CURRENT/Rfam.cm.gz - Network accessline 11
conda install -c bioconda infernal wget https://ftp.ebi.ac.uk/pub/databases/Rfam/CURRENT/Rfam.cm.gz gunzip Rfam.cm.gz && cmpress Rfam.cm - Network accessline 12
wget https://ftp.ebi.ac.uk/pub/databases/Rfam/CURRENT/Rfam.cm.gz - Network accessline 14
wget https://ftp.ebi.ac.uk/pub/databases/Rfam/CURRENT/Rfam.clanin
skills/bioskills/bio-genome-annotation-ncrna-annotation/scripts/ncrna_annotation.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-genome-annotation-ncrna-annotation/scripts/parse_ncrna.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-genome-annotation-prokaryotic-annotation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-genome-annotation-prokaryotic-annotation/scripts/bakta_annotation.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-genome-annotation-prokaryotic-annotation/scripts/parse_annotation.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-genome-annotation-repeat-annotation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-genome-annotation-repeat-annotation/scripts/repeat_annotation.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-genome-annotation-repeat-annotation/scripts/repeat_stats.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-genome-annotation-repeat-annotation/scripts/te_expression.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-genome-assembly-assembly-polishing/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-genome-assembly-assembly-polishing/scripts/polish_assembly.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-genome-assembly-assembly-qc/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-genome-assembly-assembly-qc/scripts/run_qc.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-genome-assembly-assembly-qc/scripts/summarize_three_axes.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-genome-assembly-contamination-detection/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-genome-assembly-contamination-detection/references/usage-guide.md
- Network accessline 10
# FCS-adaptor + FCS-GX (Docker/Singularity wrappers; large GX DB) curl -LO https://github.com/ncbi/fcs/raw/main/dist/fcs.py - Network accessline 11
curl -LO https://github.com/ncbi/fcs/raw/main/dist/fcs.py - Network accessline 12
curl -LO https://github.com/ncbi/fcs/raw/main/dist/run_fcsadaptor.sh - Network accessline 14
python3 fcs.py db get --mft "https://ftp.ncbi.nlm.nih.gov/genomes/TOOLS/FCS/database/latest/all.manifest" --dir "$GXDB_LOC/gxdb"
skills/bioskills/bio-genome-assembly-contamination-detection/scripts/fcs_screen.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-genome-assembly-contamination-detection/scripts/mag_qc_pipeline.sh
skills/bioskills/bio-genome-assembly-genome-profiling/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-genome-assembly-genome-profiling/scripts/profile_genome.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-genome-assembly-hifi-assembly/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-genome-assembly-hifi-assembly/scripts/hifi_assembly_workflow.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-genome-assembly-long-read-assembly/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-genome-assembly-long-read-assembly/scripts/flye_assembly.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-genome-assembly-metagenome-assembly/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-genome-assembly-metagenome-assembly/scripts/metagenome_workflow.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-genome-assembly-scaffolding/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-genome-assembly-scaffolding/references/usage-guide.md
- Network accessline 22
# 3D-DNA / Juicer (clone; juicer_tools is a jar) git clone https://github.com/aidenlab/3d-dna.git wget https://github.com/aidenlab/juicer/releases/download/v2.0/juicer_tools.2.0.jar - Network accessline 23
git clone https://github.com/aidenlab/3d-dna.git - Network accessline 24
wget https://github.com/aidenlab/juicer/releases/download/v2.0/juicer_tools.2.0.jar
skills/bioskills/bio-genome-assembly-scaffolding/scripts/yahs_scaffolding.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-genome-assembly-short-read-assembly/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-genome-assembly-short-read-assembly/scripts/bacterial_assembly.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-genome-engineering-base-editing-design/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-genome-engineering-base-editing-design/scripts/base_editing_design.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-genome-engineering-grna-design/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-genome-engineering-grna-design/scripts/grna_design.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-genome-engineering-hdr-template-design/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-genome-engineering-hdr-template-design/scripts/hdr_template_design.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-genome-engineering-off-target-prediction/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-genome-engineering-off-target-prediction/references/usage-guide.md
skills/bioskills/bio-genome-engineering-off-target-prediction/scripts/off_target_analysis.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-genome-engineering-prime-editing-design/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-genome-engineering-prime-editing-design/scripts/prime_editing_design.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-genome-intervals-bed-file-basics/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-genome-intervals-bed-file-basics/scripts/create_bed.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-genome-intervals-bed-file-basics/scripts/validate_bed.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-genome-intervals-bedgraph-handling/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-genome-intervals-bedgraph-handling/scripts/bam_to_bigwig.sh
skills/bioskills/bio-genome-intervals-bedgraph-handling/scripts/bedgraph_operations.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-genome-intervals-bigwig-tracks/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 219
- Kent WJ, Zweig AS, Barber G, Hinrichs AS, Karolchik D. 2010. BigWig and BigBed: enabling browsing of large distributed datasets. *Bioinformatics* 26:2204-2207. - Ramirez F, Ryan DP, Gruning B, Bhardwaj V, Kilpert F, Richter AS, Heyne S, D - Network accessline 220
- Ramirez F, Ryan DP, Gruning B, Bhardwaj V, Kilpert F, Richter AS, Heyne S, Dundar F, Manke T. 2016. deepTools2: a next generation web server for deep-sequencing data analysis. *Nucleic Acids Res* 44:W160-W165. - pyBigWig (Devon Ryan / dee - Network accessline 221
- pyBigWig (Devon Ryan / deepTools project) - C-extension wrapping libBigWig; no journal paper, see https://github.com/deeptools/pyBigWig - Network accessline 222
- UCSC Kent utilities (bedGraphToBigWig, bigWigInfo, bigWigSummary, bigWigAverageOverBed) - https://github.com/ucscGenomeBrowser/kent; cite Kent 2010 for the format.
skills/bioskills/bio-genome-intervals-bigwig-tracks/scripts/bedgraph_to_bigwig.sh
skills/bioskills/bio-genome-intervals-bigwig-tracks/scripts/pybigwig_demo.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-genome-intervals-coverage-analysis/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-genome-intervals-coverage-analysis/scripts/bedgraph_from_bam.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-genome-intervals-coverage-analysis/scripts/coverage_analysis.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-genome-intervals-gtf-gff-handling/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 183
- Pertea G, Pertea M. 2020. GFF Utilities: GffRead and GffCompare. *F1000Research* 9:304. - Stovner EB, Saetrom P. 2020. PyRanges: efficient comparison of genomic intervals in Python. *Bioinformatics* 36:918-919. - Dale R. gffutils: GFF and - Network accessline 184
- Stovner EB, Saetrom P. 2020. PyRanges: efficient comparison of genomic intervals in Python. *Bioinformatics* 36:918-919. - Dale R. gffutils: GFF and GTF file manipulation and interconversion. Software, https://github.com/daler/gffutils (n - Network accessline 185
- Dale R. gffutils: GFF and GTF file manipulation and interconversion. Software, https://github.com/daler/gffutils (no journal publication). - Network accessline 186
- Dainat J. AGAT: Another Gff Analysis Toolkit to handle annotations in any GTF/GFF format. Zenodo. doi:10.5281/zenodo.3552717. - Rubinsteyn A, et al. gtfparse: parsing tools for GTF (gene transfer format) files. Software, https://github.co - Network accessline 187
- Rubinsteyn A, et al. gtfparse: parsing tools for GTF (gene transfer format) files. Software, https://github.com/openvax/gtfparse (no journal publication).
skills/bioskills/bio-genome-intervals-gtf-gff-handling/references/usage-guide.md
- Network accessline 59
## Resources - [GENCODE](https://www.gencodegenes.org/) - Human/mouse annotations and format FAQ - Network accessline 60
- [GENCODE](https://www.gencodegenes.org/) - Human/mouse annotations and format FAQ - Network accessline 61
- [Ensembl FTP](https://ftp.ensembl.org/) - Multi-species annotations - Network accessline 62
- [GFF3 specification](https://github.com/The-Sequence-Ontology/Specifications/blob/master/gff3.md) - Sequence Ontology - Network accessline 63
- [gffread documentation](https://ccb.jhu.edu/software/stringtie/gff.shtml) - Network accessline 64
- [gffutils documentation](https://daler.github.io/gffutils/)
skills/bioskills/bio-genome-intervals-gtf-gff-handling/scripts/gffread_convert.sh
skills/bioskills/bio-genome-intervals-gtf-gff-handling/scripts/parse_gtf.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-genome-intervals-interval-arithmetic/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-genome-intervals-interval-arithmetic/references/usage-guide.md
- Network accessline 77
## Resources - [bedtools intersect](https://bedtools.readthedocs.io/en/latest/content/tools/intersect.html) - Network accessline 78
- [bedtools intersect](https://bedtools.readthedocs.io/en/latest/content/tools/intersect.html) - Network accessline 79
- [bedtools merge](https://bedtools.readthedocs.io/en/latest/content/tools/merge.html) - Network accessline 80
- [pybedtools documentation](https://daler.github.io/pybedtools/) - Network accessline 81
- [pyranges documentation](https://pyranges.readthedocs.io/) - Network accessline 82
- [bioframe documentation](https://bioframe.readthedocs.io/)
skills/bioskills/bio-genome-intervals-interval-arithmetic/scripts/intersect_examples.sh
skills/bioskills/bio-genome-intervals-interval-arithmetic/scripts/interval_operations.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-genome-intervals-overlap-significance/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-genome-intervals-overlap-significance/scripts/permutation_overlap.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-genome-intervals-overlap-significance/scripts/shuffle_permutation_test.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-genome-intervals-proximity-operations/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-genome-intervals-proximity-operations/references/usage-guide.md
- Network accessline 62
## Resources - [bedtools closest](https://bedtools.readthedocs.io/en/latest/content/tools/closest.html) - Network accessline 63
- [bedtools closest](https://bedtools.readthedocs.io/en/latest/content/tools/closest.html) - Network accessline 64
- [bedtools window](https://bedtools.readthedocs.io/en/latest/content/tools/window.html) - Network accessline 65
- [bedtools slop](https://bedtools.readthedocs.io/en/latest/content/tools/slop.html) - Network accessline 66
- [bedtools flank](https://bedtools.readthedocs.io/en/latest/content/tools/flank.html)
skills/bioskills/bio-genome-intervals-proximity-operations/scripts/create_promoters.sh
skills/bioskills/bio-genome-intervals-proximity-operations/scripts/proximity_analysis.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-hi-c-analysis-compartment-analysis/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-hi-c-analysis-compartment-analysis/scripts/call_compartments.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-hi-c-analysis-contact-pairs/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-hi-c-analysis-contact-pairs/scripts/fastq_to_pairs.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-hi-c-analysis-hic-data-io/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 31
**"Load my Hi-C contact matrix, convert it, and pull out a region."** -> Open the cooler at one resolution, fetch raw or balanced pixels, and convert across .hic/.cool/.mcool while knowing what does not survive the round trip. - Python: `co - Network accessline 32
- Python: `cooler.Cooler('file.mcool::/resolutions/10000').matrix(balance=True).fetch('chr1')` - Network accessline 66
| Imported KR/VC norms | keep their original names | the KR/VC/VC_SQRT auto-divisive rule fires only on those names | | `matrix(balance=True)` raises / all NaN | balance first; NaN rows = masked bins | unbalanced file has no weight; masked - Network accessline 67
| `matrix(balance=True)` raises / all NaN | balance first; NaN rows = masked bins | unbalanced file has no weight; masked bins are expected NaN | | `fetch('1')` returns empty on a `chr1` file | harmonize chrom naming first | chr1-vs-1 silen - Network accessline 68
| `fetch('1')` returns empty on a `chr1` file | harmonize chrom naming first | chr1-vs-1 silently zeros every join, no error | - Network accessline 84
`clr.matrix().fetch(region)` takes a UCSC region string or a bare chrom name; one arg -> symmetric square, two args -> rectangular submatrix (incl. trans). `clr.bins().fetch('chr1')`, `clr.pixels().fetch(region)` slice the tables. - Network accessline 93
balanced = clr.matrix(balance=True).fetch('chr1') # multiplicative cooler weight - Network accessline 94
raw = clr.matrix(balance=False).fetch('chr1') # observed counts - Network accessline 95
kr = clr.matrix(balance='KR').fetch('chr1') # KR/VC/VC_SQRT auto-treated as divisive by name - Network accessline 96
sparse = clr.matrix(balance=True, sparse=True).fetch('chr1') # scipy COO for large chromosomes - Network accessline 152
cooler.merge_coolers('merged.cool', ['rep1.cool', 'rep2.cool'], mergebuf=20_000_000) # bin tables MUST match np.save('chr1.npy', clr.matrix(balance=True).fetch('chr1')) - Network accessline 153
np.save('chr1.npy', clr.matrix(balance=True).fetch('chr1')) - Network accessline 164
The bin table IS the identity of a cooler. Two coolers are pixel-comparable only if their bin tables are byte-identical: same chroms, same order, same contigs present, same binsize. Dropping `chrM`/scaffolds in one pipeline shifts every `bi - Network accessline 210
| Balanced values look wrong, no error | KR/VC norm renamed to `weight` (treated multiplicative) | keep KR/VC/VC_SQRT name or set `divisive_weights=True` | | Two coolers from one `.hic` disagree | hic2cool 0.5.0 inversion boundary | regener - Network accessline 211
| Two coolers from one `.hic` disagree | hic2cool 0.5.0 inversion boundary | regenerate with one version; record it | | `fetch('1')` returns nothing | chr1-vs-1 naming mismatch | harmonize chrom naming across all inputs | | FRAG resolutions - Network accessline 212
| `fetch('1')` returns nothing | chr1-vs-1 naming mismatch | harmonize chrom naming across all inputs |
skills/bioskills/bio-hi-c-analysis-hic-data-io/references/usage-guide.md
- Network accessline 22
- "Build a cooler from my pairs file at 10kb" - "Why is my balanced matrix all NaN?" - "My fetch('1') returns nothing - what is wrong?" - Network accessline 23
- "Why is my balanced matrix all NaN?" - "My fetch('1') returns nothing - what is wrong?" - "Coarsen this 10kb cooler to 100kb correctly" - Network accessline 24
- "My fetch('1') returns nothing - what is wrong?"
skills/bioskills/bio-hi-c-analysis-hic-data-io/scripts/load_cooler.py
skills/bioskills/bio-hi-c-analysis-hic-differential/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-hi-c-analysis-hic-visualization/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 31
**"Plot my Hi-C contact matrix"** -> Choose a transform (raw / ICE-balanced / observed-over-expected), a matched colormap+norm (LogNorm for counts, symmetric-diverging for O/E and ratios), and a resolution that fits the feature; render as a - Network accessline 32
- Python: `clr.matrix(balance=True).fetch(region)` then `ax.matshow(m, norm=LogNorm(...), cmap='fall')` - Network accessline 83
clr = cooler.Cooler('matrix.mcool::/resolutions/10000') region = ('chr1', 50_000_000, 60_000_000) m = clr.matrix(balance=True).fetch(region) - Network accessline 85
m = clr.matrix(balance=True).fetch(region) - Network accessline 106
chrom = region[0] exp_by_diag = expected.query('region1 == @chrom')['balanced.avg'].to_numpy() # expected per genomic separation m = clr.matrix(balance=True).fetch(region) - Network accessline 107
exp_by_diag = expected.query('region1 == @chrom')['balanced.avg'].to_numpy() # expected per genomic separation m = clr.matrix(balance=True).fetch(region) i, j = np.indices(m.shape) - Network accessline 108
m = clr.matrix(balance=True).fetch(region) - Network accessline 148
res = clr.binsize vp_bin = (55_000_000 // res) - (50_000_000 // res) # viewpoint index within the region profile = clr.matrix(balance=True).fetch(region)[vp_bin, :] - Network accessline 149
vp_bin = (55_000_000 // res) - (50_000_000 // res) # viewpoint index within the region profile = clr.matrix(balance=True).fetch(region)[vp_bin, :] fig, ax = plt.subplots(figsize=(11, 2.5)) - Network accessline 150
profile = clr.matrix(balance=True).fetch(region)[vp_bin, :] - Network accessline 183
m1 = clr1.matrix(balance=True).fetch(region) - Network accessline 184
m2 = clr2.matrix(balance=True).fetch(region) # clr1, clr2 already depth-equalized upstream
skills/bioskills/bio-hi-c-analysis-hic-visualization/scripts/plot_hic.py
- Executable scriptline 1
Executable file - Network accessline 36
balanced = clr.matrix(balance=True).fetch(region) - Network accessline 53
clr2 = cooler.Cooler(f'condition2.mcool::/resolutions/{RESOLUTION}') # already balanced + depth-matched balanced2 = clr2.matrix(balance=True).fetch(region) - Network accessline 54
balanced2 = clr2.matrix(balance=True).fetch(region)
skills/bioskills/bio-hi-c-analysis-hichip-plac-loops/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-hi-c-analysis-hichip-plac-loops/references/usage-guide.md
- Network accessline 10
# FitHiChIP (default; config-driven CLI) - install by git clone or Docker (no bioconda package) git clone https://github.com/ay-lab/FitHiChIP - Network accessline 11
git clone https://github.com/ay-lab/FitHiChIP - Network accessline 13
conda install -c bioconda hic-pro # MAPS (PLAC-seq/HiChIP regression model) git clone https://github.com/ijuric/MAPS - Network accessline 14
# MAPS (PLAC-seq/HiChIP regression model) git clone https://github.com/ijuric/MAPS # hichipper (restriction-aware QC + loops) - Network accessline 15
git clone https://github.com/ijuric/MAPS
skills/bioskills/bio-hi-c-analysis-hichip-plac-loops/scripts/run_fithichip.sh
skills/bioskills/bio-hi-c-analysis-loop-calling/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-hi-c-analysis-loop-calling/scripts/call_loops.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-hi-c-analysis-matrix-operations/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 84
clr = cooler.Cooler('matrix.mcool::/resolutions/10000') # re-open to see the stored weights balanced = clr.matrix(balance=True).fetch('chr1') # raw[i,j] * w[i] * w[j]; masked bins -> NaN - Network accessline 85
balanced = clr.matrix(balance=True).fetch('chr1') # raw[i,j] * w[i] * w[j]; masked bins -> NaN - Network accessline 126
def oe_matrix(clr, region, cvd): obs = clr.matrix(balance=True).fetch(region) - Network accessline 127
obs = clr.matrix(balance=True).fetch(region)
skills/bioskills/bio-hi-c-analysis-matrix-operations/scripts/balance_and_oe.py
skills/bioskills/bio-hi-c-analysis-tad-detection/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-hi-c-analysis-tad-detection/scripts/call_tads.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-imaging-mass-cytometry-cell-segmentation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-imaging-mass-cytometry-cell-segmentation/scripts/segment_cells.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-imaging-mass-cytometry-data-preprocessing/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-imaging-mass-cytometry-data-preprocessing/scripts/preprocess_imc.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-imaging-mass-cytometry-differential-analysis/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-imaging-mass-cytometry-differential-analysis/scripts/differential_abundance.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-imaging-mass-cytometry-interactive-annotation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-imaging-mass-cytometry-interactive-annotation/scripts/napari_annotation.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-imaging-mass-cytometry-phenotyping/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-imaging-mass-cytometry-phenotyping/scripts/phenotype_cells.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-imaging-mass-cytometry-quality-metrics/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-imaging-mass-cytometry-quality-metrics/scripts/run_qc.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-imaging-mass-cytometry-spatial-analysis/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-imaging-mass-cytometry-spatial-analysis/scripts/spatial_analysis.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-immunoinformatics-epitope-prediction/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-immunoinformatics-epitope-prediction/scripts/epitope_prediction.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-immunoinformatics-immunogenicity-scoring/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-immunoinformatics-immunogenicity-scoring/scripts/immunogenicity_scoring.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-immunoinformatics-mhc-binding-prediction/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-immunoinformatics-mhc-binding-prediction/scripts/mhc_binding.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-immunoinformatics-mhc-class-ii-prediction/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-immunoinformatics-mhc-class-ii-prediction/scripts/class_ii_pairing.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-immunoinformatics-mhc-class-ii-prediction/scripts/run_class_ii.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/bioskills/bio-immunoinformatics-neoantigen-prediction/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-immunoinformatics-neoantigen-prediction/scripts/neoantigen_prediction.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-immunoinformatics-tcr-epitope-binding/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-immunoinformatics-tcr-epitope-binding/scripts/tcr_epitope.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-liquid-biopsy-analytical-validation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-liquid-biopsy-analytical-validation/scripts/detection_limits.py
- Executable scriptline 1
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skills/bioskills/bio-liquid-biopsy-cfdna-preprocessing/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 117
'''Summarize cfDNA fragment lengths as a QC readout.''' bam = pysam.AlignmentFile(bam_path, 'rb') sizes = [r.template_length for r in bam.fetch() - Network accessline 118
bam = pysam.AlignmentFile(bam_path, 'rb') sizes = [r.template_length for r in bam.fetch() if r.is_proper_pair and not r.is_secondary and 0 < r.template_length <= max_size] - Network accessline 119
sizes = [r.template_length for r in bam.fetch()
skills/bioskills/bio-liquid-biopsy-cfdna-preprocessing/scripts/preprocess_cfdna.py
skills/bioskills/bio-liquid-biopsy-ctdna-mutation-detection/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-liquid-biopsy-ctdna-mutation-detection/references/usage-guide.md
skills/bioskills/bio-liquid-biopsy-ctdna-mutation-detection/scripts/detect_ctdna_mutations.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-liquid-biopsy-fragment-analysis/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 85
short = np.zeros(n_bins) long = np.zeros(n_bins) for read in bam.fetch(chrom): - Network accessline 86
long = np.zeros(n_bins) for read in bam.fetch(chrom): if not read.is_proper_pair or read.is_secondary or read.template_length <= 0: - Network accessline 87
for read in bam.fetch(chrom):
skills/bioskills/bio-liquid-biopsy-fragment-analysis/scripts/fragment_analysis.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 25
bam = pysam.AlignmentFile(bam_path, 'rb') sizes = [r.template_length for r in bam.fetch() - Network accessline 26
sizes = [r.template_length for r in bam.fetch() - Network accessline 48
short = np.zeros(n_bins) long = np.zeros(n_bins) for read in bam.fetch(chrom): - Network accessline 49
long = np.zeros(n_bins) for read in bam.fetch(chrom): if not read.is_proper_pair or read.is_secondary or read.template_length <= 0: - Network accessline 50
for read in bam.fetch(chrom): - Network accessline 70
bam = pysam.AlignmentFile(bam_path, 'rb') kept = sum(1 for r in bam.fetch() - Network accessline 71
kept = sum(1 for r in bam.fetch() - Network accessline 72
if r.is_proper_pair and not r.is_secondary and lo <= r.template_length <= hi) total = sum(1 for r in bam.fetch() if r.is_proper_pair and not r.is_secondary and r.template_length > 0) - Network accessline 73
total = sum(1 for r in bam.fetch()
skills/bioskills/bio-liquid-biopsy-longitudinal-monitoring/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-liquid-biopsy-longitudinal-monitoring/scripts/longitudinal_monitoring.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-liquid-biopsy-methylation-based-detection/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-liquid-biopsy-methylation-based-detection/scripts/cfdna_methylation.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-liquid-biopsy-tumor-fraction-estimation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-liquid-biopsy-tumor-fraction-estimation/scripts/estimate_tumor_fraction.R
- Executable scriptline 1
#!/usr/bin/env Rscript
skills/bioskills/bio-long-read-sequencing-basecalling/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-long-read-sequencing-basecalling/references/usage-guide.md
- Network accessline 8
# Dorado (from ONT) - download the prebuilt binary # https://github.com/nanoporetech/dorado - Network accessline 9
# https://github.com/nanoporetech/dorado - Network accessline 70
## Resources - [Dorado GitHub](https://github.com/nanoporetech/dorado) - Network accessline 71
- [Dorado GitHub](https://github.com/nanoporetech/dorado) - Network accessline 72
- [Dorado documentation](https://software-docs.nanoporetech.com/dorado/) - Network accessline 73
- [POD5 format](https://pod5-file-format.readthedocs.io/)
skills/bioskills/bio-long-read-sequencing-basecalling/scripts/basecall_pipeline.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-long-read-sequencing-basecalling/scripts/dorado_basecall.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-long-read-sequencing-clair3-variants/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-long-read-sequencing-clair3-variants/references/usage-guide.md
- Network accessline 9
# hap.py (with RTG vcfeval) for benchmarking; GIAB truth + stratification BEDs # The full ONT model set (every version, hac/fast, _with_mv) is in the rerio repo: # https://github.com/nanoporetech/rerio -> clair3_models/ - Network accessline 11
# https://github.com/nanoporetech/rerio -> clair3_models/ - Network accessline 66
## Resources - [Clair3 GitHub](https://github.com/HKU-BAL/Clair3) - Network accessline 67
- [Clair3 GitHub](https://github.com/HKU-BAL/Clair3) - Network accessline 68
- [Clair3 models (rerio)](https://github.com/nanoporetech/rerio) - Network accessline 69
- [GIAB benchmarks and stratifications](https://www.nist.gov/programs-projects/genome-bottle)
skills/bioskills/bio-long-read-sequencing-clair3-variants/scripts/clair3_workflow.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-long-read-sequencing-haplotype-phasing/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-long-read-sequencing-haplotype-phasing/references/usage-guide.md
- Network accessline 63
## Resources - [WhatsHap docs](https://whatshap.readthedocs.io/) - Network accessline 64
- [WhatsHap docs](https://whatshap.readthedocs.io/) - Network accessline 65
- [LongPhase](https://github.com/twolinin/longphase) - Network accessline 66
- [HiPhase](https://github.com/PacificBiosciences/HiPhase)
skills/bioskills/bio-long-read-sequencing-haplotype-phasing/scripts/longphase_sv_cophasing.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-long-read-sequencing-haplotype-phasing/scripts/phase_and_haplotag.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-long-read-sequencing-isoseq-analysis/SKILL.md
- Network accessline 10
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 11
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 12
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-long-read-sequencing-isoseq-analysis/references/usage-guide.md
- Network accessline 65
## Resources - [PacBio Iso-Seq docs](https://isoseq.how/) - Network accessline 66
- [PacBio Iso-Seq docs](https://isoseq.how/) - Network accessline 67
- [SQANTI3](https://github.com/ConesaLab/SQANTI3) - Network accessline 68
- [IsoQuant](https://github.com/ablab/IsoQuant) - Network accessline 69
- [pychopper](https://github.com/epi2me-labs/pychopper)
skills/bioskills/bio-long-read-sequencing-isoseq-analysis/scripts/isoseq_workflow.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-long-read-sequencing-isoseq-analysis/scripts/sqanti3_qc.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-long-read-sequencing-long-read-alignment/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-long-read-sequencing-long-read-alignment/references/usage-guide.md
- Network accessline 9
# Optional specialized aligners: conda install -c bioconda winnowmap pbmm2 lra # VACmap: see https://github.com/micahvista/VACmap - Network accessline 11
# VACmap: see https://github.com/micahvista/VACmap - Network accessline 66
## Resources - [minimap2 manual](https://lh3.github.io/minimap2/minimap2.html) - Network accessline 67
- [minimap2 manual](https://lh3.github.io/minimap2/minimap2.html) - Network accessline 68
- [minimap2 cookbook](https://github.com/lh3/minimap2/blob/master/cookbook.md) - Network accessline 69
- [Winnowmap](https://github.com/marbl/Winnowmap) - Network accessline 70
- [pbmm2](https://github.com/PacificBiosciences/pbmm2)
skills/bioskills/bio-long-read-sequencing-long-read-alignment/scripts/methylation_passthrough.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-long-read-sequencing-long-read-alignment/scripts/minimap2_align.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-long-read-sequencing-long-read-qc/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-long-read-sequencing-long-read-qc/references/usage-guide.md
- Network accessline 63
## Resources - [NanoPack / NanoPlot](https://github.com/wdecoster/nanopack) - Network accessline 64
- [NanoPack / NanoPlot](https://github.com/wdecoster/nanopack) - Network accessline 65
- [cramino](https://github.com/wdecoster/cramino) - Network accessline 66
- [pycoQC](https://github.com/a-slide/pycoQC) - Network accessline 67
- [Filtlong](https://github.com/rrwick/Filtlong)
skills/bioskills/bio-long-read-sequencing-long-read-qc/scripts/qc_workflow.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-long-read-sequencing-medaka-polishing/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 142
- medaka. Oxford Nanopore Technologies. https://github.com/nanoporetech/medaka (no journal paper; cite the repository). - Network accessline 145
- Wick RR, Judd LM, Holt KE. 2023. Assembling the perfect bacterial genome using Oxford Nanopore and Illumina sequencing. *PLoS Comput Biol* 19(3):e1010905. - Rhie A, Walenz BP, Koren S, Phillippy AM. 2020. Merqury: reference-free quality, - Network accessline 147
- Wick RR. 2024. Medaka v2: progress and potential pitfalls. https://rrwick.github.io/2024/10/17/medaka-v2.html (blog; source of the missing-plasmid footgun).
skills/bioskills/bio-long-read-sequencing-medaka-polishing/references/usage-guide.md
- Network accessline 63
## Resources - [medaka GitHub](https://github.com/nanoporetech/medaka) - Network accessline 64
- [medaka GitHub](https://github.com/nanoporetech/medaka) - Network accessline 65
- [ARTIC fieldbioinformatics](https://github.com/artic-network/fieldbioinformatics) (medaka amplicon consensus)
skills/bioskills/bio-long-read-sequencing-medaka-polishing/scripts/medaka_polish.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-long-read-sequencing-medaka-polishing/scripts/medaka_variant.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-long-read-sequencing-nanopore-methylation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-long-read-sequencing-nanopore-methylation/references/usage-guide.md
- Network accessline 64
## Resources - [modkit](https://github.com/nanoporetech/modkit) - Network accessline 65
- [modkit](https://github.com/nanoporetech/modkit) - Network accessline 66
- [modkit docs](https://nanoporetech.github.io/modkit/) - Network accessline 67
- [pb-CpG-tools](https://github.com/PacificBiosciences/pb-CpG-tools) - Network accessline 68
- [SAM tags spec (MM/ML)](https://samtools.github.io/hts-specs/SAMtags.pdf)
skills/bioskills/bio-long-read-sequencing-nanopore-methylation/scripts/modkit_methylation.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-long-read-sequencing-structural-variants/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-long-read-sequencing-structural-variants/references/usage-guide.md
- Network accessline 64
## Resources - [Sniffles2](https://github.com/fritzsedlazeck/Sniffles) - Network accessline 65
- [Sniffles2](https://github.com/fritzsedlazeck/Sniffles) - Network accessline 66
- [cuteSV](https://github.com/tjiangHIT/cuteSV) - Network accessline 67
- [Truvari](https://github.com/ACEnglish/truvari) - Network accessline 68
- [GIAB benchmarks](https://www.nist.gov/programs-projects/genome-bottle)
skills/bioskills/bio-long-read-sequencing-structural-variants/scripts/sv_calling.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-machine-learning-atlas-mapping/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-machine-learning-atlas-mapping/scripts/ood_gating_demo.py
- Executable scriptline 1
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skills/bioskills/bio-machine-learning-atlas-mapping/scripts/scarches_annotation.py
- Executable scriptline 1
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skills/bioskills/bio-machine-learning-biomarker-discovery/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-machine-learning-biomarker-discovery/scripts/boruta_feature_selection.py
- Executable scriptline 1
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skills/bioskills/bio-machine-learning-biomarker-discovery/scripts/lasso_biomarker.py
- Executable scriptline 1
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skills/bioskills/bio-machine-learning-model-validation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-machine-learning-model-validation/scripts/nested_cv_biomarker.py
- Executable scriptline 1
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skills/bioskills/bio-machine-learning-omics-classifiers/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-machine-learning-omics-classifiers/scripts/logistic_regression.py
- Executable scriptline 1
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skills/bioskills/bio-machine-learning-omics-classifiers/scripts/rf_xgboost_classifier.py
- Executable scriptline 1
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skills/bioskills/bio-machine-learning-prediction-explanation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-machine-learning-prediction-explanation/scripts/lime_explanation.py
- Executable scriptline 1
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skills/bioskills/bio-machine-learning-prediction-explanation/scripts/shap_omics_classifier.py
- Executable scriptline 1
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skills/bioskills/bio-machine-learning-survival-analysis/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-machine-learning-survival-analysis/scripts/competing_risks_cif.py
- Executable scriptline 1
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skills/bioskills/bio-machine-learning-survival-analysis/scripts/cox_regression.py
- Executable scriptline 1
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skills/bioskills/bio-metabolomics-isotope-tracing/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-metabolomics-isotope-tracing/scripts/isotope_correction.py
- Executable scriptline 1
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skills/bioskills/bio-metabolomics-lipidomics/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-metabolomics-metabolite-annotation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-metabolomics-metabolite-annotation/references/usage-guide.md
skills/bioskills/bio-metabolomics-metabolite-annotation/scripts/annotate_features.py
- Executable scriptline 1
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skills/bioskills/bio-metabolomics-msdial-preprocessing/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-metabolomics-msdial-preprocessing/references/usage-guide.md
- Network accessline 10
# MS-DIAL: https://systemsomicslab.github.io/compms/msdial/main.html (GUI is Windows-only)
skills/bioskills/bio-metabolomics-normalization-qc/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-metabolomics-pathway-mapping/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-metabolomics-pathway-mapping/references/usage-guide.md
skills/bioskills/bio-metabolomics-statistical-analysis/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-metabolomics-statistical-analysis/scripts/metabolomics_differential.py
- Executable scriptline 1
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skills/bioskills/bio-metabolomics-targeted-analysis/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-metabolomics-targeted-analysis/references/usage-guide.md
skills/bioskills/bio-metabolomics-xcms-preprocessing/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-metagenomics-abundance-estimation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-metagenomics-abundance-estimation/references/usage-guide.md
- Network accessline 73
## Resources - [Bracken GitHub](https://github.com/jenniferlu717/Bracken) - Network accessline 74
- [Bracken GitHub](https://github.com/jenniferlu717/Bracken) - Network accessline 75
- [Bracken Paper](https://peerj.com/articles/cs-104/) - Network accessline 76
- [scikit-bio composition module](https://scikit.bio)
skills/bioskills/bio-metagenomics-abundance-estimation/scripts/bracken_abundance.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-metagenomics-abundance-estimation/scripts/compositional_transform.py
- Executable scriptline 1
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skills/bioskills/bio-metagenomics-amr-detection/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-metagenomics-amr-detection/references/usage-guide.md
skills/bioskills/bio-metagenomics-amr-detection/scripts/batch_amr_screening.py
- Executable scriptline 1
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skills/bioskills/bio-metagenomics-amr-detection/scripts/run_amrfinder.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-metagenomics-contamination-controls/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-metagenomics-contamination-controls/references/usage-guide.md
skills/bioskills/bio-metagenomics-contamination-controls/scripts/flag_kitome.py
- Executable scriptline 1
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skills/bioskills/bio-metagenomics-contamination-controls/scripts/host_depletion.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-metagenomics-functional-profiling/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-metagenomics-functional-profiling/references/usage-guide.md
skills/bioskills/bio-metagenomics-functional-profiling/scripts/humann_workflow.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-metagenomics-kraken-classification/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-metagenomics-kraken-classification/references/usage-guide.md
- Network accessline 10
# Download a prebuilt database (sizes range from ~8 GB capped to ~70 GB Standard to ~480 GB nt) wget https://genome-idx.s3.amazonaws.com/kraken/k2_standard_20240605.tar.gz - Network accessline 11
wget https://genome-idx.s3.amazonaws.com/kraken/k2_standard_20240605.tar.gz - Network accessline 74
## Resources - [Kraken2 GitHub](https://github.com/DerrickWood/kraken2) - Network accessline 75
- [Kraken2 GitHub](https://github.com/DerrickWood/kraken2) - Network accessline 76
- [Prebuilt databases](https://benlangmead.github.io/aws-indexes/k2) - Network accessline 77
- [Bracken GitHub](https://github.com/jenniferlu717/Bracken)
skills/bioskills/bio-metagenomics-kraken-classification/scripts/kraken2_classify.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-metagenomics-metagenome-visualization/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-metagenomics-metagenome-visualization/references/usage-guide.md
- Network accessline 78
## Resources - [phyloseq tutorial](https://joey711.github.io/phyloseq/) - Network accessline 79
- [phyloseq tutorial](https://joey711.github.io/phyloseq/) - Network accessline 80
- [microViz](https://david-barnett.github.io/microViz/) - Network accessline 81
- [vegan documentation](https://cran.r-project.org/web/packages/vegan/vegan.pdf)
skills/bioskills/bio-metagenomics-metagenome-visualization/scripts/visualization_python.py
- Executable scriptline 1
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skills/bioskills/bio-metagenomics-metaphlan-profiling/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-metagenomics-metaphlan-profiling/references/usage-guide.md
skills/bioskills/bio-metagenomics-metaphlan-profiling/scripts/metaphlan_profile.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-metagenomics-strain-tracking/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-metagenomics-strain-tracking/references/usage-guide.md
- Network accessline 61
## Resources - [inStrain docs](https://instrain.readthedocs.io/) - Network accessline 62
- [inStrain docs](https://instrain.readthedocs.io/) - Network accessline 63
- [StrainPhlAn (MetaPhlAn) docs](https://github.com/biobakery/MetaPhlAn) - Network accessline 64
- [skani](https://github.com/bluenote-1577/skani) - Network accessline 65
- [StrainGE](https://github.com/broadinstitute/StrainGE)
skills/bioskills/bio-metagenomics-strain-tracking/scripts/instrain_workflow.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-metagenomics-strain-tracking/scripts/mash_comparison.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-metagenomics-strain-tracking/scripts/parse_instrain_compare.py
- Executable scriptline 1
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skills/bioskills/bio-methylation-analysis-array-preprocessing/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-methylation-analysis-array-qc-filtering/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-methylation-analysis-bismark-alignment/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-methylation-analysis-bismark-alignment/scripts/bismark_basic.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-methylation-analysis-cell-type-deconvolution/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-methylation-analysis-differential-cpg-testing/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-methylation-analysis-differential-cpg-testing/scripts/differential_methylation.py
- Executable scriptline 1
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skills/bioskills/bio-methylation-analysis-dmr-detection/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-methylation-analysis-epigenetic-clocks/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-methylation-analysis-epigenetic-clocks/scripts/epigenetic_clocks.R
- Executable scriptline 1
#!/usr/bin/env Rscript
skills/bioskills/bio-methylation-analysis-ewas-design/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-methylation-analysis-ewas-design/scripts/randomize_chip_layout.py
- Executable scriptline 1
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skills/bioskills/bio-methylation-analysis-methylation-calling/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-methylation-analysis-methylation-calling/scripts/extract_methylation.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-methylation-analysis-methylkit-analysis/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-microbiome-amplicon-processing/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-microbiome-amplicon-processing/scripts/remove_primers.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/bioskills/bio-microbiome-differential-abundance/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-microbiome-diversity-analysis/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-microbiome-diversity-analysis/references/usage-guide.md
- Network accessline 17
# QIIME2 installs as its own conda env (the release tag defines the plugin API and .qza format) conda env create -n qiime2-amplicon-2024.2 --file https://data.qiime2.org/distro/amplicon/qiime2-amplicon-2024.2-py39-linux-conda.yml - Network accessline 18
conda env create -n qiime2-amplicon-2024.2 --file https://data.qiime2.org/distro/amplicon/qiime2-amplicon-2024.2-py39-linux-conda.yml
skills/bioskills/bio-microbiome-diversity-analysis/scripts/core_metrics_qiime2.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/bioskills/bio-microbiome-diversity-analysis/scripts/diversity_analysis.R
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# Public amplicon datasets / built-in demo: # - data('GlobalPatterns', package = 'phyloseq') # ships a tree, used below # - Qiita https://qiita.ucsd.edu ; Earth Microbiome Project https://earthmicrobiome.org - Network accessline 11
# - Qiita https://qiita.ucsd.edu ; Earth Microbiome Project https://earthmicrobiome.org
skills/bioskills/bio-microbiome-functional-prediction/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-microbiome-functional-prediction/scripts/run_picrust2.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-microbiome-qiime2-workflow/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-microbiome-qiime2-workflow/references/usage-guide.md
- Network accessline 17
conda env create \ --name qiime2-amplicon-2026.1 \ --file https://data.qiime2.org/distro/amplicon/qiime2-amplicon-2026.1-py310-linux-conda.yml - Network accessline 18
--name qiime2-amplicon-2026.1 \ --file https://data.qiime2.org/distro/amplicon/qiime2-amplicon-2026.1-py310-linux-conda.yml conda activate qiime2-amplicon-2026.1 - Network accessline 19
--file https://data.qiime2.org/distro/amplicon/qiime2-amplicon-2026.1-py310-linux-conda.yml
skills/bioskills/bio-microbiome-qiime2-workflow/scripts/qiime2_16s.sh
skills/bioskills/bio-microbiome-taxonomy-assignment/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-microbiome-taxonomy-assignment/scripts/assign_qiime2_region.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/bioskills/bio-multi-omics-integration-data-harmonization/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-multi-omics-integration-integration-design/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-multi-omics-integration-mixomics-analysis/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-multi-omics-integration-mofa-integration/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-multi-omics-integration-similarity-network/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-pathway-analysis-enrichment-visualization/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-pathway-analysis-go-enrichment/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-pathway-analysis-gsea/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-pathway-analysis-kegg-pathways/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 26
KEGG is a LIVE DATABASE, not a package. enrichKEGG/enrichMKEGG/gseKEGG query the KEGG REST API (https://rest.kegg.jp/) at call time, so the same code on the same genes returns DIFFERENT pathways months apart as KEGG updates. For any reporte
skills/bioskills/bio-pathway-analysis-kegg-pathways/scripts/kegg_enrichment.R
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# Reference: clusterProfiler 4.18.4+ | Verify API if version differs # KEGG over-representation (enrichKEGG) + module enrichment + reproducible gson pinning. # NOTE: enrichKEGG/enrichMKEGG/gson_KEGG query the LIVE KEGG REST API (https://res - Network accessline 2
# KEGG over-representation (enrichKEGG) + module enrichment + reproducible gson pinning. # NOTE: enrichKEGG/enrichMKEGG/gson_KEGG query the LIVE KEGG REST API (https://rest.kegg.jp/). # They need internet and are NOT reproducible acro - Network accessline 3
# NOTE: enrichKEGG/enrichMKEGG/gson_KEGG query the LIVE KEGG REST API (https://rest.kegg.jp/).
skills/bioskills/bio-pathway-analysis-reactome-pathways/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 124
# the interactive web diagram needs the R-HSA id, NOT viewPathway: browseURL(paste0('https://reactome.org/PathwayBrowser/#/', ora@result$ID[1])) - Network accessline 125
browseURL(paste0('https://reactome.org/PathwayBrowser/#/', ora@result$ID[1])) - Network accessline 160
### viewPathway misuse **Trigger:** `viewPathway('R-HSA-109582')` or expecting a browser to open. **Mechanism:** the first argument is `pathName` (the Description), and the function draws a LOCAL ggraph plot, not a browser window. **Symptom - Network accessline 161
**Trigger:** `viewPathway('R-HSA-109582')` or expecting a browser to open. **Mechanism:** the first argument is `pathName` (the Description), and the function draws a LOCAL ggraph plot, not a browser window. **Symptom:** an error / empty pl - Network accessline 191
| Top hits are parent/child of one pathway | hierarchy nesting double-counts the signal | report the deepest significant node; ancestors as context | | `viewPathway('R-HSA-...')` errors or is empty | first arg is the NAME (Description), not - Network accessline 192
| `viewPathway('R-HSA-...')` errors or is empty | first arg is the NAME (Description), not the id | `viewPathway(ora@result$Description[i], ...)` | | viewPathway did not open a browser | it draws a LOCAL ggraph plot | `browseURL('https://re - Network accessline 193
| viewPathway did not open a browser | it draws a LOCAL ggraph plot | `browseURL('https://reactome.org/PathwayBrowser/#/<id>')` for the web diagram |
skills/bioskills/bio-pathway-analysis-reactome-pathways/references/usage-guide.md
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- Always pass a background `universe` (the genes actually measured); the implicit ~11,200-gene Reactome background otherwise inflates significance. - Read the hierarchy, not the raw row order: a parent and its child enrich on the same genes - Network accessline 90
- Read the hierarchy, not the raw row order: a parent and its child enrich on the same genes, so report the deepest significant node and treat its ancestors as context. ReactomePA has no `simplify()` equivalent, so this is a manual call. - - Network accessline 91
- `viewPathway()` takes the pathway NAME (the `Description`), NOT the R-HSA id, and it draws a LOCAL ggraph reaction network in the R graphics device - it does not open a browser. For the interactive web diagram use `browseURL('https://reac
skills/bioskills/bio-pathway-analysis-reactome-pathways/scripts/reactome_ora.R
skills/bioskills/bio-pathway-analysis-wikipathways/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-phasing-imputation-genotype-imputation/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-phasing-imputation-genotype-imputation/scripts/run_beagle_imputation.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-phasing-imputation-haplotype-phasing/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-phasing-imputation-haplotype-phasing/scripts/run_shapeit.sh
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#!/bin/bash
skills/bioskills/bio-phasing-imputation-imputation-qc/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-phasing-imputation-imputation-qc/scripts/imputation_qc.py
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#!/usr/bin/env python3
skills/bioskills/bio-phasing-imputation-reference-panels/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-phasing-imputation-reference-panels/scripts/prepare_panel.sh
- Executable scriptline 1
#!/bin/bash - Network accessline 5
# chromosome naming and genome build, run the strand/allele harmonization gate, and # build the engine-specific panel format. The DOWNLOAD and FORMAT-BUILD steps need # multi-GB panels (and HRC/TOPMed are server-only, NOT free wget targets) - Network accessline 6
# build the engine-specific panel format. The DOWNLOAD and FORMAT-BUILD steps need # multi-GB panels (and HRC/TOPMed are server-only, NOT free wget targets) and are # shown as commented reference, not executed -- only the harmonization step - Network accessline 7
# multi-GB panels (and HRC/TOPMed are server-only, NOT free wget targets) and are
skills/bioskills/bio-phylogenetics-bayesian-inference/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-phylogenetics-bayesian-inference/references/usage-guide.md
- Network accessline 13
# BEAST2 (and BEAUti / TreeAnnotator / LogCombiner) conda install -c bioconda beast2 # Or download from https://www.beast2.org/ - Network accessline 15
# Or download from https://www.beast2.org/ - Network accessline 17
# Tracer (visual ESS / trace diagnostics GUI) # Download from https://github.com/beast-dev/tracer/releases - Network accessline 18
# Download from https://github.com/beast-dev/tracer/releases
skills/bioskills/bio-phylogenetics-bayesian-inference/scripts/bayesian_convergence.py
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skills/bioskills/bio-phylogenetics-distance-calculations/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-phylogenetics-distance-calculations/scripts/bootstrap_consensus.py
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skills/bioskills/bio-phylogenetics-distance-calculations/scripts/build_nj_tree.py
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skills/bioskills/bio-phylogenetics-distance-calculations/scripts/pairwise_tree_distances.py
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skills/bioskills/bio-phylogenetics-divergence-dating/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-phylogenetics-divergence-dating/scripts/mcmctree_setup.py
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repository: https://github.com/GPTomics/bioSkills
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#!/bin/bash
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skills/bioskills/bio-phylogenetics-modern-tree-inference/scripts/raxml_analysis.sh
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repository: https://github.com/GPTomics/bioSkills
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# ASTER package (provides astral/ASTRAL-III, wastral, astral-pro) conda install -c bioconda aster # or build from https://github.com/chaoszhang/ASTER - Network accessline 13
conda install -c bioconda aster # or build from https://github.com/chaoszhang/ASTER - Network accessline 18
# Optional, for short-locus and small-dataset methods # PAUP* (SVDQuartets): https://paup.phylosolutions.com/ - Network accessline 19
# PAUP* (SVDQuartets): https://paup.phylosolutions.com/
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repository: https://github.com/GPTomics/bioSkills
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repository: https://github.com/GPTomics/bioSkills
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repository: https://github.com/GPTomics/bioSkills
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repository: https://github.com/GPTomics/bioSkills
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repository: https://github.com/GPTomics/bioSkills
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-population-genetics-selection-statistics/scripts/selection_scan.py
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skills/bioskills/bio-primer-design-primer-basics/SKILL.md
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repository: https://github.com/GPTomics/bioSkills
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-primer-design-primer-specificity/scripts/three_prime_anchor.py
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-primer-design-primer-validation/scripts/validate_primers.py
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name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-primer-design-qpcr-primers/scripts/qpcr_design.py
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-proteomics-data-import/scripts/load_maxquant.py
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-proteomics-dia-analysis/scripts/diann_analysis.sh
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-proteomics-differential-abundance/scripts/differential_abundance.py
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name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-proteomics-peptide-identification/scripts/fdr_filtering.py
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-proteomics-protein-inference/scripts/protein_groups.py
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-proteomics-proteomics-qc/scripts/qc_analysis.py
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-proteomics-ptm-analysis/scripts/phospho_analysis.py
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name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-proteomics-quantification/scripts/lfq_normalization.py
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-proteomics-spectral-libraries/scripts/build_library.py
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name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-read-alignment-bowtie2-alignment/scripts/align_bowtie2.sh
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-read-alignment-bwa-alignment/scripts/align_bwa.sh
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-read-alignment-hisat2-alignment/scripts/align_hisat2.sh
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#!/usr/bin/env bash
skills/bioskills/bio-read-alignment-star-alignment/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-read-alignment-star-alignment/scripts/align_star.sh
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#!/usr/bin/env bash
skills/bioskills/bio-read-qc-adapter-trimming/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-read-qc-adapter-trimming/references/usage-guide.md
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## Resources - [Cutadapt Documentation](https://cutadapt.readthedocs.io/) - Network accessline 65
- [Cutadapt Documentation](https://cutadapt.readthedocs.io/) - Network accessline 66
- [Trimmomatic Manual](http://www.usadellab.org/cms/?page=trimmomatic) - Network accessline 67
- [Illumina Adapter Sequences](https://support.illumina.com/downloads/illumina-adapter-sequences-document-1000000002694.html)
skills/bioskills/bio-read-qc-adapter-trimming/scripts/trim_adapters.sh
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skills/bioskills/bio-read-qc-contamination-screening/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-read-qc-contamination-screening/references/usage-guide.md
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## Resources - [FastQ Screen Documentation](https://www.bioinformatics.babraham.ac.uk/projects/fastq_screen/) - Network accessline 65
- [FastQ Screen Documentation](https://www.bioinformatics.babraham.ac.uk/projects/fastq_screen/) - Network accessline 66
- [Kraken2 / Bracken](https://github.com/DerrickWood/kraken2) - Network accessline 67
- [somalier](https://github.com/brentp/somalier)
skills/bioskills/bio-read-qc-contamination-screening/scripts/screen_samples.sh
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skills/bioskills/bio-read-qc-fastp-workflow/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-read-qc-fastp-workflow/references/usage-guide.md
skills/bioskills/bio-read-qc-fastp-workflow/scripts/fastp_pipeline.sh
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skills/bioskills/bio-read-qc-quality-filtering/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-read-qc-quality-filtering/references/usage-guide.md
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## Resources - [Trimmomatic Manual](http://www.usadellab.org/cms/?page=trimmomatic) - Network accessline 72
- [Trimmomatic Manual](http://www.usadellab.org/cms/?page=trimmomatic) - Network accessline 73
- [fastp Documentation](https://github.com/OpenGene/fastp) - Network accessline 74
- [Cutadapt Documentation](https://cutadapt.readthedocs.io/)
skills/bioskills/bio-read-qc-quality-filtering/scripts/quality_filter.sh
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skills/bioskills/bio-read-qc-quality-reports/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-read-qc-quality-reports/references/usage-guide.md
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## Resources - [FastQC Documentation](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) - Network accessline 58
- [FastQC Documentation](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) - Network accessline 59
- [MultiQC Documentation](https://multiqc.info/) - Network accessline 60
- [falco](https://github.com/smithlabcode/falco) - Network accessline 61
- [NanoPlot](https://github.com/wdecoster/NanoPlot)
skills/bioskills/bio-read-qc-quality-reports/scripts/run_qc_pipeline.sh
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#!/bin/bash
skills/bioskills/bio-read-qc-rnaseq-qc/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-read-qc-rnaseq-qc/references/usage-guide.md
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## Resources - [RSeQC Documentation](http://rseqc.sourceforge.net/) - Network accessline 60
- [RSeQC Documentation](http://rseqc.sourceforge.net/) - Network accessline 61
- [Qualimap](http://qualimap.conesalab.org/) - Network accessline 62
- [RNA-SeQC 2](https://github.com/getzlab/rnaseqc) - Network accessline 63
- [Picard RNA Metrics](https://broadinstitute.github.io/picard/)
skills/bioskills/bio-read-qc-rnaseq-qc/scripts/check_rrna.sh
skills/bioskills/bio-read-qc-rnaseq-qc/scripts/rnaseq_qc.sh
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skills/bioskills/bio-read-qc-umi-processing/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-read-qc-umi-processing/references/usage-guide.md
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## Resources - [umi_tools Documentation](https://umi-tools.readthedocs.io/) - Network accessline 61
- [umi_tools Documentation](https://umi-tools.readthedocs.io/) - Network accessline 62
- [fgbio Documentation](https://fulcrumgenomics.github.io/fgbio/) - Network accessline 63
- [umi_tools Publication](https://doi.org/10.1101/gr.209601.116)
skills/bioskills/bio-read-qc-umi-processing/scripts/umi_workflow.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-reporting-automated-qc-reports/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Access to secretsline 132
[Credential-related source omitted]
skills/bioskills/bio-reporting-automated-qc-reports/scripts/multiqc_pipeline.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-reporting-figure-export/SKILL.md
- Network accessline 10
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 11
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 12
repository: https://github.com/GPTomics/bioSkills - Dangerous shell operationline 163
- Borland D, Taylor RM 2nd. Rainbow Color Map (Still) Considered Harmful. IEEE Comput Graph Appl. 2007;27(2):14-17. doi:10.1109/MCG.2007.323435
skills/bioskills/bio-reporting-figure-export/scripts/publication_figures.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-reporting-jupyter-reports/SKILL.md
- Network accessline 10
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 11
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 12
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-reporting-jupyter-reports/scripts/papermill_batch.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-reporting-publication-tables/SKILL.md
- Network accessline 10
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 11
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 12
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-reporting-publication-tables/scripts/gene_table_export.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-reporting-quarto-reports/SKILL.md
- Network accessline 10
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 11
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 12
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-reporting-quarto-reports/references/usage-guide.md
- Network accessline 10
# Install Quarto - download from https://quarto.org/docs/download/
skills/bioskills/bio-reporting-rmarkdown-reports/SKILL.md
- Network accessline 10
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 11
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 12
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-restriction-analysis-enzyme-selection/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-restriction-analysis-enzyme-selection/scripts/cloning_strategy.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-restriction-analysis-enzyme-selection/scripts/select_enzymes.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-restriction-analysis-fragment-analysis/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-restriction-analysis-fragment-analysis/scripts/gel_simulation.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-restriction-analysis-fragment-analysis/scripts/predict_fragments.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-restriction-analysis-golden-gate-assembly/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-restriction-analysis-golden-gate-assembly/scripts/domestication_check.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-restriction-analysis-golden-gate-assembly/scripts/overhang_set_validate.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-restriction-analysis-restriction-mapping/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-restriction-analysis-restriction-mapping/scripts/create_map.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-restriction-analysis-restriction-mapping/scripts/plasmid_map.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-restriction-analysis-restriction-sites/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-restriction-analysis-restriction-sites/scripts/circular_plasmid.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-restriction-analysis-restriction-sites/scripts/find_sites.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-ribo-seq-initiation-site-mapping/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-ribo-seq-initiation-site-mapping/scripts/map_initiation_sites.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-ribo-seq-orf-detection/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-ribo-seq-orf-detection/scripts/detect_orfs.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-ribo-seq-riboseq-preprocessing/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-ribo-seq-riboseq-preprocessing/scripts/preprocess_riboseq.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-ribo-seq-ribosome-periodicity/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-ribo-seq-ribosome-periodicity/scripts/periodicity_analysis.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-ribo-seq-ribosome-stalling/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-ribo-seq-ribosome-stalling/scripts/detect_stalling.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-ribo-seq-translation-efficiency/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-ribo-seq-translation-efficiency/scripts/calculate_te.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-rna-quantification-alignment-free-quant/SKILL.md
- Network accessline 10
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 11
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 12
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-rna-quantification-alignment-free-quant/references/usage-guide.md
- Network accessline 48
# Human wget https://ftp.ensembl.org/pub/release-110/fasta/homo_sapiens/cdna/Homo_sapiens.GRCh38.cdna.all.fa.gz - Network accessline 49
wget https://ftp.ensembl.org/pub/release-110/fasta/homo_sapiens/cdna/Homo_sapiens.GRCh38.cdna.all.fa.gz - Network accessline 51
# Mouse wget https://ftp.ensembl.org/pub/release-110/fasta/mus_musculus/cdna/Mus_musculus.GRCm39.cdna.all.fa.gz - Network accessline 52
wget https://ftp.ensembl.org/pub/release-110/fasta/mus_musculus/cdna/Mus_musculus.GRCm39.cdna.all.fa.gz - Network accessline 57
# Human wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_44/gencode.v44.transcripts.fa.gz - Network accessline 58
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_44/gencode.v44.transcripts.fa.gz - Network accessline 63
# Get genome and transcriptome wget genome.fa.gz - Network accessline 64
wget genome.fa.gz - Network accessline 65
wget transcripts.fa.gz
skills/bioskills/bio-rna-quantification-alignment-free-quant/scripts/kallisto_quant.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-rna-quantification-alignment-free-quant/scripts/salmon_quant.sh
skills/bioskills/bio-rna-quantification-count-matrix-qc/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-rna-quantification-count-matrix-qc/scripts/qc_analysis.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-rna-quantification-featurecounts-counting/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-rna-quantification-featurecounts-counting/references/usage-guide.md
- Dangerous shell operationline 12
sudo apt install subread - Network accessline 14
# From source wget https://sourceforge.net/projects/subread/files/subread-2.0.6/subread-2.0.6-Linux-x86_64.tar.gz - Network accessline 15
wget https://sourceforge.net/projects/subread/files/subread-2.0.6/subread-2.0.6-Linux-x86_64.tar.gz
skills/bioskills/bio-rna-quantification-featurecounts-counting/scripts/count_genes.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-rna-quantification-featurecounts-counting/scripts/process_counts.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-rna-quantification-tximport-workflow/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-rna-structure-covariation-analysis/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-rna-structure-covariation-analysis/scripts/interpret_rscape.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-rna-structure-covariation-analysis/scripts/run_rscape.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-rna-structure-ncrna-search/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 67
| `--cut_ga` | GA (gathering): the curated family-membership cutoff | the correct DEFAULT for Rfam annotation | | `--cut_tc` | TC (trusted): lowest score of any known true positive | most conservative | | `--cut_nc` | NC (noise): highest sc - Network accessline 68
| `--cut_tc` | TC (trusted): lowest score of any known true positive | most conservative | | `--cut_nc` | NC (noise): highest score of a known false positive | most permissive, exploratory | | `-E` / `--incE` | flat E-value (reporting / inc - Network accessline 69
| `--cut_nc` | NC (noise): highest score of a known false positive | most permissive, exploratory | - Network accessline 78
# One-time setup wget https://ftp.ebi.ac.uk/pub/databases/Rfam/CURRENT/Rfam.cm.gz && gunzip Rfam.cm.gz - Network accessline 79
wget https://ftp.ebi.ac.uk/pub/databases/Rfam/CURRENT/Rfam.cm.gz && gunzip Rfam.cm.gz - Network accessline 80
wget https://ftp.ebi.ac.uk/pub/databases/Rfam/CURRENT/Rfam.clanin
skills/bioskills/bio-rna-structure-ncrna-search/references/usage-guide.md
- Network accessline 11
# Rfam database (pre-calibrated: press it, never recalibrate it) wget https://ftp.ebi.ac.uk/pub/databases/Rfam/CURRENT/Rfam.cm.gz - Network accessline 12
wget https://ftp.ebi.ac.uk/pub/databases/Rfam/CURRENT/Rfam.cm.gz - Network accessline 14
wget https://ftp.ebi.ac.uk/pub/databases/Rfam/CURRENT/Rfam.clanin
skills/bioskills/bio-rna-structure-ncrna-search/scripts/custom_cm.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-rna-structure-ncrna-search/scripts/parse_infernal.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-rna-structure-ncrna-search/scripts/rfam_search.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-rna-structure-secondary-structure-prediction/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-rna-structure-secondary-structure-prediction/scripts/consensus_structure.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-rna-structure-secondary-structure-prediction/scripts/rnafold_analysis.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-rna-structure-structure-probing/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-rna-structure-structure-probing/scripts/constrained_folding.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-rna-structure-structure-probing/scripts/shapemapper_analysis.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-sequence-io-batch-processing/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-sequence-io-batch-processing/scripts/batch_process.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-sequence-io-compressed-files/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Possible obfuscationline 48
- `bgzf.open()` reads BGZF input only. Pointing it at a plain `.gz` raises `ValueError: A BGZF block should start with b'\x1f\x8b\x08\x04'...`. To read plain gzip use `gzip.open()`. - Possible obfuscationline 191
| `ValueError: A BGZF block should start with b'\x1f\x8b\x08\x04'...` | `bgzf.open()` pointed at a plain gzip file | Read plain gzip with `gzip.open()`; reserve `bgzf.open` for BGZF |
skills/bioskills/bio-sequence-io-compressed-files/scripts/compressed_io.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-sequence-io-fastq-quality/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-sequence-io-fastq-quality/scripts/fastq_quality.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-sequence-io-filter-sequences/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-sequence-io-filter-sequences/scripts/filter_seqs.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-sequence-io-format-conversion/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-sequence-io-format-conversion/scripts/convert_format.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-sequence-io-paired-end-fastq/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-sequence-io-paired-end-fastq/scripts/paired_end_io.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-sequence-io-read-sequences/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-sequence-io-read-sequences/scripts/access_patterns.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-sequence-io-read-sequences/scripts/basic_parsing.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-sequence-io-read-sequences/scripts/fastq_quality.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-sequence-io-read-sequences/scripts/genbank_features.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-sequence-io-read-sequences/scripts/random_access.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-sequence-io-sequence-statistics/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 279
- Li H (2020). "auN: a new metric to measure assembly contiguity." Technical blog post, https://lh3.github.io/2020/04/08/a-new-metric-on-assembly-contiguity (auN = area under the Nx curve; smooth, threshold-free contiguity).
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#!/usr/bin/env python3
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#!/usr/bin/env python3
skills/bioskills/bio-sequence-manipulation-codon-usage/SKILL.md
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--- name: bio-codon-usage description: Analyze codon usage and calculate CAI (Codon Adaptation Index), RSCU, and Nc with Biopython, and produce naive max-CAI codon-optimized sequences. Use when scoring a gene's codon bias against a host, op - Network accessline 2
name: bio-codon-usage description: Analyze codon usage and calculate CAI (Codon Adaptation Index), RSCU, and Nc with Biopython, and produce naive max-CAI codon-optimized sequences. Use when scoring a gene's codon bias against a host, optimi - Network accessline 3
description: Analyze codon usage and calculate CAI (Codon Adaptation Index), RSCU, and Nc with Biopython, and produce naive max-CAI codon-optimized sequences. Use when scoring a gene's codon bias against a host, optimizing a CDS for heterol - Network accessline 9
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repository: https://github.com/GPTomics/bioSkills - Network accessline 30
**"Analyze codon usage"** -> Count codons in a coding sequence, compute frequencies and bias metrics. - Python: `Counter` on in-frame triplets + RSCU/Nc helpers (BioPython + standard library) - Network accessline 31
- Python: `Counter` on in-frame triplets + RSCU/Nc helpers (BioPython + standard library) - Network accessline 172
A reference-free bias measure (lower = more biased; range ~20 fully biased to 61 unbiased). The helper below is a simplified per-amino-acid approximation: Wright's published estimator averages the homozygosity F WITHIN each degeneracy class - Network accessline 230
| CAI | 0-1 | Highly expressed genes of the host | Higher = better adapted | | RSCU | 0-N | None (within-sequence) | 1 = no bias, >1 over-used | | Nc | ~20-61 | None | Lower = more biased | - Network accessline 231
| RSCU | 0-N | None (within-sequence) | 1 = no bias, >1 over-used | | Nc | ~20-61 | None | Lower = more biased | | GC3 | 0-100% | None | GC at wobble position | - Network accessline 232
| Nc | ~20-61 | None | Lower = more biased |
skills/bioskills/bio-sequence-manipulation-codon-usage/references/usage-guide.md
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This skill enables AI agents to help you analyze codon usage in coding sequences using Biopython: codon counting, CAI (Codon Adaptation Index) scoring against a host, RSCU and Nc bias metrics, GC at codon positions, and naive max-CAI codon - Network accessline 66
### Nc (Effective Number of Codons)
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protein_seq = 'MAEGEITTFTALTEKFNLPPGNYKKPKLLYCSNGGHFLRILPDGTVDGTRDRSDQHIQLQLSAESVGEVYIKSTETGQYLAMDTSGLLYGSQTPSEECLFLERLEENHYNTYTSKKHAEKNWFVGMKNGKKIELKDLVSGFLAEKQGSPTFFGYMKFLSNSEIVVLPNNVAPNVRYIIQQYGFYHHVGTWNNNSHAKIGLIILYLNKEKTLFNNNVQNKRTSHLL
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The genetic-code tables and their organism assignments follow the NCBI Taxonomy "The Genetic Codes" page, compiled by Andrzej (Anjay) Elzanowski and Jim Ostell at NCBI (https://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi). This is a ma
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pip install mebocost scanpy anndata # If the PyPI build is unavailable or stale, install from source: # pip install git+https://github.com/kaifuchenlab/MEBOCOST - Network accessline 11
# If the PyPI build is unavailable or stale, install from source: # pip install git+https://github.com/kaifuchenlab/MEBOCOST # MEBOCOST also needs its metabolite-enzyme-sensor database and a mebocost.conf - Network accessline 12
# pip install git+https://github.com/kaifuchenlab/MEBOCOST # MEBOCOST also needs its metabolite-enzyme-sensor database and a mebocost.conf # pointing at those files (download from the MEBOCOST repository).
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# miRBase distributes RNA (U) sequences; miRDeep2 needs DNA and no whitespace. # Pin the miRBase version - accessions and sequences change between releases. wget https://www.mirbase.org/download/mature.fa - Network accessline 91
# Pin the miRBase version - accessions and sequences change between releases. wget https://www.mirbase.org/download/mature.fa wget https://www.mirbase.org/download/hairpin.fa - Network accessline 92
wget https://www.mirbase.org/download/mature.fa - Network accessline 93
wget https://www.mirbase.org/download/hairpin.fa
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if [ ! -f "$MIRBASE_DIR/mature.fa" ]; then echo "Downloading miRBase..." wget -q -O $MIRBASE_DIR/mature.fa https://www.mirbase.org/download/mature.fa - Network accessline 17
echo "Downloading miRBase..." wget -q -O $MIRBASE_DIR/mature.fa https://www.mirbase.org/download/mature.fa wget -q -O $MIRBASE_DIR/hairpin.fa https://www.mirbase.org/download/hairpin.fa - Network accessline 18
wget -q -O $MIRBASE_DIR/mature.fa https://www.mirbase.org/download/mature.fa - Network accessline 19
wget -q -O $MIRBASE_DIR/hairpin.fa https://www.mirbase.org/download/hairpin.fa
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# miRge3.0 has NO '--download-library' subcommand. Fetch the pre-built libraries from # SourceForge and extract them, then point -lib at the extracted directory. wget https://sourceforge.net/projects/mirge3/files/miRge3_Lib/human.tar.gz - Network accessline 51
# SourceForge and extract them, then point -lib at the extracted directory. wget https://sourceforge.net/projects/mirge3/files/miRge3_Lib/human.tar.gz tar -xzf human.tar.gz # creates a 'human' library tree - Network accessline 52
wget https://sourceforge.net/projects/mirge3/files/miRge3_Lib/human.tar.gz
skills/bioskills/bio-small-rna-seq-mirge3-analysis/references/usage-guide.md
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#!/bin/bash
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name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-small-rna-seq-trf-pirna-profiling/scripts/pingpong_signature.py
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skills/bioskills/bio-spatial-transcriptomics-high-resolution-binning/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-spatial-transcriptomics-high-resolution-binning/scripts/bin_to_cell_logic.py
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skills/bioskills/bio-spatial-transcriptomics-image-analysis/SKILL.md
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-spatial-transcriptomics-image-analysis/scripts/extract_features.py
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skills/bioskills/bio-spatial-transcriptomics-spatial-communication/SKILL.md
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name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-spatial-transcriptomics-spatial-communication/scripts/ligrec_analysis.py
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skills/bioskills/bio-spatial-transcriptomics-spatial-data-io/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Dangerous shell operationline 151
# write a zarr STORE (a directory) to scratch, never the repo; rm -rf when done - Dangerous shell operationline 170
| A stray `.zarr` directory left in the repo after writing | `sdata.write` makes a directory store; `git status` hides untracked dirs | Write to scratch; `rm -rf` the store; run `git status --porcelain | grep '/$'` |
skills/bioskills/bio-spatial-transcriptomics-spatial-data-io/references/usage-guide.md
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- A SpatialData `.zarr` store is a DIRECTORY -- write it to a scratch path and `rm -rf` it; never commit it.
skills/bioskills/bio-spatial-transcriptomics-spatial-data-io/scripts/load_visium.py
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skills/bioskills/bio-spatial-transcriptomics-spatial-deconvolution/SKILL.md
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name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-spatial-transcriptomics-spatial-deconvolution/scripts/deconvolve_spatial.py
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skills/bioskills/bio-spatial-transcriptomics-spatial-domains/SKILL.md
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name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-spatial-transcriptomics-spatial-domains/scripts/detect_domains.py
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skills/bioskills/bio-spatial-transcriptomics-spatial-multiomics/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-spatial-transcriptomics-spatial-multiomics/scripts/integrate_multiomics.py
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skills/bioskills/bio-spatial-transcriptomics-spatial-neighbors/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-spatial-transcriptomics-spatial-neighbors/scripts/build_spatial_graph.py
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skills/bioskills/bio-spatial-transcriptomics-spatial-preprocessing/SKILL.md
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name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-spatial-transcriptomics-spatial-preprocessing/scripts/preprocess_spatial.py
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skills/bioskills/bio-spatial-transcriptomics-spatial-proteomics/SKILL.md
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name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-spatial-transcriptomics-spatial-proteomics/scripts/spatial_proteomics_analysis.py
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#!/usr/bin/env python3
skills/bioskills/bio-spatial-transcriptomics-spatial-statistics/SKILL.md
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name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-spatial-transcriptomics-spatial-statistics/scripts/spatial_autocorr.py
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skills/bioskills/bio-spatial-transcriptomics-spatial-visualization/SKILL.md
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-spatial-transcriptomics-spatial-visualization/scripts/spatial_plot.py
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skills/bioskills/bio-structural-biology-alphafold-predictions/SKILL.md
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name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
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**"Get the AlphaFold model for my protein and tell me which parts to believe"** -> Fetch the precomputed AFDB entry by UniProt accession, then read its confidence files (pLDDT per-residue, PAE per-residue-pair) to decide which regions and w - Network accessline 29
- Python: `requests.get(f'https://alphafold.ebi.ac.uk/api/prediction/{accession}')` returns metadata with `cifUrl`/`pdbUrl`/`paeDocUrl` download links. - Network accessline 86
def afdb_metadata(accession): url = f'https://alphafold.ebi.ac.uk/api/prediction/{accession}' r = requests.get(url) - Network accessline 87
url = f'https://alphafold.ebi.ac.uk/api/prediction/{accession}' r = requests.get(url) r.raise_for_status() - Network accessline 88
r = requests.get(url) - Network accessline 95
return None # >2700-aa non-human proteins and non-UniProt sequences are often absent entry = entries[0] cif_text = requests.get(entry['cifUrl']).text - Network accessline 96
entry = entries[0] cif_text = requests.get(entry['cifUrl']).text pae_json = requests.get(entry['paeDocUrl']).json() - Network accessline 97
cif_text = requests.get(entry['cifUrl']).text - Network accessline 98
pae_json = requests.get(entry['paeDocUrl']).json()
skills/bioskills/bio-structural-biology-alphafold-predictions/references/usage-guide.md
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GET https://alphafold.ebi.ac.uk/api/prediction/{UNIPROT_ACCESSION}
skills/bioskills/bio-structural-biology-alphafold-predictions/scripts/download_alphafold.py
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def afdb_metadata(accession): r = requests.get(f'https://alphafold.ebi.ac.uk/api/prediction/{accession}') - Network accessline 9
r = requests.get(f'https://alphafold.ebi.ac.uk/api/prediction/{accession}') - Network accessline 16
return None # >2700-aa non-human proteins and non-UniProt sequences are often absent entry = entries[0] # discover URLs from metadata - version suffix (v6 as of 2025) drifts cif_text = requests.get(entry['cifUrl']).text - Network accessline 17
entry = entries[0] # discover URLs from metadata - version suffix (v6 as of 2025) drifts cif_text = requests.get(entry['cifUrl']).text pae = requests.get(entry['paeDocUrl']).json() - Network accessline 18
cif_text = requests.get(entry['cifUrl']).text - Network accessline 19
pae = requests.get(entry['paeDocUrl']).json()
skills/bioskills/bio-structural-biology-binding-site-detection/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
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CASTp 3.0 (Tian 2018 *Nucleic Acids Res* 46:W363, http://sts.bioe.uic.edu/castp/) is a web server that delineates pockets, interior cavities, and channels analytically and reports each one's molecular surface AREA and VOLUME - use it when t
skills/bioskills/bio-structural-biology-binding-site-detection/references/usage-guide.md
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- fpocket (external CLI, includes mdpocket) - install via conda (`conda install -c conda-forge fpocket`) or build from the Discngine/fpocket repository - P2Rank (external CLI, `prank`) - download the standalone release from the rdk/p2rank r - Network accessline 10
- P2Rank (external CLI, `prank`) - download the standalone release from the rdk/p2rank repository (requires Java) - CASTp and DoGSiteScorer are WEB SERVERS (no install): CASTp at http://sts.bioe.uic.edu/castp/ and DoGSiteScorer on ProteinsP - Network accessline 11
- CASTp and DoGSiteScorer are WEB SERVERS (no install): CASTp at http://sts.bioe.uic.edu/castp/ and DoGSiteScorer on ProteinsPlus at https://proteins.plus/
skills/bioskills/bio-structural-biology-binding-site-detection/scripts/detect_pockets.py
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skills/bioskills/bio-structural-biology-geometric-analysis/SKILL.md
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name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 12
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-structural-biology-geometric-analysis/scripts/measure_distance.py
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skills/bioskills/bio-structural-biology-geometric-analysis/scripts/relative_sasa.py
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skills/bioskills/bio-structural-biology-geometric-analysis/scripts/superimpose.py
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skills/bioskills/bio-structural-biology-interface-analysis/SKILL.md
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name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 193
PDBePISA computes interfaces and predicts the biological assembly from the crystal, reporting interface area, an interface solvation free energy of assembly, the number of H-bonds and salt bridges, and a Complexation Significance Score (CSS
skills/bioskills/bio-structural-biology-interface-analysis/references/usage-guide.md
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- A structure file (PDB or mmCIF), ideally the biological assembly, not just the asymmetric unit. - Familiarity with SASA basics (see structural-biology/geometric-analysis). - PDBePISA (https://www.ebi.ac.uk/pdbe/pisa/) is a web service, no - Network accessline 15
- PDBePISA (https://www.ebi.ac.uk/pdbe/pisa/) is a web service, no install needed, for the biological-assembly call.
skills/bioskills/bio-structural-biology-interface-analysis/scripts/interface_bsa.py
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skills/bioskills/bio-structural-biology-modern-structure-prediction/SKILL.md
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name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 90
url = 'https://api.esmatlas.com/foldSequence/v1/pdb/' resp = requests.post(url, data=sequence, timeout=300) # 300 s: long sequences take minutes - Network accessline 91
resp = requests.post(url, data=sequence, timeout=300) # 300 s: long sequences take minutes
skills/bioskills/bio-structural-biology-modern-structure-prediction/scripts/compare_predictions.py
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skills/bioskills/bio-structural-biology-modern-structure-prediction/scripts/esmfold_api.py
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url = 'https://api.esmatlas.com/foldSequence/v1/pdb/' resp = requests.post(url, data=sequence, timeout=300) # 300 s: long sequences take minutes - Network accessline 18
resp = requests.post(url, data=sequence, timeout=300) # 300 s: long sequences take minutes
skills/bioskills/bio-structural-biology-structure-io/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 11
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repository: https://github.com/GPTomics/bioSkills - Network accessline 143
# in the -assemblyN file, so downloading it is safer than regenerating. pdb_id = '1abc' url = f'https://files.rcsb.org/download/{pdb_id.upper()}-assembly1.cif.gz' - Network accessline 144
pdb_id = '1abc' url = f'https://files.rcsb.org/download/{pdb_id.upper()}-assembly1.cif.gz' urllib.request.urlretrieve(url, f'{pdb_id}-assembly1.cif.gz') - Network accessline 145
url = f'https://files.rcsb.org/download/{pdb_id.upper()}-assembly1.cif.gz' - Network accessline 146
urllib.request.urlretrieve(url, f'{pdb_id}-assembly1.cif.gz') - Network accessline 245
- Kim H, Mirdita M, Steinegger M (2023). Foldcomp: a library and format for compressing and indexing large protein structure sets. *Bioinformatics* 39(4):btad153. DOI 10.1093/bioinformatics/btad153. - Krissinel E, Henrick K (2007). Inferenc - Network accessline 246
- Krissinel E, Henrick K (2007). Inference of macromolecular assemblies from crystalline state. *J Mol Biol* 372(3):774-797. DOI 10.1016/j.jmb.2007.05.022. - RCSB PDB (2024). Switch from MMTF to BinaryCIF: RCSB ceased serving MMTF on 2 July - Network accessline 247
- RCSB PDB (2024). Switch from MMTF to BinaryCIF: RCSB ceased serving MMTF on 2 July 2024. https://www.rcsb.org/news/65a1af31c76ca3abcc925d0c - Network accessline 248
- wwPDB. File formats and the PDB (legacy format frozen 2012; mmCIF archive standard 2014; 99,999-atom / 62-chain limits; large entries mmCIF-only). https://www.wwpdb.org/documentation/file-formats-and-the-pdb
skills/bioskills/bio-structural-biology-structure-io/scripts/download_structure.py
skills/bioskills/bio-structural-biology-structure-io/scripts/extract_chain.py
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skills/bioskills/bio-structural-biology-structure-io/scripts/parse_pdb.py
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skills/bioskills/bio-structural-biology-structure-modification/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 11
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 12
repository: https://github.com/GPTomics/bioSkills - Network accessline 362
- Cock PJA, Antao T, Chang JT, et al. 2009. Biopython: freely available Python tools for computational molecular biology and bioinformatics. *Bioinformatics* 25(11):1422-1423. doi:10.1093/bioinformatics/btp163 - Berman HM, Westbrook J, Feng - Network accessline 364
- wwPDB / RCSB PDB. Biological assembly operators (REMARK 350; `_pdbx_struct_assembly_gen` and `_pdbx_struct_oper_list`). https://www.rcsb.org/docs/programmatic-access/file-download-services
skills/bioskills/bio-structural-biology-structure-modification/scripts/modify_bfactor.py
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skills/bioskills/bio-structural-biology-structure-modification/scripts/remove_water.py
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skills/bioskills/bio-structural-biology-structure-modification/scripts/transform_coords.py
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skills/bioskills/bio-structural-biology-structure-navigation/SKILL.md
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-structural-biology-structure-navigation/scripts/enumerate_disorder.py
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skills/bioskills/bio-structural-biology-structure-navigation/scripts/extract_sequence.py
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skills/bioskills/bio-structural-biology-structure-navigation/scripts/find_ligands.py
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skills/bioskills/bio-structural-biology-structure-navigation/scripts/iterate_structure.py
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skills/bioskills/bio-structural-biology-structure-preparation/SKILL.md
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-structural-biology-structure-preparation/scripts/prepare_structure.py
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skills/bioskills/bio-structural-biology-structure-validation/SKILL.md
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The coarse Python screen decides WHETHER to run MolProbity; MolProbity (Chen et al. 2010 *Acta Crystallogr D* 66:12-21; Williams et al. 2018 *Protein Sci* 27:293-315) gives the numbers to report. For a deposited entry, prefer the pre-comput
skills/bioskills/bio-structural-biology-structure-validation/references/usage-guide.md
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MolProbity, phenix (`phenix.molprobity`, `phenix.process_predicted_model`), and DSSP (`mkdssp`) are separate CLI installs, not pip packages; install them independently and confirm they are on PATH. The MolProbity web service (http://molprob
skills/bioskills/bio-structural-biology-structure-validation/scripts/validate_structure.py
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skills/bioskills/bio-systems-biology-community-metabolic-modeling/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-systems-biology-community-metabolic-modeling/scripts/community_modeling.py
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skills/bioskills/bio-systems-biology-context-specific-models/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-systems-biology-context-specific-models/scripts/context_specific.py
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skills/bioskills/bio-systems-biology-flux-balance-analysis/SKILL.md
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# Curated genome-scale models: http://bigg.ucsd.edu/models (King 2016). Record the model
skills/bioskills/bio-systems-biology-flux-balance-analysis/references/usage-guide.md
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Inputs: a genome-scale model in SBML (`.xml`) or COBRA JSON, or a built-in test model (`load_model('textbook')`, `load_model('iJO1366')`). Curated models are at http://bigg.ucsd.edu/models.
skills/bioskills/bio-systems-biology-flux-balance-analysis/scripts/fba_analysis.py
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skills/bioskills/bio-systems-biology-gene-essentiality/SKILL.md
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repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-systems-biology-gene-essentiality/scripts/gene_essentiality.py
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skills/bioskills/bio-systems-biology-metabolic-reconstruction/SKILL.md
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name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
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git clone https://github.com/jotech/gapseq && cd gapseq && ./gapseq test # cloned, not pip; check deps
skills/bioskills/bio-systems-biology-metabolic-reconstruction/references/usage-guide.md
- Network accessline 10
pip install carveme # CarveMe: also needs DIAMOND and an LP solver (CPLEX/Gurobi academic; SCIP fallback) git clone https://github.com/jotech/gapseq # gapseq: cloned, not pip; needs R + bedtools/hmmer etc. - Network accessline 11
git clone https://github.com/jotech/gapseq # gapseq: cloned, not pip; needs R + bedtools/hmmer etc.
skills/bioskills/bio-systems-biology-metabolic-reconstruction/scripts/model_reconstruction.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-systems-biology-model-curation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-systems-biology-model-curation/scripts/model_curation.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-systems-biology-strain-design/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-systems-biology-strain-design/scripts/strain_design.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-tcr-bcr-analysis-immcantation-analysis/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-tcr-bcr-analysis-mixcr-analysis/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 154
- de Greef PC, de Boer RJ. TCRbeta rearrangements without a D segment are common, abundant, and public. *PNAS* 118:e2104367118 (2021). - Vander Heiden JA, et al. AIRR Community standardized representations for annotated immune repertoires. - Network accessline 156
- MiXCR documentation. https://mixcr.com/mixcr/ (presets, mixins, exportClones/exportAirr, licensing, QC).
skills/bioskills/bio-tcr-bcr-analysis-mixcr-analysis/references/usage-guide.md
- Network accessline 10
conda install -c bioconda mixcr # or download the release jar: https://github.com/milaboratory/mixcr/releases - Network accessline 11
# or download the release jar: https://github.com/milaboratory/mixcr/releases - Network accessline 14
# License is mandatory for 4.x (academic use is free): # get a key at https://platforma.bio/getlicense - Network accessline 15
# get a key at https://platforma.bio/getlicense
skills/bioskills/bio-tcr-bcr-analysis-mixcr-analysis/scripts/mixcr_workflow.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-tcr-bcr-analysis-repertoire-visualization/SKILL.md
- Network accessline 10
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 11
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 12
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-tcr-bcr-analysis-repertoire-visualization/scripts/repertoire_plots.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-tcr-bcr-analysis-scirpy-analysis/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-tcr-bcr-analysis-scirpy-analysis/scripts/scirpy_integration.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-tcr-bcr-analysis-specificity-annotation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-tcr-bcr-analysis-specificity-annotation/scripts/specificity_annotation.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-tcr-bcr-analysis-vdjtools-analysis/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-tcr-bcr-analysis-vdjtools-analysis/references/usage-guide.md
skills/bioskills/bio-tcr-bcr-analysis-vdjtools-analysis/scripts/diversity_analysis.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-temporal-genomics-circadian-rhythms/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-temporal-genomics-circadian-rhythms/scripts/cosinor_analysis.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-temporal-genomics-differential-rhythmicity/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-temporal-genomics-periodicity-detection/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-temporal-genomics-periodicity-detection/scripts/lomb_scargle_periodicity.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-temporal-genomics-periodicity-detection/scripts/wavelet_temporal.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-temporal-genomics-temporal-clustering/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-temporal-genomics-temporal-clustering/scripts/tslearn_clustering.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-temporal-genomics-temporal-grn/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-temporal-genomics-temporal-grn/scripts/granger_causality_grn.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-temporal-genomics-trajectory-modeling/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-temporal-genomics-trajectory-modeling/scripts/changepoint_detection.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-variant-calling-clinical-interpretation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-variant-calling-clinical-interpretation/references/usage-guide.md
- Network accessline 10
# ClinVar VCF (match your genome build; GRCh38 shown) wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz - Network accessline 11
wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz - Network accessline 12
wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz.tbi - Network accessline 18
# InterVar (automated ACMG, 2015 rules; treat output as a starting point, not final) git clone https://github.com/WGLab/InterVar.git - Network accessline 19
git clone https://github.com/WGLab/InterVar.git
skills/bioskills/bio-variant-calling-clinical-interpretation/scripts/clinical_filter.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-variant-calling-consensus-sequences/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-variant-calling-consensus-sequences/scripts/generate_consensus.sh
skills/bioskills/bio-variant-calling-deepvariant/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-variant-calling-deepvariant/scripts/run_deepvariant.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-variant-calling-filtering-best-practices/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-variant-calling-filtering-best-practices/scripts/filter_variants.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-variant-calling-gatk-variant-calling/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-variant-calling-gatk-variant-calling/references/usage-guide.md
skills/bioskills/bio-variant-calling-gatk-variant-calling/scripts/joint_calling.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-variant-calling-gatk-variant-calling/scripts/single_sample.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-variant-calling-joint-calling/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-variant-calling-joint-calling/scripts/joint_calling_gatk.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-variant-calling-structural-variant-calling/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-variant-calling-structural-variant-calling/scripts/call_svs.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-variant-calling-structural-variant-calling/scripts/svlen_sign_filter.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-variant-calling-variant-annotation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-variant-calling-variant-annotation/references/usage-guide.md
- Network accessline 263
# Download wget https://ftp.ncbi.nih.gov/snp/latest_release/VCF/GCF_000001405.40.gz - Network accessline 264
wget https://ftp.ncbi.nih.gov/snp/latest_release/VCF/GCF_000001405.40.gz - Network accessline 265
wget https://ftp.ncbi.nih.gov/snp/latest_release/VCF/GCF_000001405.40.gz.tbi - Network accessline 276
# Download chromosome 1 wget https://gnomad-public-us-east-1.s3.amazonaws.com/release/4.0/vcf/genomes/gnomad.genomes.v4.0.sites.chr1.vcf.bgz - Network accessline 277
wget https://gnomad-public-us-east-1.s3.amazonaws.com/release/4.0/vcf/genomes/gnomad.genomes.v4.0.sites.chr1.vcf.bgz - Network accessline 278
wget https://gnomad-public-us-east-1.s3.amazonaws.com/release/4.0/vcf/genomes/gnomad.genomes.v4.0.sites.chr1.vcf.bgz.tbi - Network accessline 291
# Download wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz - Network accessline 292
wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz - Network accessline 293
wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz.tbi
skills/bioskills/bio-variant-calling-variant-annotation/scripts/annotate_vcf.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-variant-calling-variant-calling/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-variant-calling-variant-calling/scripts/call_variants.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-variant-calling-variant-normalization/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-variant-calling-variant-normalization/references/usage-guide.md
- Dangerous shell operationline 213
rm step1.vcf.gz
skills/bioskills/bio-variant-calling-variant-normalization/scripts/normalize_vcf.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-variant-calling-vcf-basics/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills - Network accessline 359
- Danecek P, Auton A, Abecasis G, et al. The variant call format and VCFtools. *Bioinformatics.* 2011;27(15):2156-2158. doi:10.1093/bioinformatics/btr330 (VCF format definition) - Danecek P, Bonfield JK, Liddle J, et al. Twelve years of SAM - Network accessline 361
- The Variant Call Format Specification (VCFv4.3/4.4). GA4GH / samtools hts-specs. https://samtools.github.io/hts-specs/ (symbolic alleles, END, `*` overlapping-deletion allele, Number=A/R/G, PL/GL/GQ, gVCF `<NON_REF>`)
skills/bioskills/bio-variant-calling-vcf-basics/scripts/view_vcf.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-variant-calling-vcf-manipulation/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-variant-calling-vcf-manipulation/scripts/compare_vcfs.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-variant-calling-vcf-statistics/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-variant-calling-vcf-statistics/scripts/vcf_stats.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-workflow-management-cwl-workflows/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflow-management-cwl-workflows/scripts/rnaseq.cwl
- Executable scriptline 1
#!/usr/bin/env cwl-runner
skills/bioskills/bio-workflow-management-nextflow-pipelines/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflow-management-nextflow-pipelines/scripts/rnaseq.nf
- Executable scriptline 1
#!/usr/bin/env nextflow
skills/bioskills/bio-workflow-management-nf-core-pipelines/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflow-management-nf-core-pipelines/scripts/run_nf_core.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/bioskills/bio-workflow-management-snakemake-workflows/SKILL.md
- Network accessline 10
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 11
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 12
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflow-management-snakemake-workflows/scripts/rnaseq_snakefile
- Executable scriptline 1
Executable file
skills/bioskills/bio-workflow-management-wdl-workflows/SKILL.md
- Network accessline 9
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 10
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 11
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-atacseq-pipeline/SKILL.md
- Network accessline 27
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 28
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 29
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-atacseq-pipeline/references/usage-guide.md
- Network accessline 14
# Optional: deep learning, ENCODE-rE2G, WASP pip install chrombpnet tangermeme modisco-lite git clone https://github.com/broadinstitute/ABC-Enhancer-Gene-Prediction - Network accessline 15
pip install chrombpnet tangermeme modisco-lite git clone https://github.com/broadinstitute/ABC-Enhancer-Gene-Prediction git clone https://github.com/bmvdgeijn/WASP - Network accessline 16
git clone https://github.com/broadinstitute/ABC-Enhancer-Gene-Prediction - Network accessline 17
git clone https://github.com/bmvdgeijn/WASP
skills/bioskills/bio-workflows-atacseq-pipeline/scripts/atacseq_workflow.sh
skills/bioskills/bio-workflows-biomarker-pipeline/SKILL.md
- Network accessline 20
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 21
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 22
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-biomarker-pipeline/scripts/biomarker_pipeline.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-workflows-causal-genomics-pipeline/SKILL.md
- Network accessline 34
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 35
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 36
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-causal-genomics-pipeline/references/usage-guide.md
- Network accessline 23
git clone https://github.com/abdenlab/ldsc-python3.git # Python 3 fork, working CLI (bulik/ldsc is Python 2.7, unmaintained) - Network accessline 24
cd ldsc-python3 && pip install . && cd .. # Poetry project (pyproject.toml); no environment.yml git clone https://github.com/hakyimlab/MetaXcan.git pip install pyfocus - Network accessline 25
git clone https://github.com/hakyimlab/MetaXcan.git - Network accessline 26
pip install pyfocus # MAGMA ships as a zip, not a git repo. Grab the current program zip from the official CNCR page # (https://cncr.nl/research/magma/) -- the old vu.data.surfsara.nl direct links now 404 after SURF's - Network accessline 27
# MAGMA ships as a zip, not a git repo. Grab the current program zip from the official CNCR page # (https://cncr.nl/research/magma/) -- the old vu.data.surfsara.nl direct links now 404 after SURF's # domain migration, so fetch the link from - Network accessline 28
# (https://cncr.nl/research/magma/) -- the old vu.data.surfsara.nl direct links now 404 after SURF's # domain migration, so fetch the link from that page rather than hard-coding one here. unzip magma_v1.10.zip - Network accessline 29
# domain migration, so fetch the link from that page rather than hard-coding one here. unzip magma_v1.10.zip git clone https://github.com/FinucaneLab/pops.git # PoPS is run as pops.py from the clone; PyPI `pops` is unrelated - Network accessline 31
git clone https://github.com/FinucaneLab/pops.git # PoPS is run as pops.py from the clone; PyPI `pops` is unrelated
skills/bioskills/bio-workflows-causal-genomics-pipeline/scripts/post_gwas_causal_pipeline.R
- Network accessline 11
# Example: LDL cholesterol (exposure) -> coronary heart disease (outcome) # Use GWAS summary statistics from public repositories: # - IEU OpenGWAS: https://gwas.mrcieu.ac.uk/ - Network accessline 12
# Use GWAS summary statistics from public repositories: # - IEU OpenGWAS: https://gwas.mrcieu.ac.uk/ # - GWAS Catalog: https://www.ebi.ac.uk/gwas/ - Network accessline 13
# - IEU OpenGWAS: https://gwas.mrcieu.ac.uk/ - Network accessline 14
# - GWAS Catalog: https://www.ebi.ac.uk/gwas/
skills/bioskills/bio-workflows-chipseq-pipeline/SKILL.md
- Network accessline 26
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 27
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 28
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-chipseq-pipeline/scripts/narrow_peak_workflow.sh
skills/bioskills/bio-workflows-clinical-trial-pipeline/SKILL.md
- Network accessline 28
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 29
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 30
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-clinical-trial-pipeline/scripts/clinical_trial_pipeline.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-workflows-clip-pipeline/SKILL.md
- Network accessline 12
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 13
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 14
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-clip-pipeline/references/usage-guide.md
- Network accessline 13
# Peak calling and downstream (CLIPper is NOT on bioconda -- the bioconda `clipper` is an # unrelated R package. Install YeoLab CLIPper from source: # git clone https://github.com/YeoLab/clipper && cd clipper && conda env create -f enviro - Network accessline 14
# unrelated R package. Install YeoLab CLIPper from source: # git clone https://github.com/YeoLab/clipper && cd clipper && conda env create -f environment3.yml && pip install .) conda install -c bioconda pureclip homer idr meme - Network accessline 15
# git clone https://github.com/YeoLab/clipper && cd clipper && conda env create -f environment3.yml && pip install .)
skills/bioskills/bio-workflows-clip-pipeline/scripts/clip_full_pipeline.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-workflows-cnv-pipeline/SKILL.md
- Network accessline 24
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 25
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 26
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-cnv-pipeline/scripts/cnvkit_workflow.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-workflows-crispr-editing-pipeline/SKILL.md
- Network accessline 20
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 21
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 22
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-crispr-editing-pipeline/scripts/crispr_editing_workflow.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-workflows-crispr-screen-pipeline/SKILL.md
- Network accessline 31
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 32
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 33
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-crispr-screen-pipeline/references/usage-guide.md
- Network accessline 11
conda install -c bioconda mageck mageck-vispr # BAGEL2 and JACKS are GitHub clones (the PyPI `jacks` is an unrelated package) git clone https://github.com/hart-lab/bagel - Network accessline 12
# BAGEL2 and JACKS are GitHub clones (the PyPI `jacks` is an unrelated package) git clone https://github.com/hart-lab/bagel git clone https://github.com/felicityallen/JACKS - Network accessline 13
git clone https://github.com/hart-lab/bagel - Network accessline 14
git clone https://github.com/felicityallen/JACKS - Network accessline 15
pip install pertpy scanpy anndata # drugZ (clone from GitHub) git clone https://github.com/hart-lab/drugz - Network accessline 16
# drugZ (clone from GitHub) git clone https://github.com/hart-lab/drugz # Editing analysis (CRISPResso2 is on bioconda, not PyPI) - Network accessline 17
git clone https://github.com/hart-lab/drugz - Network accessline 19
conda install -c bioconda crispresso2 # Chronos for DepMap-style cancer-line CN+quality jointly (GitHub, not PyPI) pip install git+https://github.com/broadinstitute/chronos - Network accessline 20
# Chronos for DepMap-style cancer-line CN+quality jointly (GitHub, not PyPI) pip install git+https://github.com/broadinstitute/chronos # R packages - Network accessline 21
pip install git+https://github.com/broadinstitute/chronos
skills/bioskills/bio-workflows-crispr-screen-pipeline/scripts/crispr_pipeline.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-workflows-cytometry-pipeline/SKILL.md
- Network accessline 25
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 26
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 27
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-edna-pipeline/SKILL.md
- Network accessline 23
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 24
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 25
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-edna-pipeline/references/usage-guide.md
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# COI: BOLD reference library or MIDORI2 # 12S: MitoFish or 12S-seqdb # ITS: UNITE (https://unite.ut.ee/) - Network accessline 23
# 12S: MitoFish or 12S-seqdb # ITS: UNITE (https://unite.ut.ee/) # 16S/18S: SILVA (https://www.arb-silva.de/) or PR2 for 18S - Network accessline 24
# ITS: UNITE (https://unite.ut.ee/) - Network accessline 25
# 16S/18S: SILVA (https://www.arb-silva.de/) or PR2 for 18S
skills/bioskills/bio-workflows-edna-pipeline/scripts/edna_pipeline.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/bioskills/bio-workflows-expression-to-pathways/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 22
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 23
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-fastq-to-variants/SKILL.md
- Network accessline 26
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 27
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 28
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-fastq-to-variants/scripts/bwa_bcftools_workflow.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-workflows-fastq-to-variants/scripts/bwa_gatk_workflow.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-workflows-genome-annotation-pipeline/SKILL.md
- Network accessline 23
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 24
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 25
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-genome-annotation-pipeline/scripts/eukaryotic_annotation_pipeline.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-workflows-genome-annotation-pipeline/scripts/prokaryotic_annotation_pipeline.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-workflows-genome-assembly-pipeline/SKILL.md
- Network accessline 29
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 30
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 31
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-genome-assembly-pipeline/references/usage-guide.md
- Network accessline 16
# Decontamination + QC (CheckM2 downloads a large DB; for MAG taxonomy add gtdbtk) conda install -c bioconda checkm2 gunc blobtoolkit yahs quast busco # FCS-GX is distributed separately by NCBI (container + ~470 GiB GX DB), not via bioconda - Network accessline 18
# FCS-GX is distributed separately by NCBI (container + ~470 GiB GX DB), not via bioconda: https://github.com/ncbi/fcs
skills/bioskills/bio-workflows-genome-assembly-pipeline/scripts/bacterial_assembly.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-workflows-grn-pipeline/SKILL.md
- Network accessline 19
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 20
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 21
repository: https://github.com/GPTomics/bioSkills - Network accessline 110
# TF list from cisTarget resources # Human: https://resources.aertslab.org/cistarget/tf_lists/ - Network accessline 111
# Human: https://resources.aertslab.org/cistarget/tf_lists/ - Network accessline 252
# Run motif enrichment on accessible regions. The first arg is the cisTarget ranking DB: pass the # feather path and run_cistarget instantiates cisTargetDatabase itself. Prebuilt DBs at # https://resources.aertslab.org/cistarget/ . The para - Network accessline 253
# feather path and run_cistarget instantiates cisTargetDatabase itself. Prebuilt DBs at # https://resources.aertslab.org/cistarget/ . The parameter is spelled `specie`, not `species`. CTX_DB = '/path/to/hg38_screen_v10_clust.regions_vs_moti - Network accessline 254
# https://resources.aertslab.org/cistarget/ . The parameter is spelled `specie`, not `species`.
skills/bioskills/bio-workflows-grn-pipeline/references/usage-guide.md
skills/bioskills/bio-workflows-grn-pipeline/scripts/multiome_grn_workflow.py
- Executable scriptline 1
Executable file - Network accessline 22
# cisTarget ranking DB for the matching genome/region set; run_cistarget accepts the feather path # directly and instantiates cisTargetDatabase itself. Prebuilt DBs: https://resources.aertslab.org/cistarget/ - Network accessline 23
# directly and instantiates cisTargetDatabase itself. Prebuilt DBs: https://resources.aertslab.org/cistarget/
skills/bioskills/bio-workflows-grn-pipeline/scripts/scenic_grn_workflow.py
- Executable scriptline 1
Executable file - Network accessline 37
# Load TF list and filter to genes in dataset # Human TFs: https://resources.aertslab.org/cistarget/tf_lists/allTFs_hg38.txt - Network accessline 38
# Human TFs: https://resources.aertslab.org/cistarget/tf_lists/allTFs_hg38.txt - Network accessline 39
# Mouse TFs: https://resources.aertslab.org/cistarget/tf_lists/allTFs_mm.txt - Network accessline 57
# Step 2: Regulon pruning with RcisTarget # cisTarget databases: ~10 GB each, download once from: # https://resources.aertslab.org/cistarget/databases/ - Network accessline 59
# https://resources.aertslab.org/cistarget/databases/
skills/bioskills/bio-workflows-gwas-pipeline/SKILL.md
- Network accessline 25
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 26
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 27
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-gwas-pipeline/scripts/gwas_workflow.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-workflows-hic-pipeline/SKILL.md
- Network accessline 24
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 25
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 26
repository: https://github.com/GPTomics/bioSkills - Network accessline 223
# A square balanced map on a log scale; importing cooltools.lib.plotting registers 'fall'. # Show O/E with a symmetric diverging cmap to see compartments/loops (see hic-visualization). mat = clr.matrix(balance=True).fetch('chr1:50000000-600 - Network accessline 224
# Show O/E with a symmetric diverging cmap to see compartments/loops (see hic-visualization). mat = clr.matrix(balance=True).fetch('chr1:50000000-60000000') fig, ax = plt.subplots(figsize=(8, 8)) - Network accessline 225
mat = clr.matrix(balance=True).fetch('chr1:50000000-60000000')
skills/bioskills/bio-workflows-hic-pipeline/scripts/hic_analysis.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-workflows-imc-pipeline/SKILL.md
- Network accessline 19
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 20
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 21
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-imc-pipeline/scripts/imc_analysis.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-workflows-liquid-biopsy-pipeline/SKILL.md
- Network accessline 25
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 26
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 27
repository: https://github.com/GPTomics/bioSkills - Network accessline 168
for read in bam.fetch():
skills/bioskills/bio-workflows-liquid-biopsy-pipeline/scripts/liquid_biopsy_pipeline.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 81
bam = pysam.AlignmentFile(bam_path, 'rb') sizes = [] for read in bam.fetch(): - Network accessline 82
sizes = [] for read in bam.fetch(): if read.is_proper_pair and not read.is_secondary and 0 < read.template_length <= 400: - Network accessline 83
for read in bam.fetch():
skills/bioskills/bio-workflows-longread-sv-pipeline/SKILL.md
- Network accessline 20
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 21
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 22
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-longread-sv-pipeline/scripts/ont_sv_calling.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-workflows-merip-pipeline/SKILL.md
- Network accessline 22
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 23
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 24
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-merip-pipeline/scripts/merip_full_pipeline.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-workflows-metabolic-modeling-pipeline/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 23
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 24
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-metabolic-modeling-pipeline/scripts/metabolic_modeling_workflow.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-workflows-metabolomics-pipeline/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 27
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 28
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-metagenomics-pipeline/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 23
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 24
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-metagenomics-pipeline/scripts/metagenomics_workflow.sh
skills/bioskills/bio-workflows-methylation-pipeline/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 22
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 23
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-microbiome-pipeline/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 22
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 23
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-multi-omics-pipeline/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 23
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 24
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-multiome-pipeline/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 26
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 27
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-neoantigen-pipeline/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 24
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 25
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-neoantigen-pipeline/scripts/neoantigen_workflow.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/bioskills/bio-workflows-outbreak-pipeline/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 22
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 23
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-outbreak-pipeline/scripts/outbreak_workflow.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-workflows-proteomics-pipeline/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 18
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 19
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-riboseq-pipeline/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 23
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 24
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-riboseq-pipeline/scripts/riboseq_full_pipeline.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-workflows-rnaseq-to-de/SKILL.md
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third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 27
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 28
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-rnaseq-to-de/scripts/salmon_deseq2_workflow.R
- Network accessline 10
# Public RNA-seq datasets: # - Bioconductor: airway package (GSE52778, dexamethasone treatment) # - recount3: https://rna.recount.bio (precomputed counts for GEO/SRA) - Network accessline 11
# - Bioconductor: airway package (GSE52778, dexamethasone treatment) # - recount3: https://rna.recount.bio (precomputed counts for GEO/SRA) # - ENCODE: https://www.encodeproject.org (cell line RNA-seq) - Network accessline 12
# - recount3: https://rna.recount.bio (precomputed counts for GEO/SRA) - Network accessline 13
# - ENCODE: https://www.encodeproject.org (cell line RNA-seq) - Network accessline 14
# - GTEx: https://gtexportal.org (tissue expression)
skills/bioskills/bio-workflows-rnaseq-to-de/scripts/star_deseq2_workflow.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-workflows-scrnaseq-pipeline/SKILL.md
- Network accessline 22
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 23
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 24
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-scrnaseq-pipeline/scripts/scanpy_workflow.py
- Executable scriptline 1
Executable file - Network accessline 13
# Public scRNA-seq datasets: # - 10x Genomics: https://www.10xgenomics.com/datasets (PBMC 3k, 10k datasets) - Network accessline 14
# - 10x Genomics: https://www.10xgenomics.com/datasets (PBMC 3k, 10k datasets) - Network accessline 15
# - CELLxGENE: https://cellxgene.cziscience.com (curated annotated datasets) - Network accessline 16
# - GEO: GSE149173 (COVID-19 PBMC), GSE136831 (human lung) # - Human Cell Atlas: https://data.humancellatlas.org # - Scanpy built-in: sc.datasets.pbmc3k_processed() - Network accessline 17
# - Human Cell Atlas: https://data.humancellatlas.org
skills/bioskills/bio-workflows-scrnaseq-pipeline/scripts/seurat_workflow.R
- Network accessline 9
# Public scRNA-seq datasets: # - 10x Genomics: https://www.10xgenomics.com/datasets (PBMC 3k, 10k) - Network accessline 10
# - 10x Genomics: https://www.10xgenomics.com/datasets (PBMC 3k, 10k) - Network accessline 11
# - SeuratData package: InstallData('pbmc3k') for built-in tutorial data # - CELLxGENE: https://cellxgene.cziscience.com (curated annotated datasets) # - GEO: GSE149173 (COVID-19 PBMC), GSE126030 (multi-tissue) - Network accessline 12
# - CELLxGENE: https://cellxgene.cziscience.com (curated annotated datasets) - Network accessline 14
# - Human Cell Atlas: https://data.humancellatlas.org
skills/bioskills/bio-workflows-smrna-pipeline/SKILL.md
- Network accessline 22
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 23
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 24
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-smrna-pipeline/scripts/smrna_full_pipeline.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-workflows-somatic-variant-pipeline/SKILL.md
- Network accessline 23
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 24
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 25
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-somatic-variant-pipeline/scripts/run_mutect2.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-workflows-spatial-pipeline/SKILL.md
- Network accessline 25
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 26
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 27
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-spatial-pipeline/scripts/visium_workflow.py
- Executable scriptline 1
Executable file
skills/bioskills/bio-workflows-splicing-pipeline/SKILL.md
- Network accessline 25
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 26
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 27
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-splicing-pipeline/scripts/splicing_pipeline.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-workflows-tcr-pipeline/SKILL.md
- Network accessline 21
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 22
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 23
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-tcr-pipeline/scripts/tcr_full_pipeline.sh
- Executable scriptline 1
#!/bin/bash
skills/bioskills/bio-workflows-timecourse-pipeline/SKILL.md
- Network accessline 22
third_party: name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills - Network accessline 23
name: GPTomics/bioSkills repository: https://github.com/GPTomics/bioSkills commit: d91ed3d563019e649dc854c56ccd62551359488a - Network accessline 24
repository: https://github.com/GPTomics/bioSkills
skills/bioskills/bio-workflows-timecourse-pipeline/scripts/timecourse_pipeline.py
- Executable scriptline 1
Executable file
skills/boltz/README.md
- Network accessline 31
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 36
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 37
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/boltz/README_zh.md
- Network accessline 20
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 25
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/boltz/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/boltz/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何东西需要安装——wheel 自带全部内置 Skill
skills/boltz/SKILL.md
- Network accessline 22
name: Boltz-2 license: MIT terms_url: https://github.com/jwohlwend/boltz/blob/main/LICENSE - Network accessline 23
license: MIT terms_url: https://github.com/jwohlwend/boltz/blob/main/LICENSE # SKILL.md — `--use_msa_server` queries api.colabfold.com; the doc says to - Network accessline 24
terms_url: https://github.com/jwohlwend/boltz/blob/main/LICENSE - Network accessline 29
name: ColabFold MSA server (api.colabfold.com) provider: Steinegger Lab info_url: https://github.com/sokrypton/ColabFold/wiki - Network accessline 30
provider: Steinegger Lab info_url: https://github.com/sokrypton/ColabFold/wiki --- - Network accessline 31
info_url: https://github.com/sokrypton/ColabFold/wiki
skills/borzoi/README.md
- Network accessline 31
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 36
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 37
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/borzoi/README_zh.md
- Network accessline 20
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 25
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/borzoi/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/borzoi/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何东西需要安装——wheel 自带全部内置 Ski
skills/borzoi/SKILL.md
- Network accessline 27
provider: Calico Life Sciences license: CC-BY-4.0 info_url: https://huggingface.co/johahi/borzoi-replicate-0 - Network accessline 28
license: CC-BY-4.0 info_url: https://huggingface.co/johahi/borzoi-replicate-0 --- - Network accessline 29
info_url: https://huggingface.co/johahi/borzoi-replicate-0
skills/catalyst_sar_screening/README.md
- Network accessline 33
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 38
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 39
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/catalyst_sar_screening/README_zh.md
- Network accessline 22
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 27
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/catalyst_sar_screening/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/catalyst_sar_screening/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S
skills/catalyst_sar_screening/SKILL.md
- Network accessline 30
**When blocked:** ask the user for `HF_TOKEN` and/or `HF_ENDPOINT` (e.g. `https://hf-mirror.com`). Do not substitute another method. - Network accessline 31
(e.g. `https://hf-mirror.com`). Do not substitute another method. - Network accessline 41
Mn、Fe、Cu；分析金属中心与电化学稳定性的关系并生成可视化报告。 huggingface 的 API 使用 <HF_TOKEN_PLACEHOLDER>； 运行代码前 export HF_ENDPOINT=https://hf-mirror.com。 - Network accessline 43
运行代码前 export HF_ENDPOINT=https://hf-mirror.com。 - Network accessline 51
[Credential-related source omitted] - Access to secretsline 51
[Credential-related source omitted] - Network accessline 52
[Credential-related source omitted] - Access to secretsline 52
[Credential-related source omitted] - Network accessline 116
[Credential-related source omitted] - Access to secretsline 117
[Credential-related source omitted] - Network accessline 118
export HF_ENDPOINT=https://hf-mirror.com
skills/catalyst_sar_screening/build_example.py
- Executable scriptline 1
Executable file
skills/catalyst_sar_screening/kernel.py
- Executable scriptline 1
Executable file - Network accessline 15
# Provenance: UMA/CalculationTools from https://github.com/ahrehd0506/Catalyst-Design-Agent; POSCAR fixtures from https://github.com/LEDlamar/chem. - Network accessline 1093
*, conda_env: str = "catagent", hf_endpoint: str = "https://hf-mirror.com", - Network accessline 1094
conda_env: str = "catagent", hf_endpoint: str = "https://hf-mirror.com", ) -> list[str]: - Network accessline 1095
hf_endpoint: str = "https://hf-mirror.com", - Access to secretsline 1099
[Credential-related source omitted] - Access to secretsline 1101
[Credential-related source omitted] - Network accessline 1108
*, endpoint: str | None = None, timeout: float = 8.0 ) -> dict[str, Any]: ep = (endpoint or os.environ.get("HF_ENDPOINT") or "https://huggingface.co").rstrip( - Network accessline 1109
) -> dict[str, Any]: ep = (endpoint or os.environ.get("HF_ENDPOINT") or "https://huggingface.co").rstrip( "/" - Network accessline 1110
[Credential-related source omitted] - Access to secretsline 1110
[Credential-related source omitted] - Network accessline 1163
[Credential-related source omitted] - Access to secretsline 1163
[Credential-related source omitted] - Network accessline 1164
[Credential-related source omitted] - Access to secretsline 1164
[Credential-related source omitted] - Network accessline 1229
ask_hints.append( "Please provide a reachable Hugging Face mirror base URL " "(example: https://hf-mirror.com) to set as HF_ENDPOINT. " - Network accessline 1230
"Please provide a reachable Hugging Face mirror base URL " "(example: https://hf-mirror.com) to set as HF_ENDPOINT. " "Do not skip UMA or switch calculators." - Network accessline 1231
"(example: https://hf-mirror.com) to set as HF_ENDPOINT. "
skills/chai1/README.md
- Network accessline 31
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 36
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 37
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/chai1/README_zh.md
- Network accessline 20
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 25
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/chai1/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/chai1/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何东西需要安装——wheel 自带全部内置 Skill
skills/chai1/SKILL.md
- Network accessline 25
provider: Chai Discovery license: Apache-2.0 terms_url: https://github.com/chaidiscovery/chai-lab/blob/main/LICENSE - Network accessline 26
license: Apache-2.0 terms_url: https://github.com/chaidiscovery/chai-lab/blob/main/LICENSE # SKILL.md — `--use-msa-server` / `use_msa_server=True` sends the sequence - Network accessline 27
terms_url: https://github.com/chaidiscovery/chai-lab/blob/main/LICENSE - Network accessline 32
name: ColabFold MSA server (api.colabfold.com) provider: Steinegger Lab info_url: https://github.com/sokrypton/ColabFold/wiki - Network accessline 33
provider: Steinegger Lab info_url: https://github.com/sokrypton/ColabFold/wiki --- - Network accessline 34
info_url: https://github.com/sokrypton/ColabFold/wiki
skills/did-analysis/README.md
- Network accessline 33
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 38
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 39
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 75
Development is recorded in [`docs/skill-development.md`](https://github.com/PKU-YuanGroup/OpenAI4S/blob/main/docs/skill-development.md). - Network accessline 76
The independent experiment is [`experiments/did-skill-validation/`](https://github.com/PKU-YuanGroup/OpenAI4S/blob/main/experiments/did-skill-validation/README.md).
skills/did-analysis/README_zh.md
- Network accessline 22
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 27
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/did-analysis/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/did-analysis/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何东西需要安装——whee - Network accessline 51
开发过程见 [`docs/skill-development_zh.md`](https://github.com/PKU-YuanGroup/OpenAI4S/blob/main/docs/skill-development_zh.md)。 - Network accessline 52
独立实验见 [`experiments/did-skill-validation/`](https://github.com/PKU-YuanGroup/OpenAI4S/blob/main/experiments/did-skill-validation/README_zh.md)。
skills/did-analysis/SKILL.md
- Network accessline 210
This bounded unconditional estimator follows the group-time identification idea in [Callaway and Sant'Anna](https://arxiv.org/abs/1803.09015v4). - Network accessline 211
in [Callaway and Sant'Anna](https://arxiv.org/abs/1803.09015v4). - Network accessline 215
the actual estimator and inference. Naive TWFE lead/lag coefficients can mix effects under staggered heterogeneous adoption; [Sun and Abraham](https://arxiv.org/abs/1804.05785v2) explain this problem. - Network accessline 217
[Sun and Abraham](https://arxiv.org/abs/1804.05785v2) explain this problem. - Network accessline 232
limited to unconditional common-time 2×2×2 identification. It does not automatically solve covariate-dependent or staggered DDD; see [Ortiz-Villavicencio and Sant'Anna](https://arxiv.org/abs/2505.09942). - Network accessline 234
[Ortiz-Villavicencio and Sant'Anna](https://arxiv.org/abs/2505.09942).
skills/did-analysis/kernel.py
- Executable scriptline 1
Executable file
skills/did-analysis/method-notes.md
- Network accessline 30
Serially dependent panel data require attention to assignment clustering: [Bertrand, Duflo and Mullainathan](https://www.nber.org/papers/w8841) show why - Network accessline 31
[Bertrand, Duflo and Mullainathan](https://www.nber.org/papers/w8841) show why - Network accessline 32
conventional DiD standard errors and placebo rejection frequencies can mislead. The covariance convention is independently comparable to [statsmodels cov_cluster](https://www.statsmodels.org/stable/generated/statsmodels.stats.sandwich_covar - Network accessline 34
[statsmodels cov_cluster](https://www.statsmodels.org/stable/generated/statsmodels.stats.sandwich_covariance.cov_cluster.html). - Network accessline 42
pre-period diagnostic is an assessment of the supplied design; nonrejection does not verify counterfactual parallel trends. See the official [DiD pre-testing vignette](https://bcallaway11.github.io/did/articles/pre-testing.html). - Network accessline 44
[DiD pre-testing vignette](https://bcallaway11.github.io/did/articles/pre-testing.html). - Network accessline 68
uses an analytic cluster approximation and cannot be described as the full conditional/doubly robust estimator or multiplier-bootstrap inference in [Callaway and Sant'Anna](https://arxiv.org/abs/1803.09015v4) or the official - Network accessline 69
conditional/doubly robust estimator or multiplier-bootstrap inference in [Callaway and Sant'Anna](https://arxiv.org/abs/1803.09015v4) or the official [att_gt API](https://bcallaway11.github.io/did/reference/att_gt.html). - Network accessline 70
[Callaway and Sant'Anna](https://arxiv.org/abs/1803.09015v4) or the official - Network accessline 71
[att_gt API](https://bcallaway11.github.io/did/reference/att_gt.html). - Network accessline 82
Do not extend this algebra automatically to conditional or staggered DDD. [Better Understanding Triple Differences Estimators](https://arxiv.org/abs/2505.09942) - Network accessline 83
[Better Understanding Triple Differences Estimators](https://arxiv.org/abs/2505.09942)
skills/diffdock/README.md
- Network accessline 31
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 36
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 37
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/diffdock/README_zh.md
- Network accessline 20
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 25
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/diffdock/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/diffdock/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何东西需要安装——wheel 自带全部内置
skills/diffdock/SKILL.md
skills/esmfold2/README.md
- Network accessline 31
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 36
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 37
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/esmfold2/README_zh.md
- Network accessline 20
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 25
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/esmfold2/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/esmfold2/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何东西需要安装——wheel 自带全部内置
skills/esmfold2/SKILL.md
- Network accessline 33
provider: Biohub license: MIT terms_url: https://github.com/Biohub/esm/blob/main/LICENSE.md - Network accessline 34
license: MIT terms_url: https://github.com/Biohub/esm/blob/main/LICENSE.md --- - Network accessline 35
terms_url: https://github.com/Biohub/esm/blob/main/LICENSE.md - Network accessline 55
uv pip install "torch>=2.5,<2.8" einops "biotite>=1.0" rdkit msgpack-numpy biopython scikit-learn brotli attrs pandas cloudpathlib httpx tenacity zstd pydssp pygtrie accelerate huggingface_hub safetensors "numpy<3" networkx sent - Network accessline 58
pygtrie accelerate huggingface_hub safetensors "numpy<3" networkx \ sentencepiece tokenizers regex packaging filelock pyyaml typing_extensions \ "transformers @ git+https://github.com/Biohub/transformers.git@3a8956fb4d4ea16b0ec8e71deef2 - Network accessline 59
sentencepiece tokenizers regex packaging filelock pyyaml typing_extensions \ "transformers @ git+https://github.com/Biohub/transformers.git@3a8956fb4d4ea16b0ec8e71deef2c2909b6a5cbf" uv pip install --no-deps "esm @ git+https://github.com/B - Network accessline 60
"transformers @ git+https://github.com/Biohub/transformers.git@3a8956fb4d4ea16b0ec8e71deef2c2909b6a5cbf" - Network accessline 61
uv pip install --no-deps "esm @ git+https://github.com/Biohub/esm.git@f652b471"
skills/esmfold2/references/esmc.md
- Network accessline 62
Paper's atlas SAE = layer 60. Feature metadata API (alpha — endpoint may change; check biohub.ai/esm docs): `https://biohub.ai/esm/protein/api/v1alpha1/features/{idx}` returns - Network accessline 63
change; check biohub.ai/esm docs): `https://biohub.ai/esm/protein/api/v1alpha1/features/{idx}` returns `{description, top_100_uniref_ids, ...}`. 5,382 features classified as - Network accessline 64
`https://biohub.ai/esm/protein/api/v1alpha1/features/{idx}` returns
skills/evaluate-model/README.md
- Network accessline 29
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 34
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 35
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/evaluate-model/README_zh.md
- Network accessline 18
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 23
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/evaluate-model/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/evaluate-model/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何东西需要安装——
skills/evaluate-model/kernel.py
- Executable scriptline 1
Executable file
skills/evidence-walkthrough/README.md
- Network accessline 47
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 52
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 53
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/evidence-walkthrough/README_zh.md
- Network accessline 33
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 38
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/evidence-walkthrough/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/evidence-walkthrough/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里
skills/evo2/README.md
- Network accessline 31
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 36
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 37
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/evo2/README_zh.md
- Network accessline 20
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 25
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/evo2/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/evo2/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何东西需要安装——wheel 自带全部内置 Skill，且
skills/evo2/SKILL.md
- Network accessline 27
provider: Arc Institute license: Apache-2.0 terms_url: https://github.com/ArcInstitute/evo2/blob/main/LICENSE - Network accessline 28
license: Apache-2.0 terms_url: https://github.com/ArcInstitute/evo2/blob/main/LICENSE --- - Network accessline 29
terms_url: https://github.com/ArcInstitute/evo2/blob/main/LICENSE
skills/example_stats/README.md
- Network accessline 29
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 34
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 35
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/example_stats/README_zh.md
- Network accessline 18
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 23
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/example_stats/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/example_stats/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何东西需要安装——wh
skills/example_stats/kernel.py
- Executable scriptline 1
Executable file
skills/fair-esm2/README.md
- Network accessline 31
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 36
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 37
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/fair-esm2/README_zh.md
- Network accessline 20
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 25
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/fair-esm2/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/fair-esm2/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何东西需要安装——wheel 自带全部
skills/fair-esm2/SKILL.md
- Network accessline 23
provider: Meta AI license: MIT terms_url: https://github.com/facebookresearch/esm/blob/main/LICENSE - Network accessline 24
license: MIT terms_url: https://github.com/facebookresearch/esm/blob/main/LICENSE --- - Network accessline 25
terms_url: https://github.com/facebookresearch/esm/blob/main/LICENSE
skills/figure-composer/README.md
- Network accessline 29
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 34
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 35
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/figure-composer/README_zh.md
- Network accessline 18
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 23
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/figure-composer/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/figure-composer/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何东西需要安装
skills/figure-composer/kernel.py
- Executable scriptline 1
Executable file
skills/figure-style/README.md
- Network accessline 29
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 34
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 35
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/figure-style/README_zh.md
- Network accessline 18
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 23
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/figure-style/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/figure-style/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何东西需要安装——whee
skills/figure-style/kernel.py
skills/indication-dossier/README.md
- Network accessline 31
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 36
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 37
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/indication-dossier/README_zh.md
- Network accessline 20
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 25
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/indication-dossier/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/indication-dossier/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何
skills/indication-dossier/references/00-research-standards.md
- Network accessline 7
**URL_Validation** — Only use URLs you successfully fetched or retrieved from MCP tools. NEVER construct or guess URLs. If a URL fails, note it in `anomaly_flags`. Academic journal links often break — try DOI resolver (`https://doi.org/[DOI - Network accessline 19
**PDF_Retrieval** — `WebSearch` results carry only the search-index snippet, never the PDF body. To read a PDF, call `WebFetch` on its URL: text extraction is native, so no separate download step is needed. `WebFetch` is text-only; when fig
skills/indication-dossier/references/06-writing-style.md
skills/is-qualitative-analysis/NOTICE.md
- Network accessline 3
This skill adapts the MIT-licensed `alterlab-qualitative-analysis` module from [AlterLab-IEU/AlterLab-Academic-Skills](https://github.com/AlterLab-IEU/AlterLab-Academic-Skills), - Network accessline 4
[AlterLab-IEU/AlterLab-Academic-Skills](https://github.com/AlterLab-IEU/AlterLab-Academic-Skills),
skills/is-qualitative-analysis/README.md
- Network accessline 37
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 42
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 43
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/is-qualitative-analysis/README_zh.md
- Network accessline 20
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 25
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/is-qualitative-analysis/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/is-qualitative-analysis/*'` 只解这一个目录）。如果你本来就在跑 OpenAI
skills/is-qualitative-analysis/UPSTREAM.json
- Network accessline 1
{ "schema_version": 1, "repository": "https://github.com/AlterLab-IEU/AlterLab-Academic-Skills", - Network accessline 2
"schema_version": 1, "repository": "https://github.com/AlterLab-IEU/AlterLab-Academic-Skills", "commit": "e4836c08a20da195a11f30f203a8cf23ec30aa95", - Network accessline 3
"repository": "https://github.com/AlterLab-IEU/AlterLab-Academic-Skills",
skills/is-qualitative-analysis/agreement-guide.md
- Network accessline 16
`n_c` are the resulting category marginals and `n = sum(n_c)`, nominal alpha is `1 - (n-1) * off_diagonal / (n*n - sum(n_c*n_c))`. This is the coincidence formulation described in [Krippendorff's method paper](https://repository.upenn.edu/b - Network accessline 18
formulation described in [Krippendorff's method paper](https://repository.upenn.edu/bitstreams/0421f871-f005-4322-b06a-a66bec328e3b/download).
skills/is-qualitative-analysis/analysis-guide.md
- Network accessline 23
impose a fixed codebook, consensus requirement, or inter-rater reliability threshold on this approach. This corrects the upstream “consensus + reflexivity” shorthand. See the authors' [quality checklist](https://www.thematicanalysis.net/edi - Network accessline 24
threshold on this approach. This corrects the upstream “consensus + reflexivity” shorthand. See the authors' [quality checklist](https://www.thematicanalysis.net/editor-checklist/) and [approach distinction](https://www.thematicanalysis.net - Network accessline 25
shorthand. See the authors' [quality checklist](https://www.thematicanalysis.net/editor-checklist/) - Network accessline 26
and [approach distinction](https://www.thematicanalysis.net/understanding-ta/).
skills/is-qualitative-analysis/kernel.py
- Executable scriptline 1
Executable file
skills/is-research-design/README.md
- Network accessline 32
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 37
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 38
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/is-research-design/README_zh.md
- Network accessline 18
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 23
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/is-research-design/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/is-research-design/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何
skills/is-research-design/UPSTREAM.json
- Network accessline 1
{ "schema_version": 1, "repository": "https://github.com/brycewang-stanford/Awesome-Journal-Skills", - Network accessline 2
"schema_version": 1, "repository": "https://github.com/brycewang-stanford/Awesome-Journal-Skills", "commit": "932eb23331721b16e967c59f093781be1928b498", - Network accessline 3
"repository": "https://github.com/brycewang-stanford/Awesome-Journal-Skills",
skills/is-survey-measurement/NOTICE.md
- Network accessline 3
This skill adapts the MIT-licensed `alterlab-sem-psychometrics` module from [AlterLab-IEU/AlterLab-Academic-Skills](https://github.com/AlterLab-IEU/AlterLab-Academic-Skills), - Network accessline 4
[AlterLab-IEU/AlterLab-Academic-Skills](https://github.com/AlterLab-IEU/AlterLab-Academic-Skills),
skills/is-survey-measurement/README.md
- Network accessline 33
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 38
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 39
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/is-survey-measurement/README_zh.md
- Network accessline 20
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 25
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/is-survey-measurement/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/is-survey-measurement/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那
skills/is-survey-measurement/SKILL.md
- Network accessline 170
an ordinal model; changing the objective alone without declaring `ordered` does not turn numeric codes into ordinal indicators. See the [lavaan categorical-data documentation](https://lavaan.ugent.be/tutorial/cat.html). - Network accessline 172
[lavaan categorical-data documentation](https://lavaan.ugent.be/tutorial/cat.html).
skills/is-survey-measurement/UPSTREAM.json
- Network accessline 1
{ "schema_version": 1, "repository": "https://github.com/AlterLab-IEU/AlterLab-Academic-Skills", - Network accessline 2
"schema_version": 1, "repository": "https://github.com/AlterLab-IEU/AlterLab-Academic-Skills", "commit": "e4836c08a20da195a11f30f203a8cf23ec30aa95", - Network accessline 3
"repository": "https://github.com/AlterLab-IEU/AlterLab-Academic-Skills", - Network accessline 38
"adaptations": [ "Renamed alterlab-sem-psychometrics to is-survey-measurement; origin openai4s identifies bundled distribution, not upstream authorship.", "The source skill declares license MIT; THIRD_PARTY_NOTICES.md was checked fo - Network accessline 39
"Renamed alterlab-sem-psychometrics to is-survey-measurement; origin openai4s identifies bundled distribution, not upstream authorship.", "The source skill declares license MIT; THIRD_PARTY_NOTICES.md was checked for per-skill exception - Network accessline 40
"The source skill declares license MIT; THIRD_PARTY_NOTICES.md was checked for per-skill exceptions. Its four CC-BY-NC core skills are not imported or adapted.",
skills/is-survey-measurement/method-notes.md
- Network accessline 73
interval, clip an inadmissible reliability to [0,1], or quietly substitute alpha when the planned estimand is unavailable. See the [semTools compRelSEM documentation](https://rdrr.io/cran/semTools/man/compRelSEM.html). - Network accessline 75
[semTools compRelSEM documentation](https://rdrr.io/cran/semTools/man/compRelSEM.html). - Network accessline 105
syntax as ordinal invariance. This recipe does not claim to implement a general automatic ordinal-invariance test. See the [semTools measEq.syntax documentation](https://rdrr.io/cran/semTools/man/measEq.syntax.html) - Network accessline 106
automatic ordinal-invariance test. See the [semTools measEq.syntax documentation](https://rdrr.io/cran/semTools/man/measEq.syntax.html) and [lavaan multiple-group guide](https://lavaan.ugent.be/tutorial/groups.html). - Network accessline 107
[semTools measEq.syntax documentation](https://rdrr.io/cran/semTools/man/measEq.syntax.html) - Network accessline 108
and [lavaan multiple-group guide](https://lavaan.ugent.be/tutorial/groups.html).
skills/ligandmpnn/README.md
- Network accessline 31
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 36
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 37
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/ligandmpnn/README_zh.md
- Network accessline 20
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 25
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/ligandmpnn/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/ligandmpnn/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何东西需要安装——wheel 自带
skills/ligandmpnn/SKILL.md
- Network accessline 23
name: LigandMPNN license: MIT terms_url: https://github.com/dauparas/LigandMPNN/blob/main/LICENSE - Network accessline 24
license: MIT terms_url: https://github.com/dauparas/LigandMPNN/blob/main/LICENSE --- - Network accessline 25
terms_url: https://github.com/dauparas/LigandMPNN/blob/main/LICENSE - Network accessline 46
pip install torch numpy biopython ProDy ml_collections dm-tree git clone --depth 1 https://github.com/dauparas/LigandMPNN.git ligandmpnn - Network accessline 47
git clone --depth 1 https://github.com/dauparas/LigandMPNN.git ligandmpnn
skills/literature-review/README.md
- Network accessline 29
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 34
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 35
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/literature-review/README_zh.md
- Network accessline 18
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 23
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/literature-review/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/literature-review/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何东西
skills/literature-review/SKILL.md
- Network accessline 25
- kind: service name: Crossref info_url: https://www.crossref.org/documentation/retrieve-metadata/ - Network accessline 26
name: Crossref info_url: https://www.crossref.org/documentation/retrieve-metadata/ privacy_url: https://www.crossref.org/operations-and-sustainability/privacy/ - Network accessline 27
info_url: https://www.crossref.org/documentation/retrieve-metadata/ - Network accessline 28
privacy_url: https://www.crossref.org/operations-and-sustainability/privacy/ - Network accessline 29
- kind: service name: OpenAlex terms_url: https://openalex.org/OpenAlex_termsofservice.pdf - Network accessline 30
name: OpenAlex terms_url: https://openalex.org/OpenAlex_termsofservice.pdf privacy_url: https://openalex.org/OpenAlex_privacy_policy.pdf - Network accessline 31
terms_url: https://openalex.org/OpenAlex_termsofservice.pdf - Network accessline 32
privacy_url: https://openalex.org/OpenAlex_privacy_policy.pdf - Network accessline 36
- kind: service name: TypeSafe info_url: https://docs.typesafe.ai/ - Network accessline 37
name: TypeSafe info_url: https://docs.typesafe.ai/ privacy_url: https://typesafe.ai/legal/privacy-policy - Network accessline 38
info_url: https://docs.typesafe.ai/ - Network accessline 39
privacy_url: https://typesafe.ai/legal/privacy-policy - Network accessline 84
Cite inline as a markdown link — `[Author Year](https://doi.org/10.xxxx/xxxxxx)` — so the rendered prose reads `(Author Year)` and the DOI rides in the href where a reader can click it and a regex can still extract it. If the DOI itself con
skills/literature-review/kernel.py
- Executable scriptline 1
Executable file - Network accessline 150
continue enc = quote_doi_path(d) j = litrev_get(f"https://api.crossref.org/works/{enc}") - Network accessline 151
enc = quote_doi_path(d) j = litrev_get(f"https://api.crossref.org/works/{enc}") time.sleep(0.06) - Network accessline 152
j = litrev_get(f"https://api.crossref.org/works/{enc}") - Network accessline 174
code = litrev_head(f"https://doi.org/{enc}") - Network accessline 188
q = urllib.parse.quote(ref_string) j = litrev_get(f"https://api.crossref.org/works?query.bibliographic={q}&rows=1") - Network accessline 189
j = litrev_get(f"https://api.crossref.org/works?query.bibliographic={q}&rows=1") - Network accessline 209
mailto = f"&mailto={urllib.parse.quote(c)}" if c else "" j = litrev_get( f"https://api.openalex.org/works?search={q}&per-page={min(n, 25)}" - Network accessline 210
j = litrev_get( f"https://api.openalex.org/works?search={q}&per-page={min(n, 25)}" f"&sort=cited_by_count:desc{flt}{mailto}" - Network accessline 211
f"https://api.openalex.org/works?search={q}&per-page={min(n, 25)}" - Network accessline 218
out.append( { "doi": (w.get("doi") or "").replace("https://doi.org/", ""), - Network accessline 219
{ "doi": (w.get("doi") or "").replace("https://doi.org/", ""), "title": w.get("title"), - Network accessline 220
"doi": (w.get("doi") or "").replace("https://doi.org/", ""), - Network accessline 240
mailto = f"&mailto={urllib.parse.quote(c)}" if c else "" enc = quote_doi_path(doi) work = litrev_get(f"https://api.openalex.org/works/doi:{enc}?select=id{mailto}") - Network accessline 241
enc = quote_doi_path(doi) work = litrev_get(f"https://api.openalex.org/works/doi:{enc}?select=id{mailto}") work_id = ((work or {}).get("id") or "").rsplit("/", 1)[-1] - Network accessline 242
work = litrev_get(f"https://api.openalex.org/works/doi:{enc}?select=id{mailto}") - Network accessline 250
out.append( { "doi": (w.get("doi") or "").replace("https://doi.org/", ""), - Network accessline 251
{ "doi": (w.get("doi") or "").replace("https://doi.org/", ""), "title": w.get("title"), - Network accessline 252
"doi": (w.get("doi") or "").replace("https://doi.org/", ""), - Network accessline 260
def _list(filter_expr: str, n: int) -> list[dict]: j = litrev_get( f"https://api.openalex.org/works?filter={filter_expr}" - Network accessline 261
j = litrev_get( f"https://api.openalex.org/works?filter={filter_expr}" f"&select=doi,title,publication_year,cited_by_count" - Network accessline 262
f"https://api.openalex.org/works?filter={filter_expr}" - Network accessline 348
{"code": "PROCNOTE", "note": "process-narration line present; delete it"} ) if re.search(r"\]\(https://doi\.org/[^)\s]*\([^)\s]*\)", draft): - Network accessline 349
) if re.search(r"\]\(https://doi\.org/[^)\s]*\([^)\s]*\)", draft): issues.append( - Network accessline 350
if re.search(r"\]\(https://doi\.org/[^)\s]*\([^)\s]*\)", draft):
skills/mineral_spectra_analysis/README.md
- Network accessline 31
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 36
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 37
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/mineral_spectra_analysis/README_zh.md
- Network accessline 20
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 25
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/mineral_spectra_analysis/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/mineral_spectra_analysis/*'` 只解这一个目录）。如果你本来就在跑 Open
skills/mineral_spectra_analysis/SKILL.md
- Network accessline 58
https://www.rruff.net/zipped_data_files/raman/excellent_oriented.zip
skills/mineral_spectra_analysis/examples/build_example.py
- Executable scriptline 1
Executable file
skills/mineral_spectra_analysis/kernel.py
skills/molecular-dynamics/README.md
- Network accessline 5
The upstream recipe was imported from [K-Dense-AI/scientific-agent-skills](https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/molecular-dynamics) at revision `a1043ef544e3a100c28e70a736b6cf32554d0677` (2026-10-16). The b - Network accessline 30
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 35
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 36
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/molecular-dynamics/README_zh.md
- Network accessline 5
本 recipe 从 [K-Dense-AI/scientific-agent-skills](https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/molecular-dynamics) 导入，来源 revision 为 `a1043ef544e3a100c28e70a736b6cf32554d0677`（2026-10-16）。随附参考资料按 OpenMM 8.6.1 和 MDAnal - Network accessline 20
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 25
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/molecular-dynamics/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/molecular-dynamics/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何
skills/molecular-dynamics/SKILL.md
- Network accessline 25
- **OpenMM** (https://openmm.org/): High-performance MD simulation engine with GPU support, Python API, and flexible force field support - Network accessline 26
- **MDAnalysis** (https://mdanalysis.org/): Python library for reading, writing, and analyzing MD trajectories from all major simulation packages - Network accessline 511
- **OpenMM documentation**: https://openmm.org/documentation.html - Network accessline 512
- **MDAnalysis user guide**: https://docs.mdanalysis.org/ - Network accessline 513
- **GROMACS** (alternative MD engine): https://manual.gromacs.org/ - Network accessline 514
- **NAMD** (alternative): https://www.ks.uiuc.edu/Research/namd/ - Network accessline 515
- **CHARMM-GUI** (web-based system builder): https://charmm-gui.org/ - Network accessline 516
- **AmberTools** (free Amber tools): https://ambermd.org/AmberTools.php
skills/molecular-dynamics/references/mdanalysis_analysis.md
- Network accessline 257
- [RMSD/RMSF](https://docs.mdanalysis.org/2.10.0/documentation_pages/analysis/rms.html) - Network accessline 258
- [Alignment](https://docs.mdanalysis.org/2.10.0/documentation_pages/analysis/align.html) - Network accessline 259
- [PCA](https://docs.mdanalysis.org/2.10.0/documentation_pages/analysis/pca.html) - Network accessline 260
- [DSSP](https://docs.mdanalysis.org/2.10.0/documentation_pages/analysis/dssp.html) - Network accessline 261
- [Hydrogen bonds](https://docs.mdanalysis.org/2.10.0/documentation_pages/analysis/hydrogenbonds.html) - Network accessline 262
- [Groups and selections](https://docs.mdanalysis.org/2.10.0/documentation_pages/core/groups.html) - Network accessline 263
- [Distances and periodic boxes](https://docs.mdanalysis.org/2.10.0/documentation_pages/lib/distances.html) - Network accessline 264
- [Units](https://docs.mdanalysis.org/2.10.0/documentation_pages/units.html)
skills/molecular-dynamics/references/system_preparation.md
- Network accessline 77
- [OpenMM running simulations and force-field inventory](https://docs.openmm.org/latest/userguide/application/02_running_sims.html) - Network accessline 78
- [Modeller and solvent semantics](https://docs.openmm.org/latest/api-python/generated/openmm.app.modeller.Modeller.html) - Network accessline 79
- [Simulation and restart APIs](https://docs.openmm.org/latest/api-python/generated/openmm.app.simulation.Simulation.html) - Network accessline 80
- [Barostat Context parameters](https://docs.openmm.org/latest/api-python/generated/openmm.openmm.MonteCarloBarostat.html) - Network accessline 81
- [PDBFixer 1.12 release](https://github.com/openmm/pdbfixer/releases/tag/v1.12) - Network accessline 82
- [PDBFixer manual](https://github.com/openmm/pdbfixer/blob/v1.12/Manual.html) - Network accessline 83
- [OpenFF installation](https://docs.openforcefield.org/projects/toolkit/en/stable/installation.html) - Network accessline 84
- [OpenFF ForceField API](https://docs.openforcefield.org/projects/toolkit/en/stable/api/generated/openff.toolkit.typing.engines.smirnoff.ForceField.html) - Network accessline 85
- [OpenFF force-field releases](https://github.com/openforcefield/openff-forcefields/releases) - Network accessline 86
- [NAGL installation](https://docs.openforcefield.org/projects/nagl/en/stable/installation.html)
skills/openfold3/README.md
- Network accessline 31
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 36
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 37
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/openfold3/README_zh.md
- Network accessline 20
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 25
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/openfold3/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/openfold3/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何东西需要安装——wheel 自带全部
skills/openfold3/SKILL.md
- Network accessline 23
provider: OpenFold Consortium license: Apache-2.0 terms_url: https://github.com/aqlaboratory/openfold-3/blob/main/LICENSE - Network accessline 24
license: Apache-2.0 terms_url: https://github.com/aqlaboratory/openfold-3/blob/main/LICENSE # SKILL.md — `--use-msa-server` DEFAULTS to true (MSA server is - Network accessline 25
terms_url: https://github.com/aqlaboratory/openfold-3/blob/main/LICENSE - Network accessline 30
name: ColabFold MSA server (api.colabfold.com) provider: Steinegger Lab info_url: https://github.com/sokrypton/ColabFold/wiki - Network accessline 31
provider: Steinegger Lab info_url: https://github.com/sokrypton/ColabFold/wiki --- - Network accessline 32
info_url: https://github.com/sokrypton/ColabFold/wiki
skills/panel-data-preprocessing/README.md
- Network accessline 82
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 87
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 88
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/panel-data-preprocessing/README_zh.md
- Network accessline 61
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 66
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/panel-data-preprocessing/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/panel-data-preprocessing/*'` 只解这一个目录）。如果你本来就在跑 Open
skills/panel-data-preprocessing/SKILL.md
- Network accessline 167
with its documented preservation and grouping choices; it does not require scikit-learn or claim identical library defaults. See the primary documentation on [imputation](https://scikit-learn.org/stable/modules/impute.html), - Network accessline 168
scikit-learn or claim identical library defaults. See the primary documentation on [imputation](https://scikit-learn.org/stable/modules/impute.html), [category preprocessing](https://scikit-learn.org/stable/modules/preprocessing.html#encodi - Network accessline 169
on [imputation](https://scikit-learn.org/stable/modules/impute.html), - Network accessline 170
[category preprocessing](https://scikit-learn.org/stable/modules/preprocessing.html#encoding-categorical-features), - Network accessline 171
[text features](https://scikit-learn.org/stable/modules/feature_extraction.html#text-feature-extraction) - Network accessline 172
and [fitting without data leakage](https://scikit-learn.org/stable/common_pitfalls.html#data-leakage). - Network accessline 308
scores divide the mean change by SD, not this mean-noise estimate, so better averaging precision does not itself imply higher detection power. See the [NIST autocorrelation guidance](https://www.itl.nist.gov/div898/handbook/eda/section3/eda - Network accessline 309
averaging precision does not itself imply higher detection power. See the [NIST autocorrelation guidance](https://www.itl.nist.gov/div898/handbook/eda/section3/eda331.htm) and [linear residual SD definition](https://www.itl.nist.gov/div898/ - Network accessline 310
[NIST autocorrelation guidance](https://www.itl.nist.gov/div898/handbook/eda/section3/eda331.htm) - Network accessline 311
and [linear residual SD definition](https://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/linressd.htm)
skills/panel-data-preprocessing/kernel.py
- Executable scriptline 1
Executable file
skills/paper-narrative/README.md
- Network accessline 29
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 34
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 35
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/paper-narrative/README_zh.md
- Network accessline 18
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 23
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/paper-narrative/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/paper-narrative/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何东西需要安装
skills/paper-narrative/kernel.py
- Executable scriptline 1
Executable file
skills/pdf-explore/README.md
- Network accessline 29
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 34
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 35
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/pdf-explore/README_zh.md
- Network accessline 18
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 23
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/pdf-explore/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/pdf-explore/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何东西需要安装——wheel
skills/pdf-explore/kernel.py
- Executable scriptline 1
Executable file - Network accessline 1308
if isinstance(results, dict) and "usage" in results: return dict(results["usage"]) it = ot = ne = nc = 0 - Network accessline 1309
return dict(results["usage"]) it = ot = ne = nc = 0 for r in results: - Network accessline 1310
it = ot = ne = nc = 0 - Network accessline 1311
for r in results: nc += 1 u = r.get("usage") - Network accessline 1312
nc += 1
skills/plan-ml-experiment/README.md
- Network accessline 29
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 34
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 35
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/plan-ml-experiment/README_zh.md
- Network accessline 18
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 23
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/plan-ml-experiment/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/plan-ml-experiment/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何
skills/plan-ml-experiment/kernel.py
- Executable scriptline 1
Executable file
skills/protein-design-mcp/README.md
- Network accessline 39
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 44
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 45
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/protein-design-mcp/README_zh.md
- Network accessline 26
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 31
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/protein-design-mcp/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/protein-design-mcp/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何
skills/protein-mutation-enhancement/README.md
- Network accessline 29
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 34
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 35
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/protein-mutation-enhancement/README_zh.md
- Network accessline 18
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 23
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/protein-mutation-enhancement/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/protein-mutation-enhancement/*'` 只解这一个目录）。如果你本来
skills/protein-mutation-enhancement/kernel.py
- Executable scriptline 1
Executable file
skills/proteinmpnn/README.md
- Network accessline 31
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 36
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 37
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/proteinmpnn/README_zh.md
- Network accessline 20
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 25
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/proteinmpnn/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/proteinmpnn/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何东西需要安装——wheel
skills/proteinmpnn/SKILL.md
- Network accessline 22
name: ProteinMPNN license: MIT terms_url: https://github.com/dauparas/ProteinMPNN/blob/main/LICENSE - Network accessline 23
license: MIT terms_url: https://github.com/dauparas/ProteinMPNN/blob/main/LICENSE --- - Network accessline 24
terms_url: https://github.com/dauparas/ProteinMPNN/blob/main/LICENSE - Network accessline 45
pip install torch numpy # if not already present git clone --depth 1 https://github.com/dauparas/ProteinMPNN.git proteinmpnn - Network accessline 46
git clone --depth 1 https://github.com/dauparas/ProteinMPNN.git proteinmpnn
skills/reaction-atom-mapping/README.md
- Network accessline 30
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 35
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 36
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/reaction-atom-mapping/README_zh.md
- Network accessline 19
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 24
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/reaction-atom-mapping/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/reaction-atom-mapping/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那
skills/reaction-atom-mapping/SKILL.md
- Network accessline 13
name: RXNMapper license: MIT terms_url: https://github.com/rxn4chemistry/rxnmapper/blob/main/LICENSE - Network accessline 14
license: MIT terms_url: https://github.com/rxn4chemistry/rxnmapper/blob/main/LICENSE --- - Network accessline 15
terms_url: https://github.com/rxn4chemistry/rxnmapper/blob/main/LICENSE - Network accessline 121
Primary source: <https://github.com/rxn4chemistry/rxnmapper>.
skills/reaction-condition-recommendation/README.md
- Network accessline 31
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 36
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 37
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/reaction-condition-recommendation/README_zh.md
- Network accessline 19
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 24
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/reaction-condition-recommendation/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/reaction-condition-recommendation/*'` 只解这一
skills/reaction-condition-recommendation/SKILL.md
- Network accessline 13
name: Parrot inference code license: MIT terms_url: https://github.com/wangxr0526/Parrot/blob/main/LICENSE - Network accessline 14
license: MIT terms_url: https://github.com/wangxr0526/Parrot/blob/main/LICENSE - kind: checkpoint - Network accessline 15
terms_url: https://github.com/wangxr0526/Parrot/blob/main/LICENSE - Network accessline 17
name: Parrot USPTO condition predictor license: MIT terms_url: https://huggingface.co/xiaoruiwang/ChemEnzyRetroPlanner_metadata - Network accessline 18
license: MIT terms_url: https://huggingface.co/xiaoruiwang/ChemEnzyRetroPlanner_metadata --- - Network accessline 19
terms_url: https://huggingface.co/xiaoruiwang/ChemEnzyRetroPlanner_metadata - Network accessline 58
mkdir -p "$PARROT_ROOT" if [ ! -d "$PARROT_ROOT/source/.git" ]; then git clone https://github.com/wangxr0526/Parrot.git "$PARROT_ROOT/source" - Network accessline 59
if [ ! -d "$PARROT_ROOT/source/.git" ]; then git clone https://github.com/wangxr0526/Parrot.git "$PARROT_ROOT/source" fi - Network accessline 60
git clone https://github.com/wangxr0526/Parrot.git "$PARROT_ROOT/source" - Network accessline 162
Primary sources: <https://github.com/wangxr0526/Parrot> and the first-author - Network accessline 163
checkpoint distribution <https://huggingface.co/xiaoruiwang/ChemEnzyRetroPlanner_metadata>. The source paper is Wang et al., *Research* (2023), DOI 10.34133/research.0231. - Network accessline 164
<https://huggingface.co/xiaoruiwang/ChemEnzyRetroPlanner_metadata>. The source
skills/reaction-forward-prediction/README.md
- Network accessline 30
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 35
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 36
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/reaction-forward-prediction/README_zh.md
- Network accessline 19
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 24
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/reaction-forward-prediction/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/reaction-forward-prediction/*'` 只解这一个目录）。如果你本来就在
skills/reaction-forward-prediction/SKILL.md
- Network accessline 13
name: ReactionT5v2-forward license: MIT terms_url: https://huggingface.co/sagawa/ReactionT5v2-forward - Network accessline 14
license: MIT terms_url: https://huggingface.co/sagawa/ReactionT5v2-forward --- - Network accessline 15
terms_url: https://huggingface.co/sagawa/ReactionT5v2-forward - Network accessline 55
mkdir -p "$REACTIONT5_ROOT" if [ ! -d "$REACTIONT5_ROOT/source/.git" ]; then git clone https://github.com/sagawatatsuya/ReactionT5v2.git \ - Network accessline 56
if [ ! -d "$REACTIONT5_ROOT/source/.git" ]; then git clone https://github.com/sagawatatsuya/ReactionT5v2.git \ "$REACTIONT5_ROOT/source" - Network accessline 57
git clone https://github.com/sagawatatsuya/ReactionT5v2.git \ - Access to secretsline 113
[Credential-related source omitted] - Network accessline 193
Primary sources: <https://github.com/sagawatatsuya/ReactionT5v2> and - Network accessline 194
<https://huggingface.co/sagawa/ReactionT5v2-forward>.
skills/reaction-yield-estimation/README.md
- Network accessline 31
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 36
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 37
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/reaction-yield-estimation/README_zh.md
- Network accessline 19
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 24
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/reaction-yield-estimation/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/reaction-yield-estimation/*'` 只解这一个目录）。如果你本来就在跑 Op
skills/reaction-yield-estimation/SKILL.md
- Network accessline 13
name: ReactionT5v2-yield license: MIT terms_url: https://huggingface.co/sagawa/ReactionT5v2-yield - Network accessline 14
license: MIT terms_url: https://huggingface.co/sagawa/ReactionT5v2-yield --- - Network accessline 15
terms_url: https://huggingface.co/sagawa/ReactionT5v2-yield - Network accessline 66
mkdir -p "$REACTIONT5_ROOT" if [ ! -d "$REACTIONT5_ROOT/source/.git" ]; then git clone https://github.com/sagawatatsuya/ReactionT5v2.git \ - Network accessline 67
if [ ! -d "$REACTIONT5_ROOT/source/.git" ]; then git clone https://github.com/sagawatatsuya/ReactionT5v2.git \ "$REACTIONT5_ROOT/source" - Network accessline 68
git clone https://github.com/sagawatatsuya/ReactionT5v2.git \ - Access to secretsline 110
[Credential-related source omitted] - Network accessline 206
Primary sources: <https://github.com/sagawatatsuya/ReactionT5v2> and - Network accessline 207
<https://huggingface.co/sagawa/ReactionT5v2-yield>.
skills/remote-compute-nvidia/README.md
- Network accessline 31
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 36
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 37
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Access to secretsline 52
[Credential-related source omitted]
skills/remote-compute-nvidia/README_zh.md
- Network accessline 20
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 25
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/remote-compute-nvidia/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/remote-compute-nvidia/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那 - Access to secretsline 32
[Credential-related source omitted]
skills/remote-compute-nvidia/SKILL.md
- Network accessline 18
- **`self_hosted`** — pull and run an NVIDIA NIM container from `nvcr.io` on a local GPU host (`--gpus all`). The NIM server is the container's own long-lived process; the job curls it at `http://localhost:8000` and - Network accessline 19
local GPU host (`--gpus all`). The NIM server is the container's own long-lived process; the job curls it at `http://localhost:8000` and health-gates on `/v1/health/ready`. Needs Docker + the NVIDIA Container - Network accessline 20
long-lived process; the job curls it at `http://localhost:8000` and - Network accessline 22
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted] - Network accessline 23
- **`hosted`** — no local GPU. A slim keepalive container is started and the job curls the fully-managed endpoint at `https://integrate.api.nvidia.com` with a `Bearer nvapi-…` key (`NVIDIA_API_KEY`). Use this when you have no - Network accessline 24
job curls the fully-managed endpoint at `https://integrate.api.nvidia.com` - Access to secretsline 25
[Credential-related source omitted] - Access to secretsline 42
[Credential-related source omitted] - Access to secretsline 43
[Credential-related source omitted] - Access to secretsline 48
[Credential-related source omitted] - Access to secretsline 82
[Credential-related source omitted] - Network accessline 98
set -eo pipefail mkdir -p out curl -sS -X POST "$OPENAI4S_NIM_URL/v1/biology/nvidia/esmfold2/predict" \ - Network accessline 99
[Credential-related source omitted] - Network accessline 100
[Credential-related source omitted] - Access to secretsline 100
[Credential-related source omitted] - Access to secretsline 101
[Credential-related source omitted] - Network accessline 124
The NIM server boots inside the container; gate on readiness before the first request, then curl `localhost`: - Network accessline 125
request, then curl `localhost`: - Network accessline 131
# health-gate: the NIM server takes a moment to load weights on cold start for i in $(seq 1 60); do curl -fsS "$OPENAI4S_NIM_URL$OPENAI4S_NIM_HEALTH" && break - Network accessline 132
for i in $(seq 1 60); do curl -fsS "$OPENAI4S_NIM_URL$OPENAI4S_NIM_HEALTH" && break sleep 5 - Network accessline 133
curl -fsS "$OPENAI4S_NIM_URL$OPENAI4S_NIM_HEALTH" && break - Network accessline 134
sleep 5 done curl -sS -X POST "$OPENAI4S_NIM_URL/v1/biology/nvidia/esmfold2/predict" \ - Network accessline 135
done curl -sS -X POST "$OPENAI4S_NIM_URL/v1/biology/nvidia/esmfold2/predict" \ -H "Content-Type: application/json" \ - Network accessline 136
curl -sS -X POST "$OPENAI4S_NIM_URL/v1/biology/nvidia/esmfold2/predict" \ - Network accessline 141
`OPENAI4S_NIM_URL` is `http://localhost:8000` (self_hosted) or - Network accessline 142
`https://integrate.api.nvidia.com` (hosted); `OPENAI4S_NIM_HEALTH` is the - Access to secretsline 208
[Credential-related source omitted] - Access to secretsline 209
[Credential-related source omitted] - Network accessline 223
A plain non-zero `exit_code` with logs is the NIM tool failing on inputs — inspect `stdout_tail`/`stderr_tail`. A `4xx`/`5xx` in the curl output means the - Network accessline 224
inspect `stdout_tail`/`stderr_tail`. A `4xx`/`5xx` in the curl output means the - Dangerous shell operationline 243
down (`docker rm -f`). **Sequential only**: each submit wipes `/work`, so call
skills/remote-compute-nvidia/provider.json
- Access to secretsline 3
[Credential-related source omitted]
skills/remote-compute-nvidia/provider.py
- Executable scriptline 1
Executable file - Network accessline 7
* self_hosted — pull and run an NVIDIA NIM container from ``nvcr.io`` on a local GPU host (``--gpus all``). The NIM server is the container's own long-lived process; the job script curls it at ``http://localhost:8000`` - Network accessline 8
local GPU host (``--gpus all``). The NIM server is the container's own long-lived process; the job script curls it at ``http://localhost:8000`` and health-gates on ``/v1/health/ready``. Auth is an NGC API key - Network accessline 9
long-lived process; the job script curls it at ``http://localhost:8000`` - Access to secretsline 11
[Credential-related source omitted] - Network accessline 13
* hosted — no GPU needed locally. A slim keepalive container is started and the job script curls the fully-managed endpoint at ``https://integrate.api.nvidia.com`` with a ``Bearer nvapi-…`` key - Network accessline 14
the job script curls the fully-managed endpoint at ``https://integrate.api.nvidia.com`` with a ``Bearer nvapi-…`` key (``NVIDIA_API_KEY``). - Network accessline 15
``https://integrate.api.nvidia.com`` with a ``Bearer nvapi-…`` key - Access to secretsline 16
[Credential-related source omitted] - Network accessline 48
SELF_HOSTED_URL = "http://localhost:8000" - Network accessline 49
HOSTED_URL = "https://integrate.api.nvidia.com" - Network accessline 50
# Slim image for the hosted form's keepalive container — only needs curl + a - Access to secretsline 77
[Credential-related source omitted] - Access to secretsline 81
[Credential-related source omitted] - Access to secretsline 141
[Credential-related source omitted] - Access to secretsline 160
[Credential-related source omitted] - Access to secretsline 163
[Credential-related source omitted] - Access to secretsline 165
[Credential-related source omitted] - Dangerous shell operationline 187
def exec( - Access to secretsline 199
[Credential-related source omitted] - Access to secretsline 209
[Credential-related source omitted] - Access to secretsline 211
[Credential-related source omitted] - Access to secretsline 336
[Credential-related source omitted]
skills/remote-compute-ssh/README.md
- Network accessline 31
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 36
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 37
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/remote-compute-ssh/README_zh.md
- Network accessline 20
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 25
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/remote-compute-ssh/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/remote-compute-ssh/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何
skills/remote-compute-ssh/SKILL.md
- Access to secretsline 103
[Credential-related source omitted]
skills/retrosynthesis_planning/MODEL_BACKENDS.md
- Network accessline 207
"code_license": "MIT", "checkpoint_license": "MIT", "source_url": "https://doi.org/10.6084/m9.figshare.30601718.v1", - Network accessline 208
"checkpoint_license": "MIT", "source_url": "https://doi.org/10.6084/m9.figshare.30601718.v1", "metadata": { - Network accessline 209
"source_url": "https://doi.org/10.6084/m9.figshare.30601718.v1",
skills/retrosynthesis_planning/MODEL_BACKENDS_zh.md
- Network accessline 191
"code_license": "MIT", "checkpoint_license": "MIT", "source_url": "https://doi.org/10.6084/m9.figshare.30601718.v1", - Network accessline 192
"checkpoint_license": "MIT", "source_url": "https://doi.org/10.6084/m9.figshare.30601718.v1", "metadata": { - Network accessline 193
"source_url": "https://doi.org/10.6084/m9.figshare.30601718.v1",
skills/retrosynthesis_planning/MODEL_TASKS.md
- Network accessline 84
- RetroChimera code, inference API, released checkpoints, and warning: <https://github.com/microsoft/retrochimera> - Network accessline 85
<https://github.com/microsoft/retrochimera> - Network accessline 86
- AiZynthFinder installation, learned policies, public data, and license: <https://github.com/MolecularAI/aizynthfinder> - ReactionT5v2 code and task CLIs: - Network accessline 87
<https://github.com/MolecularAI/aizynthfinder> - Network accessline 88
- ReactionT5v2 code and task CLIs: <https://github.com/sagawatatsuya/ReactionT5v2> - ReactionT5v2 forward and yield model cards: - Network accessline 89
<https://github.com/sagawatatsuya/ReactionT5v2> - Network accessline 90
- ReactionT5v2 forward and yield model cards: <https://huggingface.co/sagawa/ReactionT5v2-forward> and <https://huggingface.co/sagawa/ReactionT5v2-yield> - Network accessline 91
<https://huggingface.co/sagawa/ReactionT5v2-forward> and - Network accessline 92
<https://huggingface.co/sagawa/ReactionT5v2-yield> - Network accessline 93
- RXNMapper package, API, confidence output, and license: <https://github.com/rxn4chemistry/rxnmapper> - Parrot code license, checkpoint downloader, and CPU/GPU CLI: - Network accessline 94
<https://github.com/rxn4chemistry/rxnmapper> - Network accessline 95
- Parrot code license, checkpoint downloader, and CPU/GPU CLI: <https://github.com/wangxr0526/Parrot> - RXN-Sandbox container and model license: - Network accessline 96
<https://github.com/wangxr0526/Parrot> - Network accessline 98
<https://github.com/rxn4chemistry/rxn-sandbox>
skills/retrosynthesis_planning/MODEL_TASKS_zh.md
- Network accessline 66
- RetroChimera：<https://github.com/microsoft/retrochimera> - Network accessline 67
- AiZynthFinder：<https://github.com/MolecularAI/aizynthfinder> - Network accessline 68
- ReactionT5v2：<https://github.com/sagawatatsuya/ReactionT5v2> - Network accessline 69
- ReactionT5v2 forward/yield 模型卡： <https://huggingface.co/sagawa/ReactionT5v2-forward>、 <https://huggingface.co/sagawa/ReactionT5v2-yield> - Network accessline 70
<https://huggingface.co/sagawa/ReactionT5v2-forward>、 - Network accessline 71
<https://huggingface.co/sagawa/ReactionT5v2-yield> - Network accessline 72
- RXNMapper：<https://github.com/rxn4chemistry/rxnmapper> - Network accessline 73
- Parrot：<https://github.com/wangxr0526/Parrot> - Network accessline 74
- RXN-Sandbox：<https://github.com/rxn4chemistry/rxn-sandbox>
skills/retrosynthesis_planning/README.md
- Network accessline 31
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 36
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 37
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/retrosynthesis_planning/README_zh.md
- Network accessline 20
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 25
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/retrosynthesis_planning/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/retrosynthesis_planning/*'` 只解这一个目录）。如果你本来就在跑 OpenAI
skills/retrosynthesis_planning/SKILL.md
- Network accessline 335
"title": "Verified source title", "identifier": "DOI, patent, ELN run, or database record", "url": "https://example.org/record", - Network accessline 336
"identifier": "DOI, patent, ELN run, or database record", "url": "https://example.org/record", "match_level": "exact_substrate", # exact_substrate, close_analog, reaction_class - Network accessline 337
"url": "https://example.org/record", - Network accessline 466
http://127.0.0.1:9876/examples/aspirin_retrosynthesis.html
skills/retrosynthesis_planning/atom_mapping_benchmark.py
skills/retrosynthesis_planning/benchmark_common.py
- Executable scriptline 1
Executable file
skills/retrosynthesis_planning/condition_benchmark.py
skills/retrosynthesis_planning/examples/aspirin_retrosynthesis.html
- Network accessline 254
const ns = "http://www.w3.org/2000/svg"; - Network accessline 646
<dt>Interpretation</dt><dd>Target molecule being disconnected into simpler purchasable or stock precursors.</dd> <dt>Annotation</dt><dd>Example LLM molecule brief: this is a target in the displayed aspirin route set. The stock status is not - Network accessline 647
<dt>Annotation</dt><dd>Example LLM molecule brief: this is a target in the displayed aspirin route set. The stock status is not a terminal precursor; confirm identity, purity, and route-specific functional-group compatibility before executi - Network accessline 648
<dt>Query</dt><dd><a href="https://pubchem.ncbi.nlm.nih.gov/#query=CC%28%3DO%29Oc1ccccc1C%28%3DO%29O">PubChem</a></dd> - Network accessline 660
<dt>Interpretation</dt><dd>Predicted synthetic intermediate; inspect functional groups, stereochemistry, and whether downstream disconnections are plausible.</dd> <dt>Annotation</dt><dd>Example LLM molecule brief: this is a intermediate in - Network accessline 661
<dt>Annotation</dt><dd>Example LLM molecule brief: this is a intermediate in the displayed aspirin route set. The stock status is not a terminal precursor; confirm identity, purity, and route-specific functional-group compatibility before e - Network accessline 662
<dt>Query</dt><dd><a href="https://pubchem.ncbi.nlm.nih.gov/#query=CC%28%3DO%29Oc1ccccc1C%28%3DO%29OCc1ccccc1">PubChem</a></dd> - Network accessline 674
<dt>Interpretation</dt><dd>Terminal precursor found in the selected stock database; verify vendor, purity, price, and regulatory constraints.</dd> <dt>Annotation</dt><dd>Example LLM molecule brief: this is a stock precursor in the displayed - Network accessline 675
<dt>Annotation</dt><dd>Example LLM molecule brief: this is a stock precursor in the displayed aspirin route set. The stock status is in stock; confirm identity, purity, and route-specific functional-group compatibility before execution.</dd - Network accessline 676
<dt>Query</dt><dd><a href="https://pubchem.ncbi.nlm.nih.gov/#query=CC%28%3DO%29Cl">PubChem</a></dd> - Network accessline 688
<dt>Interpretation</dt><dd>Terminal precursor found in the selected stock database; verify vendor, purity, price, and regulatory constraints.</dd> <dt>Annotation</dt><dd>Example LLM molecule brief: this is a stock precursor in the displayed - Network accessline 689
<dt>Annotation</dt><dd>Example LLM molecule brief: this is a stock precursor in the displayed aspirin route set. The stock status is in stock; confirm identity, purity, and route-specific functional-group compatibility before execution.</dd - Network accessline 690
<dt>Query</dt><dd><a href="https://pubchem.ncbi.nlm.nih.gov/#query=CC%28%3DO%29O">PubChem</a></dd> - Network accessline 702
<dt>Interpretation</dt><dd>Terminal precursor found in the selected stock database; verify vendor, purity, price, and regulatory constraints.</dd> <dt>Annotation</dt><dd>Example LLM molecule brief: this is a stock precursor in the displayed - Network accessline 703
<dt>Annotation</dt><dd>Example LLM molecule brief: this is a stock precursor in the displayed aspirin route set. The stock status is in stock; confirm identity, purity, and route-specific functional-group compatibility before execution.</dd - Network accessline 704
<dt>Query</dt><dd><a href="https://pubchem.ncbi.nlm.nih.gov/#query=CC%28%3DO%29OC%28C%29%3DO">PubChem</a></dd> - Network accessline 716
<dt>Interpretation</dt><dd>Terminal precursor found in the selected stock database; verify vendor, purity, price, and regulatory constraints.</dd> <dt>Annotation</dt><dd>Example LLM molecule brief: this is a stock precursor in the displayed - Network accessline 717
<dt>Annotation</dt><dd>Example LLM molecule brief: this is a stock precursor in the displayed aspirin route set. The stock status is in stock; confirm identity, purity, and route-specific functional-group compatibility before execution.</dd - Network accessline 718
<dt>Query</dt><dd><a href="https://pubchem.ncbi.nlm.nih.gov/#query=O%3DC%28O%29c1ccccc1F">PubChem</a></dd> - Network accessline 730
<dt>Interpretation</dt><dd>Terminal precursor found in the selected stock database; verify vendor, purity, price, and regulatory constraints.</dd> <dt>Annotation</dt><dd>Example LLM molecule brief: this is a stock precursor in the displayed - Network accessline 731
<dt>Annotation</dt><dd>Example LLM molecule brief: this is a stock precursor in the displayed aspirin route set. The stock status is in stock; confirm identity, purity, and route-specific functional-group compatibility before execution.</dd - Network accessline 732
<dt>Query</dt><dd><a href="https://pubchem.ncbi.nlm.nih.gov/#query=O%3DC%28O%29c1ccccc1O">PubChem</a></dd> - Network accessline 744
<dt>Interpretation</dt><dd>Terminal precursor found in the selected stock database; verify vendor, purity, price, and regulatory constraints.</dd> <dt>Annotation</dt><dd>Example LLM molecule brief: this is a stock precursor in the displayed - Network accessline 745
<dt>Annotation</dt><dd>Example LLM molecule brief: this is a stock precursor in the displayed aspirin route set. The stock status is in stock; confirm identity, purity, and route-specific functional-group compatibility before execution.</dd - Network accessline 746
<dt>Query</dt><dd><a href="https://pubchem.ncbi.nlm.nih.gov/#query=O%3DC%28OCc1ccccc1%29c1ccccc1O">PubChem</a></dd> - Network accessline 766
</div> <div class="route-diagram"> <svg class="route-svg" role="img" aria-label="Route 1 tree" viewBox="0 0 910 296" xmlns="http://www.w3.org/2000/svg"> - Network accessline 767
<div class="route-diagram"> <svg class="route-svg" role="img" aria-label="Route 1 tree" viewBox="0 0 910 296" xmlns="http://www.w3.org/2000/svg"> <g> - Network accessline 768
<svg class="route-svg" role="img" aria-label="Route 1 tree" viewBox="0 0 910 296" xmlns="http://www.w3.org/2000/svg"> - Network accessline 877
</div> <div class="route-diagram"> <svg class="route-svg" role="img" aria-label="Route 2 tree" viewBox="0 0 910 296" xmlns="http://www.w3.org/2000/svg"> - Network accessline 878
<div class="route-diagram"> <svg class="route-svg" role="img" aria-label="Route 2 tree" viewBox="0 0 910 296" xmlns="http://www.w3.org/2000/svg"> <g> - Network accessline 879
<svg class="route-svg" role="img" aria-label="Route 2 tree" viewBox="0 0 910 296" xmlns="http://www.w3.org/2000/svg"> - Network accessline 988
</div> <div class="route-diagram"> <svg class="route-svg" role="img" aria-label="Route 3 tree" viewBox="0 0 910 296" xmlns="http://www.w3.org/2000/svg"> - Network accessline 989
<div class="route-diagram"> <svg class="route-svg" role="img" aria-label="Route 3 tree" viewBox="0 0 910 296" xmlns="http://www.w3.org/2000/svg"> <g> - Network accessline 990
<svg class="route-svg" role="img" aria-label="Route 3 tree" viewBox="0 0 910 296" xmlns="http://www.w3.org/2000/svg"> - Network accessline 1099
</div> <div class="route-diagram"> <svg class="route-svg" role="img" aria-label="Route 4 tree" viewBox="0 0 910 296" xmlns="http://www.w3.org/2000/svg"> - Network accessline 1100
<div class="route-diagram"> <svg class="route-svg" role="img" aria-label="Route 4 tree" viewBox="0 0 910 296" xmlns="http://www.w3.org/2000/svg"> <g> - Network accessline 1101
<svg class="route-svg" role="img" aria-label="Route 4 tree" viewBox="0 0 910 296" xmlns="http://www.w3.org/2000/svg"> - Network accessline 1210
</div> <div class="route-diagram"> <svg class="route-svg" role="img" aria-label="Route 5 tree" viewBox="0 0 1510 296" xmlns="http://www.w3.org/2000/svg"> - Network accessline 1211
<div class="route-diagram"> <svg class="route-svg" role="img" aria-label="Route 5 tree" viewBox="0 0 1510 296" xmlns="http://www.w3.org/2000/svg"> <g> - Network accessline 1212
<svg class="route-svg" role="img" aria-label="Route 5 tree" viewBox="0 0 1510 296" xmlns="http://www.w3.org/2000/svg">
skills/retrosynthesis_planning/examples/aspirin_retrosynthesis_report.md
- Network accessline 59
- Stock status: not a terminal precursor - Interpretation: Target molecule being disconnected into simpler purchasable or stock precursors. - Suggested query: https://pubchem.ncbi.nlm.nih.gov/#query=CC%28%3DO%29Oc1ccccc1C%28%3DO%29O - Network accessline 61
- Suggested query: https://pubchem.ncbi.nlm.nih.gov/#query=CC%28%3DO%29Oc1ccccc1C%28%3DO%29O - Network accessline 67
- Stock status: not a terminal precursor - Interpretation: Predicted synthetic intermediate; inspect functional groups, stereochemistry, and whether downstream disconnections are plausible. - Suggested query: https://pubchem.ncbi.nlm.nih.go - Network accessline 69
- Suggested query: https://pubchem.ncbi.nlm.nih.gov/#query=CC%28%3DO%29Oc1ccccc1C%28%3DO%29OCc1ccccc1 - Network accessline 75
- Stock status: in stock - Interpretation: Terminal precursor found in the selected stock database; verify vendor, purity, price, and regulatory constraints. - Suggested query: https://pubchem.ncbi.nlm.nih.gov/#query=CC%28%3DO%29Cl - Network accessline 77
- Suggested query: https://pubchem.ncbi.nlm.nih.gov/#query=CC%28%3DO%29Cl - Network accessline 83
- Stock status: in stock - Interpretation: Terminal precursor found in the selected stock database; verify vendor, purity, price, and regulatory constraints. - Suggested query: https://pubchem.ncbi.nlm.nih.gov/#query=CC%28%3DO%29O - Network accessline 85
- Suggested query: https://pubchem.ncbi.nlm.nih.gov/#query=CC%28%3DO%29O - Network accessline 91
- Stock status: in stock - Interpretation: Terminal precursor found in the selected stock database; verify vendor, purity, price, and regulatory constraints. - Suggested query: https://pubchem.ncbi.nlm.nih.gov/#query=CC%28%3DO%29OC%28C%29%3 - Network accessline 93
- Suggested query: https://pubchem.ncbi.nlm.nih.gov/#query=CC%28%3DO%29OC%28C%29%3DO - Network accessline 99
- Stock status: in stock - Interpretation: Terminal precursor found in the selected stock database; verify vendor, purity, price, and regulatory constraints. - Suggested query: https://pubchem.ncbi.nlm.nih.gov/#query=O%3DC%28O%29c1ccccc1F - Network accessline 101
- Suggested query: https://pubchem.ncbi.nlm.nih.gov/#query=O%3DC%28O%29c1ccccc1F - Network accessline 107
- Stock status: in stock - Interpretation: Terminal precursor found in the selected stock database; verify vendor, purity, price, and regulatory constraints. - Suggested query: https://pubchem.ncbi.nlm.nih.gov/#query=O%3DC%28O%29c1ccccc1O - Network accessline 109
- Suggested query: https://pubchem.ncbi.nlm.nih.gov/#query=O%3DC%28O%29c1ccccc1O - Network accessline 115
- Stock status: in stock - Interpretation: Terminal precursor found in the selected stock database; verify vendor, purity, price, and regulatory constraints. - Suggested query: https://pubchem.ncbi.nlm.nih.gov/#query=O%3DC%28OCc1ccccc1%29c1 - Network accessline 117
- Suggested query: https://pubchem.ncbi.nlm.nih.gov/#query=O%3DC%28OCc1ccccc1%29c1ccccc1O
skills/retrosynthesis_planning/examples/build_example.py
- Executable scriptline 1
Executable file
skills/retrosynthesis_planning/external_backends.py
skills/retrosynthesis_planning/forward_benchmark.py
skills/retrosynthesis_planning/gt_codebase.py
- Executable scriptline 1
Executable file
skills/retrosynthesis_planning/kernel.py
- Executable scriptline 1
Executable file - Network accessline 235
results[result_key] = {"ok": False, "error": "invalid DOI"} continue url = "https://doi.org/" + quote(doi, safe="/") - Network accessline 236
continue url = "https://doi.org/" + quote(doi, safe="/") try: - Network accessline 237
url = "https://doi.org/" + quote(doi, safe="/") - Network accessline 482
"provider has no callable fetch_reaction_evidence method" ) supplied = normalize_reaction_evidence(fetch(briefs)) - Network accessline 483
) supplied = normalize_reaction_evidence(fetch(briefs)) except Exception as exc: - Network accessline 484
supplied = normalize_reaction_evidence(fetch(briefs)) - Network accessline 642
def _call_openai4s_fetch(fetch: Any, url: str) -> Any: try: return fetch(url, format="markdown", timeout=20, max_chars=12000) - Network accessline 643
try: return fetch(url, format="markdown", timeout=20, max_chars=12000) except TypeError: - Network accessline 644
return fetch(url, format="markdown", timeout=20, max_chars=12000) - Network accessline 645
except TypeError: try: return fetch(url, format="markdown") - Network accessline 646
try: return fetch(url, format="markdown") except TypeError: - Network accessline 647
return fetch(url, format="markdown") - Network accessline 649
return fetch(url) - Network accessline 1685
return "https://pubchem.ncbi.nlm.nih.gov/#query=" + quote(smiles) - Network accessline 1694
encoded = quote(smiles, safe="") return ( "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/smiles/" - Network accessline 1695
return ( "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/smiles/" f"{encoded}/PNG?image_size={width}x{height}" - Network accessline 1696
"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/smiles/" - Possible obfuscationline 1717
decoded = base64.b64decode(encoded, validate=True) - Network accessline 1770
cx = w / 2 cy = h / 2 - 8 svg = f"""<svg xmlns="http://www.w3.org/2000/svg" width="{w}" height="{h}" viewBox="0 0 {w} {h}"> - Network accessline 1771
cy = h / 2 - 8 svg = f"""<svg xmlns="http://www.w3.org/2000/svg" width="{w}" height="{h}" viewBox="0 0 {w} {h}"> <g fill="none" stroke="#5f6f7c" stroke-width="3" stroke-linecap="round" stroke-linejoin="round" opacity="0.82"> - Network accessline 1772
svg = f"""<svg xmlns="http://www.w3.org/2000/svg" width="{w}" height="{h}" viewBox="0 0 {w} {h}"> - Network accessline 3402
return "\n".join( [ f'<svg class="route-svg" role="img" aria-label="Route {html.escape(str(route_id))} tree" viewBox="0 0 {width:.0f} {height:.0f}" xmlns="http://www.w3.org/2000/svg">', - Network accessline 3403
[ f'<svg class="route-svg" role="img" aria-label="Route {html.escape(str(route_id))} tree" viewBox="0 0 {width:.0f} {height:.0f}" xmlns="http://www.w3.org/2000/svg">', "<g>", - Network accessline 3404
f'<svg class="route-svg" role="img" aria-label="Route {html.escape(str(route_id))} tree" viewBox="0 0 {width:.0f} {height:.0f}" xmlns="http://www.w3.org/2000/svg">', - Network accessline 3782
const ns = "http://www.w3.org/2000/svg"; - Possible obfuscationline 3789
if (btoa(atob(encoded)) !== encoded) return "";
skills/retrosynthesis_planning/model_deployment.py
- Executable scriptline 1
Executable file - Network accessline 59
@property def download_url(self) -> str: return f"https://ndownloader.figshare.com/files/{self.file_id}" - Network accessline 61
return f"https://ndownloader.figshare.com/files/{self.file_id}" - Network accessline 63
@property def source_url(self) -> str: return f"https://doi.org/10.6084/m9.figshare.{self.article_id}.v1" - Network accessline 65
return f"https://doi.org/10.6084/m9.figshare.{self.article_id}.v1"
skills/retrosynthesis_planning/multistep_benchmark.py
- Executable scriptline 1
Executable file
skills/retrosynthesis_planning/parrot_mar_inference.py
skills/retrosynthesis_planning/reaction_model_backends.py
skills/retrosynthesis_planning/reaction_model_deployment.py
- Executable scriptline 1
Executable file - Executable scriptline 1
Executable file - Network accessline 67
*torch_packages, "--index-url", "https://download.pytorch.org/whl/cpu", - Network accessline 67
*torch_packages, "--index-url", "https://download.pytorch.org/whl/cpu", - Network accessline 68
"--index-url", "https://download.pytorch.org/whl/cpu", ] - Network accessline 68
"--index-url", "https://download.pytorch.org/whl/cpu", ] - Network accessline 69
"https://download.pytorch.org/whl/cpu", - Network accessline 69
"https://download.pytorch.org/whl/cpu", - Network accessline 114
python="3.11", packages=("aizynthfinder[all]==4.4.1",), source_url="https://github.com/MolecularAI/aizynthfinder", - Network accessline 114
python="3.11", packages=("aizynthfinder[all]==4.4.1",), source_url="https://github.com/MolecularAI/aizynthfinder", - Network accessline 115
packages=("aizynthfinder[all]==4.4.1",), source_url="https://github.com/MolecularAI/aizynthfinder", source_revision="9859f5bc6c04c342b828aff20001504c238d7ac1", - Network accessline 115
packages=("aizynthfinder[all]==4.4.1",), source_url="https://github.com/MolecularAI/aizynthfinder", source_revision="9859f5bc6c04c342b828aff20001504c238d7ac1", - Network accessline 116
source_url="https://github.com/MolecularAI/aizynthfinder", - Network accessline 116
source_url="https://github.com/MolecularAI/aizynthfinder", - Network accessline 137
"scipy==1.14.1", ), source_url="https://github.com/rxn4chemistry/rxnmapper", - Network accessline 137
"scipy==1.14.1", ), source_url="https://github.com/rxn4chemistry/rxnmapper", - Network accessline 138
), source_url="https://github.com/rxn4chemistry/rxnmapper", source_revision="640d9ddd304d28eb338482f4e9c2dd6b1a25de7c", - Network accessline 138
), source_url="https://github.com/rxn4chemistry/rxnmapper", source_revision="640d9ddd304d28eb338482f4e9c2dd6b1a25de7c", - Network accessline 139
source_url="https://github.com/rxn4chemistry/rxnmapper", - Network accessline 139
source_url="https://github.com/rxn4chemistry/rxnmapper", - Network accessline 155
"rdkit==2024.3.5", ), source_url="https://github.com/sagawatatsuya/ReactionT5v2", - Network accessline 155
"rdkit==2024.3.5", ), source_url="https://github.com/sagawatatsuya/ReactionT5v2", - Network accessline 156
), source_url="https://github.com/sagawatatsuya/ReactionT5v2", source_revision="76eb08068e10fe255cae5d563a91e1c1e9abac54", - Network accessline 156
), source_url="https://github.com/sagawatatsuya/ReactionT5v2", source_revision="76eb08068e10fe255cae5d563a91e1c1e9abac54", - Network accessline 157
source_url="https://github.com/sagawatatsuya/ReactionT5v2", - Network accessline 157
source_url="https://github.com/sagawatatsuya/ReactionT5v2", - Network accessline 174
"pyyaml==6.0.2", ), source_url="https://github.com/wangxr0526/Parrot", - Network accessline 174
"pyyaml==6.0.2", ), source_url="https://github.com/wangxr0526/Parrot", - Network accessline 175
), source_url="https://github.com/wangxr0526/Parrot", source_revision="0fb2325567e21011589641544e32427c8244e2a9", - Network accessline 175
), source_url="https://github.com/wangxr0526/Parrot", source_revision="0fb2325567e21011589641544e32427c8244e2a9", - Network accessline 176
source_url="https://github.com/wangxr0526/Parrot", - Network accessline 176
source_url="https://github.com/wangxr0526/Parrot", - Network accessline 198
), source_url=( "https://huggingface.co/xiaoruiwang/" "ChemEnzyRetroPlanner_metadata" - Network accessline 198
), source_url=( "https://huggingface.co/xiaoruiwang/" "ChemEnzyRetroPlanner_metadata" - Network accessline 199
source_url=( "https://huggingface.co/xiaoruiwang/" "ChemEnzyRetroPlanner_metadata" ), - Network accessline 199
source_url=( "https://huggingface.co/xiaoruiwang/" "ChemEnzyRetroPlanner_metadata" ), - Network accessline 200
"https://huggingface.co/xiaoruiwang/" "ChemEnzyRetroPlanner_metadata" - Network accessline 200
"https://huggingface.co/xiaoruiwang/" "ChemEnzyRetroPlanner_metadata" - Network accessline 216
"bytes": 133_909, "sha256": "e1259cfc45610b4801d38ed512d31290aa13204bfd5df9fe601fe845ddcbe3d6", "url": "https://pypi.org/project/aizynthfinder/4.4.1/", - Network accessline 216
"bytes": 133_909, "sha256": "e1259cfc45610b4801d38ed512d31290aa13204bfd5df9fe601fe845ddcbe3d6", "url": "https://pypi.org/project/aizynthfinder/4.4.1/", - Network accessline 217
"sha256": "e1259cfc45610b4801d38ed512d31290aa13204bfd5df9fe601fe845ddcbe3d6", "url": "https://pypi.org/project/aizynthfinder/4.4.1/", }, - Network accessline 217
"sha256": "e1259cfc45610b4801d38ed512d31290aa13204bfd5df9fe601fe845ddcbe3d6", "url": "https://pypi.org/project/aizynthfinder/4.4.1/", }, - Network accessline 218
"url": "https://pypi.org/project/aizynthfinder/4.4.1/", - Network accessline 218
"url": "https://pypi.org/project/aizynthfinder/4.4.1/", - Network accessline 222
"bytes": 3_007_424, "sha256": "27876a4286881aafd286fd6f24a6a56a4ca6ba22d68e035a0ea120106c541ba5", "url": "https://pypi.org/project/rxnmapper/0.4.3/", - Network accessline 222
"bytes": 3_007_424, "sha256": "27876a4286881aafd286fd6f24a6a56a4ca6ba22d68e035a0ea120106c541ba5", "url": "https://pypi.org/project/rxnmapper/0.4.3/", - Network accessline 223
"sha256": "27876a4286881aafd286fd6f24a6a56a4ca6ba22d68e035a0ea120106c541ba5", "url": "https://pypi.org/project/rxnmapper/0.4.3/", }, - Network accessline 223
"sha256": "27876a4286881aafd286fd6f24a6a56a4ca6ba22d68e035a0ea120106c541ba5", "url": "https://pypi.org/project/rxnmapper/0.4.3/", }, - Network accessline 224
"url": "https://pypi.org/project/rxnmapper/0.4.3/", - Network accessline 224
"url": "https://pypi.org/project/rxnmapper/0.4.3/",
skills/retrosynthesis_planning/reaction_model_worker.py
- Executable scriptline 1
Executable file
skills/retrosynthesis_planning/reproducibility_bundle.py
- Executable scriptline 1
Executable file
skills/retrosynthesis_planning/route_review.py
- Executable scriptline 1
Executable file
skills/retrosynthesis_planning/scenario_benchmark_cli.py
- Executable scriptline 1
Executable file
skills/retrosynthesis_planning/scenarios/01_single_step_retrosynthesis.md
- Network accessline 247
- Dai H, et al. *Retrosynthesis Prediction with Conditional Graph Logic Network*. NeurIPS (2019). 官方实现与 Schneider 50K 固定划分：<https://github.com/Hanjun-Dai/GLN> - Network accessline 248
- Mikulak-Klucznik B, et al. RetroChimera 官方实现、权重与许可证：<https://github.com/microsoft/retrochimera> - Network accessline 249
- Sagawa T, Kojima R. *ReactionT5: a pre-trained transformer model for accurate chemical reaction prediction with limited data*. Journal of Cheminformatics (2025). 官方实现：<https://github.com/sagawatatsuya/ReactionT5v2>
skills/retrosynthesis_planning/scenarios/02_multistep_route_planning.md
- Network accessline 17
- **PaRoutes**：MolecularAI 官方仓库以 Apache-2.0 发布评测代码；[Zenodo 记录 6275421](https://zenodo.org/records/6275421) 的版本为 `1.0.0`，[记录 API](https://zenodo.org/api/records/6275421) 明确声明数据许可为 CC BY 4.0，提供 n1/n5 targets、stocks、reference routes、约 150k 训练路 - Network accessline 238
- Genheden S, et al. *PaRoutes: towards a framework for benchmarking retrosynthesis route predictions*. Digital Discovery (2022). 官方实现：<https://github.com/MolecularAI/paroutes>；官方数据：<https://zenodo.org/records/6275421> - Network accessline 239
- Genheden S, et al. AiZynthFinder 官方实现与文档：<https://github.com/MolecularAI/aizynthfinder> - Network accessline 240
- RetroChimera 官方实现：<https://github.com/microsoft/retrochimera>
skills/retrosynthesis_planning/scenarios/03_atom_mapping.md
skills/retrosynthesis_planning/scenarios/04_forward_prediction.md
- Network accessline 236
- Schwaller P, et al. *Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction*. ACS Central Science (2019). 官方实现与 USPTO_MIT 数据入口：<https://github.com/pschwllr/MolecularTransformer> - Network accessline 237
- Sagawa T, Kojima R. ReactionT5v2 官方实现与数据/推理协议：<https://github.com/sagawatatsuya/ReactionT5v2> - Network accessline 238
- ReactionT5v2-forward 官方权重：<https://huggingface.co/sagawa/ReactionT5v2-forward>
skills/retrosynthesis_planning/scenarios/05_condition_recommendation.md
- Network accessline 239
- Wang X, et al. Parrot 官方实现、配置和数据下载脚本：<https://github.com/wangxr0526/Parrot>
skills/retrosynthesis_planning/scenarios/06_yield_estimation.md
- Network accessline 247
- Ahneman DT, et al. *Predicting reaction performance in C–N cross-coupling using machine learning*. Science 360, 186–190 (2018), DOI: 10.1126/science.aar5169. - Schwaller P, et al. `rxn_yields` 官方数据处理、划分、模型与已训练权重：<https://github.com/rxn4ch - Network accessline 248
- Schwaller P, et al. `rxn_yields` 官方数据处理、划分、模型与已训练权重：<https://github.com/rxn4chemistry/rxn_yields> - Network accessline 249
- Sagawa T, Kojima R. ReactionT5v2 官方实现与 C–N split 去重入口：<https://github.com/sagawatatsuya/ReactionT5v2> - Network accessline 250
- ReactionT5v2-yield 官方权重：<https://huggingface.co/sagawa/ReactionT5v2-yield>
skills/retrosynthesis_planning/scenarios/gt_codebases/01_single_step_retrosynthesis.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/retrosynthesis_planning/scenarios/gt_codebases/02_multistep_route_planning.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/retrosynthesis_planning/scenarios/gt_codebases/03_atom_mapping.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/retrosynthesis_planning/scenarios/gt_codebases/04_forward_prediction.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/retrosynthesis_planning/scenarios/gt_codebases/05_condition_recommendation.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/retrosynthesis_planning/scenarios/gt_codebases/06_yield_estimation.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/retrosynthesis_planning/scenarios/openai4s_codebases/01_single_step_retrosynthesis.py
- Executable scriptline 1
Executable file
skills/retrosynthesis_planning/scenarios/openai4s_codebases/02_multistep_route_planning.py
- Executable scriptline 1
Executable file
skills/retrosynthesis_planning/scenarios/openai4s_codebases/03_atom_mapping.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/retrosynthesis_planning/scenarios/openai4s_codebases/04_forward_prediction.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/retrosynthesis_planning/scenarios/openai4s_codebases/05_condition_recommendation.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/retrosynthesis_planning/scenarios/openai4s_codebases/06_yield_estimation.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/retrosynthesis_planning/scenarios/openai4s_codebases/generate.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 260
" else: raise SystemExit(24)\n" " finally: link.unlink(missing_ok=True)\n" "try:\n socket.create_connection(('127.0.0.1', port), timeout=2).close()\n" - Network accessline 261
" finally: link.unlink(missing_ok=True)\n" "try:\n socket.create_connection(('127.0.0.1', port), timeout=2).close()\n" "except OSError: pass\n" - Network accessline 262
"try:\n socket.create_connection(('127.0.0.1', port), timeout=2).close()\n" - Network accessline 276
violations = frozenset({21, 22, 23, 24, 25, 26}) try: with socket.socket() as listener: - Network accessline 277
try: with socket.socket() as listener: listener.bind(("127.0.0.1", 0)) - Network accessline 278
with socket.socket() as listener:
skills/retrosynthesis_planning/scenarios/pipelines/01_single_step_retrosynthesis.py
- Executable scriptline 1
Executable file
skills/retrosynthesis_planning/scenarios/pipelines/02_multistep_route_planning.py
- Executable scriptline 1
Executable file
skills/retrosynthesis_planning/scenarios/pipelines/03_atom_mapping.py
- Executable scriptline 1
Executable file
skills/retrosynthesis_planning/scenarios/pipelines/04_forward_prediction.py
- Executable scriptline 1
Executable file
skills/retrosynthesis_planning/scenarios/pipelines/05_condition_recommendation.py
- Executable scriptline 1
Executable file
skills/retrosynthesis_planning/scenarios/pipelines/06_yield_estimation.py
- Executable scriptline 1
Executable file
skills/retrosynthesis_planning/scenarios/test_cases/README.md
- Network accessline 53
| Scenario | Candidate source and identity | License evidence and remaining admission work | | --- | --- | --- | | `single_step` | [GLN](https://github.com/Hanjun-Dai/GLN/tree/b5bd7b181a61a8289cc1d1a33825b2c417bed0ef), commit `b5bd7b181a61a - Network accessline 54
| --- | --- | --- | | `single_step` | [GLN](https://github.com/Hanjun-Dai/GLN/tree/b5bd7b181a61a8289cc1d1a33825b2c417bed0ef), commit `b5bd7b181a61a8289cc1d1a33825b2c417bed0ef`, links the Schneider50K raw CSV directory and the RetroSim train - Network accessline 55
| `single_step` | [GLN](https://github.com/Hanjun-Dai/GLN/tree/b5bd7b181a61a8289cc1d1a33825b2c417bed0ef), commit `b5bd7b181a61a8289cc1d1a33825b2c417bed0ef`, links the Schneider50K raw CSV directory and the RetroSim train/validation/test spl - Network accessline 56
| `multistep` | [PaRoutes, Zenodo record 6275421](https://zenodo.org/records/6275421), DOI `10.5281/zenodo.6275421`, version `1.0.0`; the six n1/n5 files are listed below. | The [record API](https://zenodo.org/api/records/6275421) explicitl - Network accessline 57
| `atom_mapping` | [RXNMapper](https://github.com/rxn4chemistry/rxnmapper/tree/a01ecdcd5ac944850e9691739c1df858e005fd39), commit `a01ecdcd5ac944850e9691739c1df858e005fd39`, points to [RXNMapperData](https://ibm.box.com/v/RXNMapperData). No - Network accessline 58
| `forward` | [MolecularTransformer](https://github.com/pschwllr/MolecularTransformer/tree/aeb339daf0a029b391f8307fb3f467f461605dd2), commit `aeb339daf0a029b391f8307fb3f467f461605dd2`, links [USPTO/data.zip](https://github.com/wengong-jin/n - Network accessline 59
| `conditions` | [Parrot download script](https://github.com/wangxr0526/Parrot/blob/0fb2325567e21011589641544e32427c8244e2a9/preprocess_script/download_data.py), commit `0fb2325567e21011589641544e32427c8244e2a9`, names `USPTO_condition_fina - Network accessline 60
| `yield` | [rxn_yields](https://github.com/rxn4chemistry/rxn_yields/tree/d9e6b87ce1b881978490d68bfc00021e3b48127a), commit `d9e6b87ce1b881978490d68bfc00021e3b48127a`, lists `data/Buchwald-Hartwig/Dreher_and_Doyle_input_data.xlsx` (2,134,76 - Network accessline 62
The [Lowe original USPTO metadata](https://api.figshare.com/v2/articles/5104873) - Network accessline 65
still need review. `USPTO_MIT` identifies a dataset/split convention; the MIT code license is not a license conclusion for that derived data. The [HF metadata at the admitted model revision](https://huggingface.co/api/models/xiaoruiwang/Che - Network accessline 66
code license is not a license conclusion for that derived data. The [HF metadata at the admitted model revision](https://huggingface.co/api/models/xiaoruiwang/ChemEnzyRetroPlanner_metadata/revision/b9ef6049d341bfc62d835f09ad6ce33b6f86b047) - Network accessline 67
The [HF metadata at the admitted model revision](https://huggingface.co/api/models/xiaoruiwang/ChemEnzyRetroPlanner_metadata/revision/b9ef6049d341bfc62d835f09ad6ce33b6f86b047)
skills/retrosynthesis_planning/scenarios/test_cases/README_zh.md
- Network accessline 46
| 场景 | 候选来源与身份 | 许可证据及待完成准入 | | --- | --- | --- | | `single_step` | [GLN](https://github.com/Hanjun-Dai/GLN/tree/b5bd7b181a61a8289cc1d1a33825b2c417bed0ef)，commit `b5bd7b181a61a8289cc1d1a33825b2c417bed0ef`，指向 Schneider50K 原始 CSV 目录及 RetroSim - Network accessline 47
| --- | --- | --- | | `single_step` | [GLN](https://github.com/Hanjun-Dai/GLN/tree/b5bd7b181a61a8289cc1d1a33825b2c417bed0ef)，commit `b5bd7b181a61a8289cc1d1a33825b2c417bed0ef`，指向 Schneider50K 原始 CSV 目录及 RetroSim train/validation/test 划分。 | G - Network accessline 48
| `single_step` | [GLN](https://github.com/Hanjun-Dai/GLN/tree/b5bd7b181a61a8289cc1d1a33825b2c417bed0ef)，commit `b5bd7b181a61a8289cc1d1a33825b2c417bed0ef`，指向 Schneider50K 原始 CSV 目录及 RetroSim train/validation/test 划分。 | GLN 代码为 MIT；选用派生数据归档仍 - Network accessline 49
| `multistep` | [PaRoutes，Zenodo 记录 6275421](https://zenodo.org/records/6275421)，DOI `10.5281/zenodo.6275421`，版本 `1.0.0`；六个 n1/n5 文件见下表。 | [记录 API](https://zenodo.org/api/records/6275421) 明确声明数据许可 `cc-by-4.0`；仓库 Apache-2.0 是代码许可。维护者记录准入与署名安 - Network accessline 50
| `atom_mapping` | [RXNMapper](https://github.com/rxn4chemistry/rxnmapper/tree/a01ecdcd5ac944850e9691739c1df858e005fd39)，commit `a01ecdcd5ac944850e9691739c1df858e005fd39`，指向 [RXNMapperData](https://ibm.box.com/v/RXNMapperData)；尚未选定具体真值文件。 | - Network accessline 51
| `forward` | [MolecularTransformer](https://github.com/pschwllr/MolecularTransformer/tree/aeb339daf0a029b391f8307fb3f467f461605dd2)，commit `aeb339daf0a029b391f8307fb3f467f461605dd2`，指向 [USPTO/data.zip](https://github.com/wengong-jin/nips17 - Network accessline 52
| `conditions` | [Parrot 下载脚本](https://github.com/wangxr0526/Parrot/blob/0fb2325567e21011589641544e32427c8244e2a9/preprocess_script/download_data.py)，commit `0fb2325567e21011589641544e32427c8244e2a9`，列出 `USPTO_condition_final.zip`，Google Dr - Network accessline 53
| `yield` | [rxn_yields](https://github.com/rxn4chemistry/rxn_yields/tree/d9e6b87ce1b881978490d68bfc00021e3b48127a)，commit `d9e6b87ce1b881978490d68bfc00021e3b48127a`，列出 `data/Buchwald-Hartwig/Dreher_and_Doyle_input_data.xlsx`（2,134,762 字节）； - Network accessline 55
[Lowe 原始 USPTO 元数据](https://api.figshare.com/v2/articles/5104873) 明确声明 - Network accessline 57
证据，但仍需核对所选派生包的对应关系与条款。`USPTO_MIT` 标识数据集/划分约定， MIT 代码许可证不能作为该派生数据的许可结论。 [已准入模型 revision 的 HF 元数据](https://huggingface.co/api/models/xiaoruiwang/ChemEnzyRetroPlanner_metadata/revision/b9ef6049d341bfc62d835f09ad6ce33b6f86b047) - Network accessline 58
MIT 代码许可证不能作为该派生数据的许可结论。 [已准入模型 revision 的 HF 元数据](https://huggingface.co/api/models/xiaoruiwang/ChemEnzyRetroPlanner_metadata/revision/b9ef6049d341bfc62d835f09ad6ce33b6f86b047) 声明 MIT，但该观察不能证明 Parrot 另行存放于 Google Drive 的训练/测试数据许可。 - Network accessline 59
[已准入模型 revision 的 HF 元数据](https://huggingface.co/api/models/xiaoruiwang/ChemEnzyRetroPlanner_metadata/revision/b9ef6049d341bfc62d835f09ad6ce33b6f86b047)
skills/retrosynthesis_planning/scenarios/test_cases/evaluate.py
- Executable scriptline 1
Executable file
skills/retrosynthesis_planning/scenarios/test_cases/install.py
- Executable scriptline 1
Executable file
skills/retrosynthesis_planning/single_step_benchmark.py
skills/retrosynthesis_planning/structural_audit.py
- Executable scriptline 1
Executable file
skills/retrosynthesis_planning/syntheseus_worker.py
- Executable scriptline 1
Executable file
skills/retrosynthesis_planning/workflow.py
- Executable scriptline 1
Executable file
skills/retrosynthesis_planning/yield_benchmark.py
skills/rfdiffusion/README.md
- Network accessline 32
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 37
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 38
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/rfdiffusion/README_zh.md
- Network accessline 20
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 25
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/rfdiffusion/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/rfdiffusion/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何东西需要安装——wheel
skills/rfdiffusion/SKILL.md
- Network accessline 23
name: Adaptyv protein-design-skills RFdiffusion recipe license: MIT terms_url: https://github.com/adaptyvbio/protein-design-skills/blob/b231232025e3db41e9c637abc1e8082e915f6cb1/LICENSE - Network accessline 24
license: MIT terms_url: https://github.com/adaptyvbio/protein-design-skills/blob/b231232025e3db41e9c637abc1e8082e915f6cb1/LICENSE - kind: code-and-weights - Network accessline 25
terms_url: https://github.com/adaptyvbio/protein-design-skills/blob/b231232025e3db41e9c637abc1e8082e915f6cb1/LICENSE - Network accessline 27
name: RFdiffusion license: BSD-3-Clause terms_url: https://github.com/RosettaCommons/RFdiffusion/blob/main/LICENSE - Network accessline 28
license: BSD-3-Clause terms_url: https://github.com/RosettaCommons/RFdiffusion/blob/main/LICENSE --- - Network accessline 29
terms_url: https://github.com/RosettaCommons/RFdiffusion/blob/main/LICENSE
skills/scgpt/README.md
- Network accessline 31
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 36
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 37
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/scgpt/README_zh.md
- Network accessline 20
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 25
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/scgpt/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/scgpt/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何东西需要安装——wheel 自带全部内置 Skill
skills/scgpt/SKILL.md
- Network accessline 29
name: scGPT provider: Wang Lab (University of Toronto) info_url: https://github.com/bowang-lab/scGPT - Network accessline 30
provider: Wang Lab (University of Toronto) info_url: https://github.com/bowang-lab/scGPT --- - Network accessline 31
info_url: https://github.com/bowang-lab/scGPT
skills/scvi-tools/README.md
- Network accessline 29
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 34
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 35
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/scvi-tools/README_zh.md
- Network accessline 18
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 23
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/scvi-tools/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/scvi-tools/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何东西需要安装——wheel 自带
skills/scvi-tools/kernel.py
- Executable scriptline 1
Executable file
skills/single-cell-rna-analysis/README.md
- Network accessline 33
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 38
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 39
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/single-cell-rna-analysis/README_zh.md
- Network accessline 21
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 26
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/single-cell-rna-analysis/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/single-cell-rna-analysis/*'` 只解这一个目录）。如果你本来就在跑 Open
skills/single-cell-rna-analysis/kernel.py
- Executable scriptline 1
Executable file
skills/single-step-retrosynthesis/README.md
- Network accessline 31
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 36
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 37
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/single-step-retrosynthesis/README_zh.md
- Network accessline 19
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 24
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/single-step-retrosynthesis/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/single-step-retrosynthesis/*'` 只解这一个目录）。如果你本来就在跑
skills/single-step-retrosynthesis/SKILL.md
- Network accessline 13
name: RetroChimera 1 license: MIT terms_url: https://github.com/microsoft/retrochimera/blob/main/LICENSE - Network accessline 14
license: MIT terms_url: https://github.com/microsoft/retrochimera/blob/main/LICENSE --- - Network accessline 15
terms_url: https://github.com/microsoft/retrochimera/blob/main/LICENSE - Network accessline 171
Primary model source: <https://github.com/microsoft/retrochimera>. Read deployment
skills/solublempnn/README.md
- Network accessline 31
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 36
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 37
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/solublempnn/README_zh.md
- Network accessline 20
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 25
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/solublempnn/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/solublempnn/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何东西需要安装——wheel
skills/solublempnn/SKILL.md
- Network accessline 24
name: SolubleMPNN license: MIT terms_url: https://github.com/dauparas/ProteinMPNN/blob/main/LICENSE - Network accessline 25
license: MIT terms_url: https://github.com/dauparas/ProteinMPNN/blob/main/LICENSE --- - Network accessline 26
terms_url: https://github.com/dauparas/ProteinMPNN/blob/main/LICENSE - Network accessline 47
pip install torch numpy # if not already present git clone --depth 1 https://github.com/dauparas/ProteinMPNN.git proteinmpnn - Network accessline 48
git clone --depth 1 https://github.com/dauparas/ProteinMPNN.git proteinmpnn
skills/target_druggability_screening/README.md
- Network accessline 41
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 46
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 47
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/target_druggability_screening/README_zh.md
- Network accessline 22
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 27
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/target_druggability_screening/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/target_druggability_screening/*'` 只解这一个目录）。如果你
skills/target_druggability_screening/SKILL.md
skills/target_druggability_screening/kernel.py
- Executable scriptline 1
Executable file - Network accessline 343
_link( f"PMID {lead['pmid']}", f"https://pubmed.ncbi.nlm.nih.gov/{quote(str(lead['pmid']), safe='')}/", - Network accessline 344
f"PMID {lead['pmid']}", f"https://pubmed.ncbi.nlm.nih.gov/{quote(str(lead['pmid']), safe='')}/", ) - Network accessline 345
f"https://pubmed.ncbi.nlm.nih.gov/{quote(str(lead['pmid']), safe='')}/", - Network accessline 349
refs.append( _link( lead["doi"], f"https://doi.org/{quote(str(lead['doi']), safe='/')}" - Network accessline 350
_link( lead["doi"], f"https://doi.org/{quote(str(lead['doi']), safe='/')}" ) - Network accessline 351
lead["doi"], f"https://doi.org/{quote(str(lead['doi']), safe='/')}"
skills/text-features/README.md
- Network accessline 34
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 39
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 40
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/text-features/README_zh.md
- Network accessline 18
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 23
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/text-features/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/text-features/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何东西需要安装——wh
skills/text-features/SKILL.md
- Network accessline 17
- kind: service name: TypeSafe info_url: https://docs.typesafe.ai/ - Network accessline 18
name: TypeSafe info_url: https://docs.typesafe.ai/ privacy_url: https://typesafe.ai/legal/privacy-policy - Network accessline 19
info_url: https://docs.typesafe.ai/ - Network accessline 20
privacy_url: https://typesafe.ai/legal/privacy-policy
skills/text-features/kernel.py
- Executable scriptline 1
Executable file
skills/using-model-endpoint/README.md
- Network accessline 31
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 36
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 37
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Access to secretsline 51
[Credential-related source omitted] - Access to secretsline 53
[Credential-related source omitted]
skills/using-model-endpoint/README_zh.md
- Network accessline 20
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 25
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/using-model-endpoint/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/using-model-endpoint/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里 - Access to secretsline 31
[Credential-related source omitted] - Access to secretsline 33
[Credential-related source omitted]
skills/using-model-endpoint/SKILL.md
skills/using-model-endpoint/provider.py
- Executable scriptline 1
Executable file - Access to secretsline 90
[Credential-related source omitted] - Access to secretsline 92
[Credential-related source omitted] - Access to secretsline 94
[Credential-related source omitted] - Access to secretsline 97
[Credential-related source omitted] - Access to secretsline 100
[Credential-related source omitted] - Access to secretsline 110
[Credential-related source omitted] - Dangerous shell operationline 136
def exec(
skills/volcengine-datapro/README.md
- Network accessline 32
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 37
The click-through form of that same download is the [repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip), - Network accessline 38
[repository zip](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip),
skills/volcengine-datapro/README_zh.md
- Network accessline 19
curl -L https://codeload.github.com/PKU-YuanGroup/OpenAI4S/tar.gz/refs/heads/main \ - Network accessline 24
同一份内容的图形界面版本是点击下载整个[仓库 zip 包](https://github.com/PKU-YuanGroup/OpenAI4S/archive/main.zip)，Windows 上也走这条路：解压后把 `skills/volcengine-datapro/` 拷出来即可（`unzip main.zip 'OpenAI4S-main/skills/volcengine-datapro/*'` 只解这一个目录）。如果你本来就在跑 OpenAI4S，那这里没有任何
How the signals fit together
Facts from a static read of the files at the pinned commit. They describe what the code does, not why.
Some files could not be read; the facts below still hold.
Declared and executed: 5 facts
- Contacts www.w3.org, which SKILL.md, the README and the plugin manifest do not mentionadmet_genetic/kernel.py:270High confidence
- Contacts api.openai.com, which SKILL.md, the README and the plugin manifest do not mentionbioprobench/Scripts/generate_response.py:15High confidence
- Contacts cpc2.gao-lab.org, which SKILL.md, the README and the plugin manifest do not mentionbio-alternative-splicing-isoform-switching/scripts/isoform_switch_analysis.R:61High confidence
- Contacts services.healthtech.dtu.dk, which SKILL.md, the README and the plugin manifest do not mentionbio-alternative-splicing-isoform-switching/scripts/isoform_switch_analysis.R:63High confidence
- Contacts iupred2a.elte.hu, which SKILL.md, the README and the plugin manifest do not mentionbio-alternative-splicing-isoform-switching/scripts/isoform_switch_analysis.R:64High confidence
Signal confidence: 495 facts
- Network access in code of documentationadmet_genetic/README.md:31Low confidence
- Network access in prose of documentationadmet_genetic/README.md:36Low confidence
- Network access in prose of documentationadmet_genetic/README.md:37Low confidence
- Network access in code of documentationadmet_genetic/README_zh.md:20Low confidence
- Network access in prose of documentationadmet_genetic/README_zh.md:25Low confidence
- Network access in the frontmatter of the SKILL.mdadmet_genetic/SKILL.md:18Low confidence
- Network access in the frontmatter of the SKILL.mdadmet_genetic/SKILL.md:19Low confidence
- Network access in the frontmatter of the SKILL.mdadmet_genetic/SKILL.md:20Low confidence
- Executable script in code of a file that runsadmet_genetic/examples/build_example.py:1High confidence
- Executable script in code of a file that runsadmet_genetic/kernel.py:1High confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valueadmet_genetic/kernel.py:56Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valueadmet_genetic/kernel.py:57Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valueadmet_genetic/kernel.py:58Low confidence
- Access to secrets in code of a file that runs: a variable, placeholder or call, not a stored valueadmet_genetic/kernel.py:59Low confidence
- Network access in code of a file that runsadmet_genetic/kernel.py:268High confidence
- Network access in code of a file that runsadmet_genetic/kernel.py:269High confidence
- Network access in code of a file that runsadmet_genetic/kernel.py:270High confidence
- Network access in code of documentationalphafold2/README.md:33Low confidence
- Network access in prose of documentationalphafold2/README.md:38Low confidence
- Network access in prose of documentationalphafold2/README.md:39Low confidence
- Network access in code of documentationalphafold2/README_zh.md:22Low confidence
- Network access in prose of documentationalphafold2/README_zh.md:27Low confidence
- Network access in the frontmatter of the SKILL.mdalphafold2/SKILL.md:26Low confidence
- Network access in the frontmatter of the SKILL.mdalphafold2/SKILL.md:27Low confidence
- Network access in the frontmatter of the SKILL.mdalphafold2/SKILL.md:28Low confidence
- Network access in the frontmatter of the SKILL.mdalphafold2/SKILL.md:33Low confidence
- Network access in the frontmatter of the SKILL.mdalphafold2/SKILL.md:34Low confidence
- Network access in the frontmatter of the SKILL.mdalphafold2/SKILL.md:35Low confidence
- Network access in code of documentationaudit-dataset/README.md:29Low confidence
- Network access in prose of documentationaudit-dataset/README.md:34Low confidence
- Network access in prose of documentationaudit-dataset/README.md:35Low confidence
- Network access in code of documentationaudit-dataset/README_zh.md:18Low confidence
- Network access in prose of documentationaudit-dataset/README_zh.md:23Low confidence
- Executable script in prose of a file that runsaudit-dataset/kernel.py:1Low confidence
- Network access in code of documentationbioprobench/README.md:31Low confidence
- Network access in prose of documentationbioprobench/README.md:36Low confidence
- Network access in prose of documentationbioprobench/README.md:37Low confidence
- Network access in code of documentationbioprobench/README_zh.md:20Low confidence
- Network access in prose of documentationbioprobench/README_zh.md:25Low confidence
- Network access in the frontmatter of the SKILL.mdbioprobench/SKILL.md:13Low confidence
- Network access in the frontmatter of the SKILL.mdbioprobench/SKILL.md:14Low confidence
- Network access in the frontmatter of the SKILL.mdbioprobench/SKILL.md:15Low confidence
- Network access in prose of the SKILL.mdbioprobench/SKILL.md:80Low confidence
- Network access in prose of the SKILL.mdbioprobench/SKILL.md:81Low confidence
- Access to secrets in code of the SKILL.mdbioprobench/SKILL.md:156High confidence
- Network access in code of the SKILL.mdbioprobench/SKILL.md:177High confidence
- Network access in code of the SKILL.mdbioprobench/SKILL.md:178High confidence
- Access to secrets in code of the SKILL.mdbioprobench/SKILL.md:178High confidence
- Network access in code of the SKILL.mdbioprobench/SKILL.md:179High confidence
- Network access in prose of the SKILL.mdbioprobench/SKILL.md:211Low confidence
and 445 more
Only the first 500 facts are listed.
Works with
| Agent | Status | Evidence | Install to |
|---|---|---|---|
| Claude Code | Native | Path CLAUDE.md | .claude/skills/<skill-name>/ |
| Codex | Native | Manifest AGENTS.md | .agents/skills/<skill-name>/ |
| Cursor | Compatible | Standard SKILL.md skills/alphafold2/SKILL.md | .cursor/skills/<skill-name>/ |
| Gemini CLI | Compatible | Standard SKILL.md skills/alphafold2/SKILL.md | .gemini/skills/<skill-name>/ |
| GitHub Copilot | Compatible | Standard SKILL.md skills/alphafold2/SKILL.md | .github/skills/<skill-name>/ |
| OpenCode | Compatible | Standard SKILL.md skills/alphafold2/SKILL.md | .opencode/skills/<skill-name>/ |
- Native The repository ships a manifest, folder or frontmatter field for this agent.
- Compatible A standard SKILL.md that this agent can read.
- Unknown No evidence either way.
Derived automatically from paths, manifests and frontmatter. Not tested on each agent.
