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jaechang-hits/SciAgent-Skills
197 bioinformatics & life science skills for Claude Code and AI agents — BixBench 92.0% accuracy. RNA-seq, single-cell, drug discovery, proteomics, and more. Powers OmicsHorizon.
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
legacy/opentrons-integration/SKILL.md
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legacy/plotly-interactive-visualization/SKILL.md
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legacy/seaborn-statistical-visualization/SKILL.md
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legacy/single-cell-annotation/SKILL.md
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skills/biostatistics/pymc-bayesian-modeling/SKILL.md
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skills/biostatistics/scikit-survival-analysis/SKILL.md
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skills/biostatistics/statistical-analysis/SKILL.md
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skills/biostatistics/statsmodels-statistical-modeling/SKILL.md
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skills/cell-biology/cellpose-cell-segmentation/SKILL.md
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skills/cell-biology/flowio-flow-cytometry/SKILL.md
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skills/cell-biology/napari-image-viewer/SKILL.md
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skills/cell-biology/opencv-bioimage-analysis/SKILL.md
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skills/cell-biology/pyimagej-fiji-bridge/SKILL.md
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skills/cell-biology/scikit-image-processing/SKILL.md
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skills/cell-biology/trackpy-particle-tracking/SKILL.md
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skills/data-visualization/matplotlib-scientific-plotting/SKILL.md
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skills/data-visualization/molecular-visualization-3dmol/SKILL.md
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skills/data-visualization/multipanel/SKILL.md
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skills/data-visualization/omics-plotting/SKILL.md
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skills/data-visualization/plotly-interactive-plots/SKILL.md
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skills/data-visualization/scientific-visualization/SKILL.md
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skills/data-visualization/seaborn-statistical-plots/SKILL.md
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skills/data-visualization/statistical-significance-annotation/SKILL.md
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skills/genomics-bioinformatics/alignment/bwa-mem2-dna-aligner/SKILL.md
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skills/genomics-bioinformatics/alignment/pysam-genomic-files/SKILL.md
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skills/genomics-bioinformatics/alignment/samtools-bam-processing/SKILL.md
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skills/genomics-bioinformatics/alignment/star-rna-seq-aligner/SKILL.md
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skills/genomics-bioinformatics/annotation/bakta-genome-annotation/SKILL.md
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skills/genomics-bioinformatics/annotation/prokka-genome-annotation/SKILL.md
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skills/genomics-bioinformatics/annotation/roary-pangenome/SKILL.md
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skills/genomics-bioinformatics/arboreto-grn-inference/SKILL.md
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skills/genomics-bioinformatics/biopython-molecular-biology/SKILL.md
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skills/genomics-bioinformatics/biopython-sequence-analysis/SKILL.md
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skills/genomics-bioinformatics/databases/archs4-database/SKILL.md
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skills/genomics-bioinformatics/databases/bioservices-multi-database/SKILL.md
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skills/genomics-bioinformatics/databases/cbioportal-database/SKILL.md
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skills/genomics-bioinformatics/databases/clinpgx-database/SKILL.md
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skills/genomics-bioinformatics/databases/clinvar-database/SKILL.md
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skills/genomics-bioinformatics/databases/cosmic-database/SKILL.md
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skills/genomics-bioinformatics/databases/dbsnp-database/SKILL.md
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skills/genomics-bioinformatics/databases/depmap-crispr-essentiality/SKILL.md
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skills/genomics-bioinformatics/databases/ena-database/SKILL.md
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skills/genomics-bioinformatics/databases/encode-database/SKILL.md
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skills/genomics-bioinformatics/databases/ensembl-database/SKILL.md
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skills/genomics-bioinformatics/databases/gene-database/SKILL.md
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skills/genomics-bioinformatics/databases/geo-database/SKILL.md
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skills/genomics-bioinformatics/databases/gget-genomic-databases/SKILL.md
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skills/genomics-bioinformatics/databases/gnomad-database/SKILL.md
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skills/genomics-bioinformatics/databases/gwas-database/SKILL.md
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skills/genomics-bioinformatics/databases/jaspar-database/SKILL.md
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skills/genomics-bioinformatics/databases/kegg-database/SKILL.md
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skills/genomics-bioinformatics/databases/monarch-database/SKILL.md
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skills/genomics-bioinformatics/databases/mouse-phenome-database/SKILL.md
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skills/genomics-bioinformatics/databases/quickgo-database/SKILL.md
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skills/genomics-bioinformatics/databases/regulomedb-database/SKILL.md
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skills/genomics-bioinformatics/databases/remap-database/SKILL.md
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skills/genomics-bioinformatics/databases/ucsc-genome-browser/SKILL.md
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skills/genomics-bioinformatics/etetoolkit/SKILL.md
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skills/genomics-bioinformatics/homer-motif-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/interval-ops/bedtools-genomic-intervals/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/interval-ops/deeptools-ngs-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/interval-ops/geniml/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/interval-ops/gtars/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/macs3-peak-calling/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/qc/busco-status-interpretation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/qc/fastp-fastq-preprocessing/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/qc/multiqc-qc-reports/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/rnaseq/deseq2-differential-expression/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/rnaseq/featurecounts-rna-counting/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/rnaseq/gseapy-gene-enrichment/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/rnaseq/pydeseq2-differential-expression/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/rnaseq/salmon-rna-quantification/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/scikit-bio/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/single-cell/anndata-data-structure/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/single-cell/cell-type-annotation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/single-cell/celltypist-cell-annotation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/single-cell/cellxgene-census/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/single-cell/harmony-batch-correction/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/single-cell/popv-cell-annotation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/single-cell/scanpy-scrna-seq/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/single-cell/scvi-tools-single-cell/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/spatial-transcriptomics/multimodal-omics-integration/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/spatial-transcriptomics/spatial-domain-detection/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/spatial-transcriptomics/spatial-mapping-tangram/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/spatial-transcriptomics/spatial-panel-design/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/spatial-transcriptomics/spatial-transcriptomics-analysis-squidpy/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/spatial-transcriptomics/spatial-transcriptomics-cell-cell-communication/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/spatial-transcriptomics/spatial-transcriptomics-cell-type-annotation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/spatial-transcriptomics/spatial-transcriptomics-deconvolution-cell2location/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/spatial-transcriptomics/trajectory-inference/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/variant/bcftools-variant-manipulation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/variant/cnvkit-copy-number/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/variant/gatk-variant-calling/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/variant/plink2-gwas-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/variant/snpeff-variant-annotation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/genomics-bioinformatics/variant/vcf-variant-filtering/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/lab-automation/benchling-integration/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/lab-automation/opentrons-protocol-api/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/lab-automation/protocolsio-integration/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/lab-automation/pylabrobot/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/lab-automation/western-blot-quantification/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/medical-imaging/histolab-wsi-processing/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/medical-imaging/imaging-data-commons/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/medical-imaging/nnunet-segmentation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/medical-imaging/omero-integration/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/medical-imaging/pathml/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/medical-imaging/pydicom-medical-imaging/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/medical-imaging/simpleitk-image-registration/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/molecular-biology/plannotate-plasmid-annotation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/molecular-biology/sgrna-design-guide/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/molecular-biology/viennarna-structure-prediction/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/proteomics-protein-engineering/adaptyv-bio/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/proteomics-protein-engineering/esm-protein-language-model/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/proteomics-protein-engineering/hmdb-database/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/proteomics-protein-engineering/interpro-database/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
Diff text is unavailable under the recorded licenses. Line counts and the GitHub link remain available.
Week of Oct 26, 2026 · Line counts unavailable
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First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/proteomics-protein-engineering/matchms-spectral-matching/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
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55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/proteomics-protein-engineering/maxquant-proteomics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/proteomics-protein-engineering/metabolomics-workbench-database/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/proteomics-protein-engineering/pride-database/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/proteomics-protein-engineering/pyopenms-mass-spectrometry/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/proteomics-protein-engineering/uniprot-protein-database/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/aeon/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/astropy-astronomy/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/dask-parallel-computing/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/degenerate-input-filtering/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/exploratory-data-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/geopandas-geospatial/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/hypogenic-hypothesis-generation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/matlab-scientific-computing/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/nan-safe-correlation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/neb-irc-activation-energy/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/networkx-graph-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/neurokit2/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/neuropixels-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/nextflow-workflow-engine/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/polars-dataframes/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/pyhealth/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/pymatgen/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/pymoo/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/scikit-learn-machine-learning/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/shap-model-explainability/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/simpy-discrete-event-simulation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/snakemake-workflow-engine/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/spikeinterface-electrophysiology/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/sympy-symbolic-math/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/torch-geometric-graph-neural-networks/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/transformers-bio-nlp/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/umap-learn/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/uspto-database/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/vaex-dataframes/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-computing/zarr-python/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-writing/biorxiv-database/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-writing/cancer-research-figure-guide/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-writing/cell-figure-guide/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-writing/citation-management/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-writing/clinical-decision-support-documents/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-writing/elife-figure-guide/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-writing/general-figure-guide/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-writing/hypothesis-generation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-writing/lancet-figure-guide/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-writing/latex-research-posters/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-writing/literature-review/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-writing/nature-figure-guide/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-writing/nejm-figure-guide/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-writing/openalex-database/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-writing/peer-review-methodology/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-writing/pnas-figure-guide/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-writing/pubmed-database/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-writing/science-figure-guide/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-writing/scientific-brainstorming/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-writing/scientific-critical-thinking/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-writing/scientific-literature-search/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-writing/scientific-manuscript-writing/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-writing/scientific-schematics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/scientific-writing/scientific-slides/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
55c1bd3e863d → 55c1bd3e863d
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 Network or broad shell
First observation → 55c1bd3e863d
No earlier version was observed for comparison.
skills/structural-biology-drug-discovery/aizynthfinder-retrosynthesis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
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skills/structural-biology-drug-discovery/alphafold-database-access/SKILL.md
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skills/structural-biology-drug-discovery/autodock-vina-docking/SKILL.md
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skills/structural-biology-drug-discovery/chembl-database-bioactivity/SKILL.md
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skills/structural-biology-drug-discovery/clinicaltrials-database-search/SKILL.md
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skills/structural-biology-drug-discovery/dailymed-database/SKILL.md
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skills/structural-biology-drug-discovery/datamol-cheminformatics/SKILL.md
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skills/structural-biology-drug-discovery/ddinter-database/SKILL.md
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skills/structural-biology-drug-discovery/deepchem/SKILL.md
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skills/structural-biology-drug-discovery/diffdock/SKILL.md
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skills/structural-biology-drug-discovery/drugbank-database-access/SKILL.md
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skills/structural-biology-drug-discovery/emdb-database/SKILL.md
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skills/structural-biology-drug-discovery/fda-database/SKILL.md
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skills/structural-biology-drug-discovery/gtopdb-database/SKILL.md
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skills/structural-biology-drug-discovery/mdanalysis-trajectory/SKILL.md
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skills/structural-biology-drug-discovery/mdtraj-trajectory-analysis/SKILL.md
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skills/structural-biology-drug-discovery/medchem/SKILL.md
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skills/structural-biology-drug-discovery/molfeat-molecular-featurization/SKILL.md
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skills/structural-biology-drug-discovery/opentargets-database/SKILL.md
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skills/structural-biology-drug-discovery/pdb-database/SKILL.md
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skills/structural-biology-drug-discovery/pubchem-compound-search/SKILL.md
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skills/structural-biology-drug-discovery/pytdc-therapeutics-data-commons/SKILL.md
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skills/structural-biology-drug-discovery/rdkit-chemdraw-cdxml/SKILL.md
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skills/structural-biology-drug-discovery/rowan/SKILL.md
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skills/structural-biology-drug-discovery/sar-analysis/SKILL.md
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skills/structural-biology-drug-discovery/torchdrug/SKILL.md
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skills/structural-biology-drug-discovery/zinc-database/SKILL.md
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skills/systems-biology-multiomics/brenda-database/SKILL.md
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skills/systems-biology-multiomics/cellchat-cell-communication/SKILL.md
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skills/systems-biology-multiomics/kegg-pathway-analysis/SKILL.md
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skills/systems-biology-multiomics/lamindb-data-management/SKILL.md
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skills/systems-biology-multiomics/libsbml-network-modeling/SKILL.md
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skills/systems-biology-multiomics/mofaplus-multi-omics/SKILL.md
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skills/systems-biology-multiomics/muon-multiomics-singlecell/SKILL.md
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skills/systems-biology-multiomics/omics-analysis-guide/SKILL.md
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skills/systems-biology-multiomics/reactome-database/SKILL.md
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skills/systems-biology-multiomics/string-database-ppi/SKILL.md
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View on GitHubAdd to my skills
Verified commit 55c1bd3e863d11bf5a853d24bcdda10c88e6eddf
git clone --filter=blob:none --no-checkout https://github.com/jaechang-hits/SciAgent-Skills.git .hotskills/jaechang-hits-sciagent-skills && git -C .hotskills/jaechang-hits-sciagent-skills checkout --detach 55c1bd3e863d11bf5a853d24bcdda10c88e6eddf
# Copy each selected skill directory from .hotskills/jaechang-hits-sciagent-skills into .claude/skills/<skill-name>/git clone --filter=blob:none --no-checkout https://github.com/jaechang-hits/SciAgent-Skills.git .hotskills/jaechang-hits-sciagent-skills && git -C .hotskills/jaechang-hits-sciagent-skills checkout --detach 55c1bd3e863d11bf5a853d24bcdda10c88e6eddf
# Copy each selected skill directory from .hotskills/jaechang-hits-sciagent-skills into .agents/skills/<skill-name>/385GitHub stars
1star in 7 days
216skills in this repository
10commits in 30 days
Heat
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Individual skills in this repository
- opentrons-integration
legacy/opentrons-integration - plotly-interactive-visualization
legacy/plotly-interactive-visualization - seaborn-statistical-visualization
legacy/seaborn-statistical-visualization - single-cell-annotation
legacy/single-cell-annotation - pymc-bayesian-modeling
skills/biostatistics/pymc-bayesian-modeling - scikit-survival-analysis
skills/biostatistics/scikit-survival-analysis - statistical-analysis
skills/biostatistics/statistical-analysis - statsmodels-statistical-modeling
skills/biostatistics/statsmodels-statistical-modeling - cellpose-cell-segmentation
skills/cell-biology/cellpose-cell-segmentation - flowio-flow-cytometry
skills/cell-biology/flowio-flow-cytometry - napari-image-viewer
skills/cell-biology/napari-image-viewer - opencv-bioimage-analysis
skills/cell-biology/opencv-bioimage-analysis - pyimagej-fiji-bridge
skills/cell-biology/pyimagej-fiji-bridge - scikit-image-processing
skills/cell-biology/scikit-image-processing - trackpy-particle-tracking
skills/cell-biology/trackpy-particle-tracking - matplotlib-scientific-plotting
skills/data-visualization/matplotlib-scientific-plotting - molecular-visualization-3dmol
skills/data-visualization/molecular-visualization-3dmol - multipanel
skills/data-visualization/multipanel - omics-plotting
skills/data-visualization/omics-plotting - plotly-interactive-plots
skills/data-visualization/plotly-interactive-plots - scientific-visualization
skills/data-visualization/scientific-visualization - seaborn-statistical-plots
skills/data-visualization/seaborn-statistical-plots - statistical-significance-annotation
skills/data-visualization/statistical-significance-annotation - bwa-mem2-dna-aligner
skills/genomics-bioinformatics/alignment/bwa-mem2-dna-aligner - pysam-genomic-files
skills/genomics-bioinformatics/alignment/pysam-genomic-files - samtools-bam-processing
skills/genomics-bioinformatics/alignment/samtools-bam-processing - star-rna-seq-aligner
skills/genomics-bioinformatics/alignment/star-rna-seq-aligner - bakta-genome-annotation
skills/genomics-bioinformatics/annotation/bakta-genome-annotation - prokka-genome-annotation
skills/genomics-bioinformatics/annotation/prokka-genome-annotation - roary-pangenome
skills/genomics-bioinformatics/annotation/roary-pangenome - arboreto-grn-inference
skills/genomics-bioinformatics/arboreto-grn-inference - biopython-molecular-biology
skills/genomics-bioinformatics/biopython-molecular-biology - biopython-sequence-analysis
skills/genomics-bioinformatics/biopython-sequence-analysis - archs4-database
skills/genomics-bioinformatics/databases/archs4-database - bioservices-multi-database
skills/genomics-bioinformatics/databases/bioservices-multi-database - cbioportal-database
skills/genomics-bioinformatics/databases/cbioportal-database - clinpgx-database
skills/genomics-bioinformatics/databases/clinpgx-database - clinvar-database
skills/genomics-bioinformatics/databases/clinvar-database - cosmic-database
skills/genomics-bioinformatics/databases/cosmic-database - dbsnp-database
skills/genomics-bioinformatics/databases/dbsnp-database - depmap-crispr-essentiality
skills/genomics-bioinformatics/databases/depmap-crispr-essentiality - ena-database
skills/genomics-bioinformatics/databases/ena-database - encode-database
skills/genomics-bioinformatics/databases/encode-database - ensembl-database
skills/genomics-bioinformatics/databases/ensembl-database - gene-database
skills/genomics-bioinformatics/databases/gene-database - geo-database
skills/genomics-bioinformatics/databases/geo-database - gget-genomic-databases
skills/genomics-bioinformatics/databases/gget-genomic-databases - gnomad-database
skills/genomics-bioinformatics/databases/gnomad-database - gwas-database
skills/genomics-bioinformatics/databases/gwas-database - jaspar-database
skills/genomics-bioinformatics/databases/jaspar-database - kegg-database
skills/genomics-bioinformatics/databases/kegg-database - monarch-database
skills/genomics-bioinformatics/databases/monarch-database - mouse-phenome-database
skills/genomics-bioinformatics/databases/mouse-phenome-database - quickgo-database
skills/genomics-bioinformatics/databases/quickgo-database - regulomedb-database
skills/genomics-bioinformatics/databases/regulomedb-database - remap-database
skills/genomics-bioinformatics/databases/remap-database - ucsc-genome-browser
skills/genomics-bioinformatics/databases/ucsc-genome-browser - etetoolkit
skills/genomics-bioinformatics/etetoolkit - homer-motif-analysis
skills/genomics-bioinformatics/homer-motif-analysis - bedtools-genomic-intervals
skills/genomics-bioinformatics/interval-ops/bedtools-genomic-intervals - deeptools-ngs-analysis
skills/genomics-bioinformatics/interval-ops/deeptools-ngs-analysis - geniml
skills/genomics-bioinformatics/interval-ops/geniml - gtars
skills/genomics-bioinformatics/interval-ops/gtars - macs3-peak-calling
skills/genomics-bioinformatics/macs3-peak-calling - busco-status-interpretation
skills/genomics-bioinformatics/qc/busco-status-interpretation - fastp-fastq-preprocessing
skills/genomics-bioinformatics/qc/fastp-fastq-preprocessing - multiqc-qc-reports
skills/genomics-bioinformatics/qc/multiqc-qc-reports - deseq2-differential-expression
skills/genomics-bioinformatics/rnaseq/deseq2-differential-expression - featurecounts-rna-counting
skills/genomics-bioinformatics/rnaseq/featurecounts-rna-counting - gseapy-gene-enrichment
skills/genomics-bioinformatics/rnaseq/gseapy-gene-enrichment - pydeseq2-differential-expression
skills/genomics-bioinformatics/rnaseq/pydeseq2-differential-expression - salmon-rna-quantification
skills/genomics-bioinformatics/rnaseq/salmon-rna-quantification - scikit-bio
skills/genomics-bioinformatics/scikit-bio - anndata-data-structure
skills/genomics-bioinformatics/single-cell/anndata-data-structure - cell-type-annotation
skills/genomics-bioinformatics/single-cell/cell-type-annotation - celltypist-cell-annotation
skills/genomics-bioinformatics/single-cell/celltypist-cell-annotation - cellxgene-census
skills/genomics-bioinformatics/single-cell/cellxgene-census - harmony-batch-correction
skills/genomics-bioinformatics/single-cell/harmony-batch-correction - popv-cell-annotation
skills/genomics-bioinformatics/single-cell/popv-cell-annotation - scanpy-scrna-seq
skills/genomics-bioinformatics/single-cell/scanpy-scrna-seq - scvi-tools-single-cell
skills/genomics-bioinformatics/single-cell/scvi-tools-single-cell - multimodal-omics-integration
skills/genomics-bioinformatics/spatial-transcriptomics/multimodal-omics-integration - spatial-domain-detection
skills/genomics-bioinformatics/spatial-transcriptomics/spatial-domain-detection - spatial-mapping-tangram
skills/genomics-bioinformatics/spatial-transcriptomics/spatial-mapping-tangram - spatial-panel-design
skills/genomics-bioinformatics/spatial-transcriptomics/spatial-panel-design - spatial-transcriptomics-analysis-squidpy
skills/genomics-bioinformatics/spatial-transcriptomics/spatial-transcriptomics-analysis-squidpy - spatial-transcriptomics-cell-cell-communication
skills/genomics-bioinformatics/spatial-transcriptomics/spatial-transcriptomics-cell-cell-communication - spatial-transcriptomics-cell-type-annotation
skills/genomics-bioinformatics/spatial-transcriptomics/spatial-transcriptomics-cell-type-annotation - spatial-transcriptomics-deconvolution-cell2location
skills/genomics-bioinformatics/spatial-transcriptomics/spatial-transcriptomics-deconvolution-cell2location - trajectory-inference
skills/genomics-bioinformatics/spatial-transcriptomics/trajectory-inference - bcftools-variant-manipulation
skills/genomics-bioinformatics/variant/bcftools-variant-manipulation - cnvkit-copy-number
skills/genomics-bioinformatics/variant/cnvkit-copy-number - gatk-variant-calling
skills/genomics-bioinformatics/variant/gatk-variant-calling - plink2-gwas-analysis
skills/genomics-bioinformatics/variant/plink2-gwas-analysis - snpeff-variant-annotation
skills/genomics-bioinformatics/variant/snpeff-variant-annotation - vcf-variant-filtering
skills/genomics-bioinformatics/variant/vcf-variant-filtering - benchling-integration
skills/lab-automation/benchling-integration - opentrons-protocol-api
skills/lab-automation/opentrons-protocol-api - protocolsio-integration
skills/lab-automation/protocolsio-integration - pylabrobot
skills/lab-automation/pylabrobot - western-blot-quantification
skills/lab-automation/western-blot-quantification - histolab-wsi-processing
skills/medical-imaging/histolab-wsi-processing - imaging-data-commons
skills/medical-imaging/imaging-data-commons - nnunet-segmentation
skills/medical-imaging/nnunet-segmentation - omero-integration
skills/medical-imaging/omero-integration - pathml
skills/medical-imaging/pathml - pydicom-medical-imaging
skills/medical-imaging/pydicom-medical-imaging - simpleitk-image-registration
skills/medical-imaging/simpleitk-image-registration - plannotate-plasmid-annotation
skills/molecular-biology/plannotate-plasmid-annotation - sgrna-design-guide
skills/molecular-biology/sgrna-design-guide - viennarna-structure-prediction
skills/molecular-biology/viennarna-structure-prediction - adaptyv-bio
skills/proteomics-protein-engineering/adaptyv-bio - esm-protein-language-model
skills/proteomics-protein-engineering/esm-protein-language-model - hmdb-database
skills/proteomics-protein-engineering/hmdb-database - interpro-database
skills/proteomics-protein-engineering/interpro-database - matchms-spectral-matching
skills/proteomics-protein-engineering/matchms-spectral-matching - maxquant-proteomics
skills/proteomics-protein-engineering/maxquant-proteomics - metabolomics-workbench-database
skills/proteomics-protein-engineering/metabolomics-workbench-database - pride-database
skills/proteomics-protein-engineering/pride-database - pyopenms-mass-spectrometry
skills/proteomics-protein-engineering/pyopenms-mass-spectrometry - uniprot-protein-database
skills/proteomics-protein-engineering/uniprot-protein-database - aeon
skills/scientific-computing/aeon - astropy-astronomy
skills/scientific-computing/astropy-astronomy - dask-parallel-computing
skills/scientific-computing/dask-parallel-computing - degenerate-input-filtering
skills/scientific-computing/degenerate-input-filtering - exploratory-data-analysis
skills/scientific-computing/exploratory-data-analysis - geopandas-geospatial
skills/scientific-computing/geopandas-geospatial - hypogenic-hypothesis-generation
skills/scientific-computing/hypogenic-hypothesis-generation - matlab-scientific-computing
skills/scientific-computing/matlab-scientific-computing - nan-safe-correlation
skills/scientific-computing/nan-safe-correlation - neb-irc-activation-energy
skills/scientific-computing/neb-irc-activation-energy - networkx-graph-analysis
skills/scientific-computing/networkx-graph-analysis - neurokit2
skills/scientific-computing/neurokit2 - neuropixels-analysis
skills/scientific-computing/neuropixels-analysis - nextflow-workflow-engine
skills/scientific-computing/nextflow-workflow-engine - polars-dataframes
skills/scientific-computing/polars-dataframes - pyhealth
skills/scientific-computing/pyhealth - pymatgen
skills/scientific-computing/pymatgen - pymoo
skills/scientific-computing/pymoo - scikit-learn-machine-learning
skills/scientific-computing/scikit-learn-machine-learning - shap-model-explainability
skills/scientific-computing/shap-model-explainability - simpy-discrete-event-simulation
skills/scientific-computing/simpy-discrete-event-simulation - snakemake-workflow-engine
skills/scientific-computing/snakemake-workflow-engine - spikeinterface-electrophysiology
skills/scientific-computing/spikeinterface-electrophysiology - sympy-symbolic-math
skills/scientific-computing/sympy-symbolic-math - torch-geometric-graph-neural-networks
skills/scientific-computing/torch-geometric-graph-neural-networks - transformers-bio-nlp
skills/scientific-computing/transformers-bio-nlp - umap-learn
skills/scientific-computing/umap-learn - uspto-database
skills/scientific-computing/uspto-database - vaex-dataframes
skills/scientific-computing/vaex-dataframes - zarr-python
skills/scientific-computing/zarr-python - biorxiv-database
skills/scientific-writing/biorxiv-database - cancer-research-figure-guide
skills/scientific-writing/cancer-research-figure-guide - cell-figure-guide
skills/scientific-writing/cell-figure-guide - citation-management
skills/scientific-writing/citation-management - clinical-decision-support-documents
skills/scientific-writing/clinical-decision-support-documents - elife-figure-guide
skills/scientific-writing/elife-figure-guide - general-figure-guide
skills/scientific-writing/general-figure-guide - hypothesis-generation
skills/scientific-writing/hypothesis-generation - lancet-figure-guide
skills/scientific-writing/lancet-figure-guide - latex-research-posters
skills/scientific-writing/latex-research-posters - literature-review
skills/scientific-writing/literature-review - nature-figure-guide
skills/scientific-writing/nature-figure-guide - nejm-figure-guide
skills/scientific-writing/nejm-figure-guide - openalex-database
skills/scientific-writing/openalex-database - peer-review-methodology
skills/scientific-writing/peer-review-methodology - pnas-figure-guide
skills/scientific-writing/pnas-figure-guide - pubmed-database
skills/scientific-writing/pubmed-database - science-figure-guide
skills/scientific-writing/science-figure-guide - scientific-brainstorming
skills/scientific-writing/scientific-brainstorming - scientific-critical-thinking
skills/scientific-writing/scientific-critical-thinking - scientific-literature-search
skills/scientific-writing/scientific-literature-search - scientific-manuscript-writing
skills/scientific-writing/scientific-manuscript-writing - scientific-schematics
skills/scientific-writing/scientific-schematics - scientific-slides
skills/scientific-writing/scientific-slides - aizynthfinder-retrosynthesis
skills/structural-biology-drug-discovery/aizynthfinder-retrosynthesis - alphafold-database-access
skills/structural-biology-drug-discovery/alphafold-database-access - autodock-vina-docking
skills/structural-biology-drug-discovery/autodock-vina-docking - chembl-database-bioactivity
skills/structural-biology-drug-discovery/chembl-database-bioactivity - clinicaltrials-database-search
skills/structural-biology-drug-discovery/clinicaltrials-database-search - dailymed-database
skills/structural-biology-drug-discovery/dailymed-database - datamol-cheminformatics
skills/structural-biology-drug-discovery/datamol-cheminformatics - ddinter-database
skills/structural-biology-drug-discovery/ddinter-database - deepchem
skills/structural-biology-drug-discovery/deepchem - diffdock
skills/structural-biology-drug-discovery/diffdock - drugbank-database-access
skills/structural-biology-drug-discovery/drugbank-database-access - emdb-database
skills/structural-biology-drug-discovery/emdb-database - fda-database
skills/structural-biology-drug-discovery/fda-database - gtopdb-database
skills/structural-biology-drug-discovery/gtopdb-database - mdanalysis-trajectory
skills/structural-biology-drug-discovery/mdanalysis-trajectory - mdtraj-trajectory-analysis
skills/structural-biology-drug-discovery/mdtraj-trajectory-analysis - medchem
skills/structural-biology-drug-discovery/medchem - molfeat-molecular-featurization
skills/structural-biology-drug-discovery/molfeat-molecular-featurization - opentargets-database
skills/structural-biology-drug-discovery/opentargets-database - pdb-database
skills/structural-biology-drug-discovery/pdb-database - pubchem-compound-search
skills/structural-biology-drug-discovery/pubchem-compound-search - pytdc-therapeutics-data-commons
skills/structural-biology-drug-discovery/pytdc-therapeutics-data-commons - rdkit-chemdraw-cdxml
skills/structural-biology-drug-discovery/rdkit-chemdraw-cdxml - rdkit-cheminformatics
skills/structural-biology-drug-discovery/rdkit-cheminformatics - rowan
skills/structural-biology-drug-discovery/rowan - sar-analysis
skills/structural-biology-drug-discovery/sar-analysis - smina-molecular-docking
skills/structural-biology-drug-discovery/smina-molecular-docking - torchdrug
skills/structural-biology-drug-discovery/torchdrug - unichem-database
skills/structural-biology-drug-discovery/unichem-database - zinc-database
skills/structural-biology-drug-discovery/zinc-database - brenda-database
skills/systems-biology-multiomics/brenda-database - cellchat-cell-communication
skills/systems-biology-multiomics/cellchat-cell-communication - cobrapy-metabolic-modeling
skills/systems-biology-multiomics/cobrapy-metabolic-modeling - kegg-pathway-analysis
skills/systems-biology-multiomics/kegg-pathway-analysis - lamindb-data-management
skills/systems-biology-multiomics/lamindb-data-management - libsbml-network-modeling
skills/systems-biology-multiomics/libsbml-network-modeling - mofaplus-multi-omics
skills/systems-biology-multiomics/mofaplus-multi-omics - muon-multiomics-singlecell
skills/systems-biology-multiomics/muon-multiomics-singlecell - omics-analysis-guide
skills/systems-biology-multiomics/omics-analysis-guide - reactome-database
skills/systems-biology-multiomics/reactome-database - string-database-ppi
skills/systems-biology-multiomics/string-database-ppi
How it works
opentrons-integration
When your agent uses it
Opentrons Protocol API v2 for OT-2/Flex: Python protocols for pipetting, serial dilutions, PCR, plate replication; control thermocycler, heater-shaker, magnetic, temperature modules. Use pylabrobot for multi-vendor.
What it loads into context
Frontmatter
71 tokensAlways available to the agent
SKILL.md
4,731 tokensWhen the skill is activated
Reference documents
1,291,723 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Scripts executed separately
../../skills/data-visualization/molecular-visualization-3dmol/scripts/mol_viewer.py../../skills/scientific-computing/neb-irc-activation-energy/scripts/check_result.py../../skills/scientific-computing/neb-irc-activation-energy/scripts/plot_irc.py../../skills/scientific-computing/neb-irc-activation-energy/scripts/setup_env.sh../../skills/structural-biology-drug-discovery/rdkit-chemdraw-cdxml/scripts/build_reaction_scheme.py../../skills/structural-biology-drug-discovery/rdkit-chemdraw-cdxml/scripts/check_scheme.py
Files
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bytes../../skills/structural-biology-drug-discovery/rdkit-chemdraw-cdxml/SKILL.mddoc27,795 bytes../../skills/structural-biology-drug-discovery/rdkit-chemdraw-cdxml/references/cdxml-schema-reference.mddoc8,422 bytes../../skills/structural-biology-drug-discovery/rdkit-chemdraw-cdxml/scripts/build_reaction_scheme.pyruns12,548 bytes../../skills/structural-biology-drug-discovery/rdkit-chemdraw-cdxml/scripts/check_scheme.pyruns11,471 bytes../../skills/structural-biology-drug-discovery/rdkit-cheminformatics/SKILL.mddoc17,876 bytes../../skills/structural-biology-drug-discovery/rdkit-cheminformatics/references/api_reference.mddoc5,958 bytes../../skills/structural-biology-drug-discovery/rdkit-cheminformatics/references/descriptors_guide.mddoc6,297 bytes../../skills/structural-biology-drug-discovery/rdkit-cheminformatics/references/smarts_patterns.mddoc5,233 bytes../../skills/structural-biology-drug-discovery/rowan/SKILL.mddoc15,465 bytes../../skills/structural-biology-drug-discovery/sar-analysis/SKILL.mddoc23,276 bytes../../skills/structural-biology-drug-discovery/smina-molecular-docking/SKILL.mddoc14,751 bytes../../skills/structural-biology-drug-discovery/torchdrug/SKILL.mddoc18,811 bytes../../skills/structural-biology-drug-discovery/unichem-database/SKILL.mddoc35,006 bytes../../skills/structural-biology-drug-discovery/zinc-database/SKILL.mddoc21,195 bytes../../skills/systems-biology-multiomics/brenda-database/SKILL.mddoc18,330 bytes../../skills/systems-biology-multiomics/cellchat-cell-communication/SKILL.mddoc27,402 bytes../../skills/systems-biology-multiomics/cobrapy-metabolic-modeling/SKILL.mddoc19,640 bytes../../skills/systems-biology-multiomics/cobrapy-metabolic-modeling/references/api_workflows.mddoc14,731 bytes../../skills/systems-biology-multiomics/kegg-pathway-analysis/SKILL.mddoc19,365 bytes../../skills/systems-biology-multiomics/lamindb-data-management/SKILL.mddoc15,998 bytes../../skills/systems-biology-multiomics/libsbml-network-modeling/SKILL.mddoc35,168 bytes../../skills/systems-biology-multiomics/mofaplus-multi-omics/SKILL.mddoc26,254 bytes../../skills/systems-biology-multiomics/muon-multiomics-singlecell/SKILL.mddoc32,596 bytes../../skills/systems-biology-multiomics/omics-analysis-guide/SKILL.mddoc28,566 bytes../../skills/systems-biology-multiomics/reactome-database/SKILL.mddoc20,810 bytes../../skills/systems-biology-multiomics/string-database-ppi/SKILL.mddoc16,859 bytes../../skills/systems-biology-multiomics/string-database-ppi/references/api_advanced.mddoc7,052 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubplotly-interactive-visualization
When your agent uses it
Interactive visualization with Plotly. 40+ chart types (scatter, line, heatmap, 3D, geographic) with hover, zoom, pan. Two APIs: Plotly Express (DataFrame) and Graph Objects (fine control). For static publication figures use matplotlib; for statistical grammar use seaborn.
What it loads into context
Frontmatter
79 tokensAlways available to the agent
SKILL.md
4,992 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore18,178 bytesSKILL.mdcore18,178 bytesSKILL.mdcore18,178 bytesSKILL.mdcore18,178 bytesSKILL.mdcore18,178 bytesSKILL.mdcore18,178 bytesSKILL.mdcore18,178 bytesSKILL.mdcore18,178 bytesSKILL.mdcore18,178 bytesSKILL.mdcore18,178 bytesSKILL.mdcore18,178 bytesSKILL.mdcore18,178 bytesSKILL.mdcore18,178 bytesSKILL.mdcore18,178 bytesSKILL.mdcore18,178 bytesSKILL.mdcore18,178 bytesSKILL.mdcore18,178 bytesSKILL.mdcore18,178 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubseaborn-statistical-visualization
When your agent uses it
Statistical visualization on matplotlib + pandas. Distributions (histplot, kdeplot, violin, box), relational (scatter, line), categorical, regression, correlation heatmaps. Auto aggregation/CIs. Use plotly for interactive; matplotlib for low-level.
What it loads into context
Frontmatter
70 tokensAlways available to the agent
SKILL.md
4,382 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore16,506 bytesSKILL.mdcore16,506 bytesSKILL.mdcore16,506 bytesSKILL.mdcore16,506 bytesSKILL.mdcore16,506 bytesSKILL.mdcore16,506 bytesSKILL.mdcore16,506 bytesSKILL.mdcore16,506 bytesSKILL.mdcore16,506 bytesSKILL.mdcore16,506 bytesSKILL.mdcore16,506 bytesSKILL.mdcore16,506 bytesSKILL.mdcore16,506 bytesSKILL.mdcore16,506 bytesSKILL.mdcore16,506 bytesSKILL.mdcore16,506 bytesSKILL.mdcore16,506 bytesSKILL.mdcore16,506 bytesSKILL.mdcore16,506 bytesSKILL.mdcore16,506 bytesSKILL.mdcore16,506 bytesSKILL.mdcore16,506 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubsingle-cell-annotation
When your agent uses it
Best practices for single-cell RNA-seq cell type annotation including marker-based, reference-based, and automated classification approaches.
What it loads into context
Frontmatter
35 tokensAlways available to the agent
SKILL.md
3,006 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Metadata
- Overview
- Key Concepts
- Decision Framework
- Three Annotation Approaches
- Recommended Workflow
- Best Practices
- Common Pitfalls
- Tool Selection Guide
- Key Marker Genes by Cell Type
- Validation Checklist
- References
- Troubleshooting
Files
SKILL.mdcore12,811 bytesSKILL.mdcore12,811 bytesSKILL.mdcore12,811 bytesSKILL.mdcore12,811 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpymc-bayesian-modeling
When your agent uses it
Bayesian modeling with PyMC 5: priors, likelihood, NUTS/ADVI sampling, diagnostics (R-hat, ESS), LOO/WAIC comparison, prediction. Hierarchical, logistic, GP variants; predictive checks.
What it loads into context
Frontmatter
74 tokensAlways available to the agent
SKILL.md
6,007 tokensWhen the skill is activated
Reference documents
6,596 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Workflow
- Key Parameters
- Key Concepts
- Common Recipes
- Expected Outputs
- Troubleshooting
- Best Practices
- Bundled Resources
- Related Skills
- References
Files
SKILL.mdcore22,869 bytesSKILL.mdcore22,869 bytesSKILL.mdcore22,869 bytesSKILL.mdcore22,869 bytesSKILL.mdcore22,869 bytesSKILL.mdcore22,869 bytesSKILL.mdcore22,869 bytesSKILL.mdcore22,869 bytesSKILL.mdcore22,869 bytesSKILL.mdcore22,869 bytesSKILL.mdcore22,869 bytesSKILL.mdcore22,869 bytesSKILL.mdcore22,869 bytesSKILL.mdcore22,869 bytesSKILL.mdcore22,869 bytesreferences/advanced_workflows.mddoc10,796 bytesreferences/distributions_inference.mddoc14,012 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscikit-survival-analysis
When your agent uses it
Time-to-event modeling with scikit-survival: Cox PH (elastic net), Random Survival Forests, Boosting, SVMs for censored data. C-index, Brier, time-dependent AUC; Kaplan-Meier, Nelson-Aalen, competing risks. Pipeline/GridSearchCV compatible. Use statsmodels for frequentist, pymc for Bayesian, lifelines for parametric.
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
7,359 tokensWhen the skill is activated
Reference documents
7,952 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
SKILL.mdcore27,779 bytesSKILL.mdcore27,779 bytesSKILL.mdcore27,779 bytesSKILL.mdcore27,779 bytesSKILL.mdcore27,779 bytesSKILL.mdcore27,779 bytesSKILL.mdcore27,779 bytesSKILL.mdcore27,779 bytesSKILL.mdcore27,779 bytesSKILL.mdcore27,779 bytesSKILL.mdcore27,779 bytesSKILL.mdcore27,779 bytesSKILL.mdcore27,779 bytesSKILL.mdcore27,779 bytesSKILL.mdcore27,779 bytesSKILL.mdcore27,779 bytesSKILL.mdcore27,779 bytesSKILL.mdcore27,779 bytesSKILL.mdcore27,779 bytesreferences/data_competing_risks.mddoc14,226 bytesreferences/models_evaluation.mddoc15,685 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubstatistical-analysis
When your agent uses it
Guided statistical analysis: test choice, assumption checks, effect sizes, power, APA reporting. Pick tests, verify assumptions, or format results for publication. Covers frequentist (t-test, ANOVA, chi-square, regression, correlation, survival, count, reliability) and Bayesian. Use statsmodels or pymc-bayesian-modeling to fit.
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
4,634 tokensWhen the skill is activated
Reference documents
11,354 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Workflow
- Bundled Resources
- Further Reading
- Related Skills
Files
SKILL.mdcore19,303 bytesreferences/bayesian_statistics.mddoc16,032 bytesreferences/effect_sizes_and_power.mddoc12,664 bytesreferences/reporting_standards.mddoc12,496 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubstatsmodels-statistical-modeling
When your agent uses it
Python statistical modeling: regression (OLS, WLS, GLM), discrete (Logit, Poisson, NegBin), time series (ARIMA, SARIMAX, VAR), with rigorous inference, diagnostics, and hypothesis tests. Use scikit-learn for ML; statistical-analysis for test choice.
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
4,999 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- References
Files
SKILL.mdcore16,837 bytesSKILL.mdcore16,837 bytesSKILL.mdcore16,837 bytesSKILL.mdcore16,837 bytesSKILL.mdcore16,837 bytesSKILL.mdcore16,837 bytesSKILL.mdcore16,837 bytesSKILL.mdcore16,837 bytesSKILL.mdcore16,837 bytesSKILL.mdcore16,837 bytesSKILL.mdcore16,837 bytesSKILL.mdcore16,837 bytesSKILL.mdcore16,837 bytesSKILL.mdcore16,837 bytesSKILL.mdcore16,837 bytesSKILL.mdcore16,837 bytesSKILL.mdcore16,837 bytesSKILL.mdcore16,837 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubcellpose-cell-segmentation
When your agent uses it
DL cell/nucleus segmentation for fluorescence and brightfield microscopy. Pre-trained models (cyto3, nuclei, tissuenet) and a generalist flow-based algorithm segment cells without retraining. Outputs label masks for morphology and tracking. Use scikit-image watershed for rule-based; Cellpose when DL generalization across staining is needed.
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
3,859 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubflowio-flow-cytometry
When your agent uses it
Parse/write FCS (Flow Cytometry) files v2.0-3.1. Events as NumPy, channel metadata, multi-dataset files, CSV/FCS export. Use FlowKit for gating/compensation.
What it loads into context
Frontmatter
64 tokensAlways available to the agent
SKILL.md
3,221 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore12,215 bytesSKILL.mdcore12,215 bytesSKILL.mdcore12,215 bytesSKILL.mdcore12,215 bytesSKILL.mdcore12,215 bytesSKILL.mdcore12,215 bytesSKILL.mdcore12,215 bytesSKILL.mdcore12,215 bytesSKILL.mdcore12,215 bytesSKILL.mdcore12,215 bytesSKILL.mdcore12,215 bytesSKILL.mdcore12,215 bytesSKILL.mdcore12,215 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubnapari-image-viewer
When your agent uses it
Interactive viewer for microscopy. Displays 2D/3D/4D arrays as Image, Labels, Points, Shapes, Tracks layers; supports annotation, plugin analysis, headless screenshots. Core visualization for Python bioimage workflows. Use ImageJ/FIJI for macro processing; napari for Python-native interactive visualization and DL segmentation review.
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
3,539 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Parameters
- Common Workflows
- Common Recipes
- Troubleshooting
- References
Files
SKILL.mdcore13,161 bytesSKILL.mdcore13,161 bytesSKILL.mdcore13,161 bytesSKILL.mdcore13,161 bytesSKILL.mdcore13,161 bytesSKILL.mdcore13,161 bytesSKILL.mdcore13,161 bytesSKILL.mdcore13,161 bytesSKILL.mdcore13,161 bytesSKILL.mdcore13,161 bytesSKILL.mdcore13,161 bytesSKILL.mdcore13,161 bytesSKILL.mdcore13,161 bytesSKILL.mdcore13,161 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubopencv-bioimage-analysis
When your agent uses it
Computer vision for bio-image preprocessing, feature detection, real-time microscopy. Color conversion, morphology, contour/blob detection, template matching, optical flow on fluorescence/brightfield. 10-100× faster than pure Python via C++. Use scikit-image for scientific morphometry/regionprops; OpenCV for real-time, video, classical feature extraction.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
4,436 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Parameters
- Common Workflows
- Common Recipes
- Troubleshooting
- References
Files
SKILL.mdcore15,783 bytesSKILL.mdcore15,783 bytesSKILL.mdcore15,783 bytesSKILL.mdcore15,783 bytesSKILL.mdcore15,783 bytesSKILL.mdcore15,783 bytesSKILL.mdcore15,783 bytesSKILL.mdcore15,783 bytesSKILL.mdcore15,783 bytesSKILL.mdcore15,783 bytesSKILL.mdcore15,783 bytesSKILL.mdcore15,783 bytesSKILL.mdcore15,783 bytesSKILL.mdcore15,783 bytesSKILL.mdcore15,783 bytesSKILL.mdcore15,783 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpyimagej-fiji-bridge
When your agent uses it
Python bridge to ImageJ2/Fiji for macros, plugins (Bio-Formats, TrackMate, Analyze Particles), NumPy↔ImagePlus/ImgLib2 exchange, and ImageJ Ops. Automates Fiji headlessly from Python. Use scikit-image for pure Python without Fiji plugins; napari for visualization.
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
6,462 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore24,908 bytesSKILL.mdcore24,908 bytesSKILL.mdcore24,908 bytesSKILL.mdcore24,908 bytesSKILL.mdcore24,908 bytesSKILL.mdcore24,908 bytesSKILL.mdcore24,908 bytesSKILL.mdcore24,908 bytesSKILL.mdcore24,908 bytesSKILL.mdcore24,908 bytesSKILL.mdcore24,908 bytesSKILL.mdcore24,908 bytesSKILL.mdcore24,908 bytesSKILL.mdcore24,908 bytesSKILL.mdcore24,908 bytesSKILL.mdcore24,908 bytesSKILL.mdcore24,908 bytesSKILL.mdcore24,908 bytesSKILL.mdcore24,908 bytesSKILL.mdcore24,908 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscikit-image-processing
When your agent uses it
Python image processing for microscopy and bioimage analysis. Read/write images, filter (Gaussian, median, LoG), segment (thresholding, watershed, active contours), measure region properties, detect features. SciPy/NumPy ecosystem. Use OpenCV for real-time video; CellPose for DL cell segmentation; napari for visualization.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
4,517 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtrackpy-particle-tracking
When your agent uses it
Python library for single-particle tracking (SPT) in video microscopy via the Crocker-Grier algorithm. Locate particles (fluorescent spots, colloids, vesicles, cells) per frame, link into trajectories, filter short tracks, and compute MSD for diffusion analysis. 2D/3D with subpixel accuracy; reads TIF stacks, AVI, image series via pims. Use for quantitative SPT and diffusion coefficient extraction from fluorescence or brightfield video.
What it loads into context
Frontmatter
117 tokensAlways available to the agent
SKILL.md
7,338 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- References
Files
SKILL.mdcore27,751 bytesSKILL.mdcore27,751 bytesSKILL.mdcore27,751 bytesSKILL.mdcore27,751 bytesSKILL.mdcore27,751 bytesSKILL.mdcore27,751 bytesSKILL.mdcore27,751 bytesSKILL.mdcore27,751 bytesSKILL.mdcore27,751 bytesSKILL.mdcore27,751 bytesSKILL.mdcore27,751 bytesSKILL.mdcore27,751 bytesSKILL.mdcore27,751 bytesSKILL.mdcore27,751 bytesSKILL.mdcore27,751 bytesSKILL.mdcore27,751 bytesSKILL.mdcore27,751 bytesSKILL.mdcore27,751 bytesSKILL.mdcore27,751 bytesSKILL.mdcore27,751 bytesSKILL.mdcore27,751 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmatplotlib-scientific-plotting
When your agent uses it
Low-level Python plotting for scientific figures: publication-quality line, scatter, bar, heatmap, contour, 3D; multi-panel layouts; fine control of every element. PNG/PDF/SVG export. Use seaborn for quick stats, plotly for interactive.
What it loads into context
Frontmatter
76 tokensAlways available to the agent
SKILL.md
4,784 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- References
Files
SKILL.mdcore15,927 bytesSKILL.mdcore15,927 bytesSKILL.mdcore15,927 bytesSKILL.mdcore15,927 bytesSKILL.mdcore15,927 bytesSKILL.mdcore15,927 bytesSKILL.mdcore15,927 bytesSKILL.mdcore15,927 bytesSKILL.mdcore15,927 bytesSKILL.mdcore15,927 bytesSKILL.mdcore15,927 bytesSKILL.mdcore15,927 bytesSKILL.mdcore15,927 bytesSKILL.mdcore15,927 bytesSKILL.mdcore15,927 bytesSKILL.mdcore15,927 bytesSKILL.mdcore15,927 bytesSKILL.mdcore15,927 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmolecular-visualization-3dmol
When your agent uses it
3Dmol.js WebGL molecular visualization emitted as self-contained HTML. Render structures (PDB/SDF/XYZ/MOL2/cube) with stick, sphere, cartoon, line, and surface styles; animate trajectories with a frame-delay (interval, ms) control; and animate vibrational normal modes via vibrate() from per-atom dx/dy/dz displacements or from precomputed frames. Output standalone HTML that loads 3Dmol from a CDN, with optional play/pause and speed controls. Use for transition-state imaginary-mode animations, MD or reaction-path playback, docking poses, and orbital/density isosurfaces. For static 2D chemical structure drawings use rdkit-chemdraw-cdxml; for 2D statistical plots use matplotlib or plotly.
What it loads into context
Frontmatter
183 tokensAlways available to the agent
SKILL.md
3,591 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Scripts executed separately
scripts/mol_viewer.py
Files
SKILL.mdcore12,837 bytesSKILL.mdcore12,837 bytesSKILL.mdcore12,837 bytesSKILL.mdcore12,837 bytesSKILL.mdcore12,837 bytesSKILL.mdcore12,837 bytesSKILL.mdcore12,837 bytesSKILL.mdcore12,837 bytesSKILL.mdcore12,837 bytesscripts/mol_viewer.pyruns6,025 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmultipanel
When your agent uses it
Assemble multiple plots into ONE publication-ready multi-panel journal figure (e.g. Figure 1 with panels A, B, C). Use whenever the user asks to combine, compose, or lay out several plots as a single composite figure — newly plotted from data or from already-rendered panels the user supplies (PNG/PDF). Ask the user to pick one of two approaches: (1) redraw every panel into one unified figure using independent, tightly packed `subfigures` (each sized to its own labels, so axes need NOT align), consistent style, correctly placed panel letters, and per-panel legends/colorbars; (2) composite already-rendered PNG/PDF panels onto a mosaic canvas and add panel letters (image compositing, not plotting). Both export vector PDF + high-DPI PNG. For a SINGLE plot from a data table, use the sibling `omics-plotting` skill instead.
What it loads into context
Frontmatter
216 tokensAlways available to the agent
SKILL.md
6,757 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to use
- Do NOT use for
- Key Concepts
- Pre-flight Interview
- Decision Framework
- Workflow
- Shared style — paste at the top of the script
- Multi-panel discipline
- Panel-label helper
- Exporting individual panels
- Best Practices
- Common Pitfalls
- Further Reading
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubomics-plotting
When your agent uses it
omics-plotting: publication-style figure authoring for omics / bioinformatics results with matplotlib / seaborn. Read this before writing any plotting or figure code in any omics analysis — RNA-seq, proteomics, single-cell, variant, or database results — not only when a plot is explicitly requested: whenever an analysis will produce a figure, load this first and follow its recipes. Covers volcano, MA, expression / correlation heatmap, GSEA bar / dot plot, box / violin / bar / ridgeline, PCA / UMAP / t-SNE scatter, Kaplan–Meier, Manhattan / QQ / forest. Supplies a shared journal-ready style and copy-paste recipes so every figure looks like one consistent system. To combine several plots into ONE multi-panel composite figure, use the sibling `multipanel` skill.
What it loads into context
Frontmatter
205 tokensAlways available to the agent
SKILL.md
7,471 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to use
- Do NOT use for
- Key Concepts
- Decision Framework
- Workflow
- Shared style — paste at the top of every plot script
- Multi-panel / composite figures
- Plot catalogue
- Recipes
- Best Practices
- Common Pitfalls
- Further Reading
Files
SKILL.mdcore25,192 bytesSKILL.mdcore25,192 bytesSKILL.mdcore25,192 bytesSKILL.mdcore25,192 bytesSKILL.mdcore25,192 bytesSKILL.mdcore25,192 bytesSKILL.mdcore25,192 bytesSKILL.mdcore25,192 bytesSKILL.mdcore25,192 bytesSKILL.mdcore25,192 bytesSKILL.mdcore25,192 bytesSKILL.mdcore25,192 bytesSKILL.mdcore25,192 bytesSKILL.mdcore25,192 bytesSKILL.mdcore25,192 bytesSKILL.mdcore25,192 bytesSKILL.mdcore25,192 bytesSKILL.mdcore25,192 bytesSKILL.mdcore25,192 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubplotly-interactive-plots
When your agent uses it
Interactive scientific visualization with Plotly. Two APIs: plotly.express (px) for one-liner DataFrame plots, plotly.graph_objects (go) for trace-level control. 40+ chart types with hover, zoom, pan, animation. Exports HTML or static PNG/SVG/PDF via kaleido. Use for volcano plots with gene hover, dose-response dashboards, expression heatmaps, 3D molecular views. Use seaborn for stats; matplotlib for publication figures.
What it loads into context
Frontmatter
116 tokensAlways available to the agent
SKILL.md
9,920 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore32,944 bytesSKILL.mdcore32,944 bytesSKILL.mdcore32,944 bytesSKILL.mdcore32,944 bytesSKILL.mdcore32,944 bytesSKILL.mdcore32,944 bytesSKILL.mdcore32,944 bytesSKILL.mdcore32,944 bytesSKILL.mdcore32,944 bytesSKILL.mdcore32,944 bytesSKILL.mdcore32,944 bytesSKILL.mdcore32,944 bytesSKILL.mdcore32,944 bytesSKILL.mdcore32,944 bytesSKILL.mdcore32,944 bytesSKILL.mdcore32,944 bytesSKILL.mdcore32,944 bytesSKILL.mdcore32,944 bytesSKILL.mdcore32,944 bytesSKILL.mdcore32,944 bytesSKILL.mdcore32,944 bytesSKILL.mdcore32,944 bytesSKILL.mdcore32,944 bytesSKILL.mdcore32,944 bytesSKILL.mdcore32,944 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscientific-visualization
When your agent uses it
Guide for choosing and creating scientific visualizations for publications and talks. Covers chart-type selection by data structure, color theory for accessibility/print, figure composition, journal formatting (Nature, Cell, ACS), and common pitfalls. Consult when visualizing data or preparing submission figures.
What it loads into context
Frontmatter
77 tokensAlways available to the agent
SKILL.md
3,468 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Workflow
- Protocol Guidelines
- Further Reading
- Related Skills
Files
SKILL.mdcore14,573 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubseaborn-statistical-plots
When your agent uses it
Statistical visualization on matplotlib with native pandas support. Auto aggregation, CIs, grouping for distributions (histplot, kdeplot), categorical (boxplot, violinplot), relational (scatterplot, lineplot), regression (regplot, lmplot), matrix (heatmap, clustermap), grids (pairplot, FacetGrid). Use for quick statistical summaries; matplotlib for fine control; plotly for interactive HTML.
What it loads into context
Frontmatter
105 tokensAlways available to the agent
SKILL.md
9,849 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore34,417 bytesSKILL.mdcore34,417 bytesSKILL.mdcore34,417 bytesSKILL.mdcore34,417 bytesSKILL.mdcore34,417 bytesSKILL.mdcore34,417 bytesSKILL.mdcore34,417 bytesSKILL.mdcore34,417 bytesSKILL.mdcore34,417 bytesSKILL.mdcore34,417 bytesSKILL.mdcore34,417 bytesSKILL.mdcore34,417 bytesSKILL.mdcore34,417 bytesSKILL.mdcore34,417 bytesSKILL.mdcore34,417 bytesSKILL.mdcore34,417 bytesSKILL.mdcore34,417 bytesSKILL.mdcore34,417 bytesSKILL.mdcore34,417 bytesSKILL.mdcore34,417 bytesSKILL.mdcore34,417 bytesSKILL.mdcore34,417 bytesSKILL.mdcore34,417 bytesSKILL.mdcore34,417 bytesSKILL.mdcore34,417 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubstatistical-significance-annotation
When your agent uses it
Guide for annotating statistical significance (p-value asterisks) on comparison plots. Covers standard notation (ns, *, **, ***, ****), matplotlib bracket+asterisk implementation, and use with seaborn box/violin/bar plots. Use when preparing publication-ready figures with significance markers.
What it loads into context
Frontmatter
84 tokensAlways available to the agent
SKILL.md
3,035 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Workflow
- Protocol Guidelines
- Further Reading
- Related Skills
Files
SKILL.mdcore11,896 bytesSKILL.mdcore11,896 bytesSKILL.mdcore11,896 bytesSKILL.mdcore11,896 bytesSKILL.mdcore11,896 bytesSKILL.mdcore11,896 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbwa-mem2-dna-aligner
When your agent uses it
Fast short-read DNA aligner for WGS/WES/ChIP-seq. 2× faster BWA-MEM successor; outputs SAM/BAM with read group headers for GATK. Primary plus supplementary records for chimeric reads. Use STAR for RNA-seq splice-aware alignment; Bowtie2 is a comparable alternative.
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
4,125 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore13,984 bytesSKILL.mdcore13,984 bytesSKILL.mdcore13,984 bytesSKILL.mdcore13,984 bytesSKILL.mdcore13,984 bytesSKILL.mdcore13,984 bytesSKILL.mdcore13,984 bytesSKILL.mdcore13,984 bytesSKILL.mdcore13,984 bytesSKILL.mdcore13,984 bytesSKILL.mdcore13,984 bytesSKILL.mdcore13,984 bytesSKILL.mdcore13,984 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpysam-genomic-files
When your agent uses it
Read/write SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ. Region queries, pileup, variant filtering, read groups. Python htslib wrapper exposing samtools/bcftools CLI. Use STAR/BWA for alignment; GATK/DeepVariant for variant calling.
What it loads into context
Frontmatter
78 tokensAlways available to the agent
SKILL.md
5,535 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore20,762 bytesSKILL.mdcore20,762 bytesSKILL.mdcore20,762 bytesSKILL.mdcore20,762 bytesSKILL.mdcore20,762 bytesSKILL.mdcore20,762 bytesSKILL.mdcore20,762 bytesSKILL.mdcore20,762 bytesSKILL.mdcore20,762 bytesSKILL.mdcore20,762 bytesSKILL.mdcore20,762 bytesSKILL.mdcore20,762 bytesSKILL.mdcore20,762 bytesSKILL.mdcore20,762 bytesSKILL.mdcore20,762 bytesSKILL.mdcore20,762 bytesSKILL.mdcore20,762 bytesSKILL.mdcore20,762 bytesSKILL.mdcore20,762 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubsamtools-bam-processing
When your agent uses it
CLI toolkit for SAM/BAM/CRAM: sort, index, convert, filter, QC alignments. Core commands: view, sort, index, flagstat, stats, depth, markdup, merge. Required between alignment and variant/peak calling. Use pysam for Python-native BAM access; deeptools for normalized coverage tracks.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
4,489 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore16,395 bytesSKILL.mdcore16,395 bytesSKILL.mdcore16,395 bytesSKILL.mdcore16,395 bytesSKILL.mdcore16,395 bytesSKILL.mdcore16,395 bytesSKILL.mdcore16,395 bytesSKILL.mdcore16,395 bytesSKILL.mdcore16,395 bytesSKILL.mdcore16,395 bytesSKILL.mdcore16,395 bytesSKILL.mdcore16,395 bytesSKILL.mdcore16,395 bytesSKILL.mdcore16,395 bytesSKILL.mdcore16,395 bytesSKILL.mdcore16,395 bytesSKILL.mdcore16,395 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubstar-rna-seq-aligner
When your agent uses it
Splice-aware RNA-seq aligner producing sorted BAM and splice junction tables. Builds genome index, runs two-pass alignment for better junctions. Outputs sorted BAM, junctions (SJ.out.tab), stats (Log.final.out), optional gene counts. Use Salmon for fast pseudoalignment; STAR when a BAM is needed for variant calling, IGV, or ENCODE pipelines.
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
4,424 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore16,440 bytesSKILL.mdcore16,440 bytesSKILL.mdcore16,440 bytesSKILL.mdcore16,440 bytesSKILL.mdcore16,440 bytesSKILL.mdcore16,440 bytesSKILL.mdcore16,440 bytesSKILL.mdcore16,440 bytesSKILL.mdcore16,440 bytesSKILL.mdcore16,440 bytesSKILL.mdcore16,440 bytesSKILL.mdcore16,440 bytesSKILL.mdcore16,440 bytesSKILL.mdcore16,440 bytesSKILL.mdcore16,440 bytesSKILL.mdcore16,440 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbakta-genome-annotation
When your agent uses it
Annotate bacterial and archaeal genomes and plasmids with Bakta's Prodigal/HMM/diamond pipeline. Identifies CDS, ncRNA, tRNA, rRNA, tmRNA, sORFs, CRISPR arrays, oriC/oriV/oriT, and gaps against a curated UniRef-derived database. Produces NCBI-compatible GFF3, GenBank, EMBL, JSON, FASTA, TSV, and a circular genome plot. Use Prokka for legacy pipelines or non-bacterial kingdoms; PGAP for NCBI GenBank submission.
What it loads into context
Frontmatter
140 tokensAlways available to the agent
SKILL.md
6,443 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore23,530 bytesSKILL.mdcore23,530 bytesSKILL.mdcore23,530 bytesSKILL.mdcore23,530 bytesSKILL.mdcore23,530 bytesSKILL.mdcore23,530 bytesSKILL.mdcore23,530 bytesSKILL.mdcore23,530 bytesSKILL.mdcore23,530 bytesSKILL.mdcore23,530 bytesSKILL.mdcore23,530 bytesSKILL.mdcore23,530 bytesSKILL.mdcore23,530 bytesSKILL.mdcore23,530 bytesSKILL.mdcore23,530 bytesSKILL.mdcore23,530 bytesSKILL.mdcore23,530 bytesSKILL.mdcore23,530 bytesSKILL.mdcore23,530 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubprokka-genome-annotation
When your agent uses it
Annotate prokaryotic genomes (bacteria, archaea, viruses) via Prokka's BLAST/HMM pipeline. Identifies CDS, rRNA, tRNA, tmRNA, signal peptides against Pfam, TIGRFAMs, RefSeq. Outputs GFF3, GenBank, FASTA, TSV. Use PGAP for NCBI GenBank submission; Bakta for faster NCBI-compatible annotation.
What it loads into context
Frontmatter
109 tokensAlways available to the agent
SKILL.md
6,651 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore24,422 bytesSKILL.mdcore24,422 bytesSKILL.mdcore24,422 bytesSKILL.mdcore24,422 bytesSKILL.mdcore24,422 bytesSKILL.mdcore24,422 bytesSKILL.mdcore24,422 bytesSKILL.mdcore24,422 bytesSKILL.mdcore24,422 bytesSKILL.mdcore24,422 bytesSKILL.mdcore24,422 bytesSKILL.mdcore24,422 bytesSKILL.mdcore24,422 bytesSKILL.mdcore24,422 bytesSKILL.mdcore24,422 bytesSKILL.mdcore24,422 bytesSKILL.mdcore24,422 bytesSKILL.mdcore24,422 bytesSKILL.mdcore24,422 bytesSKILL.mdcore24,422 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubroary-pangenome
When your agent uses it
Compute the bacterial pan-genome from Prokka/Bakta GFF3 annotations with Roary's CD-HIT + BLAST + MCL clustering pipeline. Builds gene presence/absence matrices, core/soft-core/shell/cloud partitions, multi-FASTA core gene alignments (with `-e`), and a pan-genome reference. Use Panaroo for higher-accuracy pan-genomes from highly fragmented assemblies, PIRATE for paralog-aware clustering, or PPanGGOLiN for graph-based partitioning.
What it loads into context
Frontmatter
126 tokensAlways available to the agent
SKILL.md
6,360 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore23,125 bytesSKILL.mdcore23,125 bytesSKILL.mdcore23,125 bytesSKILL.mdcore23,125 bytesSKILL.mdcore23,125 bytesSKILL.mdcore23,125 bytesSKILL.mdcore23,125 bytesSKILL.mdcore23,125 bytesSKILL.mdcore23,125 bytesSKILL.mdcore23,125 bytesSKILL.mdcore23,125 bytesSKILL.mdcore23,125 bytesSKILL.mdcore23,125 bytesSKILL.mdcore23,125 bytesSKILL.mdcore23,125 bytesSKILL.mdcore23,125 bytesSKILL.mdcore23,125 bytesSKILL.mdcore23,125 bytesSKILL.mdcore23,125 bytesSKILL.mdcore23,125 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubarboreto-grn-inference
When your agent uses it
GRN inference from expression via GRNBoost2 (gradient boosting) or GENIE3 (Random Forest). Load matrix, filter by TFs, infer TF-target-importance links, save network. Dask-parallelized to single-cell scale. Core SCENIC component.
What it loads into context
Frontmatter
78 tokensAlways available to the agent
SKILL.md
5,198 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Workflow
- Key Parameters
- Key Concepts
- Common Recipes
- Expected Outputs
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
SKILL.mdcore21,130 bytesSKILL.mdcore21,130 bytesSKILL.mdcore21,130 bytesSKILL.mdcore21,130 bytesSKILL.mdcore21,130 bytesSKILL.mdcore21,130 bytesSKILL.mdcore21,130 bytesSKILL.mdcore21,130 bytesSKILL.mdcore21,130 bytesSKILL.mdcore21,130 bytesSKILL.mdcore21,130 bytesSKILL.mdcore21,130 bytesSKILL.mdcore21,130 bytesSKILL.mdcore21,130 bytesSKILL.mdcore21,130 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbiopython-molecular-biology
When your agent uses it
Molecular biology toolkit: sequence manipulation, FASTA/GenBank/PDB I/O, NCBI Entrez, BLAST automation, pairwise/MSA alignment, Bio.PDB, phylogenetic trees. Use for batch processing, custom pipelines, format conversion, PubMed/GenBank queries. For quick gene lookups use gget; for multi-service REST APIs use bioservices.
What it loads into context
Frontmatter
104 tokensAlways available to the agent
SKILL.md
6,789 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Common Workflows
- Key Parameters
- Common Recipes
- Troubleshooting
- References
Files
SKILL.mdcore24,218 bytesSKILL.mdcore24,218 bytesSKILL.mdcore24,218 bytesSKILL.mdcore24,218 bytesSKILL.mdcore24,218 bytesSKILL.mdcore24,218 bytesSKILL.mdcore24,218 bytesSKILL.mdcore24,218 bytesSKILL.mdcore24,218 bytesSKILL.mdcore24,218 bytesSKILL.mdcore24,218 bytesSKILL.mdcore24,218 bytesSKILL.mdcore24,218 bytesSKILL.mdcore24,218 bytesSKILL.mdcore24,218 bytesSKILL.mdcore24,218 bytesSKILL.mdcore24,218 bytesSKILL.mdcore24,218 bytesSKILL.mdcore24,218 bytesSKILL.mdcore24,218 bytesSKILL.mdcore24,218 bytesSKILL.mdcore24,218 bytesSKILL.mdcore24,218 bytesSKILL.mdcore24,218 bytesSKILL.mdcore24,218 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbiopython-sequence-analysis
When your agent uses it
Biopython sequence analysis: parse FASTA/FASTQ/GenBank/GFF (SeqIO), NCBI Entrez (esearch/efetch/elink), remote/local BLAST, pairwise/MSA alignment (PairwiseAligner, MUSCLE/ClustalW), phylogenetic trees (Phylo). Use for gene family studies, phylogenomics, comparative genomics, NCBI pipelines. For PCR/restriction/cloning use biopython-molecular-biology; for SAM/BAM use pysam.
What it loads into context
Frontmatter
135 tokensAlways available to the agent
SKILL.md
9,397 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore33,989 bytesSKILL.mdcore33,989 bytesSKILL.mdcore33,989 bytesSKILL.mdcore33,989 bytesSKILL.mdcore33,989 bytesSKILL.mdcore33,989 bytesSKILL.mdcore33,989 bytesSKILL.mdcore33,989 bytesSKILL.mdcore33,989 bytesSKILL.mdcore33,989 bytesSKILL.mdcore33,989 bytesSKILL.mdcore33,989 bytesSKILL.mdcore33,989 bytesSKILL.mdcore33,989 bytesSKILL.mdcore33,989 bytesSKILL.mdcore33,989 bytesSKILL.mdcore33,989 bytesSKILL.mdcore33,989 bytesSKILL.mdcore33,989 bytesSKILL.mdcore33,989 bytesSKILL.mdcore33,989 bytesSKILL.mdcore33,989 bytesSKILL.mdcore33,989 bytesSKILL.mdcore33,989 bytesSKILL.mdcore33,989 bytesSKILL.mdcore33,989 bytesSKILL.mdcore33,989 bytesSKILL.mdcore33,989 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubarchs4-database
When your agent uses it
Query ARCHS4 REST API for uniformly processed RNA-seq expression, tissue patterns, co-expression across 1M+ human/mouse samples. Retrieve z-scores, co-expressed genes, samples by metadata, HDF5 matrices. For variant population genetics use gnomad-database; for pathway enrichment use gget-genomic-databases (Enrichr).
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
6,897 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore26,447 bytesSKILL.mdcore26,447 bytesSKILL.mdcore26,447 bytesSKILL.mdcore26,447 bytesSKILL.mdcore26,447 bytesSKILL.mdcore26,447 bytesSKILL.mdcore26,447 bytesSKILL.mdcore26,447 bytesSKILL.mdcore26,447 bytesSKILL.mdcore26,447 bytesSKILL.mdcore26,447 bytesSKILL.mdcore26,447 bytesSKILL.mdcore26,447 bytesSKILL.mdcore26,447 bytesSKILL.mdcore26,447 bytesSKILL.mdcore26,447 bytesSKILL.mdcore26,447 bytesSKILL.mdcore26,447 bytesSKILL.mdcore26,447 bytesSKILL.mdcore26,447 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbioservices-multi-database
When your agent uses it
Unified Python interface to 40+ bioinformatics web services: UniProt proteins, KEGG pathways, ChEMBL/ChEBI/PubChem, BLAST, cross-database ID mapping, GO annotations, PPI. For deep single-DB queries use dedicated tools (gget for Ensembl, pubchempy for PubChem); bioservices excels at cross-database workflows.
What it loads into context
Frontmatter
102 tokensAlways available to the agent
SKILL.md
6,629 tokensWhen the skill is activated
Reference documents
6,524 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
SKILL.mdcore24,477 bytesSKILL.mdcore24,477 bytesSKILL.mdcore24,477 bytesSKILL.mdcore24,477 bytesSKILL.mdcore24,477 bytesSKILL.mdcore24,477 bytesSKILL.mdcore24,477 bytesSKILL.mdcore24,477 bytesSKILL.mdcore24,477 bytesSKILL.mdcore24,477 bytesSKILL.mdcore24,477 bytesSKILL.mdcore24,477 bytesSKILL.mdcore24,477 bytesSKILL.mdcore24,477 bytesSKILL.mdcore24,477 bytesSKILL.mdcore24,477 bytesSKILL.mdcore24,477 bytesSKILL.mdcore24,477 bytesSKILL.mdcore24,477 bytesSKILL.mdcore24,477 bytesSKILL.mdcore24,477 bytesSKILL.mdcore24,477 bytesreferences/identifier_mapping_guide.mddoc13,481 bytesreferences/services_catalog.mddoc10,139 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubcbioportal-database
When your agent uses it
Cancer genomics (TCGA et al.) via cBioPortal REST API. Retrieve somatic mutations, CNAs, expression, clinical data (survival/stage/treatment) across thousands of studies. Use for TMB, oncoprints, survival analysis. For population frequencies use gnomad-database; for drug-gene interactions use opentargets-database.
What it loads into context
Frontmatter
98 tokensAlways available to the agent
SKILL.md
8,658 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore32,819 bytesSKILL.mdcore32,819 bytesSKILL.mdcore32,819 bytesSKILL.mdcore32,819 bytesSKILL.mdcore32,819 bytesSKILL.mdcore32,819 bytesSKILL.mdcore32,819 bytesSKILL.mdcore32,819 bytesSKILL.mdcore32,819 bytesSKILL.mdcore32,819 bytesSKILL.mdcore32,819 bytesSKILL.mdcore32,819 bytesSKILL.mdcore32,819 bytesSKILL.mdcore32,819 bytesSKILL.mdcore32,819 bytesSKILL.mdcore32,819 bytesSKILL.mdcore32,819 bytesSKILL.mdcore32,819 bytesSKILL.mdcore32,819 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubclinpgx-database
When your agent uses it
Query the ClinPGx (formerly PharmGKB) REST API plus the CPIC PostgREST companion API for pharmacogenomic clinical annotations, CPIC/DPWG dosing guidelines, gene-drug pairs, variant-drug associations, FDA/EMA drug labels, and PGx pathways. Two-host architecture: api.clinpgx.org for annotation records, api.cpicpgx.org for genotype→recommendation lookups. No auth. For germline pathogenicity use clinvar-database; for somatic cancer PGx use cosmic-database or opentargets-database; for drug bioactivity use chembl-database-bioactivity.
What it loads into context
Frontmatter
154 tokensAlways available to the agent
SKILL.md
7,155 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore29,037 bytesSKILL.mdcore29,037 bytesSKILL.mdcore29,037 bytesSKILL.mdcore29,037 bytesSKILL.mdcore29,037 bytesSKILL.mdcore29,037 bytesSKILL.mdcore29,037 bytesSKILL.mdcore29,037 bytesSKILL.mdcore29,037 bytesSKILL.mdcore29,037 bytesSKILL.mdcore29,037 bytesSKILL.mdcore29,037 bytesSKILL.mdcore29,037 bytesSKILL.mdcore29,037 bytesSKILL.mdcore29,037 bytesSKILL.mdcore29,037 bytesSKILL.mdcore29,037 bytesSKILL.mdcore29,037 bytesSKILL.mdcore29,037 bytesSKILL.mdcore29,037 bytesSKILL.mdcore29,037 bytesSKILL.mdcore29,037 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubclinvar-database
When your agent uses it
Query NCBI ClinVar via E-utilities for variant clinical significance, pathogenicity, disease associations. Search by gene/rsID/condition/review status; returns ClinSig, submitter data, conditions, HGVS. For GWAS use gwas-database; for variant consequence prediction use Ensembl VEP.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
5,122 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore19,710 bytesSKILL.mdcore19,710 bytesSKILL.mdcore19,710 bytesSKILL.mdcore19,710 bytesSKILL.mdcore19,710 bytesSKILL.mdcore19,710 bytesSKILL.mdcore19,710 bytesSKILL.mdcore19,710 bytesSKILL.mdcore19,710 bytesSKILL.mdcore19,710 bytesSKILL.mdcore19,710 bytesSKILL.mdcore19,710 bytesSKILL.mdcore19,710 bytesSKILL.mdcore19,710 bytesSKILL.mdcore19,710 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubcosmic-database
When your agent uses it
Query COSMIC for cancer somatic mutations, gene census, mutational signatures, drug resistance variants. REST API v3.1 supports gene/sample/variant queries; free registration. For germline use clinvar-database; for drug-target data use opentargets-database or chembl-database-bioactivity.
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
4,737 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore18,255 bytesSKILL.mdcore18,255 bytesSKILL.mdcore18,255 bytesSKILL.mdcore18,255 bytesSKILL.mdcore18,255 bytesSKILL.mdcore18,255 bytesSKILL.mdcore18,255 bytesSKILL.mdcore18,255 bytesSKILL.mdcore18,255 bytesSKILL.mdcore18,255 bytesSKILL.mdcore18,255 bytesSKILL.mdcore18,255 bytesSKILL.mdcore18,255 bytesSKILL.mdcore18,255 bytesSKILL.mdcore18,255 bytesSKILL.mdcore18,255 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubdbsnp-database
When your agent uses it
Query NCBI dbSNP for SNP records by rsID, gene, or region via E-utilities and Variation Services REST API. Retrieve alleles, MAF, variant class (SNV/indel/MNV), clinical links, cross-DB IDs (ClinVar, dbVar, 1000G). Free; 3 req/sec (10 with key). For clinical pathogenicity use clinvar-database; for population frequencies use gnomad-database.
What it loads into context
Frontmatter
118 tokensAlways available to the agent
SKILL.md
7,972 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore29,143 bytesSKILL.mdcore29,143 bytesSKILL.mdcore29,143 bytesSKILL.mdcore29,143 bytesSKILL.mdcore29,143 bytesSKILL.mdcore29,143 bytesSKILL.mdcore29,143 bytesSKILL.mdcore29,143 bytesSKILL.mdcore29,143 bytesSKILL.mdcore29,143 bytesSKILL.mdcore29,143 bytesSKILL.mdcore29,143 bytesSKILL.mdcore29,143 bytesSKILL.mdcore29,143 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubdepmap-crispr-essentiality
When your agent uses it
DepMap CRISPR gene effect (Chronos) analysis: sign convention for essentiality, per-gene NaN-safe Spearman correlation, data loading/alignment. For general NaN-safe correlation see nan-safe-correlation; for quality filtering see degenerate-input-filtering.
What it loads into context
Frontmatter
78 tokensAlways available to the agent
SKILL.md
3,435 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Workflow
- Further Reading
- Related Skills
Files
SKILL.mdcore14,938 bytesSKILL.mdcore14,938 bytesSKILL.mdcore14,938 bytesSKILL.mdcore14,938 bytesSKILL.mdcore14,938 bytesSKILL.mdcore14,938 bytesSKILL.mdcore14,938 bytesSKILL.mdcore14,938 bytesSKILL.mdcore14,938 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubena-database
When your agent uses it
ENA REST API for sequences, reads, assemblies, and annotations. Portal API search, Browser API retrieval (XML/FASTA/EMBL), file reports for FASTQ/BAM URLs, taxonomy, cross-refs. For multi-DB Python use bioservices; for NCBI-only use pubmed-database or Biopython Entrez.
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
5,841 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
SKILL.mdcore21,392 bytesSKILL.mdcore21,392 bytesSKILL.mdcore21,392 bytesSKILL.mdcore21,392 bytesSKILL.mdcore21,392 bytesSKILL.mdcore21,392 bytesSKILL.mdcore21,392 bytesSKILL.mdcore21,392 bytesSKILL.mdcore21,392 bytesSKILL.mdcore21,392 bytesSKILL.mdcore21,392 bytesSKILL.mdcore21,392 bytesSKILL.mdcore21,392 bytesSKILL.mdcore21,392 bytesSKILL.mdcore21,392 bytesSKILL.mdcore21,392 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubencode-database
When your agent uses it
ENCODE Portal REST API for regulatory genomics: TF ChIP-seq, ATAC-seq/DNase-seq peaks, histone marks, and RNA-seq across 1000+ cell types. Search experiments by assay/biosample/target; download BED/bigWig; retrieve SCREEN cCREs by region or gene. Use to annotate variants with regulatory tracks, find open chromatin in a cell type, or fetch peak files for ChIP/ATAC analysis. For regulatory variant scoring use regulomedb-database; for GWAS associations use gwas-database.
What it loads into context
Frontmatter
141 tokensAlways available to the agent
SKILL.md
8,945 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore34,061 bytesSKILL.mdcore34,061 bytesSKILL.mdcore34,061 bytesSKILL.mdcore34,061 bytesSKILL.mdcore34,061 bytesSKILL.mdcore34,061 bytesSKILL.mdcore34,061 bytesSKILL.mdcore34,061 bytesSKILL.mdcore34,061 bytesSKILL.mdcore34,061 bytesSKILL.mdcore34,061 bytesSKILL.mdcore34,061 bytesSKILL.mdcore34,061 bytesSKILL.mdcore34,061 bytesSKILL.mdcore34,061 bytesSKILL.mdcore34,061 bytesSKILL.mdcore34,061 bytesSKILL.mdcore34,061 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubensembl-database
When your agent uses it
Ensembl REST API for gene/transcript/variant annotations in 300+ species. Gene info by symbol/ID, sequence, cross-refs (HGNC, RefSeq, UniProt), regulatory features. For bulk local use pyensembl; for pathways use kegg-database.
What it loads into context
Frontmatter
80 tokensAlways available to the agent
SKILL.md
4,337 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore15,983 bytesSKILL.mdcore15,983 bytesSKILL.mdcore15,983 bytesSKILL.mdcore15,983 bytesSKILL.mdcore15,983 bytesSKILL.mdcore15,983 bytesSKILL.mdcore15,983 bytesSKILL.mdcore15,983 bytesSKILL.mdcore15,983 bytesSKILL.mdcore15,983 bytesSKILL.mdcore15,983 bytesSKILL.mdcore15,983 bytesSKILL.mdcore15,983 bytesSKILL.mdcore15,983 bytesSKILL.mdcore15,983 bytesSKILL.mdcore15,983 bytesSKILL.mdcore15,983 bytesSKILL.mdcore15,983 bytesSKILL.mdcore15,983 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubgene-database
When your agent uses it
NCBI Gene via E-utilities: curated records across 1M+ taxa. Official symbols, aliases, RefSeq IDs, summaries, coordinates, GO, interactions. Use for gene ID resolution and cross-species function queries. For sequences use Ensembl; for expression use geo-database.
What it loads into context
Frontmatter
79 tokensAlways available to the agent
SKILL.md
4,854 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore17,748 bytesSKILL.mdcore17,748 bytesSKILL.mdcore17,748 bytesSKILL.mdcore17,748 bytesSKILL.mdcore17,748 bytesSKILL.mdcore17,748 bytesSKILL.mdcore17,748 bytesSKILL.mdcore17,748 bytesSKILL.mdcore17,748 bytesSKILL.mdcore17,748 bytesSKILL.mdcore17,748 bytesSKILL.mdcore17,748 bytesSKILL.mdcore17,748 bytesSKILL.mdcore17,748 bytesSKILL.mdcore17,748 bytesSKILL.mdcore17,748 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubgeo-database
When your agent uses it
NCBI GEO access via GEOparse and E-utilities. Search by keyword/organism/platform, download GSE series matrices, parse GPL annotations, extract GSM metadata, load expression matrices into pandas. For single-cell use cellxgene-census; for multi-DB access use gget-genomic-databases.
What it loads into context
Frontmatter
79 tokensAlways available to the agent
SKILL.md
4,502 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore17,136 bytesSKILL.mdcore17,136 bytesSKILL.mdcore17,136 bytesSKILL.mdcore17,136 bytesSKILL.mdcore17,136 bytesSKILL.mdcore17,136 bytesSKILL.mdcore17,136 bytesSKILL.mdcore17,136 bytesSKILL.mdcore17,136 bytesSKILL.mdcore17,136 bytesSKILL.mdcore17,136 bytesSKILL.mdcore17,136 bytesSKILL.mdcore17,136 bytesSKILL.mdcore17,136 bytesSKILL.mdcore17,136 bytesSKILL.mdcore17,136 bytesSKILL.mdcore17,136 bytesSKILL.mdcore17,136 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubgget-genomic-databases
When your agent uses it
Unified CLI/Python interface to 20+ genomic databases. Gene lookups (Ensembl search/info/seq), BLAST/BLAT, AlphaFold, Enrichr enrichment, OpenTargets disease/drug, CELLxGENE single-cell, cBioPortal/COSMIC cancer, ARCHS4 expression. Spans genomics, proteomics, disease. For batch/advanced BLAST use biopython; for multi-DB Python SDK use bioservices.
What it loads into context
Frontmatter
113 tokensAlways available to the agent
SKILL.md
5,693 tokensWhen the skill is activated
Reference documents
8,305 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubgnomad-database
When your agent uses it
gnomAD v4 population variant frequencies via GraphQL API. Allele counts and frequencies stratified by ancestry (AFR, AMR, EAS, NFE, SAS, FIN, ASJ, MID), gene-level constraint (pLI, LOEUF, missense z), and coverage. Identify rare or constrained variants. For clinical pathogenicity use clinvar-database; for GWAS use gwas-database.
What it loads into context
Frontmatter
110 tokensAlways available to the agent
SKILL.md
8,272 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubgwas-database
When your agent uses it
NHGRI-EBI GWAS Catalog REST API for SNP-trait associations from published GWAS. Query studies, associations, variants, traits, genes, summary stats. Build PRS candidates, analyze pleiotropy, fetch stats for Manhattan plots. No auth.
What it loads into context
Frontmatter
71 tokensAlways available to the agent
SKILL.md
7,135 tokensWhen the skill is activated
Reference documents
2,170 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
SKILL.mdcore26,552 bytesSKILL.mdcore26,552 bytesSKILL.mdcore26,552 bytesSKILL.mdcore26,552 bytesSKILL.mdcore26,552 bytesSKILL.mdcore26,552 bytesSKILL.mdcore26,552 bytesSKILL.mdcore26,552 bytesSKILL.mdcore26,552 bytesSKILL.mdcore26,552 bytesSKILL.mdcore26,552 bytesSKILL.mdcore26,552 bytesSKILL.mdcore26,552 bytesSKILL.mdcore26,552 bytesSKILL.mdcore26,552 bytesSKILL.mdcore26,552 bytesSKILL.mdcore26,552 bytesSKILL.mdcore26,552 bytesSKILL.mdcore26,552 bytesreferences/api_endpoints.mddoc8,276 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubjaspar-database
When your agent uses it
JASPAR 2024 TF binding profiles via REST API and pyJASPAR. Retrieve PFMs/PWMs by TF name, JASPAR ID, species, or structural class. Scan DNA for TFBS; browse by taxon (human, mouse) or TF family (bHLH, zinc finger). Use for motif enrichment input, TFBS scanning, and regulatory sequence analysis. For ChIP-seq peak motif discovery use homer-motif-analysis; for regulatory variant scoring use regulomedb-database.
What it loads into context
Frontmatter
127 tokensAlways available to the agent
SKILL.md
8,476 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubkegg-database
When your agent uses it
KEGG REST API (academic only). Pathways, genes, compounds, enzymes, diseases, drugs via 7 ops (info/list/find/get/conv/link/ddi). ID conversion (NCBI/UniProt/PubChem). Use bioservices for multi-DB Python.
What it loads into context
Frontmatter
79 tokensAlways available to the agent
SKILL.md
5,196 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore18,418 bytesSKILL.mdcore18,418 bytesSKILL.mdcore18,418 bytesSKILL.mdcore18,418 bytesSKILL.mdcore18,418 bytesSKILL.mdcore18,418 bytesSKILL.mdcore18,418 bytesSKILL.mdcore18,418 bytesSKILL.mdcore18,418 bytesSKILL.mdcore18,418 bytesSKILL.mdcore18,418 bytesSKILL.mdcore18,418 bytesSKILL.mdcore18,418 bytesSKILL.mdcore18,418 bytesSKILL.mdcore18,418 bytesSKILL.mdcore18,418 bytesSKILL.mdcore18,418 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmonarch-database
When your agent uses it
Monarch Initiative knowledge graph REST API for disease-gene-phenotype associations and cross-species orthology. MONDO disease-to-gene/phenotype, HP phenotype profiles, cross-species comparisons. Use for rare disease gene prioritization and phenotype-based candidate ranking. For GWAS use gwas-database; for clinical pathogenicity use clinvar-database.
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
6,904 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore26,569 bytesSKILL.mdcore26,569 bytesSKILL.mdcore26,569 bytesSKILL.mdcore26,569 bytesSKILL.mdcore26,569 bytesSKILL.mdcore26,569 bytesSKILL.mdcore26,569 bytesSKILL.mdcore26,569 bytesSKILL.mdcore26,569 bytesSKILL.mdcore26,569 bytesSKILL.mdcore26,569 bytesSKILL.mdcore26,569 bytesSKILL.mdcore26,569 bytesSKILL.mdcore26,569 bytesSKILL.mdcore26,569 bytesSKILL.mdcore26,569 bytesSKILL.mdcore26,569 bytesSKILL.mdcore26,569 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmouse-phenome-database
When your agent uses it
Retrieve mouse phenotype data from the Jackson Laboratory Mouse Phenome Database (MPD) via its REST API. Browse 520+ projects, look up per-project measure metadata, pull strain-level means (raw or LS-mean adjusted) and per-animal values, find measures by MP/VT ontology terms, and resolve strain nomenclature or gene coordinates. Use for QTL support, cross-strain comparison, mouse model selection, and ontology-driven phenotype discovery. Use monarch-database for disease-gene-phenotype knowledge graphs; ensembl-database for mouse genome annotations.
What it loads into context
Frontmatter
138 tokensAlways available to the agent
SKILL.md
7,718 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubquickgo-database
When your agent uses it
Query EBI QuickGO REST API for GO terms and protein annotations. Fetch term metadata by ID, search by keyword, walk ancestor/descendant hierarchies, download annotations filtered by taxon, evidence code, aspect. Use for GO resolution, ontology traversal, annotation retrieval before enrichment. Use gseapy-gene-enrichment for enrichment; uniprot-protein-database for proteins.
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
7,326 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore27,758 bytesSKILL.mdcore27,758 bytesSKILL.mdcore27,758 bytesSKILL.mdcore27,758 bytesSKILL.mdcore27,758 bytesSKILL.mdcore27,758 bytesSKILL.mdcore27,758 bytesSKILL.mdcore27,758 bytesSKILL.mdcore27,758 bytesSKILL.mdcore27,758 bytesSKILL.mdcore27,758 bytesSKILL.mdcore27,758 bytesSKILL.mdcore27,758 bytesSKILL.mdcore27,758 bytesSKILL.mdcore27,758 bytesSKILL.mdcore27,758 bytesSKILL.mdcore27,758 bytesSKILL.mdcore27,758 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubregulomedb-database
When your agent uses it
Query RegulomeDB v2 GET REST API to score variants for regulatory function and retrieve overlapping evidence (TF binding, histone marks, DNase peaks, footprints, motifs, eQTLs, chromatin state). Scores range 1a (strongest) to 7 (none). Use for GWAS hit prioritization, regulatory variant annotation, cis-regulatory discovery. Use clinvar-database for pathogenicity; gwas-database for trait associations.
What it loads into context
Frontmatter
117 tokensAlways available to the agent
SKILL.md
5,754 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore21,261 bytesSKILL.mdcore21,261 bytesSKILL.mdcore21,261 bytesSKILL.mdcore21,261 bytesSKILL.mdcore21,261 bytesSKILL.mdcore21,261 bytesSKILL.mdcore21,261 bytesSKILL.mdcore21,261 bytesSKILL.mdcore21,261 bytesSKILL.mdcore21,261 bytesSKILL.mdcore21,261 bytesSKILL.mdcore21,261 bytesSKILL.mdcore21,261 bytesSKILL.mdcore21,261 bytesSKILL.mdcore21,261 bytesSKILL.mdcore21,261 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubremap-database
When your agent uses it
Query ReMap 2022 TF ChIP-seq peak database via REST API and BED downloads. Retrieve TF peaks overlapping a region (chr:start-end), peaks near a gene, TFs by species, peaks filtered by biotype (promoter, enhancer), and BED files for a TF-cell type pair. Use for TF co-occupancy, regulatory annotation, and TF binding atlases. Use jaspar-database for PWM motifs; encode-database for ENCODE tracks.
What it loads into context
Frontmatter
118 tokensAlways available to the agent
SKILL.md
8,112 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore28,744 bytesSKILL.mdcore28,744 bytesSKILL.mdcore28,744 bytesSKILL.mdcore28,744 bytesSKILL.mdcore28,744 bytesSKILL.mdcore28,744 bytesSKILL.mdcore28,744 bytesSKILL.mdcore28,744 bytesSKILL.mdcore28,744 bytesSKILL.mdcore28,744 bytesSKILL.mdcore28,744 bytesSKILL.mdcore28,744 bytesSKILL.mdcore28,744 bytesSKILL.mdcore28,744 bytesSKILL.mdcore28,744 bytesSKILL.mdcore28,744 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubucsc-genome-browser
When your agent uses it
Query UCSC Genome Browser REST API for DNA sequences, tracks, gene models, and conservation across 100+ assemblies. Retrieve sequence by region, list/fetch BED/bigWig tracks, chromosome sizes, RefSeq/GENCODE gene structures, PhyloP/PhastCons scores. Use for UCSC annotations; Ensembl REST API for Ensembl gene IDs and VEP variant annotation.
What it loads into context
Frontmatter
102 tokensAlways available to the agent
SKILL.md
6,764 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore24,007 bytesSKILL.mdcore24,007 bytesSKILL.mdcore24,007 bytesSKILL.mdcore24,007 bytesSKILL.mdcore24,007 bytesSKILL.mdcore24,007 bytesSKILL.mdcore24,007 bytesSKILL.mdcore24,007 bytesSKILL.mdcore24,007 bytesSKILL.mdcore24,007 bytesSKILL.mdcore24,007 bytesSKILL.mdcore24,007 bytesSKILL.mdcore24,007 bytesSKILL.mdcore24,007 bytesSKILL.mdcore24,007 bytesSKILL.mdcore24,007 bytesSKILL.mdcore24,007 bytesSKILL.mdcore24,007 bytesSKILL.mdcore24,007 bytesSKILL.mdcore24,007 bytesSKILL.mdcore24,007 bytesSKILL.mdcore24,007 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubetetoolkit
When your agent uses it
ETE Toolkit (ETE3): Python phylogenetic tree analysis and visualization. Parse Newick/NHX/PhyloXML, traverse/annotate nodes, render figures with TreeStyle/NodeStyle, integrate NCBI taxonomy, run PhyloTree comparative genomics. Use for species trees, gene family evolution, annotated tree figures.
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
6,273 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytesSKILL.mdcore21,381 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubhomer-motif-analysis
When your agent uses it
De novo and known TF motif enrichment in ChIP-seq/ATAC-seq peaks via HOMER. findMotifsGenome.pl finds over-represented patterns vs background; annotatePeaks.pl assigns context (TSS distance, gene, repeat). Use after MACS3 to identify enriched TFs, annotate peaks with nearest genes, and validate ChIP-seq via the target motif.
What it loads into context
Frontmatter
100 tokensAlways available to the agent
SKILL.md
7,228 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore25,744 bytesSKILL.mdcore25,744 bytesSKILL.mdcore25,744 bytesSKILL.mdcore25,744 bytesSKILL.mdcore25,744 bytesSKILL.mdcore25,744 bytesSKILL.mdcore25,744 bytesSKILL.mdcore25,744 bytesSKILL.mdcore25,744 bytesSKILL.mdcore25,744 bytesSKILL.mdcore25,744 bytesSKILL.mdcore25,744 bytesSKILL.mdcore25,744 bytesSKILL.mdcore25,744 bytesSKILL.mdcore25,744 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbedtools-genomic-intervals
When your agent uses it
Genomic interval ops on BED/BAM/GFF/VCF. Find overlaps, merge intervals, compute coverage, extract FASTA, find nearest features. Core for ChIP-seq peak annotation, region filtering, genome arithmetic. Use tabix for indexed single-region queries; use deeptools for normalized bigWig coverage.
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
4,787 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore17,306 bytesSKILL.mdcore17,306 bytesSKILL.mdcore17,306 bytesSKILL.mdcore17,306 bytesSKILL.mdcore17,306 bytesSKILL.mdcore17,306 bytesSKILL.mdcore17,306 bytesSKILL.mdcore17,306 bytesSKILL.mdcore17,306 bytesSKILL.mdcore17,306 bytesSKILL.mdcore17,306 bytesSKILL.mdcore17,306 bytesSKILL.mdcore17,306 bytesSKILL.mdcore17,306 bytesSKILL.mdcore17,306 bytesSKILL.mdcore17,306 bytesSKILL.mdcore17,306 bytesSKILL.mdcore17,306 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubdeeptools-ngs-analysis
When your agent uses it
NGS CLI for ChIP/RNA/ATAC-seq. BAM→bigWig with RPGC/CPM/RPKM, sample correlation/PCA, heatmaps/profiles around features, fingerprints. For alignment use STAR/BWA; for peak calling use MACS2.
What it loads into context
Frontmatter
76 tokensAlways available to the agent
SKILL.md
4,460 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore16,339 bytesSKILL.mdcore16,339 bytesSKILL.mdcore16,339 bytesSKILL.mdcore16,339 bytesSKILL.mdcore16,339 bytesSKILL.mdcore16,339 bytesSKILL.mdcore16,339 bytesSKILL.mdcore16,339 bytesSKILL.mdcore16,339 bytesSKILL.mdcore16,339 bytesSKILL.mdcore16,339 bytesSKILL.mdcore16,339 bytesSKILL.mdcore16,339 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubgeniml
When your agent uses it
Python library for genomic interval ML. Train/apply region2vec embeddings turning BED regions into vectors, index interval datasets for ML, search embedding space with BEDSpace, and evaluate embedding quality. Use for chromatin accessibility clustering, regulatory element classification, and cross-sample region comparison.
What it loads into context
Frontmatter
74 tokensAlways available to the agent
SKILL.md
4,493 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore17,372 bytesSKILL.mdcore17,372 bytesSKILL.mdcore17,372 bytesSKILL.mdcore17,372 bytesSKILL.mdcore17,372 bytesSKILL.mdcore17,372 bytesSKILL.mdcore17,372 bytesSKILL.mdcore17,372 bytesSKILL.mdcore17,372 bytesSKILL.mdcore17,372 bytesSKILL.mdcore17,372 bytesSKILL.mdcore17,372 bytesSKILL.mdcore17,372 bytesSKILL.mdcore17,372 bytesSKILL.mdcore17,372 bytesSKILL.mdcore17,372 bytesSKILL.mdcore17,372 bytesSKILL.mdcore17,372 bytesSKILL.mdcore17,372 bytesSKILL.mdcore17,372 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubgtars
When your agent uses it
Rust-backed Python library for fast genomic token arithmetic and BED processing. High-performance BED I/O, interval set ops (intersect, merge, complement, subtract), region tokenization against a universe, universe construction. Use for preprocessing large BED collections and ML token vocabularies.
What it loads into context
Frontmatter
69 tokensAlways available to the agent
SKILL.md
4,267 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore16,638 bytesSKILL.mdcore16,638 bytesSKILL.mdcore16,638 bytesSKILL.mdcore16,638 bytesSKILL.mdcore16,638 bytesSKILL.mdcore16,638 bytesSKILL.mdcore16,638 bytesSKILL.mdcore16,638 bytesSKILL.mdcore16,638 bytesSKILL.mdcore16,638 bytesSKILL.mdcore16,638 bytesSKILL.mdcore16,638 bytesSKILL.mdcore16,638 bytesSKILL.mdcore16,638 bytesSKILL.mdcore16,638 bytesSKILL.mdcore16,638 bytesSKILL.mdcore16,638 bytesSKILL.mdcore16,638 bytesSKILL.mdcore16,638 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmacs3-peak-calling
When your agent uses it
Poisson-model peak caller for ChIP-seq/ATAC-seq BAMs. MACS3 callpeak finds enriched regions (TF sites or histone marks) vs input/IgG; outputs BED narrowPeak/broadPeak for motif analysis, annotation, and differential binding. Use narrow peaks for TF ChIP-seq and ATAC-seq; broad for H3K27me3, H3K9me3, and other broad marks.
What it loads into context
Frontmatter
115 tokensAlways available to the agent
SKILL.md
4,201 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore13,892 bytesSKILL.mdcore13,892 bytesSKILL.mdcore13,892 bytesSKILL.mdcore13,892 bytesSKILL.mdcore13,892 bytesSKILL.mdcore13,892 bytesSKILL.mdcore13,892 bytesSKILL.mdcore13,892 bytesSKILL.mdcore13,892 bytesSKILL.mdcore13,892 bytesSKILL.mdcore13,892 bytesSKILL.mdcore13,892 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbusco-status-interpretation
When your agent uses it
Guide to interpreting BUSCO completeness statuses: why Duplicated BUSCOs count as complete, parsing output files, computing/comparing completeness across proteomes/genomes, common counting mistakes. Use when running BUSCO QC, comparing assemblies, or reporting completeness. See also: prokka-genome-annotation for annotation workflows feeding BUSCO.
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
3,664 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Workflow
- Further Reading
- Related Skills
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubfastp-fastq-preprocessing
When your agent uses it
All-in-one FASTQ QC and adapter trimming. Auto-detects Illumina adapters, filters low-quality reads, corrects paired-end overlaps, emits HTML+JSON QC in one pass. 3-10x faster than Trim Galore/Trimmomatic. First step before STAR, BWA-MEM2, or Salmon.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
4,538 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore16,285 bytesSKILL.mdcore16,285 bytesSKILL.mdcore16,285 bytesSKILL.mdcore16,285 bytesSKILL.mdcore16,285 bytesSKILL.mdcore16,285 bytesSKILL.mdcore16,285 bytesSKILL.mdcore16,285 bytesSKILL.mdcore16,285 bytesSKILL.mdcore16,285 bytesSKILL.mdcore16,285 bytesSKILL.mdcore16,285 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmultiqc-qc-reports
When your agent uses it
Aggregates QC from 150+ bioinformatics tools into one interactive HTML report. Scans FastQC, samtools, STAR, HISAT2, Trim Galore, featureCounts, Kallisto, Salmon, Picard, GATK logs; merges per-sample stats with plots. For NGS pipeline-wide QC. Use FastQC directly for single-sample; MultiQC for multi-sample reporting.
What it loads into context
Frontmatter
105 tokensAlways available to the agent
SKILL.md
3,017 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore11,700 bytesSKILL.mdcore11,700 bytesSKILL.mdcore11,700 bytesSKILL.mdcore11,700 bytesSKILL.mdcore11,700 bytesSKILL.mdcore11,700 bytesSKILL.mdcore11,700 bytesSKILL.mdcore11,700 bytesSKILL.mdcore11,700 bytesSKILL.mdcore11,700 bytesSKILL.mdcore11,700 bytesSKILL.mdcore11,700 bytesSKILL.mdcore11,700 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubdeseq2-differential-expression
When your agent uses it
Bulk RNA-seq DE with R/Bioconductor DESeq2. Negative binomial GLM, empirical Bayes shrinkage, Wald/LRT tests, multi-factor designs, Salmon tximeta import, apeglm LFC shrinkage, MA/volcano/heatmap viz. R gold standard. Use pydeseq2-differential-expression for Python; use edgeR for TMM normalization.
What it loads into context
Frontmatter
108 tokensAlways available to the agent
SKILL.md
6,721 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Workflow
- Key Parameters
- Key Concepts
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore25,818 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubfeaturecounts-rna-counting
When your agent uses it
Counts RNA-seq reads overlapping GTF gene features. Takes sorted STAR BAMs plus GTF; outputs a per-gene tab-delimited matrix across samples. Handles strandedness (0/1/2), paired-end, multi-sample batch counting in one command, and outputs assignment statistics. Use Salmon for alignment-free quantification; use featureCounts when STAR BAMs already exist.
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
3,797 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore14,795 bytesSKILL.mdcore14,795 bytesSKILL.mdcore14,795 bytesSKILL.mdcore14,795 bytesSKILL.mdcore14,795 bytesSKILL.mdcore14,795 bytesSKILL.mdcore14,795 bytesSKILL.mdcore14,795 bytesSKILL.mdcore14,795 bytesSKILL.mdcore14,795 bytesSKILL.mdcore14,795 bytesSKILL.mdcore14,795 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubgseapy-gene-enrichment
When your agent uses it
GSEA and over-representation analysis (ORA) for RNA-seq and proteomics. Wraps Enrichr for ORA against MSigDB, KEGG, GO, and 200+ databases; runs preranked GSEA on ranked DE gene lists. Outputs enrichment tables and running-score plots. Use after DESeq2 or edgeR for pathway-level interpretation.
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
4,673 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpydeseq2-differential-expression
When your agent uses it
Bulk RNA-seq DE with PyDESeq2: load counts, normalize, fit negative binomial models, Wald test (BH-FDR), LFC shrinkage, volcano/MA plots. Use for two-group comparisons, multi-factor designs with batch correction, multiple contrasts.
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
4,056 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore15,357 bytesSKILL.mdcore15,357 bytesSKILL.mdcore15,357 bytesSKILL.mdcore15,357 bytesSKILL.mdcore15,357 bytesSKILL.mdcore15,357 bytesSKILL.mdcore15,357 bytesSKILL.mdcore15,357 bytesSKILL.mdcore15,357 bytesSKILL.mdcore15,357 bytesSKILL.mdcore15,357 bytesSKILL.mdcore15,357 bytesSKILL.mdcore15,357 bytesSKILL.mdcore15,357 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubsalmon-rna-quantification
When your agent uses it
Ultra-fast RNA-seq transcript/gene quantification via quasi-mapping (no BAM). Builds a k-mer index from transcriptome FASTA, quantifies in minutes. Outputs TPM/count tables (quant.sf) with optional GC- and sequence-bias correction. Integrates with tximeta/tximport for DESeq2/edgeR. Use STAR when a genome-aligned BAM is needed.
What it loads into context
Frontmatter
103 tokensAlways available to the agent
SKILL.md
4,348 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore16,037 bytesSKILL.mdcore16,037 bytesSKILL.mdcore16,037 bytesSKILL.mdcore16,037 bytesSKILL.mdcore16,037 bytesSKILL.mdcore16,037 bytesSKILL.mdcore16,037 bytesSKILL.mdcore16,037 bytesSKILL.mdcore16,037 bytesSKILL.mdcore16,037 bytesSKILL.mdcore16,037 bytesSKILL.mdcore16,037 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscikit-bio
When your agent uses it
Python library for biology: sequence manipulation (DNA/RNA/protein), pairwise/multiple alignment, phylogenetic trees (NJ, UPGMA), diversity (Shannon, Faith PD, Bray-Curtis, UniFrac), ordination (PCoA, CCA, RDA), stats (PERMANOVA, ANOSIM, Mantel), file I/O (FASTA, FASTQ, Newick, BIOM). Use for microbiome, community ecology, or phylogenetics.
What it loads into context
Frontmatter
125 tokensAlways available to the agent
SKILL.md
4,921 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
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- References
- Related Skills
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubanndata-data-structure
When your agent uses it
Annotated matrices for single-cell genomics. Stores X with obs/var metadata, layers, embeddings (obsm/varm), graphs (obsp/varp), uns. Use for .h5ad/.zarr I/O, concatenation, scverse integration. For analysis use scanpy; for probabilistic models use scvi-tools.
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
6,483 tokensWhen the skill is activated
Reference documents
7,053 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Ecosystem Integration
- Bundled Resources
- Related Skills
- References
Files
SKILL.mdcore23,287 bytesSKILL.mdcore23,287 bytesSKILL.mdcore23,287 bytesSKILL.mdcore23,287 bytesSKILL.mdcore23,287 bytesSKILL.mdcore23,287 bytesSKILL.mdcore23,287 bytesSKILL.mdcore23,287 bytesSKILL.mdcore23,287 bytesSKILL.mdcore23,287 bytesSKILL.mdcore23,287 bytesSKILL.mdcore23,287 bytesSKILL.mdcore23,287 bytesSKILL.mdcore23,287 bytesSKILL.mdcore23,287 bytesSKILL.mdcore23,287 bytesSKILL.mdcore23,287 bytesSKILL.mdcore23,287 bytesSKILL.mdcore23,287 bytesSKILL.mdcore23,287 bytesreferences/data_structure_io.mddoc11,373 bytesreferences/manipulation_concatenation.mddoc14,258 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubcell-type-annotation
When your agent uses it
scRNA-seq cell type annotation - read before any annotation skills. Chooses and runs the route: name clusters manually from canonical markers (scanpy, runnable code here), transfer labels with CellTypist or a popV/scANVI on a reference, or do both and validate one against other. Covers package availability, tissue- and state-matched references, marker panels, confidence and marker validation of transferred labels, doublets, unresolved clusters. Tool-specific code lives in celltypist/popv-cell-annotation and scvi-tools-single-cell.
What it loads into context
Frontmatter
132 tokensAlways available to the agent
SKILL.md
7,684 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Routes at a Glance
- Overview
- Key Concepts
- Pre-flight Interview
- Decision Framework
- Best Practices
- Common Pitfalls
- Workflow
- Protocol Guidelines
- Further Reading
- Related Skills
Files
SKILL.mdcore33,096 bytesSKILL.mdcore33,096 bytesSKILL.mdcore33,096 bytesSKILL.mdcore33,096 bytesSKILL.mdcore33,096 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubcelltypist-cell-annotation
When your agent uses it
Automated scRNA-seq cell type annotation via pre-trained logistic regression. 45+ models: immune, gut, lung, brain, fetal, cancer microenvironments. Input normalized AnnData; outputs per-cell labels, majority-vote cluster labels, confidence scores. Use for fast, reference-backed annotation without manual marker inspection; read cell-type-annotation first.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
5,246 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Workflow
- Key Parameters
- Key Concepts
- Common Recipes
- Expected Outputs
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore21,177 bytesSKILL.mdcore21,177 bytesSKILL.mdcore21,177 bytesSKILL.mdcore21,177 bytesSKILL.mdcore21,177 bytesSKILL.mdcore21,177 bytesSKILL.mdcore21,177 bytesSKILL.mdcore21,177 bytesSKILL.mdcore21,177 bytesSKILL.mdcore21,177 bytesSKILL.mdcore21,177 bytesSKILL.mdcore21,177 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubcellxgene-census
When your agent uses it
Query CELLxGENE Census (61M+ cells). Search by cell type/tissue/disease/organism; get AnnData, stream out-of-core, train PyTorch models. For your own data use scanpy; for annotated data use anndata.
What it loads into context
Frontmatter
67 tokensAlways available to the agent
SKILL.md
3,850 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubharmony-batch-correction
When your agent uses it
Harmony batch correction for scRNA-seq and other omics. Removes batch effects from PCA embeddings while preserving biology. Run after PCA, before UMAP. Scales to millions of cells. Python (harmonypy, scanpy) and R (Seurat).
What it loads into context
Frontmatter
72 tokensAlways available to the agent
SKILL.md
5,799 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Workflow
- Key Parameters
- Key Concepts
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore22,517 bytesSKILL.mdcore22,517 bytesSKILL.mdcore22,517 bytesSKILL.mdcore22,517 bytesSKILL.mdcore22,517 bytesSKILL.mdcore22,517 bytesSKILL.mdcore22,517 bytesSKILL.mdcore22,517 bytesSKILL.mdcore22,517 bytesSKILL.mdcore22,517 bytesSKILL.mdcore22,517 bytesSKILL.mdcore22,517 bytesSKILL.mdcore22,517 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpopv-cell-annotation
When your agent uses it
Consensus cell type annotation: runs 10+ algorithms (KNN-Harmony/BBKNN/Scanorama/scVI, CellTypist, ONCLASS, Random Forest, SCANVI, SVM, XGBoost) on a labeled reference and transfers labels via majority voting. Outputs per-method labels, consensus, agreement score. Use when single-method annotation is insufficient or you need ensemble uncertainty for novel states; to choose between manual markers, CellTypist and popV, read cell-type-annotation first.
What it loads into context
Frontmatter
123 tokensAlways available to the agent
SKILL.md
6,909 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscanpy-scrna-seq
When your agent uses it
scRNA-seq with Scanpy: QC, normalization, HVG selection, PCA, neighborhood graph, UMAP/t-SNE, Leiden clustering, markers, cell annotation, trajectory inference. Standard scRNA-seq exploration.
What it loads into context
Frontmatter
70 tokensAlways available to the agent
SKILL.md
5,226 tokensWhen the skill is activated
Reference documents
5,620 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- Bundled Resources
- References
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscvi-tools-single-cell
When your agent uses it
Deep generative models for single-cell omics: probabilistic batch correction (scVI), semi-supervised annotation (scANVI), CITE-seq RNA+protein (totalVI), transfer learning (scARCHES), and DE with uncertainty. Unified setup→train→extract API on AnnData. Use harmony-batch-correction for fast linear correction without deep learning; muon for multi-modal MuData workflows.
What it loads into context
Frontmatter
104 tokensAlways available to the agent
SKILL.md
7,535 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore28,794 bytesSKILL.mdcore28,794 bytesSKILL.mdcore28,794 bytesSKILL.mdcore28,794 bytesSKILL.mdcore28,794 bytesSKILL.mdcore28,794 bytesSKILL.mdcore28,794 bytesSKILL.mdcore28,794 bytesSKILL.mdcore28,794 bytesSKILL.mdcore28,794 bytesSKILL.mdcore28,794 bytesSKILL.mdcore28,794 bytesSKILL.mdcore28,794 bytesSKILL.mdcore28,794 bytesSKILL.mdcore28,794 bytesSKILL.mdcore28,794 bytesSKILL.mdcore28,794 bytesSKILL.mdcore28,794 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmultimodal-omics-integration
When your agent uses it
Multimodal and multi-section integration for spatial and single-cell omics: totalVI joint RNA + protein (Visium CytAssist Gene and Protein, CITE-seq), MultiVI joint RNA + ATAC (10x Multiome, spatial ATAC-RNA), BBKNN integration of several tissue sections, scanpy ingest label transfer between sections, and gene signature scores mapped on tissue. Outputs joint latent spaces, denoised protein maps, integrated clusters, transferred labels. For dissociated CITE-seq and scVI/scANVI use scvi-tools-single-cell; for MOFA+ factor analysis use mofaplus-multi-omics or muon-multiomics-singlecell; for Harmony use harmony-batch-correction.
What it loads into context
Frontmatter
173 tokensAlways available to the agent
SKILL.md
5,071 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Common Workflows
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore18,470 bytesSKILL.mdcore18,470 bytesSKILL.mdcore18,470 bytesSKILL.mdcore18,470 bytesSKILL.mdcore18,470 bytesSKILL.mdcore18,470 bytesSKILL.mdcore18,470 bytesSKILL.mdcore18,470 bytesSKILL.mdcore18,470 bytesSKILL.mdcore18,470 bytesSKILL.mdcore18,470 bytesSKILL.mdcore18,470 bytesSKILL.mdcore18,470 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubspatial-domain-detection
When your agent uses it
Spatial domain detection (tissue regions / niches) for spatial transcriptomics: SpaGCN (expression + spatial graph + H&E histology, Visium/ST), GraphST (self-supervised graph contrastive embedding, spots or cells) and UTAG (spatial message passing for Xenium/MERFISH/CosMx/IMC cells). Outputs a domain label per spot or cell, boundary refinement, cross-method agreement (ARI) and domain marker genes. For niches defined by the cell type composition of annotated cells use Step 7 of spatial-transcriptomics-cell-type-annotation; for neighbourhood enrichment and spatial statistics use spatial-transcriptomics-analysis-squidpy.
What it loads into context
Frontmatter
158 tokensAlways available to the agent
SKILL.md
5,320 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore18,901 bytesSKILL.mdcore18,901 bytesSKILL.mdcore18,901 bytesSKILL.mdcore18,901 bytesSKILL.mdcore18,901 bytesSKILL.mdcore18,901 bytesSKILL.mdcore18,901 bytesSKILL.mdcore18,901 bytesSKILL.mdcore18,901 bytesSKILL.mdcore18,901 bytesSKILL.mdcore18,901 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubspatial-mapping-tangram
When your agent uses it
Tangram (tangram-sc) mapping of scRNA-seq onto spatial transcriptomics (Visium, Slide-seq, MERFISH, Xenium), one skill for every Tangram task: marker training genes, cells/clusters/constrained mode, per-spot cell type proportions, held-out gene validation (training scores, compare_spatial_geneexp, AUC, k-fold CV), imputation of unmeasured or dropout genes (project_genes; gimVI alternative), and Visium deconvolution into segmented nuclei with the tissue image (squidpy watershed, target_count, deconvolve_cell_annotations). Task-specific routes follow the main workflow. For probabilistic spot abundances with uncertainty use spatial-transcriptomics-deconvolution-cell2location.
What it loads into context
Frontmatter
179 tokensAlways available to the agent
SKILL.md
9,899 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Workflow
- Task Routes
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
SKILL.mdcore36,492 bytesSKILL.mdcore36,492 bytesSKILL.mdcore36,492 bytesSKILL.mdcore36,492 bytesSKILL.mdcore36,492 bytesSKILL.mdcore36,492 bytesSKILL.mdcore36,492 bytesSKILL.mdcore36,492 bytesSKILL.mdcore36,492 bytesSKILL.mdcore36,492 bytesSKILL.mdcore36,492 bytesSKILL.mdcore36,492 bytesSKILL.mdcore36,492 bytesSKILL.mdcore36,492 bytesSKILL.mdcore36,492 bytesSKILL.mdcore36,492 bytesSKILL.mdcore36,492 bytesSKILL.mdcore36,492 bytesSKILL.mdcore36,492 bytesSKILL.mdcore36,492 bytesSKILL.mdcore36,492 bytesSKILL.mdcore36,492 bytesSKILL.mdcore36,492 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubspatial-panel-design
When your agent uses it
Spatial transcriptomics gene panel design (Xenium, MERFISH, CosMx custom panels) for cell type identification: rank markers on a tissue-matched CELLxGENE scRNA-seq reference, add PanglaoDB and CellMarker 2.0 marker evidence, build a per-gene evidence table, allocate per-cell-type quotas, and check that the panel separates cell types in the reference. Output: final_gene_panel.csv with N genes, cell type and reason. For annotating data from an existing panel use spatial-transcriptomics-cell-type-annotation.
What it loads into context
Frontmatter
138 tokensAlways available to the agent
SKILL.md
5,000 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore18,051 bytesSKILL.mdcore18,051 bytesSKILL.mdcore18,051 bytesSKILL.mdcore18,051 bytesSKILL.mdcore18,051 bytesSKILL.mdcore18,051 bytesSKILL.mdcore18,051 bytesSKILL.mdcore18,051 bytesSKILL.mdcore18,051 bytesSKILL.mdcore18,051 bytesSKILL.mdcore18,051 bytesSKILL.mdcore18,051 bytesSKILL.mdcore18,051 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubspatial-transcriptomics-analysis-squidpy
When your agent uses it
Squidpy spatial statistics on annotated spatial transcriptomics: build the spatial graph (Visium grid, kNN, radius, Delaunay), neighbourhood enrichment z-scores, distance-resolved co-occurrence, interaction matrix and centrality scores, Ripley's L/F/G point patterns, spatially variable genes by Moran's I / Geary's C, and permutation ligand-receptor tests (ligrec, OmniPath). Works on Visium spots and single-cell-resolution data (Xenium, MERFISH, CosMx, Slide-seq). Needs cluster or cell type labels for all but the spatially variable gene step. For an end-to-end ligand-receptor workflow use spatial-transcriptomics-cell-cell-communication.
What it loads into context
Frontmatter
172 tokensAlways available to the agent
SKILL.md
4,723 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Common Workflows
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore16,726 bytesSKILL.mdcore16,726 bytesSKILL.mdcore16,726 bytesSKILL.mdcore16,726 bytesSKILL.mdcore16,726 bytesSKILL.mdcore16,726 bytesSKILL.mdcore16,726 bytesSKILL.mdcore16,726 bytesSKILL.mdcore16,726 bytesSKILL.mdcore16,726 bytesSKILL.mdcore16,726 bytesSKILL.mdcore16,726 bytesSKILL.mdcore16,726 bytesSKILL.mdcore16,726 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubspatial-transcriptomics-cell-cell-communication
When your agent uses it
Spatial cell-cell communication / ligand-receptor (LR) interaction analysis, one skill for every route: LIANA consensus ranking between annotated cell types, CellPhoneDB v5 permutation p-values, DEG method and spatial microenvironments, squidpy neighbourhood enrichment to keep spatially adjacent sender-receiver pairs, LIANA bivariate local LR scores on the tissue (works on Visium spots without labels), pathway-focused candidate evidence tables (immune checkpoints, chemokines, Notch, Wnt, TGF-beta) with contact vs secreted classes and PubMed co-mentions, and multi-sample Tensor-cell2cell and MISTy models. Xenium/MERFISH/CosMx cells, Visium spots, scRNA-seq. Route-specific steps follow the main workflow. Needs cell type labels first (spatial-transcriptomics-cell-type-annotation or a deconvolution skill).
What it loads into context
Frontmatter
207 tokensAlways available to the agent
SKILL.md
10,286 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Workflow
- Task Routes
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
SKILL.mdcore37,122 bytesSKILL.mdcore37,122 bytesSKILL.mdcore37,122 bytesSKILL.mdcore37,122 bytesSKILL.mdcore37,122 bytesSKILL.mdcore37,122 bytesSKILL.mdcore37,122 bytesSKILL.mdcore37,122 bytesSKILL.mdcore37,122 bytesSKILL.mdcore37,122 bytesSKILL.mdcore37,122 bytesSKILL.mdcore37,122 bytesSKILL.mdcore37,122 bytesSKILL.mdcore37,122 bytesSKILL.mdcore37,122 bytesSKILL.mdcore37,122 bytesSKILL.mdcore37,122 bytesSKILL.mdcore37,122 bytesSKILL.mdcore37,122 bytesSKILL.mdcore37,122 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubspatial-transcriptomics-cell-type-annotation
When your agent uses it
Spatial transcriptomics cell type annotation for single-cell-resolution platforms (Xenium, MERFISH, CosMx, SeqFISH): CELLxGENE reference download, Harmony label transfer, per-cluster marker evidence naming lineages then subtypes, spatial niches. Visium/Slide-seq spots: GPU -> spatial-transcriptomics-deconvolution-cell2location; CPU -> spatial-mapping-tangram (cell types: per-spot proportions); no reference -> spatial-domain-detection (regions: domains from markers). Beyond labels (microenvironment, arrangement, spatial patterns): read spatial-transcriptomics-analysis-squidpy before asking the user.
What it loads into context
Frontmatter
157 tokensAlways available to the agent
SKILL.md
7,578 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Workflow
- Key Parameters
- Key Concepts
- Common Recipes
- Expected Outputs
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore27,796 bytesSKILL.mdcore27,796 bytesSKILL.mdcore27,796 bytesSKILL.mdcore27,796 bytesSKILL.mdcore27,796 bytesSKILL.mdcore27,796 bytesSKILL.mdcore27,796 bytesSKILL.mdcore27,796 bytesSKILL.mdcore27,796 bytesSKILL.mdcore27,796 bytesSKILL.mdcore27,796 bytesSKILL.mdcore27,796 bytesSKILL.mdcore27,796 bytesSKILL.mdcore27,796 bytesSKILL.mdcore27,796 bytesSKILL.mdcore27,796 bytesSKILL.mdcore27,796 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubspatial-transcriptomics-deconvolution-cell2location
When your agent uses it
Visium and other spot-based spatial transcriptomics (Slide-seq, ST) cell type deconvolution with cell2location: load Space Ranger output, get a tissue-matched scRNA-seq reference from CELLxGENE, learn cell type signatures, estimate cell type abundance per spot with posterior uncertainty, and check the maps against marker genes. Default route for 'annotate cell types' on Visium when a GPU is available. Limits: needs a tissue- and state-matched reference; the default 30000 epochs take hours on CPU, so without a GPU it runs only with fewer epochs (exploratory maps) and can exceed one execution call's time limit. For fast per-spot proportions on CPU use spatial-mapping-tangram (clusters mode); for single-cell-resolution data (Xenium, MERFISH, CosMx) use spatial-transcriptomics-cell-type-annotation.
What it loads into context
Frontmatter
212 tokensAlways available to the agent
SKILL.md
4,165 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtrajectory-inference
When your agent uses it
Trajectory inference and cell fate mapping for single-cell and spatial transcriptomics: PAGA graph + diffusion pseudotime (no velocity needed), scVelo RNA velocity (stochastic / dynamical, from spliced/unspliced counts), CellRank 2 terminal states, fate probabilities and lineage drivers, then pseudotime and fates mapped on tissue with a spatial autocorrelation check. Outputs pseudotime, velocity streams, fate probability tables. Imaging panels (Xenium, MERFISH) have no unspliced counts: use PAGA/DPT or the CellRank pseudotime kernel. For a basic PAGA recipe inside a standard scRNA-seq workflow see scanpy-scrna-seq.
What it loads into context
Frontmatter
165 tokensAlways available to the agent
SKILL.md
4,212 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore15,355 bytesSKILL.mdcore15,355 bytesSKILL.mdcore15,355 bytesSKILL.mdcore15,355 bytesSKILL.mdcore15,355 bytesSKILL.mdcore15,355 bytesSKILL.mdcore15,355 bytesSKILL.mdcore15,355 bytesSKILL.mdcore15,355 bytesSKILL.mdcore15,355 bytesSKILL.mdcore15,355 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbcftools-variant-manipulation
When your agent uses it
CLI for VCF/BCF: filter, merge, annotate, query, normalize, compute stats. Core post-variant-calling: quality filtering, multi-sample merging, rsID annotation, genotype extraction. Samtools companion in HTSlib. Use GATK for complex indel realignment during calling; use VCFtools for population genetics stats.
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
5,595 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore19,256 bytesSKILL.mdcore19,256 bytesSKILL.mdcore19,256 bytesSKILL.mdcore19,256 bytesSKILL.mdcore19,256 bytesSKILL.mdcore19,256 bytesSKILL.mdcore19,256 bytesSKILL.mdcore19,256 bytesSKILL.mdcore19,256 bytesSKILL.mdcore19,256 bytesSKILL.mdcore19,256 bytesSKILL.mdcore19,256 bytesSKILL.mdcore19,256 bytesSKILL.mdcore19,256 bytesSKILL.mdcore19,256 bytesSKILL.mdcore19,256 bytesSKILL.mdcore19,256 bytesSKILL.mdcore19,256 bytesSKILL.mdcore19,256 bytesSKILL.mdcore19,256 bytesSKILL.mdcore19,256 bytesSKILL.mdcore19,256 bytesSKILL.mdcore19,256 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubcnvkit-copy-number
When your agent uses it
Detect somatic CNVs from WES/WGS/targeted BAMs (CNVkit v0.9.x). Bin coverage in target/antitarget regions, normalize vs reference, segment with CBS/HMM, call amps/dels, scatter/diagram plots, purity/ploidy, VCF/SEG export. CLI plus Python API (cnvlib). Use GATK CNV for deep WGS with population controls; use CNVkit for targeted/exome where antitarget bins matter.
What it loads into context
Frontmatter
125 tokensAlways available to the agent
SKILL.md
6,629 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore23,535 bytesSKILL.mdcore23,535 bytesSKILL.mdcore23,535 bytesSKILL.mdcore23,535 bytesSKILL.mdcore23,535 bytesSKILL.mdcore23,535 bytesSKILL.mdcore23,535 bytesSKILL.mdcore23,535 bytesSKILL.mdcore23,535 bytesSKILL.mdcore23,535 bytesSKILL.mdcore23,535 bytesSKILL.mdcore23,535 bytesSKILL.mdcore23,535 bytesSKILL.mdcore23,535 bytesSKILL.mdcore23,535 bytesSKILL.mdcore23,535 bytesSKILL.mdcore23,535 bytesSKILL.mdcore23,535 bytesSKILL.mdcore23,535 bytesSKILL.mdcore23,535 bytesSKILL.mdcore23,535 bytesSKILL.mdcore23,535 bytesSKILL.mdcore23,535 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubgatk-variant-calling
When your agent uses it
GATK Best Practices for germline SNP/indel calling from WGS/WES BAMs. Per-sample HaplotypeCaller GVCFs, GenomicsDBImport, GenotypeGVCFs joint calling, VQSR or hard filters. Requires BWA-MEM2-aligned, markdup, BQSR BAMs. Use DeepVariant for a faster DL alternative; GATK is the NIH/ENCODE standard.
What it loads into context
Frontmatter
111 tokensAlways available to the agent
SKILL.md
4,438 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore14,821 bytesSKILL.mdcore14,821 bytesSKILL.mdcore14,821 bytesSKILL.mdcore14,821 bytesSKILL.mdcore14,821 bytesSKILL.mdcore14,821 bytesSKILL.mdcore14,821 bytesSKILL.mdcore14,821 bytesSKILL.mdcore14,821 bytesSKILL.mdcore14,821 bytesSKILL.mdcore14,821 bytesSKILL.mdcore14,821 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubplink2-gwas-analysis
When your agent uses it
GWAS and population genetics tool. Processes PLINK (.bed/.bim/.fam), VCF, and BGEN; runs QC (MAF, HWE, missingness), IBD estimation, PCA, and linear/logistic regression GWAS. Outputs Manhattan-ready summary stats. Use regenie or SAIGE for biobanks (>100k samples) needing mixed models.
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
4,046 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore14,248 bytesSKILL.mdcore14,248 bytesSKILL.mdcore14,248 bytesSKILL.mdcore14,248 bytesSKILL.mdcore14,248 bytesSKILL.mdcore14,248 bytesSKILL.mdcore14,248 bytesSKILL.mdcore14,248 bytesSKILL.mdcore14,248 bytesSKILL.mdcore14,248 bytesSKILL.mdcore14,248 bytesSKILL.mdcore14,248 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubsnpeff-variant-annotation
When your agent uses it
Annotate and filter VCF variants with SnpEff and SnpSift. SnpEff predicts functional effects (HIGH/MODERATE/LOW/MODIFIER), genes, transcripts, AA changes, HGVS; SnpSift filters and adds ClinVar/dbSNP. Java CLI with Python subprocess integration. Use ANNOVAR for multi-database annotation; Ensembl VEP for REST API; SnpEff for fast CLI with pre-built genomes.
What it loads into context
Frontmatter
112 tokensAlways available to the agent
SKILL.md
6,129 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Workflow
- Key Parameters
- Key Concepts
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore21,768 bytesSKILL.mdcore21,768 bytesSKILL.mdcore21,768 bytesSKILL.mdcore21,768 bytesSKILL.mdcore21,768 bytesSKILL.mdcore21,768 bytesSKILL.mdcore21,768 bytesSKILL.mdcore21,768 bytesSKILL.mdcore21,768 bytesSKILL.mdcore21,768 bytesSKILL.mdcore21,768 bytesSKILL.mdcore21,768 bytesSKILL.mdcore21,768 bytesSKILL.mdcore21,768 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubvcf-variant-filtering
When your agent uses it
Guide to quality filtering raw VCF files before computing summary stats (Ts/Tv ratio, variant counts, AF distributions). Covers detecting raw VCFs via FILTER column and QUAL inspection, QUAL-based filtering with bcftools, Ts/Tv interpretation, and when NOT to filter. Read before any variant-level QC task. See bcftools-variant-manipulation for advanced filters, gatk-variant-calling for caller config, samtools-bam-processing for upstream alignment QC.
What it loads into context
Frontmatter
118 tokensAlways available to the agent
SKILL.md
4,646 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Key Concepts
- Pre-flight Interview
- Decision Framework
- Best Practices
- Common Pitfalls
- Workflow
- Protocol Guidelines
- Further Reading
- Related Skills
Files
SKILL.mdcore19,451 bytesSKILL.mdcore19,451 bytesSKILL.mdcore19,451 bytesSKILL.mdcore19,451 bytesSKILL.mdcore19,451 bytesSKILL.mdcore19,451 bytesSKILL.mdcore19,451 bytesSKILL.mdcore19,451 bytesSKILL.mdcore19,451 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbenchling-integration
When your agent uses it
Benchling R&D Python SDK: CRUD on registry entities (DNA, RNA, proteins, custom), inventory, ELN, workflow automation. Needs Benchling account and API key. Use biopython for local sequence analysis; pubchem for chemical DBs.
What it loads into context
Frontmatter
71 tokensAlways available to the agent
SKILL.md
4,118 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore17,243 bytesSKILL.mdcore17,243 bytesSKILL.mdcore17,243 bytesSKILL.mdcore17,243 bytesSKILL.mdcore17,243 bytesSKILL.mdcore17,243 bytesSKILL.mdcore17,243 bytesSKILL.mdcore17,243 bytesSKILL.mdcore17,243 bytesSKILL.mdcore17,243 bytesSKILL.mdcore17,243 bytesSKILL.mdcore17,243 bytesSKILL.mdcore17,243 bytesSKILL.mdcore17,243 bytesSKILL.mdcore17,243 bytesSKILL.mdcore17,243 bytesSKILL.mdcore17,243 bytesSKILL.mdcore17,243 bytesSKILL.mdcore17,243 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubopentrons-protocol-api
When your agent uses it
Python API v2 for Opentrons OT-2/Flex liquid handlers: protocols as Python files with metadata and run(); control pipettes, labware, and modules (thermocycler, heater-shaker, magnetic, temperature). Simulate via opentrons_simulate then upload. Use PyLabRobot for vendor-agnostic scripts (Hamilton, Tecan).
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
8,240 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubprotocolsio-integration
When your agent uses it
protocols.io REST API: search and fetch wet-lab, bioinformatics, and clinical protocols by keyword, DOI, or category, with steps, reagents, materials, equipment, timing. Public access free; auth needed for private or publishing. Pair with opentrons-protocol-api or benchling-integration to execute.
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
4,210 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore17,216 bytesSKILL.mdcore17,216 bytesSKILL.mdcore17,216 bytesSKILL.mdcore17,216 bytesSKILL.mdcore17,216 bytesSKILL.mdcore17,216 bytesSKILL.mdcore17,216 bytesSKILL.mdcore17,216 bytesSKILL.mdcore17,216 bytesSKILL.mdcore17,216 bytesSKILL.mdcore17,216 bytesSKILL.mdcore17,216 bytesSKILL.mdcore17,216 bytesSKILL.mdcore17,216 bytesSKILL.mdcore17,216 bytesSKILL.mdcore17,216 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpylabrobot
When your agent uses it
Hardware-agnostic Python liquid-handler library: portable scripts run on Hamilton STAR, Tecan Freedom EVO, Opentrons OT-2, or a simulator without vendor lock-in. For protocol automation, method dev, plate reformatting, serial dilutions, and Python lab workflows.
What it loads into context
Frontmatter
73 tokensAlways available to the agent
SKILL.md
4,225 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore16,200 bytesSKILL.mdcore16,200 bytesSKILL.mdcore16,200 bytesSKILL.mdcore16,200 bytesSKILL.mdcore16,200 bytesSKILL.mdcore16,200 bytesSKILL.mdcore16,200 bytesSKILL.mdcore16,200 bytesSKILL.mdcore16,200 bytesSKILL.mdcore16,200 bytesSKILL.mdcore16,200 bytesSKILL.mdcore16,200 bytesSKILL.mdcore16,200 bytesSKILL.mdcore16,200 bytesSKILL.mdcore16,200 bytesSKILL.mdcore16,200 bytesSKILL.mdcore16,200 bytesSKILL.mdcore16,200 bytesSKILL.mdcore16,200 bytesSKILL.mdcore16,200 bytesSKILL.mdcore16,200 bytesSKILL.mdcore16,200 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubwestern-blot-quantification
When your agent uses it
Protocols and best practices for western blot quantification and analysis including band detection, normalization, and statistical methods.
What it loads into context
Frontmatter
36 tokensAlways available to the agent
SKILL.md
4,574 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Metadata
- Overview
- Key Concepts
- Decision Framework
- Standard Workflow
- Common Experimental Designs
- Troubleshooting
- Quality Control Checklist
- Output Files
- Example Workflow Summary
- Key Formulas Reference
- Common Pitfalls
- Best Practices
- References
Files
SKILL.mdcore20,819 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubhistolab-wsi-processing
When your agent uses it
WSI processing for digital pathology. Tissue detection, tile extraction (random, grid, score-based), filter pipelines for H&E/IHC. For dataset prep, tile-based DL, slide QC. Use pathml for multiplexed imaging.
What it loads into context
Frontmatter
67 tokensAlways available to the agent
SKILL.md
5,594 tokensWhen the skill is activated
Reference documents
8,055 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
SKILL.mdcore23,035 bytesSKILL.mdcore23,035 bytesSKILL.mdcore23,035 bytesSKILL.mdcore23,035 bytesSKILL.mdcore23,035 bytesSKILL.mdcore23,035 bytesSKILL.mdcore23,035 bytesSKILL.mdcore23,035 bytesSKILL.mdcore23,035 bytesSKILL.mdcore23,035 bytesSKILL.mdcore23,035 bytesSKILL.mdcore23,035 bytesSKILL.mdcore23,035 bytesSKILL.mdcore23,035 bytesSKILL.mdcore23,035 bytesSKILL.mdcore23,035 bytesSKILL.mdcore23,035 bytesSKILL.mdcore23,035 bytesSKILL.mdcore23,035 bytesreferences/filters_preprocessing.mddoc9,858 bytesreferences/tile_extraction.mddoc9,058 bytesreferences/visualization_slides.mddoc13,901 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubimaging-data-commons
When your agent uses it
Query and download NCI Imaging Data Commons (IDC) cancer radiology and pathology datasets via the idc-index Python client. No authentication required: the parquet index ships inside the pip wheel, SQL runs locally via DuckDB, and DICOM downloads stream from public S3/GCS buckets through s5cmd. Use sql_query() for DuckDB cohort selection, get_collections/get_patients/get_dicom_studies/get_dicom_series for hierarchical browsing, download_from_selection() for downloads, and get_viewer_URL() for OHIF/Slim links. Use pydicom-medical-imaging for local DICOM reading; histolab for whole-slide pathology preprocessing.
What it loads into context
Frontmatter
150 tokensAlways available to the agent
SKILL.md
4,593 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore18,827 bytesSKILL.mdcore18,827 bytesSKILL.mdcore18,827 bytesSKILL.mdcore18,827 bytesSKILL.mdcore18,827 bytesSKILL.mdcore18,827 bytesSKILL.mdcore18,827 bytesSKILL.mdcore18,827 bytesSKILL.mdcore18,827 bytesSKILL.mdcore18,827 bytesSKILL.mdcore18,827 bytesSKILL.mdcore18,827 bytesSKILL.mdcore18,827 bytesSKILL.mdcore18,827 bytesSKILL.mdcore18,827 bytesSKILL.mdcore18,827 bytesSKILL.mdcore18,827 bytesSKILL.mdcore18,827 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubnnunet-segmentation
When your agent uses it
Medical image segmentation with nnU-Net's self-configuring framework — auto-selects architecture, preprocessing, training for any modality. CT, MRI, microscopy, ultrasound in 2D, 3D full-res, 3D low-res, cascade. Pipeline: convert → plan/preprocess → train (5-fold CV) → best config → predict → ensemble. Use when classical segmentation fails and annotated data exists.
What it loads into context
Frontmatter
105 tokensAlways available to the agent
SKILL.md
7,422 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore27,388 bytesSKILL.mdcore27,388 bytesSKILL.mdcore27,388 bytesSKILL.mdcore27,388 bytesSKILL.mdcore27,388 bytesSKILL.mdcore27,388 bytesSKILL.mdcore27,388 bytesSKILL.mdcore27,388 bytesSKILL.mdcore27,388 bytesSKILL.mdcore27,388 bytesSKILL.mdcore27,388 bytesSKILL.mdcore27,388 bytesSKILL.mdcore27,388 bytesSKILL.mdcore27,388 bytesSKILL.mdcore27,388 bytesSKILL.mdcore27,388 bytesSKILL.mdcore27,388 bytesSKILL.mdcore27,388 bytesSKILL.mdcore27,388 bytesSKILL.mdcore27,388 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubomero-integration
When your agent uses it
Open-source bio-image data management. Use the omero-py client to connect to an OMERO server, retrieve images as numpy arrays, annotate with tags and key-value pairs, manage ROIs, and feed image data into Python analysis pipelines — programmatically, no GUI.
What it loads into context
Frontmatter
75 tokensAlways available to the agent
SKILL.md
4,180 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore16,105 bytesSKILL.mdcore16,105 bytesSKILL.mdcore16,105 bytesSKILL.mdcore16,105 bytesSKILL.mdcore16,105 bytesSKILL.mdcore16,105 bytesSKILL.mdcore16,105 bytesSKILL.mdcore16,105 bytesSKILL.mdcore16,105 bytesSKILL.mdcore16,105 bytesSKILL.mdcore16,105 bytesSKILL.mdcore16,105 bytesSKILL.mdcore16,105 bytesSKILL.mdcore16,105 bytesSKILL.mdcore16,105 bytesSKILL.mdcore16,105 bytesSKILL.mdcore16,105 bytesSKILL.mdcore16,105 bytesSKILL.mdcore16,105 bytesSKILL.mdcore16,105 bytesSKILL.mdcore16,105 bytesSKILL.mdcore16,105 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpathml
When your agent uses it
Computational pathology toolkit for whole-slide images (WSIs): load slides, extract tiles, stain normalization, nuclear segmentation, feature extraction, and ML training. Supports H&E and multiplex. For end-to-end pipelines from raw WSIs to quantitative outputs.
What it loads into context
Frontmatter
68 tokensAlways available to the agent
SKILL.md
2,719 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore10,951 bytesSKILL.mdcore10,951 bytesSKILL.mdcore10,951 bytesSKILL.mdcore10,951 bytesSKILL.mdcore10,951 bytesSKILL.mdcore10,951 bytesSKILL.mdcore10,951 bytesSKILL.mdcore10,951 bytesSKILL.mdcore10,951 bytesSKILL.mdcore10,951 bytesSKILL.mdcore10,951 bytesSKILL.mdcore10,951 bytesSKILL.mdcore10,951 bytesSKILL.mdcore10,951 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpydicom-medical-imaging
When your agent uses it
Pure Python DICOM for medical imaging (CT, MRI, X-ray, ultrasound). Read/write DICOM, pixels as NumPy, edit tags, windowing (VOI LUT), PHI anonymization, build DICOM, series→3D volumes. Use histolab for WSI pathology; nibabel for NIfTI.
What it loads into context
Frontmatter
84 tokensAlways available to the agent
SKILL.md
8,116 tokensWhen the skill is activated
Reference documents
3,897 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
SKILL.mdcore30,196 bytesSKILL.mdcore30,196 bytesSKILL.mdcore30,196 bytesSKILL.mdcore30,196 bytesSKILL.mdcore30,196 bytesSKILL.mdcore30,196 bytesSKILL.mdcore30,196 bytesSKILL.mdcore30,196 bytesSKILL.mdcore30,196 bytesSKILL.mdcore30,196 bytesSKILL.mdcore30,196 bytesSKILL.mdcore30,196 bytesSKILL.mdcore30,196 bytesSKILL.mdcore30,196 bytesSKILL.mdcore30,196 bytesSKILL.mdcore30,196 bytesSKILL.mdcore30,196 bytesSKILL.mdcore30,196 bytesSKILL.mdcore30,196 bytesSKILL.mdcore30,196 bytesSKILL.mdcore30,196 bytesSKILL.mdcore30,196 bytesreferences/dicom_standards.mddoc12,503 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubsimpleitk-image-registration
When your agent uses it
Register, segment, filter, resample 3D medical images (MRI, CT, microscopy) via SimpleITK Python; DICOM, NIfTI, multi-modal. Rigid/affine/deformable registration, threshold/region-growing segmentation, Gaussian/morph filtering, label stats, format conversion. Use to align volumes across timepoints/modalities, segment fluorescence, or convert DICOM→NIfTI.
What it loads into context
Frontmatter
106 tokensAlways available to the agent
SKILL.md
9,486 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore37,030 bytesSKILL.mdcore37,030 bytesSKILL.mdcore37,030 bytesSKILL.mdcore37,030 bytesSKILL.mdcore37,030 bytesSKILL.mdcore37,030 bytesSKILL.mdcore37,030 bytesSKILL.mdcore37,030 bytesSKILL.mdcore37,030 bytesSKILL.mdcore37,030 bytesSKILL.mdcore37,030 bytesSKILL.mdcore37,030 bytesSKILL.mdcore37,030 bytesSKILL.mdcore37,030 bytesSKILL.mdcore37,030 bytesSKILL.mdcore37,030 bytesSKILL.mdcore37,030 bytesSKILL.mdcore37,030 bytesSKILL.mdcore37,030 bytesSKILL.mdcore37,030 bytesSKILL.mdcore37,030 bytesSKILL.mdcore37,030 bytesSKILL.mdcore37,030 bytesSKILL.mdcore37,030 bytesSKILL.mdcore37,030 bytesSKILL.mdcore37,030 bytesSKILL.mdcore37,030 bytesSKILL.mdcore37,030 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubplannotate-plasmid-annotation
When your agent uses it
Auto-annotate plasmids with features (promoters, terminators, resistance, origins, tags, fluorescent proteins) via BLAST against curated DBs (Addgene, fpbase, SnapGene). FASTA or raw sequence in; annotated GenBank, interactive HTML maps, CSV tables out. Handles circular topology. Use to verify synthetic constructs, prep Addgene submissions, share maps, or batch-annotate cloning libraries.
What it loads into context
Frontmatter
110 tokensAlways available to the agent
SKILL.md
4,654 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore18,690 bytesSKILL.mdcore18,690 bytesSKILL.mdcore18,690 bytesSKILL.mdcore18,690 bytesSKILL.mdcore18,690 bytesSKILL.mdcore18,690 bytesSKILL.mdcore18,690 bytesSKILL.mdcore18,690 bytesSKILL.mdcore18,690 bytesSKILL.mdcore18,690 bytesSKILL.mdcore18,690 bytesSKILL.mdcore18,690 bytesSKILL.mdcore18,690 bytesSKILL.mdcore18,690 bytesSKILL.mdcore18,690 bytesSKILL.mdcore18,690 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubsgrna-design-guide
When your agent uses it
Three-tiered sgRNA design guide using validated Addgene sequences, CRISPick pre-computed datasets, or de novo design rules for CRISPR experiments
What it loads into context
Frontmatter
44 tokensAlways available to the agent
SKILL.md
6,731 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Metadata
- Citations and Acknowledgments
- Overview
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Option 1: Search Validated sgRNA Sequences (Recommended First)
- Option 2: Download Pre-Computed sgRNAs from CRISPick
- Option 3: General sgRNA Design Guidelines (Last Resort)
- Quick Start Examples
- References
Files
SKILL.mdcore24,817 bytesSKILL.mdcore24,817 bytesSKILL.mdcore24,817 bytesSKILL.mdcore24,817 bytesSKILL.mdcore24,817 bytesSKILL.mdcore24,817 bytesSKILL.mdcore24,817 bytesSKILL.mdcore24,817 bytesSKILL.mdcore24,817 bytesSKILL.mdcore24,817 bytesSKILL.mdcore24,817 bytesSKILL.mdcore24,817 bytesSKILL.mdcore24,817 bytesSKILL.mdcore24,817 bytesSKILL.mdcore24,817 bytesSKILL.mdcore24,817 bytesSKILL.mdcore24,817 bytesSKILL.mdcore24,817 bytesSKILL.mdcore24,817 bytesresource/CRISPick_download_links.txtdoc38,053 bytesresource/addgene_grna_sequences.csvasset54,648 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubviennarna-structure-prediction
When your agent uses it
Predict RNA secondary structure, MFE folding, base-pair probabilities, RNA-RNA interactions via ViennaRNA Python bindings. Pipeline: sequence → MFE → partition function and pair-probability matrix → dot-bracket → duplex. Use for siRNA/sgRNA targeting, ribozyme design, RNA accessibility. Use RNAfold CLI for batch use without Python.
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
6,221 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore21,720 bytesSKILL.mdcore21,720 bytesSKILL.mdcore21,720 bytesSKILL.mdcore21,720 bytesSKILL.mdcore21,720 bytesSKILL.mdcore21,720 bytesSKILL.mdcore21,720 bytesSKILL.mdcore21,720 bytesSKILL.mdcore21,720 bytesSKILL.mdcore21,720 bytesSKILL.mdcore21,720 bytesSKILL.mdcore21,720 bytesSKILL.mdcore21,720 bytesSKILL.mdcore21,720 bytesSKILL.mdcore21,720 bytesSKILL.mdcore21,720 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubadaptyv-bio
When your agent uses it
API + Python SDK for ordering cell-free protein expression and binding assays. Submit sequences for expression (10–100 µg), measure binding affinity (KD) against targets, track status, and retrieve results programmatically — no wet-lab setup. Built for ML-guided directed evolution and antibody/nanobody optimization. Requires Adaptyv account and API key.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
4,628 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore18,702 bytesSKILL.mdcore18,702 bytesSKILL.mdcore18,702 bytesSKILL.mdcore18,702 bytesSKILL.mdcore18,702 bytesSKILL.mdcore18,702 bytesSKILL.mdcore18,702 bytesSKILL.mdcore18,702 bytesSKILL.mdcore18,702 bytesSKILL.mdcore18,702 bytesSKILL.mdcore18,702 bytesSKILL.mdcore18,702 bytesSKILL.mdcore18,702 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubesm-protein-language-model
When your agent uses it
Protein language models (ESM3, ESM C) for sequence generation, structure prediction, inverse folding, and embeddings. Design novel proteins, extract ML features, or fold sequences. Local GPU or EvolutionaryScale Forge API. Use AlphaFold for traditional folding; RDKit for small molecules.
What it loads into context
Frontmatter
73 tokensAlways available to the agent
SKILL.md
4,134 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore16,139 bytesSKILL.mdcore16,139 bytesSKILL.mdcore16,139 bytesSKILL.mdcore16,139 bytesSKILL.mdcore16,139 bytesSKILL.mdcore16,139 bytesSKILL.mdcore16,139 bytesSKILL.mdcore16,139 bytesSKILL.mdcore16,139 bytesSKILL.mdcore16,139 bytesSKILL.mdcore16,139 bytesSKILL.mdcore16,139 bytesSKILL.mdcore16,139 bytesSKILL.mdcore16,139 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubhmdb-database
When your agent uses it
Parse HMDB (Human Metabolome Database) local XML for metabolite info, chemical properties, biological context, disease links, spectra, and cross-DB mapping. No REST API — uses ~6 GB XML download. Use drugbank-database-access for drugs; pubchem-compound-search for live lookups.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
6,550 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
SKILL.mdcore24,509 bytesSKILL.mdcore24,509 bytesSKILL.mdcore24,509 bytesSKILL.mdcore24,509 bytesSKILL.mdcore24,509 bytesSKILL.mdcore24,509 bytesSKILL.mdcore24,509 bytesSKILL.mdcore24,509 bytesSKILL.mdcore24,509 bytesSKILL.mdcore24,509 bytesSKILL.mdcore24,509 bytesSKILL.mdcore24,509 bytesSKILL.mdcore24,509 bytesSKILL.mdcore24,509 bytesSKILL.mdcore24,509 bytesSKILL.mdcore24,509 bytesSKILL.mdcore24,509 bytesSKILL.mdcore24,509 bytesSKILL.mdcore24,509 bytesSKILL.mdcore24,509 bytesSKILL.mdcore24,509 bytesSKILL.mdcore24,509 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubinterpro-database
When your agent uses it
Query InterPro REST API for protein domain architecture, family classification, and member-DB integration. Search entries, retrieve a protein's domains, list family members, get taxonomic distribution, link to PDB. Unifies Pfam, PANTHER, PIRSF, PRINTS, PROSITE, SMART, CDD, NCBIfam. Use uniprot-protein-database for sequences; pdb-database for 3D structures.
What it loads into context
Frontmatter
112 tokensAlways available to the agent
SKILL.md
7,906 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore30,318 bytesSKILL.mdcore30,318 bytesSKILL.mdcore30,318 bytesSKILL.mdcore30,318 bytesSKILL.mdcore30,318 bytesSKILL.mdcore30,318 bytesSKILL.mdcore30,318 bytesSKILL.mdcore30,318 bytesSKILL.mdcore30,318 bytesSKILL.mdcore30,318 bytesSKILL.mdcore30,318 bytesSKILL.mdcore30,318 bytesSKILL.mdcore30,318 bytesSKILL.mdcore30,318 bytesSKILL.mdcore30,318 bytesSKILL.mdcore30,318 bytesSKILL.mdcore30,318 bytesSKILL.mdcore30,318 bytesSKILL.mdcore30,318 bytesSKILL.mdcore30,318 bytesSKILL.mdcore30,318 bytesSKILL.mdcore30,318 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmatchms-spectral-matching
When your agent uses it
MS spectral matching and metabolite ID with matchms. Import spectra (mzML, MGF, MSP, JSON), filter/normalize peaks, score similarity (cosine, modified cosine, fingerprint), build reproducible pipelines, identify unknowns vs spectral libraries. Use pyopenms for full LC-MS/MS proteomics.
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
6,095 tokensWhen the skill is activated
Reference documents
5,941 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmaxquant-proteomics
When your agent uses it
MaxQuant + Perseus proteomics pipeline: run MaxQuant for LFQ and SILAC; parse proteinGroups.txt in Python; filter contaminants/decoys; log2 + median-normalize; impute MNAR; t-test with FDR; volcano plot; GO/pathway enrichment. Use Proteome Discoverer for Thermo-native processing; FragPipe/MSFragger for GPU-accelerated DB search.
What it loads into context
Frontmatter
103 tokensAlways available to the agent
SKILL.md
7,443 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Workflow
- Key Parameters
- Key Concepts
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore29,058 bytesSKILL.mdcore29,058 bytesSKILL.mdcore29,058 bytesSKILL.mdcore29,058 bytesSKILL.mdcore29,058 bytesSKILL.mdcore29,058 bytesSKILL.mdcore29,058 bytesSKILL.mdcore29,058 bytesSKILL.mdcore29,058 bytesSKILL.mdcore29,058 bytesSKILL.mdcore29,058 bytesSKILL.mdcore29,058 bytesSKILL.mdcore29,058 bytesSKILL.mdcore29,058 bytesSKILL.mdcore29,058 bytesSKILL.mdcore29,058 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmetabolomics-workbench-database
When your agent uses it
Query Metabolomics Workbench REST API (4,200+ NIH studies) for metabolite ID, study discovery, RefMet standardization, m/z precursor searches, and gene/protein annotations. Quirks: compound input_item rejects `name` (use pubchem_cid/kegg_id/inchi_key/etc.); free-text → compound is a two-step refmet/match→refmet/name flow; moverz endpoint returns TSV text, not JSON. Use hmdb-database for local XML; pubchem-compound-search for general compound lookup.
What it loads into context
Frontmatter
138 tokensAlways available to the agent
SKILL.md
6,069 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore21,307 bytesSKILL.mdcore21,307 bytesSKILL.mdcore21,307 bytesSKILL.mdcore21,307 bytesSKILL.mdcore21,307 bytesSKILL.mdcore21,307 bytesSKILL.mdcore21,307 bytesSKILL.mdcore21,307 bytesSKILL.mdcore21,307 bytesSKILL.mdcore21,307 bytesSKILL.mdcore21,307 bytesSKILL.mdcore21,307 bytesSKILL.mdcore21,307 bytesSKILL.mdcore21,307 bytesSKILL.mdcore21,307 bytesSKILL.mdcore21,307 bytesSKILL.mdcore21,307 bytesSKILL.mdcore21,307 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpride-database
When your agent uses it
Search the PRIDE Archive v3 REST API for proteomics datasets: discover projects by keyword + faceted filters (organism, instrument, disease, software), fetch project metadata, list and download RAW/PEAK/RESULT/FASTA files (with FTP/Aspera URLs), look up which projects mention a UniProt accession, and find similar projects. PRIDE v3 no longer exposes peptide/PSM-level identification endpoints — for spectrum-level data download the project's RESULT files. Use uniprot-protein-database for protein sequences; interpro-database for domain architecture.
What it loads into context
Frontmatter
137 tokensAlways available to the agent
SKILL.md
8,626 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore32,728 bytesSKILL.mdcore32,728 bytesSKILL.mdcore32,728 bytesSKILL.mdcore32,728 bytesSKILL.mdcore32,728 bytesSKILL.mdcore32,728 bytesSKILL.mdcore32,728 bytesSKILL.mdcore32,728 bytesSKILL.mdcore32,728 bytesSKILL.mdcore32,728 bytesSKILL.mdcore32,728 bytesSKILL.mdcore32,728 bytesSKILL.mdcore32,728 bytesSKILL.mdcore32,728 bytesSKILL.mdcore32,728 bytesSKILL.mdcore32,728 bytesSKILL.mdcore32,728 bytesSKILL.mdcore32,728 bytesSKILL.mdcore32,728 bytesSKILL.mdcore32,728 bytesSKILL.mdcore32,728 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpyopenms-mass-spectrometry
When your agent uses it
MS data processing with PyOpenMS for LC-MS/MS proteomics and metabolomics — mzML/mzXML I/O, signal processing (smoothing, peak picking, centroiding), feature detection/linking, peptide/protein ID with FDR, untargeted metabolomics. Use matchms for simple spectral matching.
What it loads into context
Frontmatter
83 tokensAlways available to the agent
SKILL.md
6,240 tokensWhen the skill is activated
Reference documents
7,815 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
SKILL.mdcore23,879 bytesSKILL.mdcore23,879 bytesSKILL.mdcore23,879 bytesSKILL.mdcore23,879 bytesSKILL.mdcore23,879 bytesSKILL.mdcore23,879 bytesSKILL.mdcore23,879 bytesSKILL.mdcore23,879 bytesSKILL.mdcore23,879 bytesSKILL.mdcore23,879 bytesSKILL.mdcore23,879 bytesSKILL.mdcore23,879 bytesSKILL.mdcore23,879 bytesSKILL.mdcore23,879 bytesSKILL.mdcore23,879 bytesSKILL.mdcore23,879 bytesSKILL.mdcore23,879 bytesSKILL.mdcore23,879 bytesSKILL.mdcore23,879 bytesSKILL.mdcore23,879 bytesSKILL.mdcore23,879 bytesreferences/data_structures_reference.mddoc7,550 bytesreferences/identification_metabolomics.mddoc11,538 bytesreferences/signal_feature_processing.mddoc10,113 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubuniprot-protein-database
When your agent uses it
Query UniProt REST API: search by gene/protein name, fetch FASTA, map IDs (Ensembl, PDB, RefSeq), access Swiss-Prot annotations. Use bioservices for multi-DB access; alphafold-database-access for structures.
What it loads into context
Frontmatter
75 tokensAlways available to the agent
SKILL.md
3,565 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore13,829 bytesSKILL.mdcore13,829 bytesSKILL.mdcore13,829 bytesSKILL.mdcore13,829 bytesSKILL.mdcore13,829 bytesSKILL.mdcore13,829 bytesSKILL.mdcore13,829 bytesSKILL.mdcore13,829 bytesSKILL.mdcore13,829 bytesSKILL.mdcore13,829 bytesSKILL.mdcore13,829 bytesSKILL.mdcore13,829 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubaeon
When your agent uses it
scikit-learn compatible Python toolkit for time series ML: classify, cluster, regress, segment, transform with 30+ algorithms (ROCKET, InceptionTime, KNN-DTW, HIVE-COTE, WEASEL). Handles panel, multivariate, and unequal-length series. Maintained successor to sktime. Alternatives: sktime (larger ecosystem), tslearn (fewer algorithms), catch22 (features only).
What it loads into context
Frontmatter
106 tokensAlways available to the agent
SKILL.md
4,207 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore15,852 bytesSKILL.mdcore15,852 bytesSKILL.mdcore15,852 bytesSKILL.mdcore15,852 bytesSKILL.mdcore15,852 bytesSKILL.mdcore15,852 bytesSKILL.mdcore15,852 bytesSKILL.mdcore15,852 bytesSKILL.mdcore15,852 bytesSKILL.mdcore15,852 bytesSKILL.mdcore15,852 bytesSKILL.mdcore15,852 bytesSKILL.mdcore15,852 bytesSKILL.mdcore15,852 bytesSKILL.mdcore15,852 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubastropy-astronomy
When your agent uses it
Core Python library for astronomy/astrophysics: units with dimensional analysis, celestial coordinate transforms (ICRS/Galactic/AltAz/FK5), FITS I/O, tables (FITS/HDF5/VOTable/CSV), cosmology (Planck18, distance/age), precise time (UTC/TAI/TT/TDB, Julian, barycentric), WCS pixel-world mapping, model fitting. For general tables use pandas/polars; for radio interferometry use CASA.
What it loads into context
Frontmatter
119 tokensAlways available to the agent
SKILL.md
6,485 tokensWhen the skill is activated
Reference documents
7,558 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
SKILL.mdcore22,237 bytesSKILL.mdcore22,237 bytesSKILL.mdcore22,237 bytesSKILL.mdcore22,237 bytesSKILL.mdcore22,237 bytesSKILL.mdcore22,237 bytesSKILL.mdcore22,237 bytesSKILL.mdcore22,237 bytesSKILL.mdcore22,237 bytesSKILL.mdcore22,237 bytesSKILL.mdcore22,237 bytesSKILL.mdcore22,237 bytesSKILL.mdcore22,237 bytesSKILL.mdcore22,237 bytesSKILL.mdcore22,237 bytesSKILL.mdcore22,237 bytesSKILL.mdcore22,237 bytesSKILL.mdcore22,237 bytesSKILL.mdcore22,237 bytesSKILL.mdcore22,237 bytesSKILL.mdcore22,237 bytesSKILL.mdcore22,237 bytesSKILL.mdcore22,237 bytesreferences/auxiliary_modules.mddoc8,108 bytesreferences/coordinates_time_cosmology.mddoc8,928 bytesreferences/data_io_guide.mddoc7,459 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubdask-parallel-computing
When your agent uses it
Parallel/distributed computing for larger-than-RAM data. Components: DataFrames (parallel pandas), Arrays (parallel NumPy), Bags, Futures, Schedulers. Scales laptop to HPC cluster. For single-machine speed use polars; for out-of-core without cluster use vaex.
What it loads into context
Frontmatter
76 tokensAlways available to the agent
SKILL.md
4,136 tokensWhen the skill is activated
Reference documents
4,123 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
SKILL.mdcore16,312 bytesSKILL.mdcore16,312 bytesSKILL.mdcore16,312 bytesSKILL.mdcore16,312 bytesSKILL.mdcore16,312 bytesSKILL.mdcore16,312 bytesSKILL.mdcore16,312 bytesSKILL.mdcore16,312 bytesSKILL.mdcore16,312 bytesSKILL.mdcore16,312 bytesSKILL.mdcore16,312 bytesSKILL.mdcore16,312 bytesSKILL.mdcore16,312 bytesSKILL.mdcore16,312 bytesSKILL.mdcore16,312 bytesSKILL.mdcore16,312 bytesSKILL.mdcore16,312 bytesreferences/collections_guide.mddoc7,692 bytesreferences/distributed_computing.mddoc7,906 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubdegenerate-input-filtering
When your agent uses it
Filter degenerate, uninformative inputs before statistical tests: single-sequence alignments, empty files, constant features, zero-variance inputs, all-NaN columns. See nan-safe-correlation for NaN-aware correlation; statistical-analysis for test guidance.
What it loads into context
Frontmatter
72 tokensAlways available to the agent
SKILL.md
3,375 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Workflow
- Further Reading
- Related Skills
Files
SKILL.mdcore14,892 bytesSKILL.mdcore14,892 bytesSKILL.mdcore14,892 bytesSKILL.mdcore14,892 bytesSKILL.mdcore14,892 bytesSKILL.mdcore14,892 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubexploratory-data-analysis
When your agent uses it
Methodology for exploratory data analysis on scientific files. Decision frameworks by data type (tabular, sequence, image, spectral, structural, omics), quality assessment, report generation, format detection across 200+ formats. Use when given a data file for initial exploration or to pick an analysis before a pipeline.
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
2,835 tokensWhen the skill is activated
Reference documents
2,353 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Workflow
- Bundled Resources
- Further Reading
- Related Skills
Files
SKILL.mdcore12,613 bytesSKILL.mdcore12,613 bytesSKILL.mdcore12,613 bytesreferences/file_format_reference.mddoc8,476 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubgeopandas-geospatial
When your agent uses it
Geospatial vector analysis extending pandas. Read/write spatial formats (Shapefile, GeoJSON, GeoPackage, Parquet, PostGIS), CRS handling, geometric ops (buffer, simplify, centroid, affine), spatial analysis (joins, overlays, dissolve, clipping, distance), visualization (choropleth, interactive maps, basemaps). Use for spatial joins, overlays, CRS transforms, area/distance, maps.
What it loads into context
Frontmatter
108 tokensAlways available to the agent
SKILL.md
4,081 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- References
- Related Skills
Files
SKILL.mdcore15,039 bytesSKILL.mdcore15,039 bytesSKILL.mdcore15,039 bytesSKILL.mdcore15,039 bytesSKILL.mdcore15,039 bytesSKILL.mdcore15,039 bytesSKILL.mdcore15,039 bytesSKILL.mdcore15,039 bytesSKILL.mdcore15,039 bytesSKILL.mdcore15,039 bytesSKILL.mdcore15,039 bytesSKILL.mdcore15,039 bytesSKILL.mdcore15,039 bytesSKILL.mdcore15,039 bytesSKILL.mdcore15,039 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubhypogenic-hypothesis-generation
When your agent uses it
LLM-driven hypothesis generation/testing on tabular data. Three methods: HypoGeniC (data-driven), HypoRefine (literature+data), Union. Iterative refinement, Redis caching, multi-hypothesis inference. Manual: hypothesis-generation; ideation: scientific-brainstorming.
What it loads into context
Frontmatter
80 tokensAlways available to the agent
SKILL.md
3,860 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Workflow
- Key Parameters
- Key Concepts
- Common Recipes
- Expected Outputs
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
SKILL.mdcore15,670 bytesSKILL.mdcore15,670 bytesSKILL.mdcore15,670 bytesSKILL.mdcore15,670 bytesSKILL.mdcore15,670 bytesSKILL.mdcore15,670 bytesSKILL.mdcore15,670 bytesSKILL.mdcore15,670 bytesSKILL.mdcore15,670 bytesSKILL.mdcore15,670 bytesSKILL.mdcore15,670 bytesSKILL.mdcore15,670 bytesSKILL.mdcore15,670 bytesSKILL.mdcore15,670 bytesSKILL.mdcore15,670 bytesSKILL.mdcore15,670 bytesSKILL.mdcore15,670 bytesSKILL.mdcore15,670 bytesSKILL.mdcore15,670 bytesSKILL.mdcore15,670 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmatlab-scientific-computing
When your agent uses it
MATLAB/GNU Octave numerical computing: matrices, linear algebra, ODEs, signal processing, optimization, statistics, scientific visualization. MATLAB-syntax examples run on both. For Python use numpy/scipy; for statistical modeling use statsmodels.
What it loads into context
Frontmatter
81 tokensAlways available to the agent
SKILL.md
4,670 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore15,061 bytesSKILL.mdcore15,061 bytesSKILL.mdcore15,061 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubnan-safe-correlation
When your agent uses it
Per-feature NaN-safe Spearman/Pearson correlation across many features (genes, proteins, variants) with missing values. Covers why bulk matrix shortcuts fail, correct pairwise deletion, degenerate input filtering, and large-dataset performance. Use statistical-analysis for test choice; shap-model-explainability for interpretability.
What it loads into context
Frontmatter
83 tokensAlways available to the agent
SKILL.md
2,750 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Workflow
- Further Reading
- Related Skills
Files
SKILL.mdcore11,624 bytesSKILL.mdcore11,624 bytesSKILL.mdcore11,624 bytesSKILL.mdcore11,624 bytesSKILL.mdcore11,624 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubneb-irc-activation-energy
When your agent uses it
NEB-IRC activation energy pipeline for reaction barriers using GFN2-xTB and pysisyphus. Optimize reactant and product geometries, run CI-NEB path search, optimize the transition state with a Hessian, verify with IRC (one imaginary mode, endpoints matching reactant/product, single NEB maximum), and report the electronic and Gibbs barriers. Use when you need a transition state, reaction barrier, activation energy, minimum energy path, or intrinsic reaction coordinate. Covers reactant/product atom-ordering pitfalls, feasibility sizing for single-core runs, and thermochemistry corrections. Renders an IRC energy-profile plot and an animated TS imaginary-mode HTML viewer. For 2D reaction scheme drawing use rdkit-chemdraw-cdxml.
What it loads into context
Frontmatter
176 tokensAlways available to the agent
SKILL.md
4,402 tokensWhen the skill is activated
Reference documents
2,342 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Workflow
- Key Parameters
- Key Concepts
- Common Recipes
- Expected Outputs
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Scripts executed separately
scripts/check_result.pyscripts/plot_irc.pyscripts/setup_env.sh
Files
SKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesSKILL.mdcore16,352 bytesreferences/energetics.mddoc5,557 bytesreferences/feasibility.mddoc3,597 bytesscripts/check_result.pyruns6,798 bytesscripts/pipeline.yamlasset1,462 bytesscripts/plot_irc.pyruns7,989 bytesscripts/setup_env.shruns1,979 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubnetworkx-graph-analysis
When your agent uses it
Graph and network analysis toolkit. Four graph types (directed, undirected, multi-edge), centrality, shortest paths, community detection, generators, I/O (GraphML, GML, edge list), matplotlib viz. For large graphs (100K+ nodes) use igraph or graph-tool; for GNNs use PyG.
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
6,338 tokensWhen the skill is activated
Reference documents
7,143 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
SKILL.mdcore23,070 bytesSKILL.mdcore23,070 bytesSKILL.mdcore23,070 bytesSKILL.mdcore23,070 bytesSKILL.mdcore23,070 bytesSKILL.mdcore23,070 bytesSKILL.mdcore23,070 bytesSKILL.mdcore23,070 bytesSKILL.mdcore23,070 bytesSKILL.mdcore23,070 bytesSKILL.mdcore23,070 bytesSKILL.mdcore23,070 bytesSKILL.mdcore23,070 bytesSKILL.mdcore23,070 bytesSKILL.mdcore23,070 bytesSKILL.mdcore23,070 bytesSKILL.mdcore23,070 bytesSKILL.mdcore23,070 bytesSKILL.mdcore23,070 bytesSKILL.mdcore23,070 bytesSKILL.mdcore23,070 bytesSKILL.mdcore23,070 bytesSKILL.mdcore23,070 bytesSKILL.mdcore23,070 bytesSKILL.mdcore23,070 bytesreferences/algorithms_generators.mddoc11,365 bytesreferences/io_visualization.mddoc13,074 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubneurokit2
When your agent uses it
Python toolkit for neurophysiological signal processing: ECG (HR, HRV, R-peaks), EEG (complexity, PSD), EMG (activation onset), EDA/GSR (SCR decomposition), PPG, and RSP. Includes synthetic signal simulation. Alternatives: BioSPPy (less maintained), MNE (EEG/MEG specialist), heartpy (ECG only), scipy.signal (raw DSP).
What it loads into context
Frontmatter
103 tokensAlways available to the agent
SKILL.md
4,852 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore16,647 bytesSKILL.mdcore16,647 bytesSKILL.mdcore16,647 bytesSKILL.mdcore16,647 bytesSKILL.mdcore16,647 bytesSKILL.mdcore16,647 bytesSKILL.mdcore16,647 bytesSKILL.mdcore16,647 bytesSKILL.mdcore16,647 bytesSKILL.mdcore16,647 bytesSKILL.mdcore16,647 bytesSKILL.mdcore16,647 bytesSKILL.mdcore16,647 bytesSKILL.mdcore16,647 bytesSKILL.mdcore16,647 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubneuropixels-analysis
When your agent uses it
Pipeline for Neuropixels extracellular electrophysiology: probe geometry (ProbeInterface), Kilosort sorting via SpikeInterface, quality metrics, unit curation (ISI, firing rate, SNR), post-sort analysis (PSTH, tuning curves, population decoding). Supports Neuropixels 1.0/2.0/Ultra in rodent/primate experiments.
What it loads into context
Frontmatter
90 tokensAlways available to the agent
SKILL.md
3,920 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Workflow
- Key Parameters
- Key Concepts
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore14,810 bytesSKILL.mdcore14,810 bytesSKILL.mdcore14,810 bytesSKILL.mdcore14,810 bytesSKILL.mdcore14,810 bytesSKILL.mdcore14,810 bytesSKILL.mdcore14,810 bytesSKILL.mdcore14,810 bytesSKILL.mdcore14,810 bytesSKILL.mdcore14,810 bytesSKILL.mdcore14,810 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubnextflow-workflow-engine
When your agent uses it
Dataflow workflow engine for scalable bioinformatics pipelines. Defines processes (containerized tasks) connected by channels; runs local, HPC (SLURM/SGE), cloud (AWS/GCP/Azure), or Kubernetes via a single config change. Powers nf-core. Use Snakemake for rule-based Python workflows; use Nextflow for containerized, cloud-native, and nf-core pipelines.
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
3,719 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Parameters
- Common Workflows
- Common Recipes
- Troubleshooting
- References
Files
SKILL.mdcore14,351 bytesSKILL.mdcore14,351 bytesSKILL.mdcore14,351 bytesSKILL.mdcore14,351 bytesSKILL.mdcore14,351 bytesSKILL.mdcore14,351 bytesSKILL.mdcore14,351 bytesSKILL.mdcore14,351 bytesSKILL.mdcore14,351 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpolars-dataframes
When your agent uses it
Fast in-memory DataFrame with lazy evaluation, parallel execution, Arrow backend. Use for tabular data in RAM (1–100 GB) when pandas is too slow. Expression API: select, filter, group_by, joins, pivots, window. Lazy mode enables predicate/projection pushdown. Reads CSV, Parquet, JSON, Excel, DBs, cloud. Larger-than-RAM: Dask; GPU: cuDF.
What it loads into context
Frontmatter
107 tokensAlways available to the agent
SKILL.md
7,063 tokensWhen the skill is activated
Reference documents
13,856 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
SKILL.mdcore25,943 bytesSKILL.mdcore25,943 bytesSKILL.mdcore25,943 bytesSKILL.mdcore25,943 bytesSKILL.mdcore25,943 bytesSKILL.mdcore25,943 bytesSKILL.mdcore25,943 bytesSKILL.mdcore25,943 bytesSKILL.mdcore25,943 bytesSKILL.mdcore25,943 bytesSKILL.mdcore25,943 bytesSKILL.mdcore25,943 bytesSKILL.mdcore25,943 bytesSKILL.mdcore25,943 bytesSKILL.mdcore25,943 bytesSKILL.mdcore25,943 bytesSKILL.mdcore25,943 bytesSKILL.mdcore25,943 bytesSKILL.mdcore25,943 bytesSKILL.mdcore25,943 bytesreferences/advanced_operations.mddoc16,478 bytesreferences/io_best_practices.mddoc18,466 bytesreferences/pandas_migration.mddoc12,052 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpyhealth
When your agent uses it
Python library for healthcare ML on EHR data: process MIMIC-III/IV, eICU, OMOP-CDM; encode medical codes (ICD, ATC, NDC); build patient-level datasets; train Transformer, RETAIN, GRASP, MedBERT for mortality, drug recommendation, readmission, diagnosis prediction. Alternatives: FIDDLE (preprocessing), clinical-longformer (clinical NLP), ehr-ml (embeddings).
What it loads into context
Frontmatter
110 tokensAlways available to the agent
SKILL.md
5,059 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore19,014 bytesSKILL.mdcore19,014 bytesSKILL.mdcore19,014 bytesSKILL.mdcore19,014 bytesSKILL.mdcore19,014 bytesSKILL.mdcore19,014 bytesSKILL.mdcore19,014 bytesSKILL.mdcore19,014 bytesSKILL.mdcore19,014 bytesSKILL.mdcore19,014 bytesSKILL.mdcore19,014 bytesSKILL.mdcore19,014 bytesSKILL.mdcore19,014 bytesSKILL.mdcore19,014 bytesSKILL.mdcore19,014 bytesSKILL.mdcore19,014 bytesSKILL.mdcore19,014 bytesSKILL.mdcore19,014 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpymatgen
When your agent uses it
Python Materials Genomics library for structure analysis, thermodynamics, and electronic properties. Parse/create crystal structures (CIF, POSCAR), query Materials Project for DFT-computed properties, analyze phase and Pourbaix diagrams, compute XRD patterns, generate DFT inputs for VASP, Quantum ESPRESSO, CP2K. Alternatives: ASE (MD/geometry), AFLOW (high-throughput), OVITO (visualization).
What it loads into context
Frontmatter
104 tokensAlways available to the agent
SKILL.md
4,209 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore15,674 bytesSKILL.mdcore15,674 bytesSKILL.mdcore15,674 bytesSKILL.mdcore15,674 bytesSKILL.mdcore15,674 bytesSKILL.mdcore15,674 bytesSKILL.mdcore15,674 bytesSKILL.mdcore15,674 bytesSKILL.mdcore15,674 bytesSKILL.mdcore15,674 bytesSKILL.mdcore15,674 bytesSKILL.mdcore15,674 bytesSKILL.mdcore15,674 bytesSKILL.mdcore15,674 bytesSKILL.mdcore15,674 bytesSKILL.mdcore15,674 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpymoo
When your agent uses it
Python framework for single- and multi-objective optimization with evolutionary algorithms. Define vectorized objectives and constraints; solve with NSGA-II, NSGA-III, MOEA/D, GAs, or differential evolution. Analyze Pareto fronts, visualize trade-offs, customize operators and callbacks. For engineering design, hyperparameter search, and conflicting objectives. Alternatives: scipy.optimize (single-objective, gradient), platypus, jMetalPy (Java).
What it loads into context
Frontmatter
110 tokensAlways available to the agent
SKILL.md
5,294 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore19,550 bytesSKILL.mdcore19,550 bytesSKILL.mdcore19,550 bytesSKILL.mdcore19,550 bytesSKILL.mdcore19,550 bytesSKILL.mdcore19,550 bytesSKILL.mdcore19,550 bytesSKILL.mdcore19,550 bytesSKILL.mdcore19,550 bytesSKILL.mdcore19,550 bytesSKILL.mdcore19,550 bytesSKILL.mdcore19,550 bytesSKILL.mdcore19,550 bytesSKILL.mdcore19,550 bytesSKILL.mdcore19,550 bytesSKILL.mdcore19,550 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscikit-learn-machine-learning
When your agent uses it
Classical ML in Python: classification, regression, clustering, dim reduction, evaluation, tuning, preprocessing pipelines. Linear models, tree ensembles, SVMs, K-Means, PCA, t-SNE. Use PyTorch/TF for deep learning; XGBoost/LightGBM for scale.
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
4,201 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Common Workflows
- Key Parameters
- Common Recipes
- Troubleshooting
- References
Files
SKILL.mdcore16,057 bytesSKILL.mdcore16,057 bytesSKILL.mdcore16,057 bytesSKILL.mdcore16,057 bytesSKILL.mdcore16,057 bytesSKILL.mdcore16,057 bytesSKILL.mdcore16,057 bytesSKILL.mdcore16,057 bytesSKILL.mdcore16,057 bytesSKILL.mdcore16,057 bytesSKILL.mdcore16,057 bytesSKILL.mdcore16,057 bytesSKILL.mdcore16,057 bytesSKILL.mdcore16,057 bytesSKILL.mdcore16,057 bytesSKILL.mdcore16,057 bytesSKILL.mdcore16,057 bytesSKILL.mdcore16,057 bytesSKILL.mdcore16,057 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubshap-model-explainability
When your agent uses it
Model interpretability via SHAP (Shapley values from game theory). Covers explainer choice (Tree, Deep, Linear, Kernel, Gradient, Permutation), feature attribution, and plots (waterfall, beeswarm, bar, scatter, force, heatmap). Use to explain ML predictions, rank features, debug models, audit fairness, or compare models. Works with tree, deep, linear, and black-box models.
What it loads into context
Frontmatter
109 tokensAlways available to the agent
SKILL.md
3,583 tokensWhen the skill is activated
Reference documents
1,521 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Workflow
- Key Parameters
- Key Concepts
- Common Recipes
- Expected Outputs
- Troubleshooting
- Bundled Resources
- Best Practices
- References
- Related Skills
Files
SKILL.mdcore15,040 bytesSKILL.mdcore15,040 bytesSKILL.mdcore15,040 bytesSKILL.mdcore15,040 bytesSKILL.mdcore15,040 bytesSKILL.mdcore15,040 bytesSKILL.mdcore15,040 bytesSKILL.mdcore15,040 bytesSKILL.mdcore15,040 bytesSKILL.mdcore15,040 bytesSKILL.mdcore15,040 bytesSKILL.mdcore15,040 bytesSKILL.mdcore15,040 bytesSKILL.mdcore15,040 bytesreferences/theory.mddoc6,343 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubsimpy-discrete-event-simulation
When your agent uses it
Process-based discrete-event simulation. Model queues, shared resources, timed events: manufacturing, service ops, network traffic, logistics. Processes are Python generators yielding events. Resources: capacity-limited (Resource/Priority/Preemptive), bulk (Container), objects (Store, FilterStore). For continuous use SciPy ODEs; for agent-based use Mesa.
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
5,282 tokensWhen the skill is activated
Reference documents
5,022 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
SKILL.mdcore21,611 bytesSKILL.mdcore21,611 bytesSKILL.mdcore21,611 bytesSKILL.mdcore21,611 bytesSKILL.mdcore21,611 bytesSKILL.mdcore21,611 bytesSKILL.mdcore21,611 bytesSKILL.mdcore21,611 bytesSKILL.mdcore21,611 bytesSKILL.mdcore21,611 bytesSKILL.mdcore21,611 bytesSKILL.mdcore21,611 bytesSKILL.mdcore21,611 bytesSKILL.mdcore21,611 bytesSKILL.mdcore21,611 bytesSKILL.mdcore21,611 bytesSKILL.mdcore21,611 bytesreferences/process_events_guide.mddoc7,961 bytesreferences/resources_monitoring_guide.mddoc12,466 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubsnakemake-workflow-engine
When your agent uses it
Python-based workflow manager for reproducible, scalable pipelines. Define rules with file-based dependencies; Snakemake resolves execution order and parallelism. Runs local, SLURM, LSF, AWS, GCP via profiles; per-rule conda/Singularity envs. For NGS pipelines, ML training, and multi-step file processing. Use Nextflow for Groovy dataflow or nf-core integration.
What it loads into context
Frontmatter
100 tokensAlways available to the agent
SKILL.md
4,889 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore18,852 bytesSKILL.mdcore18,852 bytesSKILL.mdcore18,852 bytesSKILL.mdcore18,852 bytesSKILL.mdcore18,852 bytesSKILL.mdcore18,852 bytesSKILL.mdcore18,852 bytesSKILL.mdcore18,852 bytesSKILL.mdcore18,852 bytesSKILL.mdcore18,852 bytesSKILL.mdcore18,852 bytesSKILL.mdcore18,852 bytesSKILL.mdcore18,852 bytesSKILL.mdcore18,852 bytesSKILL.mdcore18,852 bytesSKILL.mdcore18,852 bytesSKILL.mdcore18,852 bytesSKILL.mdcore18,852 bytesSKILL.mdcore18,852 bytesSKILL.mdcore18,852 bytesSKILL.mdcore18,852 bytesSKILL.mdcore18,852 bytesSKILL.mdcore18,852 bytesSKILL.mdcore18,852 bytesSKILL.mdcore18,852 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubspikeinterface-electrophysiology
When your agent uses it
Unified Python framework for extracellular electrophysiology. Load 20+ formats (SpikeGLX, OpenEphys, NWB, Intan, Maxwell, Blackrock), preprocess, run 10+ sorters (Kilosort4, SpykingCircus2, Tridesclous, MountainSort5) via one API, compute quality metrics (SNR, ISI, firing rate), compare sorters, export NWB/Phy. For format-agnostic multi-sorter workflows. For Neuropixels-specific PSTH/decoding use neuropixels.
What it loads into context
Frontmatter
133 tokensAlways available to the agent
SKILL.md
7,640 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore31,474 bytesSKILL.mdcore31,474 bytesSKILL.mdcore31,474 bytesSKILL.mdcore31,474 bytesSKILL.mdcore31,474 bytesSKILL.mdcore31,474 bytesSKILL.mdcore31,474 bytesSKILL.mdcore31,474 bytesSKILL.mdcore31,474 bytesSKILL.mdcore31,474 bytesSKILL.mdcore31,474 bytesSKILL.mdcore31,474 bytesSKILL.mdcore31,474 bytesSKILL.mdcore31,474 bytesSKILL.mdcore31,474 bytesSKILL.mdcore31,474 bytesSKILL.mdcore31,474 bytesSKILL.mdcore31,474 bytesSKILL.mdcore31,474 bytesSKILL.mdcore31,474 bytesSKILL.mdcore31,474 bytesSKILL.mdcore31,474 bytesSKILL.mdcore31,474 bytesSKILL.mdcore31,474 bytesSKILL.mdcore31,474 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubsympy-symbolic-math
When your agent uses it
Symbolic math in Python: exact algebra, calculus (derivatives, integrals, limits), equation solving, symbolic matrices, ODEs, code gen (lambdify, C/Fortran). Use for exact symbolic results. For numerical use numpy/scipy; for stats use statsmodels.
What it loads into context
Frontmatter
79 tokensAlways available to the agent
SKILL.md
4,807 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore15,729 bytesSKILL.mdcore15,729 bytesSKILL.mdcore15,729 bytesSKILL.mdcore15,729 bytesSKILL.mdcore15,729 bytesSKILL.mdcore15,729 bytesSKILL.mdcore15,729 bytesSKILL.mdcore15,729 bytesSKILL.mdcore15,729 bytesSKILL.mdcore15,729 bytesSKILL.mdcore15,729 bytesSKILL.mdcore15,729 bytesSKILL.mdcore15,729 bytesSKILL.mdcore15,729 bytesSKILL.mdcore15,729 bytesSKILL.mdcore15,729 bytesSKILL.mdcore15,729 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtorch-geometric-graph-neural-networks
When your agent uses it
PyTorch Geometric (PyG) for graph neural networks: node/graph classification, link prediction with GCN, GAT, GraphSAGE, GIN. Message passing, mini-batches, heterogeneous graphs, neighbor sampling, explainability. Supports molecules (QM9, MoleculeNet), social/knowledge graphs, 3D point clouds. For non-graph DL use PyTorch; for classical graph algorithms use NetworkX.
What it loads into context
Frontmatter
106 tokensAlways available to the agent
SKILL.md
5,335 tokensWhen the skill is activated
Reference documents
7,089 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
SKILL.mdcore20,581 bytesSKILL.mdcore20,581 bytesSKILL.mdcore20,581 bytesSKILL.mdcore20,581 bytesSKILL.mdcore20,581 bytesSKILL.mdcore20,581 bytesSKILL.mdcore20,581 bytesSKILL.mdcore20,581 bytesSKILL.mdcore20,581 bytesSKILL.mdcore20,581 bytesSKILL.mdcore20,581 bytesSKILL.mdcore20,581 bytesSKILL.mdcore20,581 bytesSKILL.mdcore20,581 bytesSKILL.mdcore20,581 bytesSKILL.mdcore20,581 bytesSKILL.mdcore20,581 bytesSKILL.mdcore20,581 bytesSKILL.mdcore20,581 bytesSKILL.mdcore20,581 bytesSKILL.mdcore20,581 bytesSKILL.mdcore20,581 bytesSKILL.mdcore20,581 bytesSKILL.mdcore20,581 bytesreferences/datasets_catalog.mddoc10,667 bytesreferences/layers_transforms_reference.mddoc15,701 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtransformers-bio-nlp
When your agent uses it
HuggingFace Transformers with biomedical LMs (BioBERT, PubMedBERT, BioGPT, BioMedLM) for scientific NLP: NER (genes, diseases, chemicals), relation extraction, QA, text classification, abstract summarization. Covers loading, biomedical tokenization, inference pipelines, fine-tuning. Alternatives: spaCy en_core_sci_lg (rule-based NER), Stanza (biomedical models), NLTK.
What it loads into context
Frontmatter
114 tokensAlways available to the agent
SKILL.md
4,870 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore19,790 bytesSKILL.mdcore19,790 bytesSKILL.mdcore19,790 bytesSKILL.mdcore19,790 bytesSKILL.mdcore19,790 bytesSKILL.mdcore19,790 bytesSKILL.mdcore19,790 bytesSKILL.mdcore19,790 bytesSKILL.mdcore19,790 bytesSKILL.mdcore19,790 bytesSKILL.mdcore19,790 bytesSKILL.mdcore19,790 bytesSKILL.mdcore19,790 bytesSKILL.mdcore19,790 bytesSKILL.mdcore19,790 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubumap-learn
When your agent uses it
UMAP dimensionality reduction for visualization, clustering prep, and feature engineering. Fast nonlinear manifold learning preserving local and global structure. Standard UMAP (fit/transform, sklearn-compatible), supervised/semi-supervised, Parametric UMAP (NN encoder/decoder, TensorFlow), DensMAP (density), AlignedUMAP (temporal/batch). 15+ distance metrics, custom Numba metrics, precomputed distances. For linear reduction use PCA; for neighborhood graphs use sklearn NearestNeighbors.
What it loads into context
Frontmatter
126 tokensAlways available to the agent
SKILL.md
4,843 tokensWhen the skill is activated
Reference documents
1,918 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
SKILL.mdcore19,001 bytesSKILL.mdcore19,001 bytesSKILL.mdcore19,001 bytesSKILL.mdcore19,001 bytesSKILL.mdcore19,001 bytesSKILL.mdcore19,001 bytesSKILL.mdcore19,001 bytesSKILL.mdcore19,001 bytesSKILL.mdcore19,001 bytesSKILL.mdcore19,001 bytesSKILL.mdcore19,001 bytesSKILL.mdcore19,001 bytesSKILL.mdcore19,001 bytesSKILL.mdcore19,001 bytesSKILL.mdcore19,001 bytesSKILL.mdcore19,001 bytesreferences/api_reference.mddoc7,938 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubuspto-database
When your agent uses it
Access USPTO patent data via PatentsView REST API and Google Patents Public Data (BigQuery). Search by inventor, assignee, CPC, or keywords; download metadata and claims; analyze portfolios; track tech trends. For IP landscape analysis, competitor monitoring, prior art search, and tech forecasting in life sciences and biotech.
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
4,818 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Parameters
- Best Practices
- Common Workflows
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore18,439 bytesSKILL.mdcore18,439 bytesSKILL.mdcore18,439 bytesSKILL.mdcore18,439 bytesSKILL.mdcore18,439 bytesSKILL.mdcore18,439 bytesSKILL.mdcore18,439 bytesSKILL.mdcore18,439 bytesSKILL.mdcore18,439 bytesSKILL.mdcore18,439 bytesSKILL.mdcore18,439 bytesSKILL.mdcore18,439 bytesSKILL.mdcore18,439 bytesSKILL.mdcore18,439 bytesSKILL.mdcore18,439 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubvaex-dataframes
When your agent uses it
Out-of-core DataFrame for billion-row data via lazy evaluation and memory-mapped files. Use when data exceeds RAM (10 GB–TB) for fast aggregation, filtering, virtual columns, and visualization without loading. Supports HDF5, Arrow, Parquet, CSV with cloud (S3, GCS, Azure). Built-in ML transformers (scaling, PCA, K-means). In-memory: polars; distributed: dask.
What it loads into context
Frontmatter
108 tokensAlways available to the agent
SKILL.md
6,702 tokensWhen the skill is activated
Reference documents
11,307 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
SKILL.mdcore24,905 bytesSKILL.mdcore24,905 bytesSKILL.mdcore24,905 bytesSKILL.mdcore24,905 bytesSKILL.mdcore24,905 bytesSKILL.mdcore24,905 bytesSKILL.mdcore24,905 bytesSKILL.mdcore24,905 bytesSKILL.mdcore24,905 bytesSKILL.mdcore24,905 bytesSKILL.mdcore24,905 bytesSKILL.mdcore24,905 bytesSKILL.mdcore24,905 bytesSKILL.mdcore24,905 bytesSKILL.mdcore24,905 bytesSKILL.mdcore24,905 bytesSKILL.mdcore24,905 bytesSKILL.mdcore24,905 bytesreferences/io_performance.mddoc17,423 bytesreferences/ml_visualization.mddoc21,367 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubzarr-python
When your agent uses it
Chunked N-D arrays with compression and cloud storage. NumPy-style indexing. Backends: local, S3, GCS, ZIP, memory. Dask/Xarray integration for parallel and labeled computation. For lineage use lamindb; for labeled arrays use xarray.
What it loads into context
Frontmatter
68 tokensAlways available to the agent
SKILL.md
5,150 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore17,752 bytesSKILL.mdcore17,752 bytesSKILL.mdcore17,752 bytesSKILL.mdcore17,752 bytesSKILL.mdcore17,752 bytesSKILL.mdcore17,752 bytesSKILL.mdcore17,752 bytesSKILL.mdcore17,752 bytesSKILL.mdcore17,752 bytesSKILL.mdcore17,752 bytesSKILL.mdcore17,752 bytesSKILL.mdcore17,752 bytesSKILL.mdcore17,752 bytesSKILL.mdcore17,752 bytesSKILL.mdcore17,752 bytesSKILL.mdcore17,752 bytesSKILL.mdcore17,752 bytesSKILL.mdcore17,752 bytesSKILL.mdcore17,752 bytesSKILL.mdcore17,752 bytesSKILL.mdcore17,752 bytesSKILL.mdcore17,752 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbiorxiv-database
When your agent uses it
Query bioRxiv/medRxiv preprints via REST API. Search by DOI, category, or date range; retrieve metadata (title, abstract, authors, category, DOI, version history) and PDFs. No auth. For peer-reviewed biomedical use pubmed-database; broader scholarly search use openalex-database.
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
5,319 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore20,370 bytesSKILL.mdcore20,370 bytesSKILL.mdcore20,370 bytesSKILL.mdcore20,370 bytesSKILL.mdcore20,370 bytesSKILL.mdcore20,370 bytesSKILL.mdcore20,370 bytesSKILL.mdcore20,370 bytesSKILL.mdcore20,370 bytesSKILL.mdcore20,370 bytesSKILL.mdcore20,370 bytesSKILL.mdcore20,370 bytesSKILL.mdcore20,370 bytesSKILL.mdcore20,370 bytesSKILL.mdcore20,370 bytesSKILL.mdcore20,370 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubcancer-research-figure-guide
When your agent uses it
Cancer Research (AACR) figures: resolution (300-1200 DPI), formats (EPS/TIFF/AI), hierarchical panel labels (Ai, Aii, Bi), figure/table limits, legend requirements with replicate counts.
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
2,838 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Resolution Requirements
- File Format
- Figure Size and Dimensions
- Color Mode
- Font Requirements
- Labeling Conventions
- Image Integrity and Manipulation Policy
- Python Quick Start: Full Validation
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Pre-Submission Checklist
- References
Files
SKILL.mdcore11,326 bytesSKILL.mdcore11,326 bytesSKILL.mdcore11,326 bytesSKILL.mdcore11,326 bytesSKILL.mdcore11,326 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubcell-figure-guide
When your agent uses it
Cell (Cell Press) figure preparation: resolution (300-1000 DPI), formats (TIFF/PDF), RGB color, Avenir/Arial fonts, uppercase panel labels, strict image manipulation policies.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
3,720 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Resolution Requirements
- File Format
- Figure Size and Dimensions
- Color Mode
- Font Requirements
- Labeling Conventions
- Image Integrity and Manipulation Policy
- Python Quick Start: Full Validation
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Pre-Submission Checklist
- References
Files
SKILL.mdcore14,573 bytesSKILL.mdcore14,573 bytesSKILL.mdcore14,573 bytesSKILL.mdcore14,573 bytesSKILL.mdcore14,573 bytesSKILL.mdcore14,573 bytesSKILL.mdcore14,573 bytesSKILL.mdcore14,573 bytesSKILL.mdcore14,573 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubcitation-management
When your agent uses it
Selecting a reference manager and applying citation styles. Compares Zotero, Mendeley, EndNote, Paperpile; covers APA/Vancouver/ACS/Nature styles, DOI management, citation tracking, and Word/Google Docs/LaTeX integration. Use when setting up a reference workflow or fixing citation formatting.
What it loads into context
Frontmatter
83 tokensAlways available to the agent
SKILL.md
4,320 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Workflow
- Protocol Guidelines
- Further Reading
- Related Skills
Files
SKILL.mdcore19,755 bytesSKILL.mdcore19,755 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubclinical-decision-support-documents
When your agent uses it
Guidelines for clinical decision support (CDS) documents: biomarker-stratified cohort analyses and GRADE-graded treatment reports. Covers structure, executive summaries, evidence grading (1A–2C), stats (HR, CI, survival), and biomarker integration. Use for pharma research docs, clinical guidelines, regulatory submissions.
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
2,658 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Workflow
- Protocol Guidelines
- Further Reading
- Related Skills
Files
SKILL.mdcore12,207 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubelife-figure-guide
When your agent uses it
eLife figure preparation: file formats (TIFF/EPS/PDF), striking image requirements (1800x900 px), figure supplement naming, and image screening policy treating selective enhancement as misconduct.
What it loads into context
Frontmatter
90 tokensAlways available to the agent
SKILL.md
3,071 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Resolution Requirements
- File Format
- Figure Size and Dimensions
- Color Mode
- Font Requirements
- Labeling Conventions
- Image Integrity and Manipulation Policy
- Python Quick Start: Full Validation
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Pre-Submission Checklist
- References
Files
SKILL.mdcore12,902 bytesSKILL.mdcore12,902 bytesSKILL.mdcore12,902 bytesSKILL.mdcore12,902 bytesSKILL.mdcore12,902 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubgeneral-figure-guide
When your agent uses it
Universal QA checklist for generated scientific plots: overlapping labels, clipped text, missing axes/legends, overcrowded data, and cross-journal resolution/format guidance.
What it loads into context
Frontmatter
52 tokensAlways available to the agent
SKILL.md
1,590 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Protocol Guidelines
- Further Reading
- Related Skills
Files
SKILL.mdcore7,247 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubhypothesis-generation
When your agent uses it
Structured hypothesis formulation: turn observations into testable hypotheses with predictions, propose mechanisms, design experiments. Follows the scientific method. Use scientific-brainstorming for open ideation; hypogenic for automated LLM hypothesis testing on datasets.
What it loads into context
Frontmatter
64 tokensAlways available to the agent
SKILL.md
2,149 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Workflow
- Further Reading
- Related Skills
Files
SKILL.mdcore10,669 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHublancet-figure-guide
When your agent uses it
The Lancet figure preparation: resolution (300+ DPI at 120%), preferred editable formats (PowerPoint/Word/SVG), column widths (75/154 mm), Times New Roman, in-house redraw policy.
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
2,820 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Resolution Requirements
- File Format
- Figure Size and Dimensions
- Color Mode
- Font Requirements
- Labeling Conventions
- Image Integrity and Manipulation Policy
- Python Quick Start: Full Validation
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Pre-Submission Checklist
- References
Files
SKILL.mdcore11,350 bytesSKILL.mdcore11,350 bytesSKILL.mdcore11,350 bytesSKILL.mdcore11,350 bytesSKILL.mdcore11,350 bytesSKILL.mdcore11,350 bytesSKILL.mdcore11,350 bytesSKILL.mdcore11,350 bytesSKILL.mdcore11,350 bytesSKILL.mdcore11,350 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHublatex-research-posters
When your agent uses it
Research posters in LaTeX using beamerposter, tikzposter, or baposter. Layout, typography, color schemes, figure integration, accessibility, and QA for conferences. Includes templates. For figure generation use matplotlib-scientific-plotting or plotly-interactive-plots.
What it loads into context
Frontmatter
78 tokensAlways available to the agent
SKILL.md
3,345 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Workflow
- Protocol Guidelines
- Further Reading
- Related Skills
- Companion Assets
Files
SKILL.mdcore13,803 bytesSKILL.mdcore13,803 bytesSKILL.mdcore13,803 bytesSKILL.mdcore13,803 bytesassets/baposter_template.texasset3,955 bytesassets/beamerposter_template.texasset5,340 bytesassets/tikzposter_template.texasset4,298 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubliterature-review
When your agent uses it
Conducting systematic, scoping, and narrative literature reviews. Covers PRISMA/PRISMA-ScR protocols, search strategy (Boolean, MeSH), database selection (PubMed, Scopus, Web of Science, Embase), screening, data extraction, evidence synthesis (narrative, meta-analysis, thematic), and reporting. Use when planning or executing a formal literature review.
What it loads into context
Frontmatter
100 tokensAlways available to the agent
SKILL.md
3,425 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Workflow
- Further Reading
- Related Skills
Files
SKILL.mdcore16,418 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubnature-figure-guide
When your agent uses it
Nature figure preparation: resolution (300+ DPI), formats (AI/EPS/TIFF), RGB color, Helvetica/Arial fonts, lowercase panel labels, image integrity requirements.
What it loads into context
Frontmatter
84 tokensAlways available to the agent
SKILL.md
3,561 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Resolution Requirements
- File Format
- Figure Size and Dimensions
- Color Mode
- Font Requirements
- Labeling Conventions
- Image Integrity and Manipulation Policy
- Python Quick Start: Full Validation
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Pre-Submission Checklist
- References
Files
SKILL.mdcore14,625 bytesSKILL.mdcore14,625 bytesSKILL.mdcore14,625 bytesSKILL.mdcore14,625 bytesSKILL.mdcore14,625 bytesSKILL.mdcore14,625 bytesSKILL.mdcore14,625 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubnejm-figure-guide
When your agent uses it
NEJM figure preparation: resolution (300-1200 DPI), editable vector formats (AI/EPS/SVG), in-house medical illustration policy, and strict image integrity requirements.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
2,869 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Resolution Requirements
- File Format
- Figure Size and Dimensions
- Color Mode
- Font Requirements
- Labeling Conventions
- Image Integrity and Manipulation Policy
- Python Quick Start: Full Validation
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Pre-Submission Checklist
- References
Files
SKILL.mdcore12,487 bytesSKILL.mdcore12,487 bytesSKILL.mdcore12,487 bytesSKILL.mdcore12,487 bytesSKILL.mdcore12,487 bytesSKILL.mdcore12,487 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubopenalex-database
When your agent uses it
Query OpenAlex REST API for 250M+ scholarly works, authors, institutions, journals, concepts. Search by keyword, author, DOI, ORCID, or ID; filter by year, OA, citations, field; retrieve citations, references, author disambiguation. Free, no auth. For PubMed use pubmed-database; preprints use biorxiv-database.
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
4,792 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore19,206 bytesSKILL.mdcore19,206 bytesSKILL.mdcore19,206 bytesSKILL.mdcore19,206 bytesSKILL.mdcore19,206 bytesSKILL.mdcore19,206 bytesSKILL.mdcore19,206 bytesSKILL.mdcore19,206 bytesSKILL.mdcore19,206 bytesSKILL.mdcore19,206 bytesSKILL.mdcore19,206 bytesSKILL.mdcore19,206 bytesSKILL.mdcore19,206 bytesSKILL.mdcore19,206 bytesSKILL.mdcore19,206 bytesSKILL.mdcore19,206 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpeer-review-methodology
When your agent uses it
Structured peer review of manuscripts and grants. 7-stage evaluation: initial assessment, section review, statistical rigor, reproducibility, figure integrity, ethics, writing. Covers CONSORT/STROBE/PRISMA and report structure. For evidence quality see scientific-critical-thinking; scoring see scholar-evaluation.
What it loads into context
Frontmatter
79 tokensAlways available to the agent
SKILL.md
2,422 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Workflow
- Further Reading
- Related Skills
Files
SKILL.mdcore11,745 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpnas-figure-guide
When your agent uses it
PNAS figure preparation: resolution (300-1000 PPI), formats (TIFF/EPS/PDF), strict RGB-only color, Arial/Helvetica fonts, italicized uppercase panel labels, automated image screening.
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
3,767 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Resolution Requirements
- File Format
- Figure Size and Dimensions
- Color Mode
- Font Requirements
- Labeling Conventions
- Image Integrity and Manipulation Policy
- Python Quick Start: Full Validation
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Pre-Submission Checklist
- References
Files
SKILL.mdcore14,803 bytesSKILL.mdcore14,803 bytesSKILL.mdcore14,803 bytesSKILL.mdcore14,803 bytesSKILL.mdcore14,803 bytesSKILL.mdcore14,803 bytesSKILL.mdcore14,803 bytesSKILL.mdcore14,803 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpubmed-database
When your agent uses it
Programmatic PubMed access via NCBI E-utilities REST API. Covers Boolean/MeSH queries, field-tagged search, endpoints (ESearch, EFetch, ESummary, EPost, ELink), history server for batches, citation matching, systematic review strategies. Use for biomedical literature search or automated pipelines.
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
4,983 tokensWhen the skill is activated
Reference documents
4,098 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Troubleshooting
- Common Recipes
- Bundled Resources
- References
- Related Skills
Files
SKILL.mdcore17,749 bytesSKILL.mdcore17,749 bytesSKILL.mdcore17,749 bytesSKILL.mdcore17,749 bytesSKILL.mdcore17,749 bytesSKILL.mdcore17,749 bytesSKILL.mdcore17,749 bytesSKILL.mdcore17,749 bytesSKILL.mdcore17,749 bytesSKILL.mdcore17,749 bytesSKILL.mdcore17,749 bytesSKILL.mdcore17,749 bytesSKILL.mdcore17,749 bytesSKILL.mdcore17,749 bytesreferences/common_queries.mddoc7,331 bytesreferences/search_syntax.mddoc6,410 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscience-figure-guide
When your agent uses it
Science (AAAS) figure preparation: resolution (150-300+ DPI), formats (PDF/EPS/TIFF), RGB color, Myriad/Helvetica fonts, strict image manipulation policies including gamma adjustment disclosure.
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
3,065 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Resolution Requirements
- File Format
- Figure Size and Dimensions
- Color Mode
- Font Requirements
- Labeling Conventions
- Image Integrity and Manipulation Policy
- Python Quick Start: Full Validation
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Pre-Submission Checklist
- References
Files
SKILL.mdcore12,537 bytesSKILL.mdcore12,537 bytesSKILL.mdcore12,537 bytesSKILL.mdcore12,537 bytesSKILL.mdcore12,537 bytesSKILL.mdcore12,537 bytesSKILL.mdcore12,537 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscientific-brainstorming
When your agent uses it
Structured ideation methods: SCAMPER, Six Thinking Hats, Morphological Analysis, TRIZ, Biomimicry, plus more. Decision framework for picking methods by challenge type (stuck, improving, systematic exploration, contradiction). Use when generating research ideas or exploring interdisciplinary connections.
What it loads into context
Frontmatter
81 tokensAlways available to the agent
SKILL.md
5,699 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Workflow
- Further Reading
- Related Skills
Files
SKILL.mdcore28,847 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscientific-critical-thinking
When your agent uses it
Evaluating scientific evidence and claims. Covers study design hierarchy (RCT to expert opinion), effect sizes (OR, RR, NNT, Cohen's d), confounding, p-value vs clinical significance, GRADE quality assessment, reproducibility, and bias types (selection, information, confounding, reporting). Use when reading a paper or assessing claims.
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
4,062 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Workflow
- Further Reading
- Related Skills
Files
SKILL.mdcore19,169 bytesSKILL.mdcore19,169 bytesSKILL.mdcore19,169 bytesSKILL.mdcore19,169 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscientific-literature-search
When your agent uses it
Systematic strategies for searching scientific literature across PubMed, arXiv, Google Scholar, and AI-assisted tools. Covers PICO framework for clinical questions, three-tiered search (database-specific, AI-assisted, content extraction), PubMed field tags and MeSH, boolean query construction, and full-text extraction. Use when planning a literature search or choosing a search tier.
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
5,246 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Workflow
- Common Search Scenarios
- Quality Assessment Checklist
- Further Reading
- Related Skills
Files
SKILL.mdcore22,847 bytesSKILL.mdcore22,847 bytesSKILL.mdcore22,847 bytesSKILL.mdcore22,847 bytesSKILL.mdcore22,847 bytesSKILL.mdcore22,847 bytesSKILL.mdcore22,847 bytesSKILL.mdcore22,847 bytesSKILL.mdcore22,847 bytesSKILL.mdcore22,847 bytesSKILL.mdcore22,847 bytesSKILL.mdcore22,847 bytesSKILL.mdcore22,847 bytesSKILL.mdcore22,847 bytesSKILL.mdcore22,847 bytesSKILL.mdcore22,847 bytesSKILL.mdcore22,847 bytesSKILL.mdcore22,847 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscientific-manuscript-writing
When your agent uses it
Scientific manuscript writing: IMRAD, citation styles (APA/AMA/Vancouver/IEEE), figures/tables, reporting guidelines (CONSORT/STROBE/PRISMA/ARRIVE), writing principles (clarity/conciseness/accuracy), venue-specific style. For LaTeX see companion assets.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
3,979 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Workflow
- Bundled Resources
- Further Reading
- Related Skills
- Companion Assets
Files
SKILL.mdcore19,146 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscientific-schematics
When your agent uses it
Designing scientific schematics, diagrams, and graphical abstracts. Covers tool selection (BioRender, Inkscape, Affinity, PowerPoint), design principles for pathway diagrams, mechanism schematics, experimental workflows, and journal graphical abstracts. Includes composition, icon sourcing, color for biological entities, and accessibility. Use when creating illustrative (not data-driven) scientific figures.
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
3,390 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Workflow
- Protocol Guidelines
- Further Reading
- Related Skills
Files
SKILL.mdcore15,467 bytesSKILL.mdcore15,467 bytesSKILL.mdcore15,467 bytesSKILL.mdcore15,467 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubscientific-slides
When your agent uses it
Scientific presentations for conferences, seminars, thesis defenses, and grant pitches. Slide design, talk structure, timing, data viz for slides, QA. PowerPoint and LaTeX Beamer. For posters use latex-research-posters.
What it loads into context
Frontmatter
65 tokensAlways available to the agent
SKILL.md
3,370 tokensWhen the skill is activated
Reference documents
5,963 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Workflow
- Protocol Guidelines
- Bundled Resources
- Further Reading
- Related Skills
- Companion Assets
Files
SKILL.mdcore14,152 bytesSKILL.mdcore14,152 bytesassets/beamer_template_conference.texasset5,167 bytesassets/beamer_template_defense.texasset10,624 bytesassets/beamer_template_seminar.texasset8,804 bytesassets/timing_guidelines.mddoc5,330 bytesreferences/slide_design_guide.mddoc8,117 bytesreferences/talk_types_guide.mddoc9,553 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubaizynthfinder-retrosynthesis
When your agent uses it
AiZynthFinder retrosynthetic route planning (CASP) from AstraZeneca Molecular AI. Monte Carlo tree search guided by a template-based neural expansion policy recursively disconnects a target SMILES until precursors are found in a purchasable stock. Covers config.yml (v4 format), aizynthcli batch screening, the AiZynthFinder/AiZynthExpander Python API, one-step disconnections, custom stocks via smiles2stock, scorers, Retro*/breadth-first/DFPN search alternatives, and reading output.json.gz / trees.json. Use for synthesis route planning, synthesizability screening, and building-block/precursor search. For reaction barriers use neb-irc-activation-energy; for 2D reaction scheme drawing use rdkit-chemdraw-cdxml.
What it loads into context
Frontmatter
178 tokensAlways available to the agent
SKILL.md
5,036 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Workflow
- Key Parameters
- Key Concepts
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore19,893 bytesSKILL.mdcore19,893 bytesSKILL.mdcore19,893 bytesSKILL.mdcore19,893 bytesSKILL.mdcore19,893 bytesSKILL.mdcore19,893 bytesSKILL.mdcore19,893 bytesSKILL.mdcore19,893 bytesSKILL.mdcore19,893 bytesSKILL.mdcore19,893 bytesSKILL.mdcore19,893 bytesSKILL.mdcore19,893 bytesSKILL.mdcore19,893 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubalphafold-database-access
When your agent uses it
Access AlphaFold DB's 200M+ predicted structures by UniProt ID. Download PDB/mmCIF, analyze pLDDT/PAE, bulk-fetch proteomes via Google Cloud. For experimental structures use PDB; for prediction use ColabFold or ESMFold.
What it loads into context
Frontmatter
83 tokensAlways available to the agent
SKILL.md
4,466 tokensWhen the skill is activated
Reference documents
2,265 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
SKILL.mdcore16,631 bytesSKILL.mdcore16,631 bytesSKILL.mdcore16,631 bytesSKILL.mdcore16,631 bytesSKILL.mdcore16,631 bytesSKILL.mdcore16,631 bytesSKILL.mdcore16,631 bytesSKILL.mdcore16,631 bytesSKILL.mdcore16,631 bytesSKILL.mdcore16,631 bytesSKILL.mdcore16,631 bytesSKILL.mdcore16,631 bytesSKILL.mdcore16,631 bytesSKILL.mdcore16,631 bytesSKILL.mdcore16,631 bytesSKILL.mdcore16,631 bytesSKILL.mdcore16,631 bytesreferences/api_schemas_reference.mddoc8,086 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubautodock-vina-docking
When your agent uses it
Molecular docking with AutoDock Vina (Python API). Receptor/ligand prep (Meeko + RDKit), grid box, docking, pose and binding energy analysis, and batch virtual screening.
What it loads into context
Frontmatter
66 tokensAlways available to the agent
SKILL.md
4,564 tokensWhen the skill is activated
Reference documents
2,125 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- Bundled Resources
- References
Files
SKILL.mdcore15,860 bytesSKILL.mdcore15,860 bytesSKILL.mdcore15,860 bytesSKILL.mdcore15,860 bytesSKILL.mdcore15,860 bytesSKILL.mdcore15,860 bytesSKILL.mdcore15,860 bytesSKILL.mdcore15,860 bytesSKILL.mdcore15,860 bytesSKILL.mdcore15,860 bytesSKILL.mdcore15,860 bytesSKILL.mdcore15,860 bytesSKILL.mdcore15,860 bytesSKILL.mdcore15,860 bytesSKILL.mdcore15,860 bytesreferences/receptor_preparation_guide.mddoc4,347 bytesreferences/scoring_functions_comparison.mddoc3,612 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubchembl-database-bioactivity
When your agent uses it
Query ChEMBL (2M+ compounds, 19M+ bioactivity measurements, 13K+ targets) via the public REST/JSON API with plain `requests` — no SDK install required. Search compounds, retrieve IC50/Ki/EC50 bioactivities, find target inhibitors, run SAR, access drug mechanism/indication data.
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
7,134 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Key Concepts
- Core API
- Common Workflows
- Common Recipes
- Key Parameters
- Troubleshooting
- Best Practices
- Related Skills
- References
Files
SKILL.mdcore25,803 bytesSKILL.mdcore25,803 bytesSKILL.mdcore25,803 bytesSKILL.mdcore25,803 bytesSKILL.mdcore25,803 bytesSKILL.mdcore25,803 bytesSKILL.mdcore25,803 bytesSKILL.mdcore25,803 bytesSKILL.mdcore25,803 bytesSKILL.mdcore25,803 bytesSKILL.mdcore25,803 bytesSKILL.mdcore25,803 bytesSKILL.mdcore25,803 bytesSKILL.mdcore25,803 bytesSKILL.mdcore25,803 bytesSKILL.mdcore25,803 bytesSKILL.mdcore25,803 bytesSKILL.mdcore25,803 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubclinicaltrials-database-search
When your agent uses it
Query ClinicalTrials.gov API v2 for trial data. Search by condition, drug/intervention, location, sponsor, or phase; fetch details by NCT ID; filter by status; paginate; export CSV. For clinical research, patient matching, and trial portfolio analysis.
What it loads into context
Frontmatter
74 tokensAlways available to the agent
SKILL.md
4,689 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Key Concepts
- Core API
- Common Workflows
- Common Recipes
- Key Parameters
- Troubleshooting
- Best Practices
- Related Skills
- References
- Bundled Resources
Files
SKILL.mdcore18,605 bytesSKILL.mdcore18,605 bytesSKILL.mdcore18,605 bytesSKILL.mdcore18,605 bytesSKILL.mdcore18,605 bytesSKILL.mdcore18,605 bytesSKILL.mdcore18,605 bytesSKILL.mdcore18,605 bytesSKILL.mdcore18,605 bytesSKILL.mdcore18,605 bytesSKILL.mdcore18,605 bytesSKILL.mdcore18,605 bytesSKILL.mdcore18,605 bytesSKILL.mdcore18,605 bytesSKILL.mdcore18,605 bytesSKILL.mdcore18,605 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubdailymed-database
When your agent uses it
Query FDA drug labels (DailyMed) via REST API. Search structured product labels (SPLs) by name, NDC, set ID, or RxCUI; get indications, dosage, warnings, adverse reactions, packaging. No auth. For adverse events use fda-database; for DDIs use ddinter-database.
What it loads into context
Frontmatter
90 tokensAlways available to the agent
SKILL.md
6,240 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore24,054 bytesSKILL.mdcore24,054 bytesSKILL.mdcore24,054 bytesSKILL.mdcore24,054 bytesSKILL.mdcore24,054 bytesSKILL.mdcore24,054 bytesSKILL.mdcore24,054 bytesSKILL.mdcore24,054 bytesSKILL.mdcore24,054 bytesSKILL.mdcore24,054 bytesSKILL.mdcore24,054 bytesSKILL.mdcore24,054 bytesSKILL.mdcore24,054 bytesSKILL.mdcore24,054 bytesSKILL.mdcore24,054 bytesSKILL.mdcore24,054 bytesSKILL.mdcore24,054 bytesSKILL.mdcore24,054 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubdatamol-cheminformatics
When your agent uses it
Pythonic RDKit wrapper with sensible defaults for drug discovery. SMILES parsing, standardization, descriptors, fingerprints, similarity, clustering, diversity selection, scaffold analysis, BRICS/RECAP fragmentation, 3D conformers, and visualization. Returns native rdkit.Chem.Mol. Prefer datamol for standard workflows; use RDKit directly for advanced control.
What it loads into context
Frontmatter
103 tokensAlways available to the agent
SKILL.md
5,077 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore17,739 bytesSKILL.mdcore17,739 bytesSKILL.mdcore17,739 bytesSKILL.mdcore17,739 bytesSKILL.mdcore17,739 bytesSKILL.mdcore17,739 bytesSKILL.mdcore17,739 bytesSKILL.mdcore17,739 bytesSKILL.mdcore17,739 bytesSKILL.mdcore17,739 bytesSKILL.mdcore17,739 bytesSKILL.mdcore17,739 bytesSKILL.mdcore17,739 bytesSKILL.mdcore17,739 bytesSKILL.mdcore17,739 bytesSKILL.mdcore17,739 bytesSKILL.mdcore17,739 bytesSKILL.mdcore17,739 bytesSKILL.mdcore17,739 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubddinter-database
When your agent uses it
Query DDInter drug-drug interactions via REST API (1.7M+ interactions, 2,400+ drugs). Search by drug name/ID for severity (major/moderate/minor), mechanisms, and clinical recommendations. No auth. For FDA labeling use dailymed-database; for pharmacogenomics use clinpgx-database.
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
7,626 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore29,344 bytesSKILL.mdcore29,344 bytesSKILL.mdcore29,344 bytesSKILL.mdcore29,344 bytesSKILL.mdcore29,344 bytesSKILL.mdcore29,344 bytesSKILL.mdcore29,344 bytesSKILL.mdcore29,344 bytesSKILL.mdcore29,344 bytesSKILL.mdcore29,344 bytesSKILL.mdcore29,344 bytesSKILL.mdcore29,344 bytesSKILL.mdcore29,344 bytesSKILL.mdcore29,344 bytesSKILL.mdcore29,344 bytesSKILL.mdcore29,344 bytesSKILL.mdcore29,344 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubdeepchem
When your agent uses it
Deep learning for drug discovery. 60+ models (GCN, GAT, AttentiveFP, MPNN, ChemBERTa, GROVER), 50+ featurizers, MoleculeNet benchmarks, HPO, transfer learning. Unified load-featurize-split-train-evaluate API. For fingerprints use rdkit-cheminformatics; for featurization-only use molfeat.
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
6,112 tokensWhen the skill is activated
Reference documents
4,372 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
SKILL.mdcore23,405 bytesSKILL.mdcore23,405 bytesSKILL.mdcore23,405 bytesSKILL.mdcore23,405 bytesSKILL.mdcore23,405 bytesSKILL.mdcore23,405 bytesSKILL.mdcore23,405 bytesSKILL.mdcore23,405 bytesSKILL.mdcore23,405 bytesSKILL.mdcore23,405 bytesSKILL.mdcore23,405 bytesSKILL.mdcore23,405 bytesSKILL.mdcore23,405 bytesSKILL.mdcore23,405 bytesSKILL.mdcore23,405 bytesSKILL.mdcore23,405 bytesSKILL.mdcore23,405 bytesSKILL.mdcore23,405 bytesSKILL.mdcore23,405 bytesSKILL.mdcore23,405 bytesreferences/workflows_model_catalog.mddoc16,589 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubdiffdock
When your agent uses it
Diffusion-based docking that predicts protein-ligand poses without a predefined site. Use for blind docking, when traditional docking fails, or exploring multiple binding modes. Pipeline: prep protein (PDB) and ligand (SMILES/SDF), run inference, analyze confidence-ranked poses.
What it loads into context
Frontmatter
70 tokensAlways available to the agent
SKILL.md
3,346 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore12,952 bytesSKILL.mdcore12,952 bytesSKILL.mdcore12,952 bytesSKILL.mdcore12,952 bytesSKILL.mdcore12,952 bytesSKILL.mdcore12,952 bytesSKILL.mdcore12,952 bytesSKILL.mdcore12,952 bytesSKILL.mdcore12,952 bytesSKILL.mdcore12,952 bytesSKILL.mdcore12,952 bytesSKILL.mdcore12,952 bytesSKILL.mdcore12,952 bytesSKILL.mdcore12,952 bytesSKILL.mdcore12,952 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubdrugbank-database-access
When your agent uses it
Parse local DrugBank XML for drug info, interactions, targets, and properties. Search by ID/name/CAS, extract DDIs with severity, map targets/enzymes/transporters, compute SMILES similarity. Primary via local XML; REST API rate-limited (3k/month dev). For live bioactivity use chembl-database-bioactivity; for compound properties use pubchem-compound-search.
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
6,258 tokensWhen the skill is activated
Reference documents
7,763 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
SKILL.mdcore23,267 bytesSKILL.mdcore23,267 bytesSKILL.mdcore23,267 bytesSKILL.mdcore23,267 bytesSKILL.mdcore23,267 bytesSKILL.mdcore23,267 bytesSKILL.mdcore23,267 bytesSKILL.mdcore23,267 bytesSKILL.mdcore23,267 bytesSKILL.mdcore23,267 bytesSKILL.mdcore23,267 bytesSKILL.mdcore23,267 bytesSKILL.mdcore23,267 bytesSKILL.mdcore23,267 bytesSKILL.mdcore23,267 bytesSKILL.mdcore23,267 bytesSKILL.mdcore23,267 bytesSKILL.mdcore23,267 bytesSKILL.mdcore23,267 bytesSKILL.mdcore23,267 bytesSKILL.mdcore23,267 bytesSKILL.mdcore23,267 bytesreferences/chemical_analysis.mddoc10,424 bytesreferences/interactions_targets.mddoc16,947 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubemdb-database
When your agent uses it
Look up EMDB cryo-EM density maps and fitted atomic models via the entry REST API + EBI Search WS. Fetch entry metadata (resolution, method, organism, sample), map download URLs, fitted PDB IDs, and citations. Keyword search via EBI Search. No auth. For atomic coordinates use pdb-database; for AlphaFold predictions use alphafold-database-access.
What it loads into context
Frontmatter
101 tokensAlways available to the agent
SKILL.md
5,644 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore19,861 bytesSKILL.mdcore19,861 bytesSKILL.mdcore19,861 bytesSKILL.mdcore19,861 bytesSKILL.mdcore19,861 bytesSKILL.mdcore19,861 bytesSKILL.mdcore19,861 bytesSKILL.mdcore19,861 bytesSKILL.mdcore19,861 bytesSKILL.mdcore19,861 bytesSKILL.mdcore19,861 bytesSKILL.mdcore19,861 bytesSKILL.mdcore19,861 bytesSKILL.mdcore19,861 bytesSKILL.mdcore19,861 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubfda-database
When your agent uses it
Query openFDA REST API for adverse events (FAERS), labeling, product info, recalls, enforcement. Search by drug name, ingredient, MedDRA, or NDC. 1k req/day no key; 120k with free key. For trials use clinicaltrials-database-search; for structures use drugbank-database-access or chembl-database-bioactivity.
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
4,418 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore18,045 bytesSKILL.mdcore18,045 bytesSKILL.mdcore18,045 bytesSKILL.mdcore18,045 bytesSKILL.mdcore18,045 bytesSKILL.mdcore18,045 bytesSKILL.mdcore18,045 bytesSKILL.mdcore18,045 bytesSKILL.mdcore18,045 bytesSKILL.mdcore18,045 bytesSKILL.mdcore18,045 bytesSKILL.mdcore18,045 bytesSKILL.mdcore18,045 bytesSKILL.mdcore18,045 bytesSKILL.mdcore18,045 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubgtopdb-database
When your agent uses it
Query IUPHAR/BPS Guide to Pharmacology (GtoPdb) for receptor-ligand interactions, target/ligand metadata, families, and approved drugs. Affinities (pKi/pIC50/pKd), action (Agonist/Antagonist/etc.), species, structures (SMILES/InChI). No auth. Always resolve targets via geneSymbol/accession; most metadata lives in sub-resources (/databaseLinks, /structure, /synonyms).
What it loads into context
Frontmatter
122 tokensAlways available to the agent
SKILL.md
5,291 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore19,496 bytesSKILL.mdcore19,496 bytesSKILL.mdcore19,496 bytesSKILL.mdcore19,496 bytesSKILL.mdcore19,496 bytesSKILL.mdcore19,496 bytesSKILL.mdcore19,496 bytesSKILL.mdcore19,496 bytesSKILL.mdcore19,496 bytesSKILL.mdcore19,496 bytesSKILL.mdcore19,496 bytesSKILL.mdcore19,496 bytesSKILL.mdcore19,496 bytesSKILL.mdcore19,496 bytesSKILL.mdcore19,496 bytesSKILL.mdcore19,496 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmdanalysis-trajectory
When your agent uses it
Analyze MD trajectories from GROMACS, AMBER, NAMD, CHARMM, LAMMPS. Reads topology/trajectory into Universe objects; supports RMSD, RMSF, radius of gyration, contact maps, H-bonds, PCA, and custom distance/angle calculations. Use for post-simulation structural analysis; use OpenMM/GROMACS for running simulations.
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
3,924 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Parameters
- Common Workflows
- Common Recipes
- Troubleshooting
- References
Files
SKILL.mdcore14,272 bytesSKILL.mdcore14,272 bytesSKILL.mdcore14,272 bytesSKILL.mdcore14,272 bytesSKILL.mdcore14,272 bytesSKILL.mdcore14,272 bytesSKILL.mdcore14,272 bytesSKILL.mdcore14,272 bytesSKILL.mdcore14,272 bytesSKILL.mdcore14,272 bytesSKILL.mdcore14,272 bytesSKILL.mdcore14,272 bytesSKILL.mdcore14,272 bytesSKILL.mdcore14,272 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmdtraj-trajectory-analysis
When your agent uses it
mdtraj molecular dynamics trajectory analysis (Python). Reads DCD/XTC/TRR/NetCDF/H5/PDB topologies and trajectories; computes RMSD vs time, radius of gyration, per-residue RMSF, residue-residue contact frequency maps, phi/psi torsions for Ramachandran plots (general + Gly/Pro), and 8-state DSSP secondary structure. Modules: trajectory I/O, geometry (distances/angles/dihedrals), structural analysis (RMSD/Rg/RMSF/SASA), contacts, hydrogen bonds, secondary structure (DSSP), NMR observables. For broader atom-selection grammar use mdanalysis-trajectory; for running MD simulations use OpenMM/GROMACS.
What it loads into context
Frontmatter
174 tokensAlways available to the agent
SKILL.md
6,259 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore20,046 bytesSKILL.mdcore20,046 bytesSKILL.mdcore20,046 bytesSKILL.mdcore20,046 bytesSKILL.mdcore20,046 bytesSKILL.mdcore20,046 bytesSKILL.mdcore20,046 bytesSKILL.mdcore20,046 bytesSKILL.mdcore20,046 bytesSKILL.mdcore20,046 bytesSKILL.mdcore20,046 bytesSKILL.mdcore20,046 bytesSKILL.mdcore20,046 bytesSKILL.mdcore20,046 bytesSKILL.mdcore20,046 bytesSKILL.mdcore20,046 bytesSKILL.mdcore20,046 bytesSKILL.mdcore20,046 bytesSKILL.mdcore20,046 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmedchem
When your agent uses it
Medicinal chemistry filters for compound triage. Drug-likeness rules (Lipinski Ro5, Veber, Oprea, CNS, leadlike, REOS, Golden Triangle, Ro3), structural alerts (PAINS, NIBR, Lilly Demerits), chemical group detectors, complexity metrics, and filter composition query language. Built on RDKit/datamol. For hit-to-lead filtering, library design, ADMET pre-screening. For molecular I/O use rdkit-cheminformatics or datamol.
What it loads into context
Frontmatter
132 tokensAlways available to the agent
SKILL.md
4,933 tokensWhen the skill is activated
Reference documents
1,965 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
SKILL.mdcore17,851 bytesSKILL.mdcore17,851 bytesSKILL.mdcore17,851 bytesSKILL.mdcore17,851 bytesSKILL.mdcore17,851 bytesSKILL.mdcore17,851 bytesSKILL.mdcore17,851 bytesSKILL.mdcore17,851 bytesSKILL.mdcore17,851 bytesSKILL.mdcore17,851 bytesSKILL.mdcore17,851 bytesSKILL.mdcore17,851 bytesSKILL.mdcore17,851 bytesSKILL.mdcore17,851 bytesSKILL.mdcore17,851 bytesSKILL.mdcore17,851 bytesSKILL.mdcore17,851 bytesSKILL.mdcore17,851 bytesreferences/rules_catalog.mddoc7,020 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmolfeat-molecular-featurization
When your agent uses it
Molecular featurization hub (100+ featurizers) for ML. SMILES to fingerprints (ECFP, MACCS, MAP4), descriptors (RDKit 2D, Mordred), pretrained embeddings (ChemBERTa, GIN, Graphormer), pharmacophores. Scikit-learn compatible with parallelization/caching. For QSAR, virtual screening, similarity, and molecular DL.
What it loads into context
Frontmatter
102 tokensAlways available to the agent
SKILL.md
4,520 tokensWhen the skill is activated
Reference documents
3,663 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Key Concepts
- Core API
- Common Workflows
- Common Recipes
- Key Parameters
- Best Practices
- Troubleshooting
- Related Skills
- References
- Bundled Resources
Files
SKILL.mdcore17,401 bytesSKILL.mdcore17,401 bytesSKILL.mdcore17,401 bytesSKILL.mdcore17,401 bytesSKILL.mdcore17,401 bytesSKILL.mdcore17,401 bytesSKILL.mdcore17,401 bytesSKILL.mdcore17,401 bytesSKILL.mdcore17,401 bytesSKILL.mdcore17,401 bytesSKILL.mdcore17,401 bytesSKILL.mdcore17,401 bytesSKILL.mdcore17,401 bytesSKILL.mdcore17,401 bytesSKILL.mdcore17,401 bytesSKILL.mdcore17,401 bytesreferences/api_reference.mddoc8,083 bytesreferences/available_featurizers.mddoc6,453 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubopentargets-database
When your agent uses it
Query Open Targets GraphQL API for target-disease associations, evidence, drug links, safety. Search targets by gene, diseases by EFO ID; scores from 20+ sources, drug mechanisms, tractability. For ChEMBL use chembl-database-bioactivity; for trials use clinicaltrials-database-search.
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
5,360 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore20,412 bytesSKILL.mdcore20,412 bytesSKILL.mdcore20,412 bytesSKILL.mdcore20,412 bytesSKILL.mdcore20,412 bytesSKILL.mdcore20,412 bytesSKILL.mdcore20,412 bytesSKILL.mdcore20,412 bytesSKILL.mdcore20,412 bytesSKILL.mdcore20,412 bytesSKILL.mdcore20,412 bytesSKILL.mdcore20,412 bytesSKILL.mdcore20,412 bytesSKILL.mdcore20,412 bytesSKILL.mdcore20,412 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpdb-database
When your agent uses it
Query RCSB PDB (200K+ structures) via the public REST + GraphQL APIs with plain `requests` (no SDK). Search by text, attribute, sequence, or 3D structure similarity (Search API); retrieve metadata via GraphQL (Data API); download PDB/mmCIF from files.rcsb.org. For AlphaFold predictions use alphafold-database-access; for protein sequences only use uniprot-protein-database.
What it loads into context
Frontmatter
112 tokensAlways available to the agent
SKILL.md
8,487 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore31,067 bytesSKILL.mdcore31,067 bytesSKILL.mdcore31,067 bytesSKILL.mdcore31,067 bytesSKILL.mdcore31,067 bytesSKILL.mdcore31,067 bytesSKILL.mdcore31,067 bytesSKILL.mdcore31,067 bytesSKILL.mdcore31,067 bytesSKILL.mdcore31,067 bytesSKILL.mdcore31,067 bytesSKILL.mdcore31,067 bytesSKILL.mdcore31,067 bytesSKILL.mdcore31,067 bytesSKILL.mdcore31,067 bytesSKILL.mdcore31,067 bytesSKILL.mdcore31,067 bytesSKILL.mdcore31,067 bytesSKILL.mdcore31,067 bytesSKILL.mdcore31,067 bytesSKILL.mdcore31,067 bytesSKILL.mdcore31,067 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpubchem-compound-search
When your agent uses it
Query PubChem (110M+ compounds) directly via the PUG-REST/JSON API with plain `requests` — no SDK install required. Search by name/CID/SMILES/InChIKey/formula, retrieve properties (MW, XLogP, TPSA, H-bond counts), do similarity/substructure searches with async ListKey polling, fetch synonyms, descriptions, assay summaries, and download SDF/PNG. For local cheminformatics use rdkit; for bioactivity-centric workflows use chembl-database-bioactivity.
What it loads into context
Frontmatter
134 tokensAlways available to the agent
SKILL.md
7,366 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Expected Outputs
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore28,306 bytesSKILL.mdcore28,306 bytesSKILL.mdcore28,306 bytesSKILL.mdcore28,306 bytesSKILL.mdcore28,306 bytesSKILL.mdcore28,306 bytesSKILL.mdcore28,306 bytesSKILL.mdcore28,306 bytesSKILL.mdcore28,306 bytesSKILL.mdcore28,306 bytesSKILL.mdcore28,306 bytesSKILL.mdcore28,306 bytesSKILL.mdcore28,306 bytesSKILL.mdcore28,306 bytesSKILL.mdcore28,306 bytesSKILL.mdcore28,306 bytesSKILL.mdcore28,306 bytesSKILL.mdcore28,306 bytesSKILL.mdcore28,306 bytesSKILL.mdcore28,306 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubpytdc-therapeutics-data-commons
When your agent uses it
Therapeutics Data Commons (TDC) AI-ready drug discovery datasets. Curated ADME, toxicity, DTI, DDI with scaffold/cold splits, standardized metrics, molecular oracles, and ADMET benchmarks for therapeutic ML and property prediction. For chemical database queries use chembl-database-bioactivity; for featurization use molfeat.
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
4,838 tokensWhen the skill is activated
Reference documents
7,204 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
SKILL.mdcore17,933 bytesSKILL.mdcore17,933 bytesSKILL.mdcore17,933 bytesSKILL.mdcore17,933 bytesSKILL.mdcore17,933 bytesSKILL.mdcore17,933 bytesSKILL.mdcore17,933 bytesSKILL.mdcore17,933 bytesSKILL.mdcore17,933 bytesSKILL.mdcore17,933 bytesSKILL.mdcore17,933 bytesSKILL.mdcore17,933 bytesSKILL.mdcore17,933 bytesreferences/datasets_catalog.mddoc8,646 bytesreferences/oracles_utilities.mddoc16,384 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubrdkit-chemdraw-cdxml
When your agent uses it
Read, write, and edit ChemDraw CDX/CDXML files with RDKit's rdkit.Chem.rdChemDraw plus direct XML editing, always paired with a rendered PNG. Parse molecules and reactions from .cdxml/.cdx, write structures with good 2D depiction, and hand-build or modify the parts RDKit cannot write: reaction arrows, plus signs, schemes/steps, and text/labels. Use for reaction schemes, synthesis routes, mechanisms, retrosynthesis, or SI figures. Critical: RDKit writes structures only — round-tripping a reaction through a Mol silently drops arrows and text; this skill shows the XML layer that preserves them. For pure molecular analysis (descriptors, fingerprints, SMARTS) use rdkit-cheminformatics; for multi-format 3D conversion use openbabel.
What it loads into context
Frontmatter
188 tokensAlways available to the agent
SKILL.md
7,766 tokensWhen the skill is activated
Reference documents
2,585 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Output contract
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Scripts executed separately
scripts/build_reaction_scheme.pyscripts/check_scheme.py
Files
SKILL.mdcore27,795 bytesSKILL.mdcore27,795 bytesSKILL.mdcore27,795 bytesSKILL.mdcore27,795 bytesSKILL.mdcore27,795 bytesSKILL.mdcore27,795 bytesSKILL.mdcore27,795 bytesSKILL.mdcore27,795 bytesSKILL.mdcore27,795 bytesSKILL.mdcore27,795 bytesSKILL.mdcore27,795 bytesSKILL.mdcore27,795 bytesSKILL.mdcore27,795 bytesSKILL.mdcore27,795 bytesSKILL.mdcore27,795 bytesSKILL.mdcore27,795 bytesSKILL.mdcore27,795 bytesSKILL.mdcore27,795 bytesSKILL.mdcore27,795 bytesSKILL.mdcore27,795 bytesSKILL.mdcore27,795 bytesSKILL.mdcore27,795 bytesreferences/cdxml-schema-reference.mddoc8,422 bytesscripts/build_reaction_scheme.pyruns12,548 bytesscripts/check_scheme.pyruns11,471 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubrdkit-cheminformatics
When your agent uses it
Cheminformatics toolkit for molecular analysis and virtual screening: SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints (Morgan/ECFP, MACCS), Tanimoto similarity, SMARTS substructure filtering, Lipinski drug-likeness, reaction enumeration, 2D/3D coordinates. For simpler API use datamol; use RDKit for fine-grained sanitization, custom fingerprints, or SMARTS/reaction control.
What it loads into context
Frontmatter
116 tokensAlways available to the agent
SKILL.md
4,997 tokensWhen the skill is activated
Reference documents
5,732 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- Bundled Resources
- References
Files
SKILL.mdcore17,876 bytesSKILL.mdcore17,876 bytesSKILL.mdcore17,876 bytesSKILL.mdcore17,876 bytesSKILL.mdcore17,876 bytesSKILL.mdcore17,876 bytesSKILL.mdcore17,876 bytesSKILL.mdcore17,876 bytesSKILL.mdcore17,876 bytesSKILL.mdcore17,876 bytesSKILL.mdcore17,876 bytesSKILL.mdcore17,876 bytesSKILL.mdcore17,876 bytesSKILL.mdcore17,876 bytesSKILL.mdcore17,876 bytesreferences/api_reference.mddoc5,958 bytesreferences/descriptors_guide.mddoc6,297 bytesreferences/smarts_patterns.mddoc5,233 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubrowan
When your agent uses it
Cloud quantum chemistry platform with Python SDK. Run geometry optimization, conformer generation, torsional scans, and energy minimization (DFT/semiempirical), and retrieve properties (dipole, partial charges, frontier orbitals) — no local QC software or HPC needed.
What it loads into context
Frontmatter
71 tokensAlways available to the agent
SKILL.md
4,253 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Parameters
- Best Practices
- Common Workflows
- Expected Outputs
- Common Recipes
- Troubleshooting
- References
Files
SKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytesSKILL.mdcore15,465 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubsar-analysis
When your agent uses it
Structure-activity relationship (SAR) analysis guide for drug discovery including molecular descriptor analysis, scaffold analysis, and activity cliff detection.
What it loads into context
Frontmatter
35 tokensAlways available to the agent
SKILL.md
5,186 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Metadata
- Overview
- Key Concepts
- Decision Framework
- Best Practices
- Common Pitfalls
- Workflow
- References
Files
SKILL.mdcore23,276 bytesSKILL.mdcore23,276 bytesSKILL.mdcore23,276 bytesSKILL.mdcore23,276 bytesSKILL.mdcore23,276 bytesSKILL.mdcore23,276 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubsmina-molecular-docking
When your agent uses it
smina molecular docking CLI. AutoDock Vina fork with customizable scoring functions, native SDF/MOL2/PDB ligand input, autoboxing, local energy minimization, and per-atom score breakdowns. Pipeline: receptor PDBQT prep -> ligand prep (RDKit/OpenBabel) -> dock via autobox or explicit grid -> rescore/minimize with custom scoring -> rank poses by affinity. Choose smina over Vina when you need custom scoring terms (--custom_scoring), local optimization of an existing pose (--local_only), per-atom contributions (--atom_term_data), or SDF/MOL2 ligands without manual PDBQT conversion. For unknown binding sites use diffdock; for the Python-bindings/Vinardo workflow use autodock-vina-docking.
What it loads into context
Frontmatter
181 tokensAlways available to the agent
SKILL.md
4,370 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Workflow
- Key Parameters
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore14,751 bytesSKILL.mdcore14,751 bytesSKILL.mdcore14,751 bytesSKILL.mdcore14,751 bytesSKILL.mdcore14,751 bytesSKILL.mdcore14,751 bytesSKILL.mdcore14,751 bytesSKILL.mdcore14,751 bytesSKILL.mdcore14,751 bytesSKILL.mdcore14,751 bytesSKILL.mdcore14,751 bytesSKILL.mdcore14,751 bytesSKILL.mdcore14,751 bytesSKILL.mdcore14,751 bytesSKILL.mdcore14,751 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubtorchdrug
When your agent uses it
PyTorch-based ML platform for drug discovery: graph molecular representation learning, property prediction (ADMET, activity), retrosynthesis, drug-target interaction (DTI), and pretraining on large molecular datasets. Provides GNN layers (GraphConv, GAT, MPNN), pretrained models, and benchmark datasets.
What it loads into context
Frontmatter
80 tokensAlways available to the agent
SKILL.md
4,647 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubunichem-database
When your agent uses it
Cross-reference compound IDs across 20+ databases (ChEMBL, DrugBank, PubChem, ChEBI, PDB, SureChEMBL, HMDB, DrugCentral, BindingDB) via UniChem REST API. Resolve InChIKeys to source IDs, translate between source-specific IDs, find structurally related compounds by connectivity. POST with a JSON body for all cross-reference queries; only /sources is GET. No auth required.
What it loads into context
Frontmatter
111 tokensAlways available to the agent
SKILL.md
9,479 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubzinc-database
When your agent uses it
Query the ZINC22 virtual compound library (CartBlanche API, billions of make-on-demand + purchasable molecules). Look up substances by ZINC ID, resolve a SMILES to its ZINC ID (exact match), inspect purchasability/catalogs, and assemble compound sets for docking. Property (MW/logP) filtering is done locally with RDKit. For bioactivity use chembl-database-bioactivity; for approved drugs use drugbank-database-access.
What it loads into context
Frontmatter
118 tokensAlways available to the agent
SKILL.md
5,995 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubbrenda-database
When your agent uses it
BRENDA Enzyme DB SOAP/REST queries: kinetic parameters (Km, Vmax, kcat, Ki), EC classes, substrate specificity, inhibitors, cofactors, organism data. 80K+ enzymes, 7M+ values. Free academic registration. For metabolic modeling use cobrapy-metabolic-modeling; metabolites use hmdb-database.
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
4,948 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubcellchat-cell-communication
When your agent uses it
Infer and visualize intercellular communication from scRNA-seq with CellChat (R). Build CellChat from Seurat/counts → subset CellChatDB ligand-receptor pairs → over-expressed genes per group → communication probabilities → pathway signaling → network centrality (senders/receivers/influencers) → chord/heatmap/bubble plots → cross-condition compare. Human, mouse. Use liana for pure-Python.
What it loads into context
Frontmatter
104 tokensAlways available to the agent
SKILL.md
6,708 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Workflow
- Key Parameters
- Key Concepts
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubcobrapy-metabolic-modeling
When your agent uses it
Constraint-based (COBRA) analysis of genome-scale metabolic models: FBA, FVA, knockouts, flux sampling, production envelopes, gapfilling, media optimization. Use for strain design, essential gene ID, flux analysis. For kinetic modeling use tellurium; for visualization use Escher.
What it loads into context
Frontmatter
81 tokensAlways available to the agent
SKILL.md
5,056 tokensWhen the skill is activated
Reference documents
3,857 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubkegg-pathway-analysis
When your agent uses it
Guide to KEGG pathway enrichment for DEG results. Covers ORA vs GSEA, mandatory directionality splitting, KEGG organism codes, API failure handling with offline fallbacks, cross-condition comparisons, and answer-first reporting. Consult when running enrichment with clusterProfiler or gseapy.
What it loads into context
Frontmatter
76 tokensAlways available to the agent
SKILL.md
4,638 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Key Concepts
- Pre-flight Interview
- Decision Framework
- Best Practices
- Common Pitfalls
- Workflow
- Further Reading
- Related Skills
Files
SKILL.mdcore19,365 bytesSKILL.mdcore19,365 bytesSKILL.mdcore19,365 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHublamindb-data-management
When your agent uses it
Open-source FAIR biology data framework. Version artifacts (AnnData, DataFrame, Zarr), track lineage, validate via ontologies (Bionty), query datasets. Integrates with Nextflow, Snakemake, W&B, scVI. For scRNA-seq use scanpy; for ontology lookups use bionty.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
4,072 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHublibsbml-network-modeling
When your agent uses it
Build, read, validate, modify SBML biological network models via the libSBML Python API. SBML Levels 1–3, reactions/kinetic laws, species, rules, FBC extension for flux balance, conversion. Interoperates with COBRApy, Tellurium/RoadRunner, COPASI. Use when programmatically constructing ODE or constraint-based metabolic/signaling models in SBML.
What it loads into context
Frontmatter
106 tokensAlways available to the agent
SKILL.md
9,548 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
SKILL.mdcore35,168 bytesSKILL.mdcore35,168 bytesSKILL.mdcore35,168 bytesSKILL.mdcore35,168 bytesSKILL.mdcore35,168 bytesSKILL.mdcore35,168 bytesSKILL.mdcore35,168 bytesSKILL.mdcore35,168 bytesSKILL.mdcore35,168 bytesSKILL.mdcore35,168 bytesSKILL.mdcore35,168 bytesSKILL.mdcore35,168 bytesSKILL.mdcore35,168 bytesSKILL.mdcore35,168 bytesSKILL.mdcore35,168 bytesSKILL.mdcore35,168 bytesSKILL.mdcore35,168 bytesSKILL.mdcore35,168 bytesSKILL.mdcore35,168 bytesSKILL.mdcore35,168 bytesSKILL.mdcore35,168 bytesSKILL.mdcore35,168 bytesSKILL.mdcore35,168 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmofaplus-multi-omics
When your agent uses it
Multi-Omics Factor Analysis v2 (MOFA+) with mofapy2. Jointly decompose omics layers (scRNA, ATAC, proteomics, methylation) into latent factors capturing major variation. Multi-group designs. AnnData views → MOFA object → train → variance explained → correlate factors with metadata → visualize/cluster → enrich top loadings.
What it loads into context
Frontmatter
100 tokensAlways available to the agent
SKILL.md
6,723 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Workflow
- Key Parameters
- Key Concepts
- Common Recipes
- Expected Outputs
- Troubleshooting
- References
Files
SKILL.mdcore26,254 bytesSKILL.mdcore26,254 bytesSKILL.mdcore26,254 bytesSKILL.mdcore26,254 bytesSKILL.mdcore26,254 bytesSKILL.mdcore26,254 bytesSKILL.mdcore26,254 bytesSKILL.mdcore26,254 bytesSKILL.mdcore26,254 bytesSKILL.mdcore26,254 bytesSKILL.mdcore26,254 bytesSKILL.mdcore26,254 bytesSKILL.mdcore26,254 bytesSKILL.mdcore26,254 bytesSKILL.mdcore26,254 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubmuon-multiomics-singlecell
When your agent uses it
Multi-modal single-cell analysis with muon/MuData. Joint RNA+ATAC (10x Multiome), CITE-seq (RNA+protein), other multi-omics. MuData holds per-modality AnnData with shared obs. WNN joint embedding, per-modality preprocessing, MOFA factor analysis. Use scanpy-scrna-seq for single-modality RNA; use muon when combining 2+ omics from the same cells.
What it loads into context
Frontmatter
115 tokensAlways available to the agent
SKILL.md
9,278 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Pre-flight Interview
- Quick Start
- Core API
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Related Skills
- References
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubomics-analysis-guide
When your agent uses it
Three-tiered approach to omics data analysis (transcriptomics, proteomics) covering validated pipelines, standard workflows, and custom methods
What it loads into context
Frontmatter
40 tokensAlways available to the agent
SKILL.md
6,581 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Metadata
- Citations and Acknowledgments
- Overview
- Key Concepts
- Decision Framework
- Option 1: Search for Validated Analysis Methods (Recommended First)
- Option 2: Use Standard Analysis Workflows
- Option 3: Custom Analysis Methods (Last Resort)
- Quick Start Examples
- Data Type-Specific Considerations
- Best Practices
- Common Pitfalls
- Related Skills
- References
Files
SKILL.mdcore28,566 bytes
No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubreactome-database
When your agent uses it
Query Reactome pathways via REST: pathway queries, entity lookup, keyword search, gene list enrichment, hierarchy, cross-refs. Content + Analysis services. Python wrapper: reactome2py. For KEGG use kegg-database; for PPIs use string-database-ppi.
What it loads into context
Frontmatter
77 tokensAlways available to the agent
SKILL.md
5,612 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Key Concepts
- Common Workflows
- Key Parameters
- Best Practices
- Common Recipes
- Troubleshooting
- Bundled Resources
- Related Skills
- References
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubstring-database-ppi
When your agent uses it
Query STRING REST API for PPIs (59M proteins, 20B interactions, 5000+ species). Retrieve networks, run GO/KEGG enrichment, find partners, test PPI significance, visualize networks, analyze homology. For chemical interactions use chembl-database-bioactivity; pathways use kegg-database.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
4,427 tokensWhen the skill is activated
Reference documents
1,651 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- When to Use
- Prerequisites
- Quick Start
- Key Concepts
- Core API
- Common Workflows
- Common Recipes
- Key Parameters
- Troubleshooting
- Best Practices
- Related Skills
- References
- Bundled Resources
Files
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No excerpt is shown for this license. Read the source on GitHub.
View the complete source on GitHubSkills included
- opentrons-integration
- plotly-interactive-visualization
- seaborn-statistical-visualization
- single-cell-annotation
- pymc-bayesian-modeling
- scikit-survival-analysis
- statistical-analysis
- statsmodels-statistical-modeling
- cellpose-cell-segmentation
- flowio-flow-cytometry
- napari-image-viewer
- opencv-bioimage-analysis
- pyimagej-fiji-bridge
- scikit-image-processing
- trackpy-particle-tracking
- matplotlib-scientific-plotting
- molecular-visualization-3dmol
- multipanel
- omics-plotting
- plotly-interactive-plots
- scientific-visualization
- seaborn-statistical-plots
- statistical-significance-annotation
- bwa-mem2-dna-aligner
- pysam-genomic-files
- samtools-bam-processing
- star-rna-seq-aligner
- bakta-genome-annotation
- prokka-genome-annotation
- roary-pangenome
- arboreto-grn-inference
- biopython-molecular-biology
- biopython-sequence-analysis
- archs4-database
- bioservices-multi-database
- cbioportal-database
- clinpgx-database
- clinvar-database
- cosmic-database
- dbsnp-database
- depmap-crispr-essentiality
- ena-database
- encode-database
- ensembl-database
- gene-database
- geo-database
- gget-genomic-databases
- gnomad-database
- gwas-database
- jaspar-database
- kegg-database
- monarch-database
- mouse-phenome-database
- quickgo-database
- regulomedb-database
- remap-database
- ucsc-genome-browser
- etetoolkit
- homer-motif-analysis
- bedtools-genomic-intervals
- deeptools-ngs-analysis
- geniml
- gtars
- macs3-peak-calling
- busco-status-interpretation
- fastp-fastq-preprocessing
- multiqc-qc-reports
- deseq2-differential-expression
- featurecounts-rna-counting
- gseapy-gene-enrichment
- pydeseq2-differential-expression
- salmon-rna-quantification
- scikit-bio
- anndata-data-structure
- cell-type-annotation
- celltypist-cell-annotation
- cellxgene-census
- harmony-batch-correction
- popv-cell-annotation
- scanpy-scrna-seq
- scvi-tools-single-cell
- multimodal-omics-integration
- spatial-domain-detection
- spatial-mapping-tangram
- spatial-panel-design
- spatial-transcriptomics-analysis-squidpy
- spatial-transcriptomics-cell-cell-communication
- spatial-transcriptomics-cell-type-annotation
- spatial-transcriptomics-deconvolution-cell2location
- trajectory-inference
- bcftools-variant-manipulation
- cnvkit-copy-number
- gatk-variant-calling
- plink2-gwas-analysis
- snpeff-variant-annotation
- vcf-variant-filtering
- benchling-integration
- opentrons-protocol-api
- protocolsio-integration
- pylabrobot
- western-blot-quantification
- histolab-wsi-processing
- imaging-data-commons
- nnunet-segmentation
- omero-integration
- pathml
- pydicom-medical-imaging
- simpleitk-image-registration
- plannotate-plasmid-annotation
- sgrna-design-guide
- viennarna-structure-prediction
- adaptyv-bio
- esm-protein-language-model
- hmdb-database
- interpro-database
- matchms-spectral-matching
- maxquant-proteomics
- metabolomics-workbench-database
- pride-database
- pyopenms-mass-spectrometry
- uniprot-protein-database
- aeon
- astropy-astronomy
- dask-parallel-computing
- degenerate-input-filtering
- exploratory-data-analysis
- geopandas-geospatial
- hypogenic-hypothesis-generation
- matlab-scientific-computing
- nan-safe-correlation
- neb-irc-activation-energy
- networkx-graph-analysis
- neurokit2
- neuropixels-analysis
- nextflow-workflow-engine
- polars-dataframes
- pyhealth
- pymatgen
- pymoo
- scikit-learn-machine-learning
- shap-model-explainability
- simpy-discrete-event-simulation
- snakemake-workflow-engine
- spikeinterface-electrophysiology
- sympy-symbolic-math
- torch-geometric-graph-neural-networks
- transformers-bio-nlp
- umap-learn
- uspto-database
- vaex-dataframes
- zarr-python
- biorxiv-database
- cancer-research-figure-guide
- cell-figure-guide
- citation-management
- clinical-decision-support-documents
- elife-figure-guide
- general-figure-guide
- hypothesis-generation
- lancet-figure-guide
- latex-research-posters
- literature-review
- nature-figure-guide
- nejm-figure-guide
- openalex-database
- peer-review-methodology
- pnas-figure-guide
- pubmed-database
- science-figure-guide
- scientific-brainstorming
- scientific-critical-thinking
- scientific-literature-search
- scientific-manuscript-writing
- scientific-schematics
- scientific-slides
- aizynthfinder-retrosynthesis
- alphafold-database-access
- autodock-vina-docking
- chembl-database-bioactivity
- clinicaltrials-database-search
- dailymed-database
- datamol-cheminformatics
- ddinter-database
- deepchem
- diffdock
- drugbank-database-access
- emdb-database
- fda-database
- gtopdb-database
- mdanalysis-trajectory
- mdtraj-trajectory-analysis
- medchem
- molfeat-molecular-featurization
- opentargets-database
- pdb-database
- pubchem-compound-search
- pytdc-therapeutics-data-commons
- rdkit-chemdraw-cdxml
- rdkit-cheminformatics
- rowan
- sar-analysis
- smina-molecular-docking
- torchdrug
- unichem-database
- zinc-database
- brenda-database
- cellchat-cell-communication
- cobrapy-metabolic-modeling
- kegg-pathway-analysis
- lamindb-data-management
- libsbml-network-modeling
- mofaplus-multi-omics
- muon-multiomics-singlecell
- omics-analysis-guide
- reactome-database
- string-database-ppi
Repository facts
- License
- No license detected
- Created
- Feb 18, 2026
- Last pushed
- Nov 4, 2026
- Forks
- 45
- Open issues
- 7
- Project activity
- Active
Before you install
Network or broad shellAutomated signals, not a guarantee
Checked Nov 5, 2026
Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.
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legacy/opentrons-integration/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedlegacy/opentrons-integration/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedlegacy/opentrons-integration/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedlegacy/opentrons-integration/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedlegacy/opentrons-integration/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedlegacy/opentrons-integration/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedlegacy/opentrons-integration/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedlegacy/opentrons-integration/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedlegacy/opentrons-integration/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedlegacy/opentrons-integration/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedlegacy/opentrons-integration/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedlegacy/opentrons-integration/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedlegacy/opentrons-integration/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedlegacy/opentrons-integration/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedlegacy/opentrons-integration/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedlegacy/opentrons-integration/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedlegacy/opentrons-integration/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedlegacy/opentrons-integration/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedlegacy/plotly-interactive-visualization/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedlegacy/plotly-interactive-visualization/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzed
- No broad tool permissions
- 29 dangerous shell commandsin 9 files: skills/cell-biology/pyimagej-fiji-bridge/SKILL.md:298 skills/proteomics-protein-engineering/metabolomics-workbench-database/SKILL.md:286 skills/scientific-computing/nextflow-workflow-engine/SKILL.md:32 and 6 more
- No hidden or obfuscated text
- No instruction overrides
- 155 references to secretsin 21 files: skills/scientific-computing/pymatgen/SKILL.md:32 skills/proteomics-protein-engineering/adaptyv-bio/SKILL.md:32 skills/lab-automation/benchling-integration/SKILL.md:40 and 18 more
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.claude-plugin/marketplace.json
- Network accessline 17
"name": "jaechang-hits" }, "homepage": "https://github.com/jaechang-hits/SciAgent-Skills", - Network accessline 18
}, "homepage": "https://github.com/jaechang-hits/SciAgent-Skills", "repository": "https://github.com/jaechang-hits/SciAgent-Skills", - Network accessline 19
"homepage": "https://github.com/jaechang-hits/SciAgent-Skills", - Network accessline 20
"repository": "https://github.com/jaechang-hits/SciAgent-Skills",
.claude-plugin/plugin.json
- Network accessline 6
"name": "jaechang-hits" }, "repository": "https://github.com/jaechang-hits/SciAgent-Skills", - Network accessline 7
}, "repository": "https://github.com/jaechang-hits/SciAgent-Skills", "license": "CC-BY-4.0", - Network accessline 8
"repository": "https://github.com/jaechang-hits/SciAgent-Skills",
legacy/opentrons-integration/SKILL.md
- Network accessline 429
- [Opentrons Protocol API v2 docs](https://docs.opentrons.com/v2/) — official API reference - Network accessline 430
- [Opentrons Labware Library](https://labware.opentrons.com/) — searchable labware definitions - Network accessline 431
- [Opentrons Python Protocol Tutorial](https://docs.opentrons.com/v2/tutorial.html) — step-by-step getting started guide
legacy/plotly-interactive-visualization/SKILL.md
- Network accessline 482
- [Plotly Python documentation](https://plotly.com/python/) — official guides and API reference - Network accessline 483
- [Plotly Express API](https://plotly.com/python-api-reference/plotly.express.html) — high-level API reference - Network accessline 484
- [Dash documentation](https://dash.plotly.com/) — interactive web application framework - Network accessline 485
- [Plotly community forum](https://community.plotly.com/) — community support and examples
legacy/seaborn-statistical-visualization/SKILL.md
- Network accessline 466
- [Seaborn documentation](https://seaborn.pydata.org/) — official API reference and tutorial - Network accessline 467
- [Seaborn gallery](https://seaborn.pydata.org/examples/index.html) — visual examples of all plot types - Network accessline 468
- Waskom (2021) "seaborn: statistical data visualization" — [JOSS](https://doi.org/10.21105/joss.03021)
legacy/single-cell-annotation/SKILL.md
- Network accessline 27
**Source**: https://www.sc-best-practices.org/cellular_structure/annotation.html - Network accessline 248
### Tools: - **Scanpy**: https://scanpy.readthedocs.io/ - Network accessline 249
- **Scanpy**: https://scanpy.readthedocs.io/ - Network accessline 250
- **CellTypist**: https://www.celltypist.org/ - Network accessline 251
- **scArches**: https://scarches.readthedocs.io/ - Network accessline 252
- **Seurat**: https://satijalab.org/seurat/ - Network accessline 254
### Marker Databases & Atlases: - **PanglaoDB**: https://panglaodb.se/ (Database of marker genes) - Network accessline 255
- **PanglaoDB**: https://panglaodb.se/ (Database of marker genes) - Network accessline 256
- **CellMarker**: http://bio-bigdata.hrbmu.edu.cn/CellMarker/ (Curated cell marker database) - Network accessline 257
- **Human Cell Atlas**: https://www.humancellatlas.org/ (Reference datasets) - Network accessline 258
- **Single Cell Best Practices**: https://www.sc-best-practices.org/cellular_structure/annotation.html
skills/biostatistics/pymc-bayesian-modeling/SKILL.md
- Network accessline 466
- [PyMC documentation](https://www.pymc.io/projects/docs/en/stable/) -- official API reference and tutorials - Network accessline 466
- [PyMC documentation](https://www.pymc.io/projects/docs/en/stable/) -- official API reference and tutorials - Network accessline 467
- [PyMC GitHub](https://github.com/pymc-devs/pymc) -- source code and issue tracker - Network accessline 467
- [PyMC GitHub](https://github.com/pymc-devs/pymc) -- source code and issue tracker - Network accessline 468
- [ArviZ documentation](https://python.arviz.org/en/stable/) -- posterior analysis and visualization - Network accessline 468
- [ArviZ documentation](https://python.arviz.org/en/stable/) -- posterior analysis and visualization - Network accessline 469
- [Bayesian Modeling and Computation in Python](https://bayesiancomputationbook.com/) -- textbook using PyMC - Network accessline 469
- [Bayesian Modeling and Computation in Python](https://bayesiancomputationbook.com/) -- textbook using PyMC - Network accessline 470
- [PyMC examples gallery](https://www.pymc.io/projects/examples/en/latest/) -- curated notebook collection - Network accessline 470
- [PyMC examples gallery](https://www.pymc.io/projects/examples/en/latest/) -- curated notebook collection
skills/biostatistics/scikit-survival-analysis/SKILL.md
- Network accessline 666
- [scikit-survival documentation](https://scikit-survival.readthedocs.io/) -- official API reference - Network accessline 666
- [scikit-survival documentation](https://scikit-survival.readthedocs.io/) -- official API reference - Network accessline 667
- [scikit-survival GitHub](https://github.com/sebp/scikit-survival) -- source code and examples - Network accessline 667
- [scikit-survival GitHub](https://github.com/sebp/scikit-survival) -- source code and examples - Network accessline 668
- [API reference](https://scikit-survival.readthedocs.io/en/stable/api/index.html) -- complete class/function list - Network accessline 668
- [API reference](https://scikit-survival.readthedocs.io/en/stable/api/index.html) -- complete class/function list - Network accessline 669
- Polsterl (2020) "scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn" -- [JMLR 21(212):1-6](https://jmlr.org/papers/v21/20-729.html) - Network accessline 669
- Polsterl (2020) "scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn" -- [JMLR 21(212):1-6](https://jmlr.org/papers/v21/20-729.html)
skills/biostatistics/statistical-analysis/SKILL.md
- Network accessline 290
- Gelman, A., & Hill, J. (2006). *Data Analysis Using Regression and Multilevel/Hierarchical Models* - Kruschke, J. K. (2014). *Doing Bayesian Data Analysis* (2nd ed.) - APA Publication Manual: https://apastyle.apa.org/ - Network accessline 290
- Gelman, A., & Hill, J. (2006). *Data Analysis Using Regression and Multilevel/Hierarchical Models* - Kruschke, J. K. (2014). *Doing Bayesian Data Analysis* (2nd ed.) - APA Publication Manual: https://apastyle.apa.org/ - Network accessline 291
- Kruschke, J. K. (2014). *Doing Bayesian Data Analysis* (2nd ed.) - APA Publication Manual: https://apastyle.apa.org/ - Cross Validated (stats Q&A): https://stats.stackexchange.com/ - Network accessline 291
- Kruschke, J. K. (2014). *Doing Bayesian Data Analysis* (2nd ed.) - APA Publication Manual: https://apastyle.apa.org/ - Cross Validated (stats Q&A): https://stats.stackexchange.com/ - Network accessline 292
- APA Publication Manual: https://apastyle.apa.org/ - Network accessline 292
- APA Publication Manual: https://apastyle.apa.org/ - Network accessline 293
- Cross Validated (stats Q&A): https://stats.stackexchange.com/ - Network accessline 293
- Cross Validated (stats Q&A): https://stats.stackexchange.com/
skills/biostatistics/statsmodels-statistical-modeling/SKILL.md
- Network accessline 494
- [statsmodels documentation](https://www.statsmodels.org/stable/) — official docs - Network accessline 494
- [statsmodels documentation](https://www.statsmodels.org/stable/) — official docs - Network accessline 495
- [statsmodels User Guide](https://www.statsmodels.org/stable/user-guide.html) — tutorials - Network accessline 495
- [statsmodels User Guide](https://www.statsmodels.org/stable/user-guide.html) — tutorials - Network accessline 496
- [statsmodels API Reference](https://www.statsmodels.org/stable/api.html) — complete API - Network accessline 496
- [statsmodels API Reference](https://www.statsmodels.org/stable/api.html) — complete API
skills/cell-biology/cellpose-cell-segmentation/SKILL.md
- Network accessline 36
# Install with GPU (PyTorch CUDA) pip install cellpose torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 - Network accessline 36
# Install with GPU (PyTorch CUDA) pip install cellpose torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 - Network accessline 37
pip install cellpose torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 - Network accessline 37
pip install cellpose torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 - Network accessline 379
- [Cellpose GitHub: MouseLand/cellpose](https://github.com/MouseLand/cellpose) — source code, documentation, and model zoo - Network accessline 379
- [Cellpose GitHub: MouseLand/cellpose](https://github.com/MouseLand/cellpose) — source code, documentation, and model zoo - Network accessline 380
- Stringer C et al. (2021) "Cellpose: a generalist algorithm for cellular segmentation" — *Nature Methods* 18:100-106. [DOI:10.1038/s41592-020-01018-x](https://doi.org/10.1038/s41592-020-01018-x) - Network accessline 380
- Stringer C et al. (2021) "Cellpose: a generalist algorithm for cellular segmentation" — *Nature Methods* 18:100-106. [DOI:10.1038/s41592-020-01018-x](https://doi.org/10.1038/s41592-020-01018-x) - Network accessline 381
- Pachitariu M & Stringer C (2022) "Cellpose 2.0: how to train your own model" — *Nature Methods* 19:1500-1508. [DOI:10.1038/s41592-022-01663-4](https://doi.org/10.1038/s41592-022-01663-4) - Network accessline 381
- Pachitariu M & Stringer C (2022) "Cellpose 2.0: how to train your own model" — *Nature Methods* 19:1500-1508. [DOI:10.1038/s41592-022-01663-4](https://doi.org/10.1038/s41592-022-01663-4) - Network accessline 382
- [Cellpose documentation](https://cellpose.readthedocs.io/) — official API reference and training guide - Network accessline 382
- [Cellpose documentation](https://cellpose.readthedocs.io/) — official API reference and training guide
skills/cell-biology/flowio-flow-cytometry/SKILL.md
- Network accessline 343
- [FlowIO documentation](https://github.com/whitews/FlowIO) — official GitHub repository and API - Network accessline 343
- [FlowIO documentation](https://github.com/whitews/FlowIO) — official GitHub repository and API - Network accessline 344
- [FCS file format specification](https://www.isac-net.org/page/Data-Standards) — ISAC data standards for flow cytometry - Network accessline 344
- [FCS file format specification](https://www.isac-net.org/page/Data-Standards) — ISAC data standards for flow cytometry
skills/cell-biology/napari-image-viewer/SKILL.md
- Access to secretsline 202
[Credential-related source omitted] - Access to secretsline 202
[Credential-related source omitted] - Access to secretsline 245
[Credential-related source omitted] - Access to secretsline 245
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[Credential-related source omitted] - Access to secretsline 305
[Credential-related source omitted] - Access to secretsline 340
[Credential-related source omitted] - Access to secretsline 340
[Credential-related source omitted] - Network accessline 378
- [napari documentation](https://napari.org/stable/) — official API reference and tutorials - Network accessline 378
- [napari documentation](https://napari.org/stable/) — official API reference and tutorials - Network accessline 379
- [napari GitHub: napari/napari](https://github.com/napari/napari) — source code and plugin development guide - Network accessline 379
- [napari GitHub: napari/napari](https://github.com/napari/napari) — source code and plugin development guide - Network accessline 380
- Sofroniew N et al. (2022) "napari: a multi-dimensional image viewer for Python" — *Zenodo*. [DOI:10.5281/zenodo.3555620](https://doi.org/10.5281/zenodo.3555620) - Network accessline 380
- Sofroniew N et al. (2022) "napari: a multi-dimensional image viewer for Python" — *Zenodo*. [DOI:10.5281/zenodo.3555620](https://doi.org/10.5281/zenodo.3555620) - Network accessline 381
- [napari-hub](https://www.napari-hub.org/) — plugin registry with 300+ community analysis plugins - Network accessline 381
- [napari-hub](https://www.napari-hub.org/) — plugin registry with 300+ community analysis plugins
skills/cell-biology/opencv-bioimage-analysis/SKILL.md
- Network accessline 406
- [OpenCV Python documentation](https://docs.opencv.org/4.x/d6/d00/tutorial_py_root.html) — full Python API reference and tutorials - Network accessline 406
- [OpenCV Python documentation](https://docs.opencv.org/4.x/d6/d00/tutorial_py_root.html) — full Python API reference and tutorials - Network accessline 407
- [OpenCV GitHub: opencv/opencv](https://github.com/opencv/opencv) — source code and Python bindings - Network accessline 407
- [OpenCV GitHub: opencv/opencv](https://github.com/opencv/opencv) — source code and Python bindings - Network accessline 409
- [PyImageSearch tutorials](https://pyimagesearch.com/) — applied OpenCV for computer vision in biological imaging - Network accessline 409
- [PyImageSearch tutorials](https://pyimagesearch.com/) — applied OpenCV for computer vision in biological imaging
skills/cell-biology/pyimagej-fiji-bridge/SKILL.md
- Dangerous shell operationline 298
rm = RoiManager.getInstance() - Dangerous shell operationline 298
rm = RoiManager.getInstance() - Dangerous shell operationline 299
if rm is None: - Dangerous shell operationline 299
if rm is None: - Dangerous shell operationline 300
rm = RoiManager(False) # headless=False means no GUI window - Dangerous shell operationline 300
rm = RoiManager(False) # headless=False means no GUI window - Dangerous shell operationline 303
rm = get_roi_manager(ij) - Dangerous shell operationline 303
rm = get_roi_manager(ij) - Dangerous shell operationline 486
rm = get_roi_manager(ij) - Dangerous shell operationline 486
rm = get_roi_manager(ij) - Network accessline 512
run("TrackMate", ""); // For fully scripted TrackMate, use the Scripting Interface // documented at https://imagej.net/plugins/trackmate/scripting - Network accessline 512
run("TrackMate", ""); // For fully scripted TrackMate, use the Scripting Interface // documented at https://imagej.net/plugins/trackmate/scripting - Network accessline 513
// For fully scripted TrackMate, use the Scripting Interface // documented at https://imagej.net/plugins/trackmate/scripting """ - Network accessline 513
// For fully scripted TrackMate, use the Scripting Interface // documented at https://imagej.net/plugins/trackmate/scripting """ - Network accessline 514
// documented at https://imagej.net/plugins/trackmate/scripting - Network accessline 514
// documented at https://imagej.net/plugins/trackmate/scripting - Network accessline 583
This function reorders to the TZCYX convention used by tifffile, OME, etc. """ nC = imp.getNChannels() - Network accessline 583
This function reorders to the TZCYX convention used by tifffile, OME, etc. """ nC = imp.getNChannels() - Network accessline 584
""" nC = imp.getNChannels() nZ = imp.getNSlices() - Network accessline 584
""" nC = imp.getNChannels() nZ = imp.getNSlices() - Network accessline 585
nC = imp.getNChannels() - Network accessline 585
nC = imp.getNChannels() - Network accessline 593
for t in range(1, nT + 1): for z in range(1, nZ + 1): for c in range(1, nC + 1): - Network accessline 593
for t in range(1, nT + 1): for z in range(1, nZ + 1): for c in range(1, nC + 1): - Network accessline 594
for z in range(1, nZ + 1): for c in range(1, nC + 1): idx = imp.getStackIndex(c, z, t) - Network accessline 594
for z in range(1, nZ + 1): for c in range(1, nC + 1): idx = imp.getStackIndex(c, z, t) - Network accessline 595
for c in range(1, nC + 1): - Network accessline 595
for c in range(1, nC + 1): - Network accessline 645
- [PyImageJ documentation](https://pyimagej.readthedocs.io/) — official API reference, initialization guide, data conversion - Network accessline 645
- [PyImageJ documentation](https://pyimagej.readthedocs.io/) — official API reference, initialization guide, data conversion - Network accessline 646
- [PyImageJ GitHub repository](https://github.com/imagej/pyimagej) — source, issue tracker, notebooks, examples - Network accessline 646
- [PyImageJ GitHub repository](https://github.com/imagej/pyimagej) — source, issue tracker, notebooks, examples - Network accessline 647
- [ImageJ Ops framework](https://imagej.net/libs/imagej-ops/) — Ops namespace reference and algorithm catalog - Network accessline 647
- [ImageJ Ops framework](https://imagej.net/libs/imagej-ops/) — Ops namespace reference and algorithm catalog - Network accessline 648
- [TrackMate scripting guide](https://imagej.net/plugins/trackmate/scripting) — headless TrackMate Java API patterns - Network accessline 648
- [TrackMate scripting guide](https://imagej.net/plugins/trackmate/scripting) — headless TrackMate Java API patterns - Network accessline 649
- [Bio-Formats supported formats](https://bio-formats.readthedocs.io/en/latest/supported-formats.html) — list of proprietary microscopy formats openable via `ij.io().open()` - Network accessline 649
- [Bio-Formats supported formats](https://bio-formats.readthedocs.io/en/latest/supported-formats.html) — list of proprietary microscopy formats openable via `ij.io().open()` - Network accessline 650
- [ImageJ Macro Language reference](https://imagej.nih.gov/ij/developer/macro/macros.html) — built-in functions for `ij.macro.run()` strings - Network accessline 650
- [ImageJ Macro Language reference](https://imagej.nih.gov/ij/developer/macro/macros.html) — built-in functions for `ij.macro.run()` strings
skills/cell-biology/scikit-image-processing/SKILL.md
- Network accessline 484
- [scikit-image documentation](https://scikit-image.org/docs/stable/) — API reference and tutorials - Network accessline 484
- [scikit-image documentation](https://scikit-image.org/docs/stable/) — API reference and tutorials - Network accessline 485
- [GitHub: scikit-image/scikit-image](https://github.com/scikit-image/scikit-image) — source code and examples - Network accessline 485
- [GitHub: scikit-image/scikit-image](https://github.com/scikit-image/scikit-image) — source code and examples - Network accessline 486
- van der Walt et al. (2014) "scikit-image: image processing in Python" — [PeerJ 2:e453](https://doi.org/10.7717/peerj.453) - Network accessline 486
- van der Walt et al. (2014) "scikit-image: image processing in Python" — [PeerJ 2:e453](https://doi.org/10.7717/peerj.453) - Network accessline 487
- [scikit-image examples gallery](https://scikit-image.org/docs/stable/auto_examples/) — annotated code examples by category - Network accessline 487
- [scikit-image examples gallery](https://scikit-image.org/docs/stable/auto_examples/) — annotated code examples by category
skills/cell-biology/trackpy-particle-tracking/SKILL.md
- Network accessline 574
- [trackpy documentation](http://soft-matter.github.io/trackpy/) — official API reference, tutorials, and notebooks - Network accessline 574
- [trackpy documentation](http://soft-matter.github.io/trackpy/) — official API reference, tutorials, and notebooks - Network accessline 575
- [trackpy GitHub repository](https://github.com/soft-matter/trackpy) — source code, issue tracker, example notebooks - Network accessline 575
- [trackpy GitHub repository](https://github.com/soft-matter/trackpy) — source code, issue tracker, example notebooks - Network accessline 576
- [Crocker, J.C. & Grier, D.G. (1996). Methods of Digital Video Microscopy for Colloidal Studies. *J. Colloid Interface Sci.* 179, 298–310](https://doi.org/10.1006/jcis.1996.0217) — original algorithm paper - Network accessline 576
- [Crocker, J.C. & Grier, D.G. (1996). Methods of Digital Video Microscopy for Colloidal Studies. *J. Colloid Interface Sci.* 179, 298–310](https://doi.org/10.1006/jcis.1996.0217) — original algorithm paper - Network accessline 577
- [pims documentation](http://soft-matter.github.io/pims/) — image sequence reader library that integrates with trackpy - Network accessline 577
- [pims documentation](http://soft-matter.github.io/pims/) — image sequence reader library that integrates with trackpy - Network accessline 578
- [trackpy walkthrough notebook](https://github.com/soft-matter/trackpy/blob/master/doc/tutorial/walkthrough.ipynb) — step-by-step tutorial for 2D tracking - Network accessline 578
- [trackpy walkthrough notebook](https://github.com/soft-matter/trackpy/blob/master/doc/tutorial/walkthrough.ipynb) — step-by-step tutorial for 2D tracking
skills/data-visualization/matplotlib-scientific-plotting/SKILL.md
- Network accessline 444
- [matplotlib documentation](https://matplotlib.org/stable/) — official docs - Network accessline 444
- [matplotlib documentation](https://matplotlib.org/stable/) — official docs - Network accessline 445
- [matplotlib gallery](https://matplotlib.org/stable/gallery/) — examples by plot type - Network accessline 445
- [matplotlib gallery](https://matplotlib.org/stable/gallery/) — examples by plot type - Network accessline 446
- [matplotlib cheatsheets](https://matplotlib.org/cheatsheets/) — quick reference (PDF) - Network accessline 446
- [matplotlib cheatsheets](https://matplotlib.org/cheatsheets/) — quick reference (PDF)
skills/data-visualization/molecular-visualization-3dmol/SKILL.md
- Network accessline 50
All snippets assume `<script src="https://3Dmol.org/build/3Dmol-min.js"></script>` is loaded - Network accessline 50
All snippets assume `<script src="https://3Dmol.org/build/3Dmol-min.js"></script>` is loaded - Network accessline 258
- [3Dmol.js documentation](https://3dmol.org/doc/index.html) — GLViewer / GLModel API - Network accessline 258
- [3Dmol.js documentation](https://3dmol.org/doc/index.html) — GLViewer / GLModel API - Network accessline 259
- Rego & Koes, *Bioinformatics* 2015, 31(8):1322–1324 — 3Dmol.js: molecular visualization with WebGL - [GLViewer.animate](https://3dmol.org/doc/GLViewer.html) — `interval`, `loop`, `reps` - [GLModel.vibrate](https://3dmol.org/doc/GLModel.ht - Network accessline 259
- Rego & Koes, *Bioinformatics* 2015, 31(8):1322–1324 — 3Dmol.js: molecular visualization with WebGL - [GLViewer.animate](https://3dmol.org/doc/GLViewer.html) — `interval`, `loop`, `reps` - [GLModel.vibrate](https://3dmol.org/doc/GLModel.ht - Network accessline 260
- [GLViewer.animate](https://3dmol.org/doc/GLViewer.html) — `interval`, `loop`, `reps` - Network accessline 260
- [GLViewer.animate](https://3dmol.org/doc/GLViewer.html) — `interval`, `loop`, `reps` - Network accessline 261
- [GLModel.vibrate](https://3dmol.org/doc/GLModel.html) — normal-mode animation from `dx/dy/dz` - Network accessline 261
- [GLModel.vibrate](https://3dmol.org/doc/GLModel.html) — normal-mode animation from `dx/dy/dz` - Network accessline 262
- [py3Dmol on PyPI](https://pypi.org/project/py3Dmol/) — Python/Jupyter wrapper - Network accessline 262
- [py3Dmol on PyPI](https://pypi.org/project/py3Dmol/) — Python/Jupyter wrapper
skills/data-visualization/molecular-visualization-3dmol/scripts/mol_viewer.py
skills/data-visualization/multipanel/SKILL.md
- Network accessline 459
- Matplotlib subfigures / `Figure.subfigures` — https://matplotlib.org/stable/gallery/subplots_axes_and_figures/subfigures.html - Network accessline 459
- Matplotlib subfigures / `Figure.subfigures` — https://matplotlib.org/stable/gallery/subplots_axes_and_figures/subfigures.html - Network accessline 460
- Matplotlib constrained layout guide — https://matplotlib.org/stable/users/explain/axes/constrained_layout_guide.html - Network accessline 460
- Matplotlib constrained layout guide — https://matplotlib.org/stable/users/explain/axes/constrained_layout_guide.html - Network accessline 461
- Matplotlib `subplot_mosaic` tutorial — https://matplotlib.org/stable/users/explain/axes/mosaic.html - Network accessline 461
- Matplotlib `subplot_mosaic` tutorial — https://matplotlib.org/stable/users/explain/axes/mosaic.html - Network accessline 462
- adjustText (label de-overlap) — https://github.com/Phlya/adjustText - Network accessline 462
- adjustText (label de-overlap) — https://github.com/Phlya/adjustText
skills/data-visualization/omics-plotting/SKILL.md
- Network accessline 502
- Matplotlib documentation — https://matplotlib.org/stable/ - Network accessline 502
- Matplotlib documentation — https://matplotlib.org/stable/ - Network accessline 503
- Seaborn documentation — https://seaborn.pydata.org/ - Network accessline 503
- Seaborn documentation — https://seaborn.pydata.org/ - Network accessline 504
- Wong, "Points of view: Color blindness", Nature Methods (2011) — https://www.nature.com/articles/nmeth.1618 - Network accessline 504
- Wong, "Points of view: Color blindness", Nature Methods (2011) — https://www.nature.com/articles/nmeth.1618 - Network accessline 505
- ColorBrewer diverging/sequential palettes — https://colorbrewer2.org/ - Network accessline 505
- ColorBrewer diverging/sequential palettes — https://colorbrewer2.org/
skills/data-visualization/plotly-interactive-plots/SKILL.md
- Network accessline 833
- [Plotly Python Documentation](https://plotly.com/python/) — official API reference, examples gallery, and getting-started guides - Network accessline 833
- [Plotly Python Documentation](https://plotly.com/python/) — official API reference, examples gallery, and getting-started guides - Network accessline 834
- [Plotly Express API Reference](https://plotly.com/python-api-reference/plotly.express.html) — complete `px` function signatures and parameters - Network accessline 834
- [Plotly Express API Reference](https://plotly.com/python-api-reference/plotly.express.html) — complete `px` function signatures and parameters - Network accessline 835
- [Plotly Graph Objects Reference](https://plotly.com/python/graph-objects/) — full `go` trace and layout attribute reference - Network accessline 835
- [Plotly Graph Objects Reference](https://plotly.com/python/graph-objects/) — full `go` trace and layout attribute reference - Network accessline 836
- [Plotly GitHub Repository](https://github.com/plotly/plotly.py) — source code, issue tracker, changelog - Network accessline 836
- [Plotly GitHub Repository](https://github.com/plotly/plotly.py) — source code, issue tracker, changelog - Network accessline 837
- [Kaleido Static Image Export](https://github.com/plotly/Kaleido) — dependency for `write_image()` PNG/SVG/PDF export - Network accessline 837
- [Kaleido Static Image Export](https://github.com/plotly/Kaleido) — dependency for `write_image()` PNG/SVG/PDF export
skills/data-visualization/scientific-visualization/SKILL.md
- Network accessline 119
2. **Choose CVD-safe color palettes by default**: Use Okabe-Ito or `viridis`/`cividis` for sequential data. Test your figure with a CVD simulator (e.g., [Coblis](https://www.color-blindness.com/coblis-color-blindness-simulator/)) before sub - Network accessline 119
2. **Choose CVD-safe color palettes by default**: Use Okabe-Ito or `viridis`/`cividis` for sequential data. Test your figure with a CVD simulator (e.g., [Coblis](https://www.color-blindness.com/coblis-color-blindness-simulator/)) before sub - Network accessline 206
- [Fundamentals of Data Visualization — Claus O. Wilke (open access)](https://clauswilke.com/dataviz/) — comprehensive guide covering chart selection, color, and figure design - Network accessline 206
- [Fundamentals of Data Visualization — Claus O. Wilke (open access)](https://clauswilke.com/dataviz/) — comprehensive guide covering chart selection, color, and figure design - Network accessline 207
- [Ten Simple Rules for Better Figures (Rougier et al., 2014, PLOS Computational Biology)](https://doi.org/10.1371/journal.pcbi.1003833) — practical peer-reviewed guidelines - Network accessline 207
- [Ten Simple Rules for Better Figures (Rougier et al., 2014, PLOS Computational Biology)](https://doi.org/10.1371/journal.pcbi.1003833) — practical peer-reviewed guidelines - Network accessline 208
- [ColorBrewer 2.0](https://colorbrewer2.org/) — browser tool for selecting print-safe, CVD-safe color palettes - Network accessline 208
- [ColorBrewer 2.0](https://colorbrewer2.org/) — browser tool for selecting print-safe, CVD-safe color palettes - Network accessline 209
- [Coblis CVD Simulator](https://www.color-blindness.com/coblis-color-blindness-simulator/) — test figure accessibility under 8 types of color vision deficiency - Network accessline 209
- [Coblis CVD Simulator](https://www.color-blindness.com/coblis-color-blindness-simulator/) — test figure accessibility under 8 types of color vision deficiency - Network accessline 210
- [Nature Methods — Points of View column](https://www.nature.com/collections/cajwddbnjb) — monthly visualization advice from Bang Wong and colleagues - Network accessline 210
- [Nature Methods — Points of View column](https://www.nature.com/collections/cajwddbnjb) — monthly visualization advice from Bang Wong and colleagues - Network accessline 211
- [ACS Publications Figure Preparation Guide](https://publish.acs.org/publish/author_guidelines) — journal-specific technical requirements - Network accessline 211
- [ACS Publications Figure Preparation Guide](https://publish.acs.org/publish/author_guidelines) — journal-specific technical requirements
skills/data-visualization/seaborn-statistical-plots/SKILL.md
- Network accessline 816
- [Seaborn official documentation](https://seaborn.pydata.org/) — API reference, tutorial, and gallery - Network accessline 816
- [Seaborn official documentation](https://seaborn.pydata.org/) — API reference, tutorial, and gallery - Network accessline 817
- [Seaborn example gallery](https://seaborn.pydata.org/examples/index.html) — visual index of all plot types - Network accessline 817
- [Seaborn example gallery](https://seaborn.pydata.org/examples/index.html) — visual index of all plot types - Network accessline 818
- [Seaborn GitHub](https://github.com/mwaskom/seaborn) — source code and issue tracker - Network accessline 818
- [Seaborn GitHub](https://github.com/mwaskom/seaborn) — source code and issue tracker - Network accessline 819
- Waskom ML (2021). "seaborn: statistical data visualization." *Journal of Open Source Software*, 6(60), 3021. [https://doi.org/10.21105/joss.03021](https://doi.org/10.21105/joss.03021) - Network accessline 819
- Waskom ML (2021). "seaborn: statistical data visualization." *Journal of Open Source Software*, 6(60), 3021. [https://doi.org/10.21105/joss.03021](https://doi.org/10.21105/joss.03021)
skills/data-visualization/statistical-significance-annotation/SKILL.md
- Network accessline 250
- [Graphpad: How to report statistical significance](https://www.graphpad.com/support/faq/what-is-the-meaning-of--or--or--in-reports-of-statistical-significance-from-prism-or-instat/) — Asterisk notation conventions - Network accessline 250
- [Graphpad: How to report statistical significance](https://www.graphpad.com/support/faq/what-is-the-meaning-of--or--or--in-reports-of-statistical-significance-from-prism-or-instat/) — Asterisk notation conventions - Network accessline 251
- [Nature Methods: Points of Significance](https://www.nature.com/collections/qghhqm/pointsofsignificance) — Statistical best practices for figures - Network accessline 251
- [Nature Methods: Points of Significance](https://www.nature.com/collections/qghhqm/pointsofsignificance) — Statistical best practices for figures - Network accessline 252
- [Weissgerber et al. (2015) Beyond Bar and Line Graphs](https://doi.org/10.1371/journal.pbio.1002128) — Why to show individual data points alongside statistical annotations - Network accessline 252
- [Weissgerber et al. (2015) Beyond Bar and Line Graphs](https://doi.org/10.1371/journal.pbio.1002128) — Why to show individual data points alongside statistical annotations
skills/genomics-bioinformatics/alignment/bwa-mem2-dna-aligner/SKILL.md
- Network accessline 35
# Or download pre-compiled binary wget https://github.com/bwa-mem2/bwa-mem2/releases/download/v2.2.1/bwa-mem2-2.2.1_x64-linux.tar.bz2 - Network accessline 35
# Or download pre-compiled binary wget https://github.com/bwa-mem2/bwa-mem2/releases/download/v2.2.1/bwa-mem2-2.2.1_x64-linux.tar.bz2 - Network accessline 36
wget https://github.com/bwa-mem2/bwa-mem2/releases/download/v2.2.1/bwa-mem2-2.2.1_x64-linux.tar.bz2 - Network accessline 36
wget https://github.com/bwa-mem2/bwa-mem2/releases/download/v2.2.1/bwa-mem2-2.2.1_x64-linux.tar.bz2 - Network accessline 129
# Download GRCh38 primary assembly (human) wget https://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/001/405/GCA_000001405.15_GRCh38/seqs_for_alignment_pipelines.ucsc_ids/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna.gz - Network accessline 129
# Download GRCh38 primary assembly (human) wget https://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/001/405/GCA_000001405.15_GRCh38/seqs_for_alignment_pipelines.ucsc_ids/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna.gz - Network accessline 130
wget https://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/001/405/GCA_000001405.15_GRCh38/seqs_for_alignment_pipelines.ucsc_ids/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna.gz - Network accessline 130
wget https://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/001/405/GCA_000001405.15_GRCh38/seqs_for_alignment_pipelines.ucsc_ids/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna.gz - Network accessline 134
# Or use ENSEMBL/GENCODE wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_47/GRCh38.primary_assembly.genome.fa.gz - Network accessline 134
# Or use ENSEMBL/GENCODE wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_47/GRCh38.primary_assembly.genome.fa.gz - Network accessline 135
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_47/GRCh38.primary_assembly.genome.fa.gz - Network accessline 135
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_47/GRCh38.primary_assembly.genome.fa.gz - Network accessline 361
- [BWA-MEM2 GitHub: bwa-mem2/bwa-mem2](https://github.com/bwa-mem2/bwa-mem2) — source code, pre-compiled binaries, and benchmarks - Network accessline 361
- [BWA-MEM2 GitHub: bwa-mem2/bwa-mem2](https://github.com/bwa-mem2/bwa-mem2) — source code, pre-compiled binaries, and benchmarks - Network accessline 362
- Vasimuddin M et al. (2019) "Efficient Architecture-Aware Acceleration of BWA-MEM for Multicore Systems" — *IPDPS 2019*. [DOI:10.1109/IPDPS.2019.00041](https://doi.org/10.1109/IPDPS.2019.00041) - Network accessline 362
- Vasimuddin M et al. (2019) "Efficient Architecture-Aware Acceleration of BWA-MEM for Multicore Systems" — *IPDPS 2019*. [DOI:10.1109/IPDPS.2019.00041](https://doi.org/10.1109/IPDPS.2019.00041) - Network accessline 363
- Li H & Durbin R (2009) "Fast and accurate short read alignment with Burrows-Wheeler Aligner" — *Bioinformatics* 25(14):1754-1760. [DOI:10.1093/bioinformatics/btp324](https://doi.org/10.1093/bioinformatics/btp324) - Network accessline 363
- Li H & Durbin R (2009) "Fast and accurate short read alignment with Burrows-Wheeler Aligner" — *Bioinformatics* 25(14):1754-1760. [DOI:10.1093/bioinformatics/btp324](https://doi.org/10.1093/bioinformatics/btp324) - Network accessline 364
- [GATK Best Practices for germline short variant discovery](https://gatk.broadinstitute.org/hc/en-us/articles/360035535932) — GATK workflow recommending BWA-MEM2 alignment - Network accessline 364
- [GATK Best Practices for germline short variant discovery](https://gatk.broadinstitute.org/hc/en-us/articles/360035535932) — GATK workflow recommending BWA-MEM2 alignment
skills/genomics-bioinformatics/alignment/pysam-genomic-files/SKILL.md
- Network accessline 83
# Read BAM file, fetch reads in a region with pysam.AlignmentFile("sample.bam", "rb") as bam: for read in bam.fetch("chr1", 1000, 2000): - Network accessline 83
# Read BAM file, fetch reads in a region with pysam.AlignmentFile("sample.bam", "rb") as bam: for read in bam.fetch("chr1", 1000, 2000): - Network accessline 84
with pysam.AlignmentFile("sample.bam", "rb") as bam: for read in bam.fetch("chr1", 1000, 2000): print(f"{read.query_name}: pos={read.reference_start}, mapq={read.mapping_quality}") - Network accessline 84
with pysam.AlignmentFile("sample.bam", "rb") as bam: for read in bam.fetch("chr1", 1000, 2000): print(f"{read.query_name}: pos={read.reference_start}, mapq={read.mapping_quality}") - Network accessline 85
for read in bam.fetch("chr1", 1000, 2000): - Network accessline 85
for read in bam.fetch("chr1", 1000, 2000): - Network accessline 102
# Fetch reads overlapping a region (requires .bai index) for read in bam.fetch("chr1", 10000, 20000): - Network accessline 102
# Fetch reads overlapping a region (requires .bai index) for read in bam.fetch("chr1", 10000, 20000): - Network accessline 103
for read in bam.fetch("chr1", 10000, 20000): - Network accessline 103
for read in bam.fetch("chr1", 10000, 20000): - Network accessline 115
# Filter reads by quality and flags for read in bam.fetch("chr1", 10000, 20000): - Network accessline 115
# Filter reads by quality and flags for read in bam.fetch("chr1", 10000, 20000): - Network accessline 116
for read in bam.fetch("chr1", 10000, 20000): - Network accessline 116
for read in bam.fetch("chr1", 10000, 20000): - Network accessline 125
with pysam.AlignmentFile("input.bam", "rb") as inbam: with pysam.AlignmentFile("filtered.bam", "wb", header=inbam.header) as outbam: for read in inbam.fetch("chr1", 10000, 20000): - Network accessline 125
with pysam.AlignmentFile("input.bam", "rb") as inbam: with pysam.AlignmentFile("filtered.bam", "wb", header=inbam.header) as outbam: for read in inbam.fetch("chr1", 10000, 20000): - Network accessline 126
with pysam.AlignmentFile("filtered.bam", "wb", header=inbam.header) as outbam: for read in inbam.fetch("chr1", 10000, 20000): if read.mapping_quality >= 30: - Network accessline 126
with pysam.AlignmentFile("filtered.bam", "wb", header=inbam.header) as outbam: for read in inbam.fetch("chr1", 10000, 20000): if read.mapping_quality >= 30: - Network accessline 127
for read in inbam.fetch("chr1", 10000, 20000): - Network accessline 127
for read in inbam.fetch("chr1", 10000, 20000): - Network accessline 171
# Iterate all variants for record in vcf.fetch("chr1", 10000, 50000): - Network accessline 171
# Iterate all variants for record in vcf.fetch("chr1", 10000, 50000): - Network accessline 172
for record in vcf.fetch("chr1", 10000, 50000): - Network accessline 172
for record in vcf.fetch("chr1", 10000, 50000): - Network accessline 205
# FASTA: random access (requires .fai index) fasta = pysam.FastaFile("reference.fasta") seq = fasta.fetch("chr1", 10000, 10050) - Network accessline 205
# FASTA: random access (requires .fai index) fasta = pysam.FastaFile("reference.fasta") seq = fasta.fetch("chr1", 10000, 10050) - Network accessline 206
fasta = pysam.FastaFile("reference.fasta") seq = fasta.fetch("chr1", 10000, 10050) print(f"Sequence ({len(seq)} bp): {seq}") - Network accessline 206
fasta = pysam.FastaFile("reference.fasta") seq = fasta.fetch("chr1", 10000, 10050) print(f"Sequence ({len(seq)} bp): {seq}") - Network accessline 207
seq = fasta.fetch("chr1", 10000, 10050) - Network accessline 207
seq = fasta.fetch("chr1", 10000, 10050) - Network accessline 257
WARNING: Uses fetch(until_eof=True), which scans the entire BAM sequentially. - Network accessline 257
WARNING: Uses fetch(until_eof=True), which scans the entire BAM sequentially. - Network accessline 258
Multi-sample BAMs can be tens to hundreds of GB — this may be slow. For large files, prefer region-based filtering: for read in bam.fetch("chr1", start, end): ... - Network accessline 258
Multi-sample BAMs can be tens to hundreds of GB — this may be slow. For large files, prefer region-based filtering: for read in bam.fetch("chr1", start, end): ... - Network accessline 259
For large files, prefer region-based filtering: for read in bam.fetch("chr1", start, end): ... Or use the samtools CLI equivalent (faster for one-off extractions): - Network accessline 259
For large files, prefer region-based filtering: for read in bam.fetch("chr1", start, end): ... Or use the samtools CLI equivalent (faster for one-off extractions): - Network accessline 260
for read in bam.fetch("chr1", start, end): ... - Network accessline 260
for read in bam.fetch("chr1", start, end): ... - Network accessline 264
with pysam.AlignmentFile(bam_path, "rb") as bam_in: with pysam.AlignmentFile(output_path, "wb", header=bam_in.header) as bam_out: for read in bam_in.fetch(until_eof=True): - Network accessline 264
with pysam.AlignmentFile(bam_path, "rb") as bam_in: with pysam.AlignmentFile(output_path, "wb", header=bam_in.header) as bam_out: for read in bam_in.fetch(until_eof=True): - Network accessline 265
with pysam.AlignmentFile(output_path, "wb", header=bam_in.header) as bam_out: for read in bam_in.fetch(until_eof=True): if read.has_tag("RG") and read.get_tag("RG") == rg_id: - Network accessline 265
with pysam.AlignmentFile(output_path, "wb", header=bam_in.header) as bam_out: for read in bam_in.fetch(until_eof=True): if read.has_tag("RG") and read.get_tag("RG") == rg_id: - Network accessline 266
for read in bam_in.fetch(until_eof=True): - Network accessline 266
for read in bam_in.fetch(until_eof=True): - Network accessline 297
# Count reads per resolved sample name for read in bam.fetch(until_eof=True): - Network accessline 297
# Count reads per resolved sample name for read in bam.fetch(until_eof=True): - Network accessline 298
for read in bam.fetch(until_eof=True): - Network accessline 298
for read in bam.fetch(until_eof=True): - Network accessline 354
| System | Start | End | Example: "bases 1000-2000" | |--------|-------|-----|---------------------------| | pysam Python API | 0-based | exclusive | `fetch("chr1", 999, 2000)` | - Network accessline 354
| System | Start | End | Example: "bases 1000-2000" | |--------|-------|-----|---------------------------| | pysam Python API | 0-based | exclusive | `fetch("chr1", 999, 2000)` | - Network accessline 355
|--------|-------|-----|---------------------------| | pysam Python API | 0-based | exclusive | `fetch("chr1", 999, 2000)` | | samtools region string | 1-based | inclusive | `fetch("chr1:1000-2000")` | - Network accessline 355
|--------|-------|-----|---------------------------| | pysam Python API | 0-based | exclusive | `fetch("chr1", 999, 2000)` | | samtools region string | 1-based | inclusive | `fetch("chr1:1000-2000")` | - Network accessline 356
| pysam Python API | 0-based | exclusive | `fetch("chr1", 999, 2000)` | - Network accessline 356
| pysam Python API | 0-based | exclusive | `fetch("chr1", 999, 2000)` | - Network accessline 357
| samtools region string | 1-based | inclusive | `fetch("chr1:1000-2000")` | - Network accessline 357
| samtools region string | 1-based | inclusive | `fetch("chr1:1000-2000")` | - Network accessline 371
Without an index, use `fetch(until_eof=True)` for sequential reading. - Network accessline 371
Without an index, use `fetch(until_eof=True)` for sequential reading. - Network accessline 454
| `quality_threshold` | `count_coverage()` | 15 | 0–40 | Minimum base quality to count | | `truncate` | `pileup()` | False | True/False | Truncate pileup to exact region (True) vs include overlapping reads (False) | | `until_eof` | `fetch() - Network accessline 454
| `quality_threshold` | `count_coverage()` | 15 | 0–40 | Minimum base quality to count | | `truncate` | `pileup()` | False | True/False | Truncate pileup to exact region (True) vs include overlapping reads (False) | | `until_eof` | `fetch() - Network accessline 455
| `truncate` | `pileup()` | False | True/False | Truncate pileup to exact region (True) vs include overlapping reads (False) | | `until_eof` | `fetch()` | False | True/False | Read all records sequentially without index | | `multiple_iterat - Network accessline 455
| `truncate` | `pileup()` | False | True/False | Truncate pileup to exact region (True) vs include overlapping reads (False) | | `until_eof` | `fetch()` | False | True/False | Read all records sequentially without index | | `multiple_iterat - Network accessline 456
| `until_eof` | `fetch()` | False | True/False | Read all records sequentially without index | - Network accessline 456
| `until_eof` | `fetch()` | False | True/False | Read all records sequentially without index | - Network accessline 457
| `multiple_iterators` | `fetch()` | False | True/False | Allow multiple simultaneous iterators (slight overhead) | - Network accessline 457
| `multiple_iterators` | `fetch()` | False | True/False | Allow multiple simultaneous iterators (slight overhead) | - Network accessline 466
3. **Use `count()` instead of iterating to count reads**: `bam.count("chr1", 1000, 2000)` is much faster than `sum(1 for _ in bam.fetch(...))`. - Network accessline 466
3. **Use `count()` instead of iterating to count reads**: `bam.count("chr1", 1000, 2000)` is much faster than `sum(1 for _ in bam.fetch(...))`. - Network accessline 482
"""Extract gene sequence, reverse-complement if on minus strand.""" with pysam.FastaFile(fasta_path) as fasta: seq = fasta.fetch(chrom, start, end) - Network accessline 482
"""Extract gene sequence, reverse-complement if on minus strand.""" with pysam.FastaFile(fasta_path) as fasta: seq = fasta.fetch(chrom, start, end) - Network accessline 483
with pysam.FastaFile(fasta_path) as fasta: seq = fasta.fetch(chrom, start, end) if strand == "-": - Network accessline 483
with pysam.FastaFile(fasta_path) as fasta: seq = fasta.fetch(chrom, start, end) if strand == "-": - Network accessline 484
seq = fasta.fetch(chrom, start, end) - Network accessline 484
seq = fasta.fetch(chrom, start, end) - Network accessline 501
with pysam.AlignmentFile(bam_path, "rb") as bam: stats = {"total": 0, "mapped": 0, "unmapped": 0, "duplicates": 0, "mapq_ge30": 0} for read in bam.fetch(until_eof=True): - Network accessline 501
with pysam.AlignmentFile(bam_path, "rb") as bam: stats = {"total": 0, "mapped": 0, "unmapped": 0, "duplicates": 0, "mapq_ge30": 0} for read in bam.fetch(until_eof=True): - Network accessline 502
stats = {"total": 0, "mapped": 0, "unmapped": 0, "duplicates": 0, "mapq_ge30": 0} for read in bam.fetch(until_eof=True): stats["total"] += 1 - Network accessline 502
stats = {"total": 0, "mapped": 0, "unmapped": 0, "duplicates": 0, "mapq_ge30": 0} for read in bam.fetch(until_eof=True): stats["total"] += 1 - Network accessline 503
for read in bam.fetch(until_eof=True): - Network accessline 503
for read in bam.fetch(until_eof=True): - Network accessline 528
| `PileupProxy accessed after iterator finished` | Pileup iterator went out of scope | Store needed data from pileup columns immediately, don't save PileupProxy references | | `SamtoolsError` from CLI calls | Invalid arguments or missing in - Network accessline 528
| `PileupProxy accessed after iterator finished` | Pileup iterator went out of scope | Store needed data from pileup columns immediately, don't save PileupProxy references | | `SamtoolsError` from CLI calls | Invalid arguments or missing in - Network accessline 529
| `SamtoolsError` from CLI calls | Invalid arguments or missing input | Wrap in `try/except pysam.SamtoolsError`; check samtools docs for argument syntax | | Very slow iteration | Iterating all reads without region query | Use `fetch("chr1" - Network accessline 529
| `SamtoolsError` from CLI calls | Invalid arguments or missing input | Wrap in `try/except pysam.SamtoolsError`; check samtools docs for argument syntax | | Very slow iteration | Iterating all reads without region query | Use `fetch("chr1" - Network accessline 530
| Very slow iteration | Iterating all reads without region query | Use `fetch("chr1", start, end)` for targeted queries; use indexed files | - Network accessline 530
| Very slow iteration | Iterating all reads without region query | Use `fetch("chr1", start, end)` for targeted queries; use indexed files | - Network accessline 541
- [Pysam documentation](https://pysam.readthedocs.io/) — official API reference - Network accessline 541
- [Pysam documentation](https://pysam.readthedocs.io/) — official API reference - Network accessline 542
- [htslib](https://github.com/samtools/htslib) — underlying C library for genomic file formats - Network accessline 542
- [htslib](https://github.com/samtools/htslib) — underlying C library for genomic file formats - Network accessline 543
- [SAM format specification](https://samtools.github.io/hts-specs/SAMv1.pdf) — SAM/BAM format details - Network accessline 543
- [SAM format specification](https://samtools.github.io/hts-specs/SAMv1.pdf) — SAM/BAM format details - Network accessline 544
- Li et al. (2009) "The Sequence Alignment/Map format and SAMtools" — [Bioinformatics](https://doi.org/10.1093/bioinformatics/btp352) - Network accessline 544
- Li et al. (2009) "The Sequence Alignment/Map format and SAMtools" — [Bioinformatics](https://doi.org/10.1093/bioinformatics/btp352)
skills/genomics-bioinformatics/alignment/samtools-bam-processing/SKILL.md
- Dangerous shell operationline 347
rm collated.bam fixmated.bam sorted.bam - Dangerous shell operationline 347
rm collated.bam fixmated.bam sorted.bam - Network accessline 442
- [samtools documentation](https://www.htslib.org/doc/samtools.html) — official man pages and command reference - Network accessline 442
- [samtools documentation](https://www.htslib.org/doc/samtools.html) — official man pages and command reference - Network accessline 443
- [GitHub: samtools/samtools](https://github.com/samtools/samtools) — source, releases, issue tracker - Network accessline 443
- [GitHub: samtools/samtools](https://github.com/samtools/samtools) — source, releases, issue tracker - Network accessline 444
- Danecek et al. (2021) "Twelve years of SAMtools and BCFtools" — [GigaScience 10(2)](https://doi.org/10.1093/gigascience/giab008) - Network accessline 444
- Danecek et al. (2021) "Twelve years of SAMtools and BCFtools" — [GigaScience 10(2)](https://doi.org/10.1093/gigascience/giab008) - Network accessline 445
- [SAM format specification](https://samtools.github.io/hts-specs/SAMv1.pdf) — FLAG bits, CIGAR strings, optional tags - Network accessline 445
- [SAM format specification](https://samtools.github.io/hts-specs/SAMv1.pdf) — FLAG bits, CIGAR strings, optional tags
skills/genomics-bioinformatics/alignment/star-rna-seq-aligner/SKILL.md
- Network accessline 39
# Or compile from source git clone https://github.com/alexdobin/STAR - Network accessline 39
# Or compile from source git clone https://github.com/alexdobin/STAR - Network accessline 40
git clone https://github.com/alexdobin/STAR - Network accessline 40
git clone https://github.com/alexdobin/STAR - Network accessline 150
# Download GRCh38 genome and GENCODE annotation wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_47/GRCh38.primary_assembly.genome.fa.gz - Network accessline 150
# Download GRCh38 genome and GENCODE annotation wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_47/GRCh38.primary_assembly.genome.fa.gz - Network accessline 151
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_47/GRCh38.primary_assembly.genome.fa.gz - Network accessline 151
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_47/GRCh38.primary_assembly.genome.fa.gz - Network accessline 152
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_47/gencode.v47.primary_assembly.annotation.gtf.gz - Network accessline 152
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_47/gencode.v47.primary_assembly.annotation.gtf.gz - Network accessline 419
- [STAR GitHub repository](https://github.com/alexdobin/STAR) — source code, releases, and STAR manual PDF - Network accessline 419
- [STAR GitHub repository](https://github.com/alexdobin/STAR) — source code, releases, and STAR manual PDF - Network accessline 420
- Dobin A et al. (2013) "STAR: ultrafast universal RNA-seq aligner" — *Bioinformatics* 29(1):15-21. [DOI:10.1093/bioinformatics/bts635](https://doi.org/10.1093/bioinformatics/bts635) - Network accessline 420
- Dobin A et al. (2013) "STAR: ultrafast universal RNA-seq aligner" — *Bioinformatics* 29(1):15-21. [DOI:10.1093/bioinformatics/bts635](https://doi.org/10.1093/bioinformatics/bts635) - Network accessline 421
- [ENCODE RNA-seq alignment standards](https://www.encodeproject.org/rna-seq/) — ENCODE project guidelines using STAR - Network accessline 421
- [ENCODE RNA-seq alignment standards](https://www.encodeproject.org/rna-seq/) — ENCODE project guidelines using STAR - Network accessline 422
- [GENCODE genome annotations](https://www.gencodegenes.org/) — recommended GTF source for human/mouse STAR indices - Network accessline 422
- [GENCODE genome annotations](https://www.gencodegenes.org/) — recommended GTF source for human/mouse STAR indices
skills/genomics-bioinformatics/annotation/bakta-genome-annotation/SKILL.md
- Network accessline 603
- [Bakta GitHub: oschwengers/bakta](https://github.com/oschwengers/bakta) — source code, installation, parameter reference, and changelog - Network accessline 603
- [Bakta GitHub: oschwengers/bakta](https://github.com/oschwengers/bakta) — source code, installation, parameter reference, and changelog - Network accessline 604
- [Schwengers et al. (2021) Microb Genom 7(11):000685](https://doi.org/10.1099/mgen.0.000685) — original Bakta publication with benchmarks vs. Prokka and PGAP - Network accessline 604
- [Schwengers et al. (2021) Microb Genom 7(11):000685](https://doi.org/10.1099/mgen.0.000685) — original Bakta publication with benchmarks vs. Prokka and PGAP - Network accessline 605
- [Bakta database releases on Zenodo](https://doi.org/10.5281/zenodo.4247252) — versioned light/full database archives - Network accessline 605
- [Bakta database releases on Zenodo](https://doi.org/10.5281/zenodo.4247252) — versioned light/full database archives - Network accessline 606
- [Prodigal documentation](https://github.com/hyattpd/Prodigal/wiki) — gene prediction engine used by Bakta for CDS calling - Network accessline 606
- [Prodigal documentation](https://github.com/hyattpd/Prodigal/wiki) — gene prediction engine used by Bakta for CDS calling - Network accessline 607
- [BioPython GenBank parsing tutorial](https://biopython.org/wiki/SeqIO) — reference for parsing Bakta `.gbff` output in Python - Network accessline 607
- [BioPython GenBank parsing tutorial](https://biopython.org/wiki/SeqIO) — reference for parsing Bakta `.gbff` output in Python - Network accessline 608
- [DIAMOND aligner](https://github.com/bbuchfink/diamond) — protein search engine used for the UniRef-derived database lookups - Network accessline 608
- [DIAMOND aligner](https://github.com/bbuchfink/diamond) — protein search engine used for the UniRef-derived database lookups
skills/genomics-bioinformatics/annotation/prokka-genome-annotation/SKILL.md
- Network accessline 638
- [Prokka GitHub: tseemann/prokka](https://github.com/tseemann/prokka) — source code, installation instructions, and detailed parameter reference - Network accessline 638
- [Prokka GitHub: tseemann/prokka](https://github.com/tseemann/prokka) — source code, installation instructions, and detailed parameter reference - Network accessline 639
- [Seemann T (2014) Bioinformatics 30(14):2068–2069](https://doi.org/10.1093/bioinformatics/btu153) — original Prokka paper with benchmarks vs. RAST - Network accessline 639
- [Seemann T (2014) Bioinformatics 30(14):2068–2069](https://doi.org/10.1093/bioinformatics/btu153) — original Prokka paper with benchmarks vs. RAST - Network accessline 640
- [Prodigal documentation](https://github.com/hyattpd/Prodigal/wiki) — gene prediction engine used by Prokka for CDS calling - Network accessline 640
- [Prodigal documentation](https://github.com/hyattpd/Prodigal/wiki) — gene prediction engine used by Prokka for CDS calling - Network accessline 641
- [Barrnap GitHub: tseemann/barrnap](https://github.com/tseemann/barrnap) — rRNA prediction tool bundled with Prokka - Network accessline 641
- [Barrnap GitHub: tseemann/barrnap](https://github.com/tseemann/barrnap) — rRNA prediction tool bundled with Prokka - Network accessline 642
- [BioPython GenBank parsing tutorial](https://biopython.org/wiki/SeqIO) — reference for parsing Prokka GenBank output in Python - Network accessline 642
- [BioPython GenBank parsing tutorial](https://biopython.org/wiki/SeqIO) — reference for parsing Prokka GenBank output in Python
skills/genomics-bioinformatics/annotation/roary-pangenome/SKILL.md
- Network accessline 370
# Use the bundled python helper from the Roary install # (download from: https://github.com/sanger-pathogens/Roary/blob/master/contrib/roary_plots/roary_plots.py) - Network accessline 370
# Use the bundled python helper from the Roary install # (download from: https://github.com/sanger-pathogens/Roary/blob/master/contrib/roary_plots/roary_plots.py) - Network accessline 371
# (download from: https://github.com/sanger-pathogens/Roary/blob/master/contrib/roary_plots/roary_plots.py) - Network accessline 371
# (download from: https://github.com/sanger-pathogens/Roary/blob/master/contrib/roary_plots/roary_plots.py) - Network accessline 558
- [Roary GitHub: sanger-pathogens/Roary](https://github.com/sanger-pathogens/Roary) — source code, parameter reference, and roary_plots.py helper - Network accessline 558
- [Roary GitHub: sanger-pathogens/Roary](https://github.com/sanger-pathogens/Roary) — source code, parameter reference, and roary_plots.py helper - Network accessline 559
- [Page et al. (2015) Bioinformatics 31(22):3691–3693](https://doi.org/10.1093/bioinformatics/btv421) — original Roary publication describing the CD-HIT + MCL pipeline - Network accessline 559
- [Page et al. (2015) Bioinformatics 31(22):3691–3693](https://doi.org/10.1093/bioinformatics/btv421) — original Roary publication describing the CD-HIT + MCL pipeline - Network accessline 560
- [Roary tutorial](https://sanger-pathogens.github.io/Roary/) — step-by-step worked example with sample data - Network accessline 560
- [Roary tutorial](https://sanger-pathogens.github.io/Roary/) — step-by-step worked example with sample data - Network accessline 561
- [Panaroo (alternative tool)](https://github.com/gtonkinhill/panaroo) — graph-based pan-genome with stricter annotation cleaning - Network accessline 561
- [Panaroo (alternative tool)](https://github.com/gtonkinhill/panaroo) — graph-based pan-genome with stricter annotation cleaning - Network accessline 562
- [PIRATE (alternative tool)](https://github.com/SionBayliss/PIRATE) — paralog-aware pan-genome at multiple identity thresholds - Network accessline 562
- [PIRATE (alternative tool)](https://github.com/SionBayliss/PIRATE) — paralog-aware pan-genome at multiple identity thresholds - Network accessline 563
- [PPanGGOLiN (alternative tool)](https://github.com/labgem/PPanGGOLiN) — graph-based statistical partitioning of pan-genomes - Network accessline 563
- [PPanGGOLiN (alternative tool)](https://github.com/labgem/PPanGGOLiN) — graph-based statistical partitioning of pan-genomes - Network accessline 564
- [FastTree documentation](http://www.microbesonline.org/fasttree/) — reference for ML phylogenetic inference from core gene alignments - Network accessline 564
- [FastTree documentation](http://www.microbesonline.org/fasttree/) — reference for ML phylogenetic inference from core gene alignments
skills/genomics-bioinformatics/arboreto-grn-inference/SKILL.md
- Network accessline 174
client = Client(local_cluster) print(f"Dashboard: {client.dashboard_link}") # Example: Dashboard: http://127.0.0.1:8787/status - Network accessline 174
client = Client(local_cluster) print(f"Dashboard: {client.dashboard_link}") # Example: Dashboard: http://127.0.0.1:8787/status - Network accessline 176
# Example: Dashboard: http://127.0.0.1:8787/status - Network accessline 176
# Example: Dashboard: http://127.0.0.1:8787/status - Network accessline 475
- [Arboreto GitHub repository](https://github.com/aertslab/arboreto) -- source code, README, examples - Network accessline 475
- [Arboreto GitHub repository](https://github.com/aertslab/arboreto) -- source code, README, examples - Network accessline 476
- [Arboreto PyPI](https://pypi.org/project/arboreto/) -- installation and version history - Network accessline 476
- [Arboreto PyPI](https://pypi.org/project/arboreto/) -- installation and version history - Network accessline 477
- Moerman et al. (2019) "GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks" *Bioinformatics* 35(12):2159-2161 -- algorithm paper - Aibar et al. (2017) "SCENIC: single-cell regulatory network inference and - Network accessline 477
- Moerman et al. (2019) "GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks" *Bioinformatics* 35(12):2159-2161 -- algorithm paper - Aibar et al. (2017) "SCENIC: single-cell regulatory network inference and - Network accessline 479
- [Dask distributed documentation](https://distributed.dask.org/) -- Dask client, cluster setup, dashboard - Network accessline 479
- [Dask distributed documentation](https://distributed.dask.org/) -- Dask client, cluster setup, dashboard
skills/genomics-bioinformatics/biopython-molecular-biology/SKILL.md
- Access to secretsline 150
[Credential-related source omitted] - Access to secretsline 150
[Credential-related source omitted] - Access to secretsline 520
[Credential-related source omitted] - Access to secretsline 520
[Credential-related source omitted] - Access to secretsline 655
[Credential-related source omitted] - Access to secretsline 655
[Credential-related source omitted] - Network accessline 667
- [Biopython Tutorial and Cookbook](https://biopython.org/docs/latest/Tutorial/) — Official comprehensive tutorial - Network accessline 667
- [Biopython Tutorial and Cookbook](https://biopython.org/docs/latest/Tutorial/) — Official comprehensive tutorial - Network accessline 668
- [Biopython API Documentation](https://biopython.org/docs/latest/api/) — Complete API reference - Network accessline 668
- [Biopython API Documentation](https://biopython.org/docs/latest/api/) — Complete API reference - Network accessline 669
- [Cock, P.J.A. et al. (2009) Bioinformatics 25:1422-1423](https://doi.org/10.1093/bioinformatics/btp163) — Original Biopython paper - Network accessline 669
- [Cock, P.J.A. et al. (2009) Bioinformatics 25:1422-1423](https://doi.org/10.1093/bioinformatics/btp163) — Original Biopython paper - Network accessline 670
- [NCBI Entrez Programming Utilities](https://www.ncbi.nlm.nih.gov/books/NBK25497/) — E-utilities documentation for programmatic NCBI access - Network accessline 670
- [NCBI Entrez Programming Utilities](https://www.ncbi.nlm.nih.gov/books/NBK25497/) — E-utilities documentation for programmatic NCBI access - Network accessline 671
- [Biopython GitHub Repository](https://github.com/biopython/biopython) — Source code, issues, and release notes - Network accessline 671
- [Biopython GitHub Repository](https://github.com/biopython/biopython) — Source code, issues, and release notes
skills/genomics-bioinformatics/biopython-sequence-analysis/SKILL.md
- Network accessline 30
- **Python packages**: `biopython`, `numpy`, `matplotlib` - **Optional tools**: MUSCLE or ClustalW installed locally (for `Bio.Align.Applications` wrappers) - **NCBI access**: Set `Entrez.email` before any E-utilities call; obtain a free AP - Network accessline 30
- **Python packages**: `biopython`, `numpy`, `matplotlib` - **Optional tools**: MUSCLE or ClustalW installed locally (for `Bio.Align.Applications` wrappers) - **NCBI access**: Set `Entrez.email` before any E-utilities call; obtain a free AP - Network accessline 31
- **Optional tools**: MUSCLE or ClustalW installed locally (for `Bio.Align.Applications` wrappers) - **NCBI access**: Set `Entrez.email` before any E-utilities call; obtain a free API key at https://www.ncbi.nlm.nih.gov/account/ for 10 req/ - Network accessline 31
- **Optional tools**: MUSCLE or ClustalW installed locally (for `Bio.Align.Applications` wrappers) - **NCBI access**: Set `Entrez.email` before any E-utilities call; obtain a free API key at https://www.ncbi.nlm.nih.gov/account/ for 10 req/ - Network accessline 32
- **NCBI access**: Set `Entrez.email` before any E-utilities call; obtain a free API key at https://www.ncbi.nlm.nih.gov/account/ for 10 req/s (default is 3 req/s) - Network accessline 32
- **NCBI access**: Set `Entrez.email` before any E-utilities call; obtain a free API key at https://www.ncbi.nlm.nih.gov/account/ for 10 req/s (default is 3 req/s) - Access to secretsline 173
[Credential-related source omitted] - Access to secretsline 173
[Credential-related source omitted] - Access to secretsline 610
[Credential-related source omitted] - Access to secretsline 610
[Credential-related source omitted] - Network accessline 613
[Credential-related source omitted] - Network accessline 613
[Credential-related source omitted] - Network accessline 614
[Credential-related source omitted] - Network accessline 614
[Credential-related source omitted] - Network accessline 615
[Credential-related source omitted] - Network accessline 615
[Credential-related source omitted] - Access to secretsline 615
[Credential-related source omitted] - Access to secretsline 615
[Credential-related source omitted] - Network accessline 763
| Problem | Cause | Solution | |---------|-------|----------| | `urllib.error.HTTPError: 429 Too Many Requests` | Exceeding NCBI rate limit (3 req/s) | Add `time.sleep(0.4)` between calls, or register a free API key at https://www.ncbi.nlm. - Network accessline 763
| Problem | Cause | Solution | |---------|-------|----------| | `urllib.error.HTTPError: 429 Too Many Requests` | Exceeding NCBI rate limit (3 req/s) | Add `time.sleep(0.4)` between calls, or register a free API key at https://www.ncbi.nlm. - Network accessline 764
|---------|-------|----------| | `urllib.error.HTTPError: 429 Too Many Requests` | Exceeding NCBI rate limit (3 req/s) | Add `time.sleep(0.4)` between calls, or register a free API key at https://www.ncbi.nlm.nih.gov/account/ for 10 req/s | - Network accessline 764
|---------|-------|----------| | `urllib.error.HTTPError: 429 Too Many Requests` | Exceeding NCBI rate limit (3 req/s) | Add `time.sleep(0.4)` between calls, or register a free API key at https://www.ncbi.nlm.nih.gov/account/ for 10 req/s | - Network accessline 765
| `urllib.error.HTTPError: 429 Too Many Requests` | Exceeding NCBI rate limit (3 req/s) | Add `time.sleep(0.4)` between calls, or register a free API key at https://www.ncbi.nlm.nih.gov/account/ for 10 req/s | - Network accessline 765
| `urllib.error.HTTPError: 429 Too Many Requests` | Exceeding NCBI rate limit (3 req/s) | Add `time.sleep(0.4)` between calls, or register a free API key at https://www.ncbi.nlm.nih.gov/account/ for 10 req/s | - Network accessline 785
- [Biopython Tutorial and Cookbook](https://biopython.org/DIST/docs/tutorial/Tutorial.html) — comprehensive official tutorial (Chapters 2, 5, 6, 7, 9, 11) - Network accessline 785
- [Biopython Tutorial and Cookbook](https://biopython.org/DIST/docs/tutorial/Tutorial.html) — comprehensive official tutorial (Chapters 2, 5, 6, 7, 9, 11) - Network accessline 786
- [Biopython API Reference](https://biopython.org/docs/latest/api/) — complete module and class documentation - Network accessline 786
- [Biopython API Reference](https://biopython.org/docs/latest/api/) — complete module and class documentation - Network accessline 787
- [GitHub: biopython/biopython](https://github.com/biopython/biopython) — source, changelog, and issue tracker - Network accessline 787
- [GitHub: biopython/biopython](https://github.com/biopython/biopython) — source, changelog, and issue tracker - Network accessline 788
- [NCBI E-utilities Documentation](https://www.ncbi.nlm.nih.gov/books/NBK25499/) — Entrez API reference for esearch, efetch, elink, esummary - Network accessline 788
- [NCBI E-utilities Documentation](https://www.ncbi.nlm.nih.gov/books/NBK25499/) — Entrez API reference for esearch, efetch, elink, esummary - Network accessline 789
- [PairwiseAligner Migration Guide](https://biopython.org/docs/latest/api/Bio.Align.html) — replacing the deprecated `Bio.pairwise2` module - Network accessline 789
- [PairwiseAligner Migration Guide](https://biopython.org/docs/latest/api/Bio.Align.html) — replacing the deprecated `Bio.pairwise2` module
skills/genomics-bioinformatics/databases/archs4-database/SKILL.md
- Network accessline 11
ARCHS4 (All RNA-seq and ChIP-seq Sample and Signature Search) is a resource of uniformly aligned and processed human and mouse RNA-seq data from NCBI GEO and SRA, covering 1 million+ samples. The REST API at `https://maayanlab.cloud/archs4/ - Network accessline 11
ARCHS4 (All RNA-seq and ChIP-seq Sample and Signature Search) is a resource of uniformly aligned and processed human and mouse RNA-seq data from NCBI GEO and SRA, covering 1 million+ samples. The REST API at `https://maayanlab.cloud/archs4/ - Network accessline 41
ARCHS4_BASE = "https://maayanlab.cloud/archs4/api/v1" - Network accessline 41
ARCHS4_BASE = "https://maayanlab.cloud/archs4/api/v1" - Network accessline 43
def archs4_get(endpoint: str, params: dict = None) -> dict: """Send a GET request to the ARCHS4 API and return parsed JSON.""" r = requests.get(f"{ARCHS4_BASE}/{endpoint}", params=params, timeout=30) - Network accessline 43
def archs4_get(endpoint: str, params: dict = None) -> dict: """Send a GET request to the ARCHS4 API and return parsed JSON.""" r = requests.get(f"{ARCHS4_BASE}/{endpoint}", params=params, timeout=30) - Network accessline 44
"""Send a GET request to the ARCHS4 API and return parsed JSON.""" r = requests.get(f"{ARCHS4_BASE}/{endpoint}", params=params, timeout=30) r.raise_for_status() - Network accessline 44
"""Send a GET request to the ARCHS4 API and return parsed JSON.""" r = requests.get(f"{ARCHS4_BASE}/{endpoint}", params=params, timeout=30) r.raise_for_status() - Network accessline 45
r = requests.get(f"{ARCHS4_BASE}/{endpoint}", params=params, timeout=30) - Network accessline 45
r = requests.get(f"{ARCHS4_BASE}/{endpoint}", params=params, timeout=30) - Network accessline 71
ARCHS4_BASE = "https://maayanlab.cloud/archs4/api/v1" - Network accessline 71
ARCHS4_BASE = "https://maayanlab.cloud/archs4/api/v1" - Network accessline 82
""" endpoint = f"meta/genes/{gene_symbol}/zscore" r = requests.get( - Network accessline 82
""" endpoint = f"meta/genes/{gene_symbol}/zscore" r = requests.get( - Network accessline 83
endpoint = f"meta/genes/{gene_symbol}/zscore" r = requests.get( f"{ARCHS4_BASE}/{endpoint}", - Network accessline 83
endpoint = f"meta/genes/{gene_symbol}/zscore" r = requests.get( f"{ARCHS4_BASE}/{endpoint}", - Network accessline 84
r = requests.get( - Network accessline 84
r = requests.get( - Network accessline 120
ARCHS4_BASE = "https://maayanlab.cloud/archs4/api/v1" - Network accessline 120
ARCHS4_BASE = "https://maayanlab.cloud/archs4/api/v1" - Network accessline 133
'human' or 'mouse' (default: 'human'). """ r = requests.get( - Network accessline 133
'human' or 'mouse' (default: 'human'). """ r = requests.get( - Network accessline 134
""" r = requests.get( f"{ARCHS4_BASE}/meta/genes/{gene_symbol}/correlations", - Network accessline 134
""" r = requests.get( f"{ARCHS4_BASE}/meta/genes/{gene_symbol}/correlations", - Network accessline 135
r = requests.get( - Network accessline 135
r = requests.get( - Network accessline 171
ARCHS4_BASE = "https://maayanlab.cloud/archs4/api/v1" - Network accessline 171
ARCHS4_BASE = "https://maayanlab.cloud/archs4/api/v1" - Network accessline 184
Maximum number of samples to return. """ r = requests.get( - Network accessline 184
Maximum number of samples to return. """ r = requests.get( - Network accessline 185
""" r = requests.get( f"{ARCHS4_BASE}/samples/search", - Network accessline 185
""" r = requests.get( f"{ARCHS4_BASE}/samples/search", - Network accessline 186
r = requests.get( - Network accessline 186
r = requests.get( - Network accessline 212
ARCHS4_BASE = "https://maayanlab.cloud/archs4/api/v1" - Network accessline 212
ARCHS4_BASE = "https://maayanlab.cloud/archs4/api/v1" - Network accessline 214
def get_gene_metadata(gene_symbol: str, species: str = "human") -> dict: """Return metadata and expression summary for a gene.""" r = requests.get( - Network accessline 214
def get_gene_metadata(gene_symbol: str, species: str = "human") -> dict: """Return metadata and expression summary for a gene.""" r = requests.get( - Network accessline 215
"""Return metadata and expression summary for a gene.""" r = requests.get( f"{ARCHS4_BASE}/meta/genes/{gene_symbol}", - Network accessline 215
"""Return metadata and expression summary for a gene.""" r = requests.get( f"{ARCHS4_BASE}/meta/genes/{gene_symbol}", - Network accessline 216
r = requests.get( - Network accessline 216
r = requests.get( - Network accessline 250
ARCHS4_BASE = "https://maayanlab.cloud/archs4/api/v1" - Network accessline 250
ARCHS4_BASE = "https://maayanlab.cloud/archs4/api/v1" - Network accessline 252
def tissue_zscores(gene_symbol: str, top_n: int = 20, species: str = "human") -> pd.DataFrame: """Fetch top tissue z-scores for a gene and save for plotting.""" r = requests.get( - Network accessline 252
def tissue_zscores(gene_symbol: str, top_n: int = 20, species: str = "human") -> pd.DataFrame: """Fetch top tissue z-scores for a gene and save for plotting.""" r = requests.get( - Network accessline 253
"""Fetch top tissue z-scores for a gene and save for plotting.""" r = requests.get( f"{ARCHS4_BASE}/meta/genes/{gene_symbol}/zscore", - Network accessline 253
"""Fetch top tissue z-scores for a gene and save for plotting.""" r = requests.get( f"{ARCHS4_BASE}/meta/genes/{gene_symbol}/zscore", - Network accessline 254
r = requests.get( - Network accessline 254
r = requests.get( - Network accessline 276
# HDF5 files are available for bulk download from the ARCHS4 data portal # URL pattern: https://maayanlab.cloud/archs4/download#expression - Network accessline 276
# HDF5 files are available for bulk download from the ARCHS4 data portal # URL pattern: https://maayanlab.cloud/archs4/download#expression - Network accessline 277
# URL pattern: https://maayanlab.cloud/archs4/download#expression - Network accessline 277
# URL pattern: https://maayanlab.cloud/archs4/download#expression - Network accessline 281
def get_h5_download_urls() -> dict: """Return download URLs for ARCHS4 HDF5 expression matrices.""" base = "https://maayanlab.cloud/archs4" - Network accessline 281
def get_h5_download_urls() -> dict: """Return download URLs for ARCHS4 HDF5 expression matrices.""" base = "https://maayanlab.cloud/archs4" - Network accessline 282
"""Return download URLs for ARCHS4 HDF5 expression matrices.""" base = "https://maayanlab.cloud/archs4" return { - Network accessline 282
"""Return download URLs for ARCHS4 HDF5 expression matrices.""" base = "https://maayanlab.cloud/archs4" return { - Network accessline 283
base = "https://maayanlab.cloud/archs4" - Network accessline 283
base = "https://maayanlab.cloud/archs4" - Network accessline 358
ARCHS4_BASE = "https://maayanlab.cloud/archs4/api/v1" - Network accessline 358
ARCHS4_BASE = "https://maayanlab.cloud/archs4/api/v1" - Network accessline 363
def get_tissue_zscores(gene: str) -> pd.Series: r = requests.get( - Network accessline 363
def get_tissue_zscores(gene: str) -> pd.Series: r = requests.get( - Network accessline 364
r = requests.get( - Network accessline 364
r = requests.get( - Network accessline 404
ARCHS4_BASE = "https://maayanlab.cloud/archs4/api/v1" - Network accessline 404
ARCHS4_BASE = "https://maayanlab.cloud/archs4/api/v1" - Network accessline 406
def get_coexp(gene: str, top_n: int = 20, species: str = "human") -> list: r = requests.get( - Network accessline 406
def get_coexp(gene: str, top_n: int = 20, species: str = "human") -> list: r = requests.get( - Network accessline 407
r = requests.get( - Network accessline 407
r = requests.get( - Network accessline 451
ARCHS4_BASE = "https://maayanlab.cloud/archs4/api/v1" - Network accessline 451
ARCHS4_BASE = "https://maayanlab.cloud/archs4/api/v1" - Network accessline 454
limit: int = 200) -> pd.DataFrame: """Search samples and return a tidy metadata DataFrame.""" r = requests.get( - Network accessline 454
limit: int = 200) -> pd.DataFrame: """Search samples and return a tidy metadata DataFrame.""" r = requests.get( - Network accessline 455
"""Search samples and return a tidy metadata DataFrame.""" r = requests.get( f"{ARCHS4_BASE}/samples/search", - Network accessline 455
"""Search samples and return a tidy metadata DataFrame.""" r = requests.get( f"{ARCHS4_BASE}/samples/search", - Network accessline 456
r = requests.get( - Network accessline 456
r = requests.get( - Network accessline 515
ARCHS4_BASE = "https://maayanlab.cloud/archs4/api/v1" - Network accessline 515
ARCHS4_BASE = "https://maayanlab.cloud/archs4/api/v1" - Network accessline 517
def tissue_specificity_summary(gene_symbol: str) -> None: """Print a summary of high and low expression tissues for a gene.""" r = requests.get( - Network accessline 517
def tissue_specificity_summary(gene_symbol: str) -> None: """Print a summary of high and low expression tissues for a gene.""" r = requests.get( - Network accessline 518
"""Print a summary of high and low expression tissues for a gene.""" r = requests.get( f"{ARCHS4_BASE}/meta/genes/{gene_symbol}/zscore", - Network accessline 518
"""Print a summary of high and low expression tissues for a gene.""" r = requests.get( f"{ARCHS4_BASE}/meta/genes/{gene_symbol}/zscore", - Network accessline 519
r = requests.get( - Network accessline 519
r = requests.get( - Network accessline 550
ARCHS4_BASE = "https://maayanlab.cloud/archs4/api/v1" - Network accessline 550
ARCHS4_BASE = "https://maayanlab.cloud/archs4/api/v1" - Network accessline 555
for gene in gene_list: try: r = requests.get( - Network accessline 555
for gene in gene_list: try: r = requests.get( - Network accessline 556
try: r = requests.get( f"{ARCHS4_BASE}/meta/genes/{gene}/correlations", - Network accessline 556
try: r = requests.get( f"{ARCHS4_BASE}/meta/genes/{gene}/correlations", - Network accessline 557
r = requests.get( - Network accessline 557
r = requests.get( - Network accessline 590
ARCHS4_BASE = "https://maayanlab.cloud/archs4/api/v1" - Network accessline 590
ARCHS4_BASE = "https://maayanlab.cloud/archs4/api/v1" - Network accessline 592
keyword = "glioblastoma" r = requests.get( - Network accessline 592
keyword = "glioblastoma" r = requests.get( - Network accessline 593
r = requests.get( - Network accessline 593
r = requests.get( - Network accessline 634
- [ARCHS4 web portal](https://maayanlab.cloud/archs4/) — Interactive expression browser and dataset download - Network accessline 634
- [ARCHS4 web portal](https://maayanlab.cloud/archs4/) — Interactive expression browser and dataset download - Network accessline 635
- [ARCHS4 REST API documentation](https://maayanlab.cloud/archs4/api/) — Endpoint reference and parameters - Network accessline 635
- [ARCHS4 REST API documentation](https://maayanlab.cloud/archs4/api/) — Endpoint reference and parameters - Network accessline 636
- [Lachmann et al., Nature Communications 2018](https://doi.org/10.1038/s41467-018-03751-6) — ARCHS4 original publication describing uniform alignment pipeline - Network accessline 636
- [Lachmann et al., Nature Communications 2018](https://doi.org/10.1038/s41467-018-03751-6) — ARCHS4 original publication describing uniform alignment pipeline - Network accessline 637
- [ARCHS4 GitHub](https://github.com/MaayanLab/archs4) — Source code and HDF5 schema documentation - Network accessline 637
- [ARCHS4 GitHub](https://github.com/MaayanLab/archs4) — Source code and HDF5 schema documentation
skills/genomics-bioinformatics/databases/bioservices-multi-database/SKILL.md
- Network accessline 599
- BioServices documentation: https://bioservices.readthedocs.io/ - Network accessline 599
- BioServices documentation: https://bioservices.readthedocs.io/ - Network accessline 600
- BioServices source: https://github.com/cokelaer/bioservices - Network accessline 600
- BioServices source: https://github.com/cokelaer/bioservices - Network accessline 601
- Cokelaer et al. (2013) BioServices: a common Python package to access biological Web Services programmatically. *Bioinformatics* 29(24): 3241-3242 - UniProt REST API: https://www.uniprot.org/help/api - KEGG API: https://www.kegg.jp/kegg/r - Network accessline 601
- Cokelaer et al. (2013) BioServices: a common Python package to access biological Web Services programmatically. *Bioinformatics* 29(24): 3241-3242 - UniProt REST API: https://www.uniprot.org/help/api - KEGG API: https://www.kegg.jp/kegg/r - Network accessline 602
- UniProt REST API: https://www.uniprot.org/help/api - Network accessline 602
- UniProt REST API: https://www.uniprot.org/help/api - Network accessline 603
- KEGG API: https://www.kegg.jp/kegg/rest/keggapi.html - Network accessline 603
- KEGG API: https://www.kegg.jp/kegg/rest/keggapi.html - Network accessline 604
- QuickGO API: https://www.ebi.ac.uk/QuickGO/api/index.html - Network accessline 604
- QuickGO API: https://www.ebi.ac.uk/QuickGO/api/index.html
skills/genomics-bioinformatics/databases/bioservices-multi-database/references/services_catalog.md
- Network accessline 54
h = HGNC() # h.search("ZAP70") — search gene symbols/names # h.fetch("symbol", "ZAP70") — retrieve gene information - Network accessline 54
h = HGNC() # h.search("ZAP70") — search gene symbols/names # h.fetch("symbol", "ZAP70") — retrieve gene information - Network accessline 56
# h.fetch("symbol", "ZAP70") — retrieve gene information - Network accessline 56
# h.fetch("symbol", "ZAP70") — retrieve gene information
skills/genomics-bioinformatics/databases/cbioportal-database/SKILL.md
- Network accessline 11
cBioPortal for Cancer Genomics is a public repository of cancer genomics data including TCGA, ICGC, and hundreds of curated studies spanning 100+ cancer types. It provides somatic mutation profiles, copy number alterations (CNA), gene expre - Network accessline 11
cBioPortal for Cancer Genomics is a public repository of cancer genomics data including TCGA, ICGC, and hundreds of curated studies spanning 100+ cancer types. It provides somatic mutation profiles, copy number alterations (CNA), gene expre - Network accessline 42
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 42
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 44
def cbio_get(endpoint, params=None): """GET request to cBioPortal REST API, returns parsed JSON.""" r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 44
def cbio_get(endpoint, params=None): """GET request to cBioPortal REST API, returns parsed JSON.""" r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 45
"""GET request to cBioPortal REST API, returns parsed JSON.""" r = requests.get(f"{BASE_URL}/{endpoint}", params=params, headers={"Accept": "application/json"}, timeout=30) - Network accessline 45
"""GET request to cBioPortal REST API, returns parsed JSON.""" r = requests.get(f"{BASE_URL}/{endpoint}", params=params, headers={"Accept": "application/json"}, timeout=30) - Network accessline 46
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 46
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 79
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 79
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 81
def cbio_get(endpoint, params=None): r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 81
def cbio_get(endpoint, params=None): r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 82
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 82
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 123
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 123
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 125
def cbio_post(endpoint, body): """POST request to cBioPortal REST API.""" r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 125
def cbio_post(endpoint, body): """POST request to cBioPortal REST API.""" r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 126
"""POST request to cBioPortal REST API.""" r = requests.post(f"{BASE_URL}/{endpoint}", json=body, headers={"Accept": "application/json", - Network accessline 126
"""POST request to cBioPortal REST API.""" r = requests.post(f"{BASE_URL}/{endpoint}", json=body, headers={"Accept": "application/json", - Network accessline 127
r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 127
r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 134
def cbio_get(endpoint, params=None): r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 134
def cbio_get(endpoint, params=None): r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 135
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 135
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 175
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 175
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 177
def cbio_post(endpoint, body): r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 177
def cbio_post(endpoint, body): r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 178
r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 178
r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 185
def cbio_get(endpoint, params=None): r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 185
def cbio_get(endpoint, params=None): r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 186
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 186
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 230
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 230
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 232
def cbio_get(endpoint, params=None): r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 232
def cbio_get(endpoint, params=None): r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 233
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 233
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 275
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 275
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 277
def cbio_post(endpoint, body): r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 277
def cbio_post(endpoint, body): r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 278
r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 278
r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 285
def cbio_get(endpoint, params=None): r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 285
def cbio_get(endpoint, params=None): r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 286
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 286
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 326
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 326
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 328
def cbio_get(endpoint, params=None): r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 328
def cbio_get(endpoint, params=None): r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 329
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 329
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 334
def cbio_post(endpoint, body): r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 334
def cbio_post(endpoint, body): r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 335
r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 335
r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 370
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 370
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 372
def cbio_get(endpoint, params=None): r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 372
def cbio_get(endpoint, params=None): r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 373
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 373
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 378
def cbio_post(endpoint, body): r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 378
def cbio_post(endpoint, body): r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 379
r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 379
r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 453
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 453
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 455
def cbio_get(endpoint, params=None): r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 455
def cbio_get(endpoint, params=None): r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 456
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 456
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 461
def cbio_post(endpoint, body): r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 461
def cbio_post(endpoint, body): r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 462
r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 462
r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 513
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 513
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 515
def cbio_get(endpoint, params=None): r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 515
def cbio_get(endpoint, params=None): r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 516
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 516
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 521
def cbio_post(endpoint, body): r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 521
def cbio_post(endpoint, body): r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 522
r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 522
r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 585
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 585
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 587
def cbio_get(endpoint, params=None): r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 587
def cbio_get(endpoint, params=None): r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 588
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 588
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 593
def cbio_post(endpoint, body): r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 593
def cbio_post(endpoint, body): r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 594
r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 594
r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 676
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 676
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 678
def cbio_get(endpoint, params=None): r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 678
def cbio_get(endpoint, params=None): r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 679
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 679
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 702
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 702
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 704
def cbio_get(endpoint, params=None): r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 704
def cbio_get(endpoint, params=None): r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 705
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 705
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 710
def cbio_post(endpoint, body): r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 710
def cbio_post(endpoint, body): r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 711
r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 711
r = requests.post(f"{BASE_URL}/{endpoint}", json=body, - Network accessline 749
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 749
BASE_URL = "https://www.cbioportal.org/api" - Network accessline 751
def cbio_get(endpoint, params=None): r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 751
def cbio_get(endpoint, params=None): r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 752
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 752
r = requests.get(f"{BASE_URL}/{endpoint}", params=params, - Network accessline 792
- [cBioPortal API Swagger UI](https://www.cbioportal.org/api/swagger-ui) — interactive REST API explorer with all endpoints - Network accessline 792
- [cBioPortal API Swagger UI](https://www.cbioportal.org/api/swagger-ui) — interactive REST API explorer with all endpoints - Network accessline 793
- [cBioPortal GitHub](https://github.com/cBioPortal/cbioportal) — source code and issue tracker - Network accessline 793
- [cBioPortal GitHub](https://github.com/cBioPortal/cbioportal) — source code and issue tracker - Network accessline 794
- [Cerami E et al., Cancer Discovery 2012](https://doi.org/10.1158/2159-8290.CD-12-0095) — original cBioPortal paper - Network accessline 794
- [Cerami E et al., Cancer Discovery 2012](https://doi.org/10.1158/2159-8290.CD-12-0095) — original cBioPortal paper - Network accessline 795
- [Gao J et al., Science Signaling 2013](https://doi.org/10.1126/scisignal.2004088) — integrative analysis methods using cBioPortal - Network accessline 795
- [Gao J et al., Science Signaling 2013](https://doi.org/10.1126/scisignal.2004088) — integrative analysis methods using cBioPortal - Network accessline 796
- [TCGA PanCancer Atlas](https://www.cell.com/pb-assets/consortium/pancanceratlas/pancani3/index.html) — primary data source for PanCancer Atlas studies - Network accessline 796
- [TCGA PanCancer Atlas](https://www.cell.com/pb-assets/consortium/pancanceratlas/pancani3/index.html) — primary data source for PanCancer Atlas studies
skills/genomics-bioinformatics/databases/clinpgx-database/SKILL.md
- Network accessline 48
CLINPGX = "https://api.clinpgx.org/v1" - Network accessline 48
CLINPGX = "https://api.clinpgx.org/v1" - Network accessline 49
CPIC = "https://api.cpicpgx.org/v1" - Network accessline 49
CPIC = "https://api.cpicpgx.org/v1" - Network accessline 51
# CPIC genotype → recommendation: clopidogrel + CYP2C19 Poor Metabolizer drug = requests.get(f"{CPIC}/drug", params={"name": "eq.clopidogrel"}).json()[0] - Network accessline 51
# CPIC genotype → recommendation: clopidogrel + CYP2C19 Poor Metabolizer drug = requests.get(f"{CPIC}/drug", params={"name": "eq.clopidogrel"}).json()[0] - Network accessline 52
drug = requests.get(f"{CPIC}/drug", params={"name": "eq.clopidogrel"}).json()[0] - Network accessline 52
drug = requests.get(f"{CPIC}/drug", params={"name": "eq.clopidogrel"}).json()[0] - Network accessline 53
recs = requests.get(f"{CPIC}/recommendation", - Network accessline 53
recs = requests.get(f"{CPIC}/recommendation", - Network accessline 60
# ClinPGx side: how many CPIC guideline annotations cover CYP2C19? glines = requests.get(f"{CLINPGX}/data/guidelineAnnotation", - Network accessline 60
# ClinPGx side: how many CPIC guideline annotations cover CYP2C19? glines = requests.get(f"{CLINPGX}/data/guidelineAnnotation", - Network accessline 61
glines = requests.get(f"{CLINPGX}/data/guidelineAnnotation", - Network accessline 61
glines = requests.get(f"{CLINPGX}/data/guidelineAnnotation", - Network accessline 76
CLINPGX = "https://api.clinpgx.org/v1" - Network accessline 76
CLINPGX = "https://api.clinpgx.org/v1" - Network accessline 78
r = requests.post(f"{CLINPGX}/site/search", - Network accessline 78
r = requests.post(f"{CLINPGX}/site/search", - Network accessline 88
# Broader concept search r = requests.post(f"{CLINPGX}/site/search", - Network accessline 88
# Broader concept search r = requests.post(f"{CLINPGX}/site/search", - Network accessline 89
r = requests.post(f"{CLINPGX}/site/search", - Network accessline 89
r = requests.post(f"{CLINPGX}/site/search", - Network accessline 104
CLINPGX = "https://api.clinpgx.org/v1" - Network accessline 104
CLINPGX = "https://api.clinpgx.org/v1" - Network accessline 106
# Gene by HGNC symbol gene = requests.get(f"{CLINPGX}/data/gene", - Network accessline 106
# Gene by HGNC symbol gene = requests.get(f"{CLINPGX}/data/gene", - Network accessline 107
gene = requests.get(f"{CLINPGX}/data/gene", - Network accessline 107
gene = requests.get(f"{CLINPGX}/data/gene", - Network accessline 111
# Drug by name (lowercase generic preferred) drug = requests.get(f"{CLINPGX}/data/drug", - Network accessline 111
# Drug by name (lowercase generic preferred) drug = requests.get(f"{CLINPGX}/data/drug", - Network accessline 112
drug = requests.get(f"{CLINPGX}/data/drug", - Network accessline 112
drug = requests.get(f"{CLINPGX}/data/drug", - Network accessline 116
# Variant by rsID var = requests.get(f"{CLINPGX}/data/variant", - Network accessline 116
# Variant by rsID var = requests.get(f"{CLINPGX}/data/variant", - Network accessline 117
var = requests.get(f"{CLINPGX}/data/variant", - Network accessline 117
var = requests.get(f"{CLINPGX}/data/variant", - Network accessline 123
# Direct record fetch when you already have a PA ID r = requests.get(f"{CLINPGX}/data/drug/PA449088", params={"view": "max"}).json() - Network accessline 123
# Direct record fetch when you already have a PA ID r = requests.get(f"{CLINPGX}/data/drug/PA449088", params={"view": "max"}).json() - Network accessline 124
r = requests.get(f"{CLINPGX}/data/drug/PA449088", params={"view": "max"}).json() - Network accessline 124
r = requests.get(f"{CLINPGX}/data/drug/PA449088", params={"view": "max"}).json() - Network accessline 136
CLINPGX = "https://api.clinpgx.org/v1" - Network accessline 136
CLINPGX = "https://api.clinpgx.org/v1" - Network accessline 138
# All clinical annotations for clopidogrel data = requests.get(f"{CLINPGX}/data/clinicalAnnotation", - Network accessline 138
# All clinical annotations for clopidogrel data = requests.get(f"{CLINPGX}/data/clinicalAnnotation", - Network accessline 139
data = requests.get(f"{CLINPGX}/data/clinicalAnnotation", - Network accessline 139
data = requests.get(f"{CLINPGX}/data/clinicalAnnotation", - Network accessline 160
# All Level 1A clinical annotations (highest evidence) data = requests.get(f"{CLINPGX}/data/clinicalAnnotation", - Network accessline 160
# All Level 1A clinical annotations (highest evidence) data = requests.get(f"{CLINPGX}/data/clinicalAnnotation", - Network accessline 161
data = requests.get(f"{CLINPGX}/data/clinicalAnnotation", - Network accessline 161
data = requests.get(f"{CLINPGX}/data/clinicalAnnotation", - Network accessline 182
CLINPGX = "https://api.clinpgx.org/v1" - Network accessline 182
CLINPGX = "https://api.clinpgx.org/v1" - Network accessline 184
# All CPIC guidelines mentioning CYP2C19 data = requests.get(f"{CLINPGX}/data/guidelineAnnotation", - Network accessline 184
# All CPIC guidelines mentioning CYP2C19 data = requests.get(f"{CLINPGX}/data/guidelineAnnotation", - Network accessline 185
data = requests.get(f"{CLINPGX}/data/guidelineAnnotation", - Network accessline 185
data = requests.get(f"{CLINPGX}/data/guidelineAnnotation", - Network accessline 195
# Guidelines for a specific drug across all bodies (CPIC, DPWG, …) data = requests.get(f"{CLINPGX}/data/guidelineAnnotation", - Network accessline 195
# Guidelines for a specific drug across all bodies (CPIC, DPWG, …) data = requests.get(f"{CLINPGX}/data/guidelineAnnotation", - Network accessline 196
data = requests.get(f"{CLINPGX}/data/guidelineAnnotation", - Network accessline 196
data = requests.get(f"{CLINPGX}/data/guidelineAnnotation", - Network accessline 214
CLINPGX = "https://api.clinpgx.org/v1" - Network accessline 214
CLINPGX = "https://api.clinpgx.org/v1" - Network accessline 216
data = requests.get(f"{CLINPGX}/data/label", - Network accessline 216
data = requests.get(f"{CLINPGX}/data/label", - Network accessline 238
CPIC = "https://api.cpicpgx.org/v1" - Network accessline 238
CPIC = "https://api.cpicpgx.org/v1" - Network accessline 240
# Resolve drug name to drugid (RxNorm-prefixed) drug = requests.get(f"{CPIC}/drug", - Network accessline 240
# Resolve drug name to drugid (RxNorm-prefixed) drug = requests.get(f"{CPIC}/drug", - Network accessline 241
drug = requests.get(f"{CPIC}/drug", - Network accessline 241
drug = requests.get(f"{CPIC}/drug", - Network accessline 245
# All phenotype-specific recommendations for clopidogrel recs = requests.get(f"{CPIC}/recommendation", - Network accessline 245
# All phenotype-specific recommendations for clopidogrel recs = requests.get(f"{CPIC}/recommendation", - Network accessline 246
recs = requests.get(f"{CPIC}/recommendation", - Network accessline 246
recs = requests.get(f"{CPIC}/recommendation", - Network accessline 255
# Phenotype filter via jsonb containment (cs.{...}) # The phenotypes column is a jsonb dict; cs. checks that the query is a subset. recs = requests.get(f"{CPIC}/recommendation", - Network accessline 255
# Phenotype filter via jsonb containment (cs.{...}) # The phenotypes column is a jsonb dict; cs. checks that the query is a subset. recs = requests.get(f"{CPIC}/recommendation", - Network accessline 256
# The phenotypes column is a jsonb dict; cs. checks that the query is a subset. recs = requests.get(f"{CPIC}/recommendation", params={"drugid": f"eq.{drug['drugid']}", - Network accessline 256
# The phenotypes column is a jsonb dict; cs. checks that the query is a subset. recs = requests.get(f"{CPIC}/recommendation", params={"drugid": f"eq.{drug['drugid']}", - Network accessline 257
recs = requests.get(f"{CPIC}/recommendation", - Network accessline 257
recs = requests.get(f"{CPIC}/recommendation", - Network accessline 264
# Gene-driven: list every drug with a CPIC pair for CYP2C19 pairs = requests.get(f"{CPIC}/pair", - Network accessline 264
# Gene-driven: list every drug with a CPIC pair for CYP2C19 pairs = requests.get(f"{CPIC}/pair", - Network accessline 265
pairs = requests.get(f"{CPIC}/pair", - Network accessline 265
pairs = requests.get(f"{CPIC}/pair", - Network accessline 312
CLINPGX = "https://api.clinpgx.org/v1" - Network accessline 312
CLINPGX = "https://api.clinpgx.org/v1" - Network accessline 315
rows = [] for g in pharmacogenes: data = requests.get(f"{CLINPGX}/data/guidelineAnnotation", - Network accessline 315
rows = [] for g in pharmacogenes: data = requests.get(f"{CLINPGX}/data/guidelineAnnotation", - Network accessline 316
for g in pharmacogenes: data = requests.get(f"{CLINPGX}/data/guidelineAnnotation", params={"relatedGenes.symbol": g, - Network accessline 316
for g in pharmacogenes: data = requests.get(f"{CLINPGX}/data/guidelineAnnotation", params={"relatedGenes.symbol": g, - Network accessline 317
data = requests.get(f"{CLINPGX}/data/guidelineAnnotation", - Network accessline 317
data = requests.get(f"{CLINPGX}/data/guidelineAnnotation", - Network accessline 339
CPIC = "https://api.cpicpgx.org/v1" - Network accessline 339
CPIC = "https://api.cpicpgx.org/v1" - Network accessline 343
rows = [] for name in drugs: drug = requests.get(f"{CPIC}/drug", params={"name": f"eq.{name}"}, timeout=15).json() - Network accessline 343
rows = [] for name in drugs: drug = requests.get(f"{CPIC}/drug", params={"name": f"eq.{name}"}, timeout=15).json() - Network accessline 344
for name in drugs: drug = requests.get(f"{CPIC}/drug", params={"name": f"eq.{name}"}, timeout=15).json() if not drug: - Network accessline 344
for name in drugs: drug = requests.get(f"{CPIC}/drug", params={"name": f"eq.{name}"}, timeout=15).json() if not drug: - Network accessline 345
drug = requests.get(f"{CPIC}/drug", params={"name": f"eq.{name}"}, timeout=15).json() - Network accessline 345
drug = requests.get(f"{CPIC}/drug", params={"name": f"eq.{name}"}, timeout=15).json() - Network accessline 347
rows.append({"drug": name, "in_cpic": False, "n_recs": 0, "phenotypes": ""}); continue did = drug[0]["drugid"] recs = requests.get(f"{CPIC}/recommendation", - Network accessline 347
rows.append({"drug": name, "in_cpic": False, "n_recs": 0, "phenotypes": ""}); continue did = drug[0]["drugid"] recs = requests.get(f"{CPIC}/recommendation", - Network accessline 348
did = drug[0]["drugid"] recs = requests.get(f"{CPIC}/recommendation", params={"drugid": f"eq.{did}"}, timeout=15).json() - Network accessline 348
did = drug[0]["drugid"] recs = requests.get(f"{CPIC}/recommendation", params={"drugid": f"eq.{did}"}, timeout=15).json() - Network accessline 349
recs = requests.get(f"{CPIC}/recommendation", - Network accessline 349
recs = requests.get(f"{CPIC}/recommendation", - Network accessline 370
CLINPGX = "https://api.clinpgx.org/v1" - Network accessline 370
CLINPGX = "https://api.clinpgx.org/v1" - Network accessline 373
hits = requests.post(f"{CLINPGX}/site/search", - Network accessline 373
hits = requests.post(f"{CLINPGX}/site/search", - Network accessline 383
# Dereference one annotation by ID for full detail if ann_hits: ann = requests.get(f"{CLINPGX}/data/clinicalAnnotation/{ann_hits[0]['id']}", - Network accessline 383
# Dereference one annotation by ID for full detail if ann_hits: ann = requests.get(f"{CLINPGX}/data/clinicalAnnotation/{ann_hits[0]['id']}", - Network accessline 384
if ann_hits: ann = requests.get(f"{CLINPGX}/data/clinicalAnnotation/{ann_hits[0]['id']}", params={"view": "max"}, timeout=15).json()["data"] - Network accessline 384
if ann_hits: ann = requests.get(f"{CLINPGX}/data/clinicalAnnotation/{ann_hits[0]['id']}", params={"view": "max"}, timeout=15).json()["data"] - Network accessline 385
ann = requests.get(f"{CLINPGX}/data/clinicalAnnotation/{ann_hits[0]['id']}", - Network accessline 385
ann = requests.get(f"{CLINPGX}/data/clinicalAnnotation/{ann_hits[0]['id']}", - Network accessline 421
7. **URL-encode `cs.{...}` jsonb filters** when phenotype values contain spaces or special characters. `requests.get(..., params={"phenotypes": 'cs.{"CYP2C19":"Poor Metabolizer"}'})` works because `requests` does the encoding; a manual URL - Network accessline 421
7. **URL-encode `cs.{...}` jsonb filters** when phenotype values contain spaces or special characters. `requests.get(..., params={"phenotypes": 'cs.{"CYP2C19":"Poor Metabolizer"}'})` works because `requests` does the encoding; a manual URL - Network accessline 430
import requests r = requests.post("https://api.clinpgx.org/v1/site/search", - Network accessline 430
import requests r = requests.post("https://api.clinpgx.org/v1/site/search", - Network accessline 431
r = requests.post("https://api.clinpgx.org/v1/site/search", - Network accessline 431
r = requests.post("https://api.clinpgx.org/v1/site/search", - Network accessline 443
import requests, pandas as pd data = requests.get("https://api.clinpgx.org/v1/data/clinicalAnnotation", - Network accessline 443
import requests, pandas as pd data = requests.get("https://api.clinpgx.org/v1/data/clinicalAnnotation", - Network accessline 444
data = requests.get("https://api.clinpgx.org/v1/data/clinicalAnnotation", - Network accessline 444
data = requests.get("https://api.clinpgx.org/v1/data/clinicalAnnotation", - Network accessline 461
import requests CPIC = "https://api.cpicpgx.org/v1" - Network accessline 461
import requests CPIC = "https://api.cpicpgx.org/v1" - Network accessline 462
CPIC = "https://api.cpicpgx.org/v1" - Network accessline 462
CPIC = "https://api.cpicpgx.org/v1" - Network accessline 467
did = requests.get(f"{CPIC}/drug", params={"name": f"eq.{drug}"}).json()[0]["drugid"] - Network accessline 467
did = requests.get(f"{CPIC}/drug", params={"name": f"eq.{drug}"}).json()[0]["drugid"] - Network accessline 468
import json recs = requests.get(f"{CPIC}/recommendation", params={"drugid": f"eq.{did}", - Network accessline 468
import json recs = requests.get(f"{CPIC}/recommendation", params={"drugid": f"eq.{did}", - Network accessline 469
recs = requests.get(f"{CPIC}/recommendation", - Network accessline 469
recs = requests.get(f"{CPIC}/recommendation", - Network accessline 486
s = requests.Session() - Network accessline 486
s = requests.Session() - Network accessline 493
r = s.get("https://api.clinpgx.org/v1/data/gene", - Network accessline 493
r = s.get("https://api.clinpgx.org/v1/data/gene", - Network accessline 501
| Problem | Cause | Solution - Network accessline 501
| Problem | Cause | Solution - Network accessline 502
| ----------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- | --------------------------------------------- - Network accessline 502
| ----------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- | --------------------------------------------- - Network accessline 503
| HTTP 404/405 on `https://api.pharmgkb.org/v1/...` | Old PharmGKB host is dead; the service rebranded to ClinPGx in 2024 | Migrate to `https://api.clinpgx.org/v1/...`. - Network accessline 503
| HTTP 404/405 on `https://api.pharmgkb.org/v1/...` | Old PharmGKB host is dead; the service rebranded to ClinPGx in 2024 | Migrate to `https://api.clinpgx.org/v1/...`. - Network accessline 504
| `{"status":"fail","data":{"errors":[{"message":"No such property: 'gene'"}]}}` | `data/clinicalAnnotation` does **not** accept `gene=` or `relatedGenes.symbol=` | Use `data/guidelineAnnotation?relatedGenes.sy - Network accessline 504
| `{"status":"fail","data":{"errors":[{"message":"No such property: 'gene'"}]}}` | `data/clinicalAnnotation` does **not** accept `gene=` or `relatedGenes.symbol=` | Use `data/guidelineAnnotation?relatedGenes.sy - Network accessline 505
| `{"status":"fail","data":{"errors":[{"message":"Missing criteria."}]}}` | A `data/{type}` list query has no filter and no ID | Add at least one filter (`name=`, `symbol=`, - Network accessline 505
| `{"status":"fail","data":{"errors":[{"message":"Missing criteria."}]}}` | A `data/{type}` list query has no filter and no ID | Add at least one filter (`name=`, `symbol=`, - Network accessline 506
| HTTP 405 on `GET /site/search?query=...` | `site/search` only accepts POST with a JSON body | Use `requests.post(url, json={"query": "..."} - Network accessline 506
| HTTP 405 on `GET /site/search?query=...` | `site/search` only accepts POST with a JSON body | Use `requests.post(url, json={"query": "..."} - Network accessline 507
| HTTP 429 mid-loop | Hit ClinPGx rate limit | Insert `time.sleep(0.3–0.5)` between calls; u - Network accessline 507
| HTTP 429 mid-loop | Hit ClinPGx rate limit | Insert `time.sleep(0.3–0.5)` between calls; u - Network accessline 508
| HTTP 400 on `https://api.cpicpgx.org/v1/recommendation?phenotypes=cs.{...}` | The `cs.` JSON wasn't URL-encoded | Pass via `requests` `params={"phenotypes": 'c - Network accessline 508
| HTTP 400 on `https://api.cpicpgx.org/v1/recommendation?phenotypes=cs.{...}` | The `cs.` JSON wasn't URL-encoded | Pass via `requests` `params={"phenotypes": 'c - Network accessline 521
- [ClinPGx (formerly PharmGKB) website](https://www.clinpgx.org/) — Database front end; web links match the `data/{type}/{paId}` URL shape - Network accessline 521
- [ClinPGx (formerly PharmGKB) website](https://www.clinpgx.org/) — Database front end; web links match the `data/{type}/{paId}` URL shape - Network accessline 522
- [ClinPGx REST API documentation](https://www.clinpgx.org/page/webResources) — Official endpoint reference, `data/...` and `site/search` schemas - Network accessline 522
- [ClinPGx REST API documentation](https://www.clinpgx.org/page/webResources) — Official endpoint reference, `data/...` and `site/search` schemas - Network accessline 523
- [CPIC API documentation](https://api.cpicpgx.org/) — Swagger UI for the PostgREST companion API - Network accessline 523
- [CPIC API documentation](https://api.cpicpgx.org/) — Swagger UI for the PostgREST companion API - Network accessline 524
- [CPIC guidelines](https://cpicpgx.org/guidelines/) — Source of `recommendation` rows; canonical genotype-prescribing oracle - Network accessline 524
- [CPIC guidelines](https://cpicpgx.org/guidelines/) — Source of `recommendation` rows; canonical genotype-prescribing oracle - Network accessline 525
- [Relling & Klein (2011) Nature Reviews Drug Discovery — PharmGKB foundational paper](https://doi.org/10.1038/nrd3499) - Network accessline 525
- [Relling & Klein (2011) Nature Reviews Drug Discovery — PharmGKB foundational paper](https://doi.org/10.1038/nrd3499) - Network accessline 526
- [Whirl-Carrillo et al. (2021) Clin Pharmacol Ther — PharmGKB data architecture update](https://doi.org/10.1002/cpt.2350) - Network accessline 526
- [Whirl-Carrillo et al. (2021) Clin Pharmacol Ther — PharmGKB data architecture update](https://doi.org/10.1002/cpt.2350)
skills/genomics-bioinformatics/databases/clinvar-database/SKILL.md
- Network accessline 25
- **Data requirements**: gene symbols, rsIDs, HGVS strings, or ClinVar Variation IDs - **Environment**: internet connection; NCBI Entrez email required (set `email` parameter) - **Rate limits**: 3 requests/second unauthenticated; 10/second - Network accessline 25
- **Data requirements**: gene symbols, rsIDs, HGVS strings, or ClinVar Variation IDs - **Environment**: internet connection; NCBI Entrez email required (set `email` parameter) - **Rate limits**: 3 requests/second unauthenticated; 10/second - Network accessline 27
- **Rate limits**: 3 requests/second unauthenticated; 10/second with API key (free at https://www.ncbi.nlm.nih.gov/account/) - Network accessline 27
- **Rate limits**: 3 requests/second unauthenticated; 10/second with API key (free at https://www.ncbi.nlm.nih.gov/account/) - Network accessline 41
def clinvar_search(query, retmax=10): """Search ClinVar and return a list of ClinVar Variation IDs.""" r = requests.get( - Network accessline 41
def clinvar_search(query, retmax=10): """Search ClinVar and return a list of ClinVar Variation IDs.""" r = requests.get( - Network accessline 42
"""Search ClinVar and return a list of ClinVar Variation IDs.""" r = requests.get( "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi", - Network accessline 42
"""Search ClinVar and return a list of ClinVar Variation IDs.""" r = requests.get( "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi", - Network accessline 43
r = requests.get( - Network accessline 43
r = requests.get( - Network accessline 44
"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi", - Network accessline 44
"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi", - Network accessline 65
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 65
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 66
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 66
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 68
def esearch(query, retmax=200): r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 68
def esearch(query, retmax=200): r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 69
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 69
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 99
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 99
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 100
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 100
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 102
def esummary(ids): """Fetch ESummary records for a list of ClinVar variation IDs.""" r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 102
def esummary(ids): """Fetch ESummary records for a list of ClinVar variation IDs.""" r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 103
"""Fetch ESummary records for a list of ClinVar variation IDs.""" r = requests.post(f"{BASE}/esummary.fcgi", data={"db": "clinvar", "id": ",".join(ids), - Network accessline 103
"""Fetch ESummary records for a list of ClinVar variation IDs.""" r = requests.post(f"{BASE}/esummary.fcgi", data={"db": "clinvar", "id": ",".join(ids), - Network accessline 104
r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 104
r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 110
ids, _ = esearch_func = lambda q: requests.get( - Network accessline 110
ids, _ = esearch_func = lambda q: requests.get( - Network accessline 138
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 138
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 139
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 139
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 142
# ClinVar 2024 XML overhaul: "clinvarset" rettype returns an empty stub. # Use rettype="vcv" + is_variationid="true" to get the new <VariationArchive> records. r = requests.post(f"{BASE}/efetch.fcgi", - Network accessline 142
# ClinVar 2024 XML overhaul: "clinvarset" rettype returns an empty stub. # Use rettype="vcv" + is_variationid="true" to get the new <VariationArchive> records. r = requests.post(f"{BASE}/efetch.fcgi", - Network accessline 143
# Use rettype="vcv" + is_variationid="true" to get the new <VariationArchive> records. r = requests.post(f"{BASE}/efetch.fcgi", data={"db": "clinvar", "id": ",".join(variation_ids), - Network accessline 143
# Use rettype="vcv" + is_variationid="true" to get the new <VariationArchive> records. r = requests.post(f"{BASE}/efetch.fcgi", data={"db": "clinvar", "id": ",".join(variation_ids), - Network accessline 144
r = requests.post(f"{BASE}/efetch.fcgi", - Network accessline 144
r = requests.post(f"{BASE}/efetch.fcgi", - Network accessline 178
# Full summary (tab-separated, ~300 MB compressed) URL = "https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/variant_summary.txt.gz" - Network accessline 178
# Full summary (tab-separated, ~300 MB compressed) URL = "https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/variant_summary.txt.gz" - Network accessline 179
URL = "https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/variant_summary.txt.gz" - Network accessline 179
URL = "https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/variant_summary.txt.gz" - Network accessline 181
# Stream and parse without full download with urllib.request.urlopen(URL) as resp: - Network accessline 181
# Stream and parse without full download with urllib.request.urlopen(URL) as resp: - Network accessline 182
with urllib.request.urlopen(URL) as resp: - Network accessline 182
with urllib.request.urlopen(URL) as resp: - Network accessline 206
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 206
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 207
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 207
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 219
terms = [f'"{star_terms[s]}"[review status]' for s in range(min_stars, 5) if s in star_terms] query = f"{gene}[gene] AND (" + " OR ".join(terms) + ")" r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 219
terms = [f'"{star_terms[s]}"[review status]' for s in range(min_stars, 5) if s in star_terms] query = f"{gene}[gene] AND (" + " OR ".join(terms) + ")" r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 220
query = f"{gene}[gene] AND (" + " OR ".join(terms) + ")" r = requests.get(f"{BASE}/esearch.fcgi", params={"db": "clinvar", "term": query, "retmax": 100, - Network accessline 220
query = f"{gene}[gene] AND (" + " OR ".join(terms) + ")" r = requests.get(f"{BASE}/esearch.fcgi", params={"db": "clinvar", "term": query, "retmax": 100, - Network accessline 221
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 221
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 237
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 237
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 238
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 238
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 240
def get_conditions(variation_ids): """Return condition data for a list of ClinVar variation IDs.""" r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 240
def get_conditions(variation_ids): """Return condition data for a list of ClinVar variation IDs.""" r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 241
"""Return condition data for a list of ClinVar variation IDs.""" r = requests.post(f"{BASE}/esummary.fcgi", data={"db": "clinvar", "id": ",".join(variation_ids), - Network accessline 241
"""Return condition data for a list of ClinVar variation IDs.""" r = requests.post(f"{BASE}/esummary.fcgi", data={"db": "clinvar", "id": ",".join(variation_ids), - Network accessline 242
r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 242
r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 285
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 285
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 286
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 286
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 288
def search_gene_pathogenic(gene, clinsig="pathogenic"): query = f"{gene}[gene] AND {clinsig}[clinsig]" r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 288
def search_gene_pathogenic(gene, clinsig="pathogenic"): query = f"{gene}[gene] AND {clinsig}[clinsig]" r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 289
query = f"{gene}[gene] AND {clinsig}[clinsig]" r = requests.get(f"{BASE}/esearch.fcgi", params={"db": "clinvar", "term": query, "retmax": 500, - Network accessline 289
query = f"{gene}[gene] AND {clinsig}[clinsig]" r = requests.get(f"{BASE}/esearch.fcgi", params={"db": "clinvar", "term": query, "retmax": 500, - Network accessline 290
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 290
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 297
for i in range(0, len(ids), 100): batch = ids[i:i+100] r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 297
for i in range(0, len(ids), 100): batch = ids[i:i+100] r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 298
batch = ids[i:i+100] r = requests.post(f"{BASE}/esummary.fcgi", data={"db": "clinvar", "id": ",".join(batch), - Network accessline 298
batch = ids[i:i+100] r = requests.post(f"{BASE}/esummary.fcgi", data={"db": "clinvar", "id": ",".join(batch), - Network accessline 299
r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 299
r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 336
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 336
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 337
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 337
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 342
for rsid in variants: r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 342
for rsid in variants: r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 343
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 343
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 351
r2 = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 351
r2 = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 372
| `retmax` | ESearch | `20` | `1`–`10000` | Max records returned per query | | `retmode` | ESearch/ESummary | `"xml"` | `"json"`, `"xml"` | Response format | | `rettype` | EFetch | `"vcv"` | `"vcv"` | Record type for XML fetch (legacy `clin - Network accessline 372
| `retmax` | ESearch | `20` | `1`–`10000` | Max records returned per query | | `retmode` | ESearch/ESummary | `"xml"` | `"json"`, `"xml"` | Response format | | `rettype` | EFetch | `"vcv"` | `"vcv"` | Record type for XML fetch (legacy `clin - Network accessline 373
| `retmode` | ESearch/ESummary | `"xml"` | `"json"`, `"xml"` | Response format | | `rettype` | EFetch | `"vcv"` | `"vcv"` | Record type for XML fetch (legacy `clinvarset` returns empty stub since 2024) | | `is_variationid` | EFetch | `"fals - Network accessline 373
| `retmode` | ESearch/ESummary | `"xml"` | `"json"`, `"xml"` | Response format | | `rettype` | EFetch | `"vcv"` | `"vcv"` | Record type for XML fetch (legacy `clinvarset` returns empty stub since 2024) | | `is_variationid` | EFetch | `"fals - Network accessline 374
| `rettype` | EFetch | `"vcv"` | `"vcv"` | Record type for XML fetch (legacy `clinvarset` returns empty stub since 2024) | - Network accessline 374
| `rettype` | EFetch | `"vcv"` | `"vcv"` | Record type for XML fetch (legacy `clinvarset` returns empty stub since 2024) | - Network accessline 388
[Credential-related source omitted] - Network accessline 388
[Credential-related source omitted] - Access to secretsline 388
[Credential-related source omitted] - Access to secretsline 388
[Credential-related source omitted] - Network accessline 404
r = requests.get( - Network accessline 404
r = requests.get( - Network accessline 405
"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi", - Network accessline 405
"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi", - Network accessline 420
url = "https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/variant_summary.txt.gz" - Network accessline 420
url = "https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/variant_summary.txt.gz" - Network accessline 440
r = requests.get( - Network accessline 440
r = requests.get( - Network accessline 441
"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi", - Network accessline 441
"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi", - Network accessline 455
| Empty `idlist` for rsID query | rsID not indexed in ClinVar | Try HGVS notation or gene+position query instead | | Missing `clinsig` in summary | Variant has no interpretation | Check `review_status`; "no interpretation for the single var - Network accessline 455
| Empty `idlist` for rsID query | rsID not indexed in ClinVar | Try HGVS notation or gene+position query instead | | Missing `clinsig` in summary | Variant has no interpretation | Check `review_status`; "no interpretation for the single var - Network accessline 456
| Missing `clinsig` in summary | Variant has no interpretation | Check `review_status`; "no interpretation for the single variant" means no ClinSig yet | | XML parse error in EFetch | Incomplete response (timeout) | Set `requests.get(..., t - Network accessline 456
| Missing `clinsig` in summary | Variant has no interpretation | Check `review_status`; "no interpretation for the single variant" means no ClinSig yet | | XML parse error in EFetch | Incomplete response (timeout) | Set `requests.get(..., t - Network accessline 457
| XML parse error in EFetch | Incomplete response (timeout) | Set `requests.get(..., timeout=30)` and retry once | - Network accessline 457
| XML parse error in EFetch | Incomplete response (timeout) | Set `requests.get(..., timeout=30)` and retry once | - Network accessline 472
- [ClinVar official site](https://www.ncbi.nlm.nih.gov/clinvar/) — Browse and download ClinVar data - Network accessline 472
- [ClinVar official site](https://www.ncbi.nlm.nih.gov/clinvar/) — Browse and download ClinVar data - Network accessline 473
- [NCBI E-utilities documentation](https://www.ncbi.nlm.nih.gov/books/NBK25499/) — Full E-utilities API reference - Network accessline 473
- [NCBI E-utilities documentation](https://www.ncbi.nlm.nih.gov/books/NBK25499/) — Full E-utilities API reference - Network accessline 474
- [ClinVar FTP downloads](https://ftp.ncbi.nlm.nih.gov/pub/clinvar/) — Bulk data files (variant_summary.txt.gz, etc.) - Network accessline 474
- [ClinVar FTP downloads](https://ftp.ncbi.nlm.nih.gov/pub/clinvar/) — Bulk data files (variant_summary.txt.gz, etc.) - Network accessline 475
- [ClinVar data model](https://www.ncbi.nlm.nih.gov/clinvar/docs/help/) — Understanding review status stars and ClinSig categories - Network accessline 475
- [ClinVar data model](https://www.ncbi.nlm.nih.gov/clinvar/docs/help/) — Understanding review status stars and ClinSig categories
skills/genomics-bioinformatics/databases/cosmic-database/SKILL.md
- Network accessline 25
- **Python packages**: `requests`, `pandas` - **Data requirements**: gene symbols (HGNC), COSMIC mutation IDs (COSM), sample IDs, or genomic coordinates - **Environment**: internet connection; free account registration at https://cancer.san - Network accessline 25
- **Python packages**: `requests`, `pandas` - **Data requirements**: gene symbols (HGNC), COSMIC mutation IDs (COSM), sample IDs, or genomic coordinates - **Environment**: internet connection; free account registration at https://cancer.san - Network accessline 26
- **Data requirements**: gene symbols (HGNC), COSMIC mutation IDs (COSM), sample IDs, or genomic coordinates - **Environment**: internet connection; free account registration at https://cancer.sanger.ac.uk/cosmic/register - **Rate limits**: - Network accessline 26
- **Data requirements**: gene symbols (HGNC), COSMIC mutation IDs (COSM), sample IDs, or genomic coordinates - **Environment**: internet connection; free account registration at https://cancer.sanger.ac.uk/cosmic/register - **Rate limits**: - Network accessline 27
- **Environment**: internet connection; free account registration at https://cancer.sanger.ac.uk/cosmic/register - Network accessline 27
- **Environment**: internet connection; free account registration at https://cancer.sanger.ac.uk/cosmic/register - Network accessline 31
pip install requests pandas # Register at https://cancer.sanger.ac.uk/cosmic/register to obtain API credentials - Network accessline 31
pip install requests pandas # Register at https://cancer.sanger.ac.uk/cosmic/register to obtain API credentials - Network accessline 32
# Register at https://cancer.sanger.ac.uk/cosmic/register to obtain API credentials - Network accessline 32
# Register at https://cancer.sanger.ac.uk/cosmic/register to obtain API credentials - Network accessline 46
BASE = "https://cancer.sanger.ac.uk/cosmic/api" - Network accessline 46
BASE = "https://cancer.sanger.ac.uk/cosmic/api" - Network accessline 49
# Get mutations for KRAS gene r = requests.get(f"{BASE}/mutations", - Network accessline 49
# Get mutations for KRAS gene r = requests.get(f"{BASE}/mutations", - Network accessline 50
r = requests.get(f"{BASE}/mutations", - Network accessline 50
r = requests.get(f"{BASE}/mutations", - Network accessline 73
BASE = "https://cancer.sanger.ac.uk/cosmic/api" - Network accessline 73
BASE = "https://cancer.sanger.ac.uk/cosmic/api" - Network accessline 78
if cancer_site: params["primary_site"] = cancer_site r = requests.get(f"{BASE}/mutations", headers=HEADERS, params=params) - Network accessline 78
if cancer_site: params["primary_site"] = cancer_site r = requests.get(f"{BASE}/mutations", headers=HEADERS, params=params) - Network accessline 79
params["primary_site"] = cancer_site r = requests.get(f"{BASE}/mutations", headers=HEADERS, params=params) r.raise_for_status() - Network accessline 79
params["primary_site"] = cancer_site r = requests.get(f"{BASE}/mutations", headers=HEADERS, params=params) r.raise_for_status() - Network accessline 80
r = requests.get(f"{BASE}/mutations", headers=HEADERS, params=params) - Network accessline 80
r = requests.get(f"{BASE}/mutations", headers=HEADERS, params=params) - Network accessline 118
BASE = "https://cancer.sanger.ac.uk/cosmic/api" - Network accessline 118
BASE = "https://cancer.sanger.ac.uk/cosmic/api" - Network accessline 121
r = requests.get(f"{BASE}/genes", headers=HEADERS, params={"limit": 100}) - Network accessline 121
r = requests.get(f"{BASE}/genes", headers=HEADERS, params={"limit": 100}) - Network accessline 126
# Get Cancer Gene Census genes r_cgc = requests.get(f"{BASE}/genes", - Network accessline 126
# Get Cancer Gene Census genes r_cgc = requests.get(f"{BASE}/genes", - Network accessline 127
r_cgc = requests.get(f"{BASE}/genes", - Network accessline 127
r_cgc = requests.get(f"{BASE}/genes", - Network accessline 157
BASE = "https://cancer.sanger.ac.uk/cosmic/api" - Network accessline 157
BASE = "https://cancer.sanger.ac.uk/cosmic/api" - Network accessline 160
# KRAS G12D mutation mutation_id = "COSM521" r = requests.get(f"{BASE}/mutations/{mutation_id}", headers=HEADERS) - Network accessline 160
# KRAS G12D mutation mutation_id = "COSM521" r = requests.get(f"{BASE}/mutations/{mutation_id}", headers=HEADERS) - Network accessline 161
mutation_id = "COSM521" r = requests.get(f"{BASE}/mutations/{mutation_id}", headers=HEADERS) r.raise_for_status() - Network accessline 161
mutation_id = "COSM521" r = requests.get(f"{BASE}/mutations/{mutation_id}", headers=HEADERS) r.raise_for_status() - Network accessline 162
r = requests.get(f"{BASE}/mutations/{mutation_id}", headers=HEADERS) - Network accessline 162
r = requests.get(f"{BASE}/mutations/{mutation_id}", headers=HEADERS) - Network accessline 186
BASE = "https://cancer.sanger.ac.uk/cosmic/api" - Network accessline 186
BASE = "https://cancer.sanger.ac.uk/cosmic/api" - Network accessline 189
# Search for a specific sample r = requests.get(f"{BASE}/samples", - Network accessline 189
# Search for a specific sample r = requests.get(f"{BASE}/samples", - Network accessline 190
r = requests.get(f"{BASE}/samples", - Network accessline 190
r = requests.get(f"{BASE}/samples", - Network accessline 200
if samples: sample_id = samples[0]["sample_id"] r2 = requests.get(f"{BASE}/samples/{sample_id}/mutations", headers=HEADERS) - Network accessline 200
if samples: sample_id = samples[0]["sample_id"] r2 = requests.get(f"{BASE}/samples/{sample_id}/mutations", headers=HEADERS) - Network accessline 201
sample_id = samples[0]["sample_id"] r2 = requests.get(f"{BASE}/samples/{sample_id}/mutations", headers=HEADERS) if r2.ok: - Network accessline 201
sample_id = samples[0]["sample_id"] r2 = requests.get(f"{BASE}/samples/{sample_id}/mutations", headers=HEADERS) if r2.ok: - Network accessline 202
r2 = requests.get(f"{BASE}/samples/{sample_id}/mutations", headers=HEADERS) - Network accessline 202
r2 = requests.get(f"{BASE}/samples/{sample_id}/mutations", headers=HEADERS) - Network accessline 221
BASE = "https://cancer.sanger.ac.uk/cosmic/api" - Network accessline 221
BASE = "https://cancer.sanger.ac.uk/cosmic/api" - Network accessline 224
# List available mutational signatures r = requests.get(f"{BASE}/signatures", headers=HEADERS) - Network accessline 224
# List available mutational signatures r = requests.get(f"{BASE}/signatures", headers=HEADERS) - Network accessline 225
r = requests.get(f"{BASE}/signatures", headers=HEADERS) - Network accessline 225
r = requests.get(f"{BASE}/signatures", headers=HEADERS) - Network accessline 234
# Get signature attributions by cancer type r2 = requests.get(f"{BASE}/signatures/attributions", - Network accessline 234
# Get signature attributions by cancer type r2 = requests.get(f"{BASE}/signatures/attributions", - Network accessline 235
r2 = requests.get(f"{BASE}/signatures/attributions", - Network accessline 235
r2 = requests.get(f"{BASE}/signatures/attributions", - Network accessline 255
BASE = "https://cancer.sanger.ac.uk/cosmic/api" - Network accessline 255
BASE = "https://cancer.sanger.ac.uk/cosmic/api" - Network accessline 258
# Get drug resistance variants r = requests.get(f"{BASE}/resistance_mutations", - Network accessline 258
# Get drug resistance variants r = requests.get(f"{BASE}/resistance_mutations", - Network accessline 259
r = requests.get(f"{BASE}/resistance_mutations", - Network accessline 259
r = requests.get(f"{BASE}/resistance_mutations", - Network accessline 297
BASE = "https://cancer.sanger.ac.uk/cosmic/api" - Network accessline 297
BASE = "https://cancer.sanger.ac.uk/cosmic/api" - Network accessline 304
limit = 200 while len(all_muts) < max_records: r = requests.get(f"{BASE}/mutations", - Network accessline 304
limit = 200 while len(all_muts) < max_records: r = requests.get(f"{BASE}/mutations", - Network accessline 305
while len(all_muts) < max_records: r = requests.get(f"{BASE}/mutations", headers=HEADERS, - Network accessline 305
while len(all_muts) < max_records: r = requests.get(f"{BASE}/mutations", headers=HEADERS, - Network accessline 306
r = requests.get(f"{BASE}/mutations", - Network accessline 306
r = requests.get(f"{BASE}/mutations", - Network accessline 343
BASE = "https://cancer.sanger.ac.uk/cosmic/api" - Network accessline 343
BASE = "https://cancer.sanger.ac.uk/cosmic/api" - Network accessline 348
skip = 0 while True: r = requests.get(f"{BASE}/genes", - Network accessline 348
skip = 0 while True: r = requests.get(f"{BASE}/genes", - Network accessline 349
while True: r = requests.get(f"{BASE}/genes", headers=HEADERS, - Network accessline 349
while True: r = requests.get(f"{BASE}/genes", headers=HEADERS, - Network accessline 350
r = requests.get(f"{BASE}/genes", - Network accessline 350
r = requests.get(f"{BASE}/genes", - Network accessline 398
4. **Check data license for commercial use**: COSMIC data is free for academic use but requires a commercial license for industry applications. Verify at https://cancer.sanger.ac.uk/cosmic/license. - Network accessline 398
4. **Check data license for commercial use**: COSMIC data is free for academic use but requires a commercial license for industry applications. Verify at https://cancer.sanger.ac.uk/cosmic/license. - Network accessline 416
[Credential-related source omitted] - Network accessline 416
[Credential-related source omitted] - Network accessline 417
r = requests.get("https://cancer.sanger.ac.uk/cosmic/api/mutations", - Network accessline 417
r = requests.get("https://cancer.sanger.ac.uk/cosmic/api/mutations", - Network accessline 441
r = requests.get("https://cancer.sanger.ac.uk/cosmic/api/mutations", - Network accessline 441
r = requests.get("https://cancer.sanger.ac.uk/cosmic/api/mutations", - Network accessline 462
[Credential-related source omitted] - Network accessline 462
[Credential-related source omitted] - Network accessline 463
r = requests.get("https://cancer.sanger.ac.uk/cosmic/api/genes", - Network accessline 463
r = requests.get("https://cancer.sanger.ac.uk/cosmic/api/genes", - Network accessline 492
- [COSMIC website](https://cancer.sanger.ac.uk/cosmic/) — Official COSMIC database and downloads - Network accessline 492
- [COSMIC website](https://cancer.sanger.ac.uk/cosmic/) — Official COSMIC database and downloads - Network accessline 493
- [COSMIC REST API v3 documentation](https://cancer.sanger.ac.uk/cosmic/api) — API endpoint reference - Network accessline 493
- [COSMIC REST API v3 documentation](https://cancer.sanger.ac.uk/cosmic/api) — API endpoint reference - Network accessline 494
- [Cancer Gene Census](https://cancer.sanger.ac.uk/census/) — Curated cancer driver gene catalog - Network accessline 494
- [Cancer Gene Census](https://cancer.sanger.ac.uk/census/) — Curated cancer driver gene catalog - Network accessline 495
- [COSMIC mutational signatures (Alexandrov et al. 2020)](https://doi.org/10.1038/s41586-020-1943-3) — Reference paper for COSMIC v3 signatures - Network accessline 495
- [COSMIC mutational signatures (Alexandrov et al. 2020)](https://doi.org/10.1038/s41586-020-1943-3) — Reference paper for COSMIC v3 signatures
skills/genomics-bioinformatics/databases/dbsnp-database/SKILL.md
- Network accessline 27
- **Data requirements**: rsIDs (`rs80357906`), gene symbols, or chromosomal coordinates - **Environment**: internet connection; NCBI Entrez email required for E-utilities (set `email` parameter) - **Rate limits**: 3 requests/second without - Network accessline 27
- **Data requirements**: rsIDs (`rs80357906`), gene symbols, or chromosomal coordinates - **Environment**: internet connection; NCBI Entrez email required for E-utilities (set `email` parameter) - **Rate limits**: 3 requests/second without - Network accessline 29
[Credential-related source omitted] - Network accessline 29
[Credential-related source omitted] - Access to secretsline 29
[Credential-related source omitted] - Access to secretsline 29
[Credential-related source omitted] - Network accessline 42
EMAIL = "your@email.com" # required by NCBI policy BASE_EUTILS = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 42
EMAIL = "your@email.com" # required by NCBI policy BASE_EUTILS = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 43
BASE_EUTILS = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 43
BASE_EUTILS = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 44
BASE_VARIATION = "https://api.ncbi.nlm.nih.gov/variation/v0" - Network accessline 44
BASE_VARIATION = "https://api.ncbi.nlm.nih.gov/variation/v0" - Network accessline 47
"""Fetch a dbSNP record by rsID using the NCBI Variation Services API (structured JSON).""" rs_num = str(rsid).lstrip("rs") r = requests.get(f"{BASE_VARIATION}/refsnp/{rs_num}", timeout=15) - Network accessline 47
"""Fetch a dbSNP record by rsID using the NCBI Variation Services API (structured JSON).""" rs_num = str(rsid).lstrip("rs") r = requests.get(f"{BASE_VARIATION}/refsnp/{rs_num}", timeout=15) - Network accessline 48
rs_num = str(rsid).lstrip("rs") r = requests.get(f"{BASE_VARIATION}/refsnp/{rs_num}", timeout=15) r.raise_for_status() - Network accessline 48
rs_num = str(rsid).lstrip("rs") r = requests.get(f"{BASE_VARIATION}/refsnp/{rs_num}", timeout=15) r.raise_for_status() - Network accessline 49
r = requests.get(f"{BASE_VARIATION}/refsnp/{rs_num}", timeout=15) - Network accessline 49
r = requests.get(f"{BASE_VARIATION}/refsnp/{rs_num}", timeout=15) - Network accessline 73
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 73
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 74
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 74
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 79
returns the simpler eSummaryResult/DocumentSummary tree without namespaces.""" rs_num = str(rsid).lstrip("rs") r = requests.get(f"{BASE}/efetch.fcgi", - Network accessline 79
returns the simpler eSummaryResult/DocumentSummary tree without namespaces.""" rs_num = str(rsid).lstrip("rs") r = requests.get(f"{BASE}/efetch.fcgi", - Network accessline 80
rs_num = str(rsid).lstrip("rs") r = requests.get(f"{BASE}/efetch.fcgi", params={"db": "snp", "id": rs_num, - Network accessline 80
rs_num = str(rsid).lstrip("rs") r = requests.get(f"{BASE}/efetch.fcgi", params={"db": "snp", "id": rs_num, - Network accessline 81
r = requests.get(f"{BASE}/efetch.fcgi", - Network accessline 81
r = requests.get(f"{BASE}/efetch.fcgi", - Network accessline 106
def esummary_snp(rsid: str) -> dict: rs_num = str(rsid).lstrip("rs") r = requests.get(f"{BASE}/esummary.fcgi", - Network accessline 106
def esummary_snp(rsid: str) -> dict: rs_num = str(rsid).lstrip("rs") r = requests.get(f"{BASE}/esummary.fcgi", - Network accessline 107
rs_num = str(rsid).lstrip("rs") r = requests.get(f"{BASE}/esummary.fcgi", params={"db": "snp", "id": rs_num, - Network accessline 107
rs_num = str(rsid).lstrip("rs") r = requests.get(f"{BASE}/esummary.fcgi", params={"db": "snp", "id": rs_num, - Network accessline 108
r = requests.get(f"{BASE}/esummary.fcgi", - Network accessline 108
r = requests.get(f"{BASE}/esummary.fcgi", - Network accessline 136
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 136
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 137
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 137
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 139
def esearch_snp(query: str, retmax: int = 100) -> tuple[list, int]: """Search dbSNP using a query string. Returns (id_list, total_count).""" r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 139
def esearch_snp(query: str, retmax: int = 100) -> tuple[list, int]: """Search dbSNP using a query string. Returns (id_list, total_count).""" r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 140
"""Search dbSNP using a query string. Returns (id_list, total_count).""" r = requests.get(f"{BASE}/esearch.fcgi", params={"db": "snp", "term": query, - Network accessline 140
"""Search dbSNP using a query string. Returns (id_list, total_count).""" r = requests.get(f"{BASE}/esearch.fcgi", params={"db": "snp", "term": query, - Network accessline 141
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 141
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 168
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 168
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 169
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 169
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 174
query = f"{chrom}[CHR] AND {start}:{stop}[CHRPOS37]" if assembly == "GRCh37" else \ f"{chrom}[CHR] AND {start}:{stop}[CHRPOS]" r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 174
query = f"{chrom}[CHR] AND {start}:{stop}[CHRPOS37]" if assembly == "GRCh37" else \ f"{chrom}[CHR] AND {start}:{stop}[CHRPOS]" r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 175
f"{chrom}[CHR] AND {start}:{stop}[CHRPOS]" r = requests.get(f"{BASE}/esearch.fcgi", params={"db": "snp", "term": query, - Network accessline 175
f"{chrom}[CHR] AND {start}:{stop}[CHRPOS]" r = requests.get(f"{BASE}/esearch.fcgi", params={"db": "snp", "term": query, - Network accessline 176
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 176
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 198
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 198
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 199
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 199
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 202
"""Fetch ESummary records for a list of rsIDs. Returns dict keyed by rs number.""" ids_str = ",".join(str(r).lstrip("rs") for r in rsids) r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 202
"""Fetch ESummary records for a list of rsIDs. Returns dict keyed by rs number.""" ids_str = ",".join(str(r).lstrip("rs") for r in rsids) r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 203
ids_str = ",".join(str(r).lstrip("rs") for r in rsids) r = requests.post(f"{BASE}/esummary.fcgi", data={"db": "snp", "id": ids_str, - Network accessline 203
ids_str = ",".join(str(r).lstrip("rs") for r in rsids) r = requests.post(f"{BASE}/esummary.fcgi", data={"db": "snp", "id": ids_str, - Network accessline 204
r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 204
r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 236
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 236
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 237
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 237
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 240
"""Upload rsIDs to NCBI history server. Returns (WebEnv, query_key).""" ids_str = ",".join(str(i).lstrip("rs") for i in id_list) r = requests.post(f"{BASE}/epost.fcgi", - Network accessline 240
"""Upload rsIDs to NCBI history server. Returns (WebEnv, query_key).""" ids_str = ",".join(str(i).lstrip("rs") for i in id_list) r = requests.post(f"{BASE}/epost.fcgi", - Network accessline 241
ids_str = ",".join(str(i).lstrip("rs") for i in id_list) r = requests.post(f"{BASE}/epost.fcgi", data={"db": "snp", "id": ids_str, "email": EMAIL}, - Network accessline 241
ids_str = ",".join(str(i).lstrip("rs") for i in id_list) r = requests.post(f"{BASE}/epost.fcgi", data={"db": "snp", "id": ids_str, "email": EMAIL}, - Network accessline 242
r = requests.post(f"{BASE}/epost.fcgi", - Network accessline 242
r = requests.post(f"{BASE}/epost.fcgi", - Network accessline 253
retstart: int = 0, retmax: int = 100) -> dict: """Retrieve records from NCBI history server using ESummary.""" r = requests.get(f"{BASE}/esummary.fcgi", - Network accessline 253
retstart: int = 0, retmax: int = 100) -> dict: """Retrieve records from NCBI history server using ESummary.""" r = requests.get(f"{BASE}/esummary.fcgi", - Network accessline 254
"""Retrieve records from NCBI history server using ESummary.""" r = requests.get(f"{BASE}/esummary.fcgi", params={"db": "snp", "WebEnv": webenv, - Network accessline 254
"""Retrieve records from NCBI history server using ESummary.""" r = requests.get(f"{BASE}/esummary.fcgi", params={"db": "snp", "WebEnv": webenv, - Network accessline 255
r = requests.get(f"{BASE}/esummary.fcgi", - Network accessline 255
r = requests.get(f"{BASE}/esummary.fcgi", - Network accessline 300
BASE_VARIATION = "https://api.ncbi.nlm.nih.gov/variation/v0" - Network accessline 300
BASE_VARIATION = "https://api.ncbi.nlm.nih.gov/variation/v0" - Network accessline 303
"""Fetch structured JSON from NCBI Variation Services API.""" rs_num = str(rsid).lstrip("rs") r = requests.get(f"{BASE_VARIATION}/refsnp/{rs_num}", timeout=15) - Network accessline 303
"""Fetch structured JSON from NCBI Variation Services API.""" rs_num = str(rsid).lstrip("rs") r = requests.get(f"{BASE_VARIATION}/refsnp/{rs_num}", timeout=15) - Network accessline 304
rs_num = str(rsid).lstrip("rs") r = requests.get(f"{BASE_VARIATION}/refsnp/{rs_num}", timeout=15) r.raise_for_status() - Network accessline 304
rs_num = str(rsid).lstrip("rs") r = requests.get(f"{BASE_VARIATION}/refsnp/{rs_num}", timeout=15) r.raise_for_status() - Network accessline 305
r = requests.get(f"{BASE_VARIATION}/refsnp/{rs_num}", timeout=15) - Network accessline 305
r = requests.get(f"{BASE_VARIATION}/refsnp/{rs_num}", timeout=15) - Network accessline 377
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 377
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 378
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 378
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 380
def epost_snp(rsids): ids = ",".join(str(r).lstrip("rs") for r in rsids) r = requests.post(f"{BASE}/epost.fcgi", - Network accessline 380
def epost_snp(rsids): ids = ",".join(str(r).lstrip("rs") for r in rsids) r = requests.post(f"{BASE}/epost.fcgi", - Network accessline 381
ids = ",".join(str(r).lstrip("rs") for r in rsids) r = requests.post(f"{BASE}/epost.fcgi", data={"db": "snp", "id": ids, "email": EMAIL}, timeout=30) - Network accessline 381
ids = ",".join(str(r).lstrip("rs") for r in rsids) r = requests.post(f"{BASE}/epost.fcgi", data={"db": "snp", "id": ids, "email": EMAIL}, timeout=30) - Network accessline 382
r = requests.post(f"{BASE}/epost.fcgi", - Network accessline 382
r = requests.post(f"{BASE}/epost.fcgi", - Network accessline 389
def esummary_history(webenv, query_key, start, retmax=100): r = requests.get(f"{BASE}/esummary.fcgi", - Network accessline 389
def esummary_history(webenv, query_key, start, retmax=100): r = requests.get(f"{BASE}/esummary.fcgi", - Network accessline 390
r = requests.get(f"{BASE}/esummary.fcgi", - Network accessline 390
r = requests.get(f"{BASE}/esummary.fcgi", - Network accessline 440
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 440
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 441
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 441
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 443
def search_gene_variants(gene: str, retmax: int = 500) -> list: r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 443
def search_gene_variants(gene: str, retmax: int = 500) -> list: r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 444
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 444
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 454
batch = ids[i:i+batch_size] ids_str = ",".join(batch) r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 454
batch = ids[i:i+batch_size] ids_str = ",".join(batch) r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 455
ids_str = ",".join(batch) r = requests.post(f"{BASE}/esummary.fcgi", data={"db": "snp", "id": ids_str, - Network accessline 455
ids_str = ",".join(batch) r = requests.post(f"{BASE}/esummary.fcgi", data={"db": "snp", "id": ids_str, - Network accessline 456
r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 456
r = requests.post(f"{BASE}/esummary.fcgi", - Access to secretsline 509
[Credential-related source omitted] - Access to secretsline 509
[Credential-related source omitted] - Network accessline 513
[Credential-related source omitted] - Network accessline 513
[Credential-related source omitted] - Access to secretsline 513
[Credential-related source omitted] - Access to secretsline 513
[Credential-related source omitted] - Network accessline 535
"""Check if an rsID exists in dbSNP and return basic info.""" rs_num = str(rsid).lstrip("rs") r = requests.get( - Network accessline 535
"""Check if an rsID exists in dbSNP and return basic info.""" rs_num = str(rsid).lstrip("rs") r = requests.get( - Network accessline 536
rs_num = str(rsid).lstrip("rs") r = requests.get( "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi", - Network accessline 536
rs_num = str(rsid).lstrip("rs") r = requests.get( "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi", - Network accessline 537
r = requests.get( - Network accessline 537
r = requests.get( - Network accessline 538
"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi", - Network accessline 538
"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi", - Network accessline 575
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 575
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 576
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 576
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 579
"""Search dbSNP for a gene and return rsIDs with GRCh38 coordinates.""" # Step 1: search r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 579
"""Search dbSNP for a gene and return rsIDs with GRCh38 coordinates.""" # Step 1: search r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 580
# Step 1: search r = requests.get(f"{BASE}/esearch.fcgi", params={"db": "snp", "term": f"{gene}[gene] AND human[orgn]", - Network accessline 580
# Step 1: search r = requests.get(f"{BASE}/esearch.fcgi", params={"db": "snp", "term": f"{gene}[gene] AND human[orgn]", - Network accessline 581
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 581
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 591
# Step 2: fetch summaries r2 = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 591
# Step 2: fetch summaries r2 = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 592
r2 = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 592
r2 = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 620
| `HTTP 429` or connection refused | Rate limit exceeded (3 req/sec) | Add `time.sleep(0.35)` between requests; register for API key to get 10 req/sec | | ESummary returns `{"error": "Invalid uid"}` | rsID does not exist in dbSNP | Check rs - Network accessline 620
| `HTTP 429` or connection refused | Rate limit exceeded (3 req/sec) | Add `time.sleep(0.35)` between requests; register for API key to get 10 req/sec | | ESummary returns `{"error": "Invalid uid"}` | rsID does not exist in dbSNP | Check rs - Network accessline 621
| ESummary returns `{"error": "Invalid uid"}` | rsID does not exist in dbSNP | Check rsID spelling; verify with NCBI browser; variant may be a novel call not yet in dbSNP | | `esearch` returns 0 results for a gene | Gene symbol mismatch or - Network accessline 621
| ESummary returns `{"error": "Invalid uid"}` | rsID does not exist in dbSNP | Check rsID spelling; verify with NCBI browser; variant may be a novel call not yet in dbSNP | | `esearch` returns 0 results for a gene | Gene symbol mismatch or - Network accessline 622
| `esearch` returns 0 results for a gene | Gene symbol mismatch or missing `human[orgn]` filter | Try adding `AND human[orgn]`; check NCBI gene symbol at https://www.ncbi.nlm.nih.gov/gene | - Network accessline 622
| `esearch` returns 0 results for a gene | Gene symbol mismatch or missing `human[orgn]` filter | Try adding `AND human[orgn]`; check NCBI gene symbol at https://www.ncbi.nlm.nih.gov/gene | - Network accessline 623
| `KeyError: 'maf'` / `'mafallele'` in ESummary parsing | Fields removed in the 2024 dbSNP ESummary schema | Use `rec["global_mafs"]` — list of `{"study": ..., "freq": "<allele>=<value>/<count>"}`; pick a study (`GnomAD_genomes`, `TOPMED`, - Network accessline 623
| `KeyError: 'maf'` / `'mafallele'` in ESummary parsing | Fields removed in the 2024 dbSNP ESummary schema | Use `rec["global_mafs"]` — list of `{"study": ..., "freq": "<allele>=<value>/<count>"}`; pick a study (`GnomAD_genomes`, `TOPMED`, - Network accessline 624
| `global_mafs` empty | Variant has no aggregated population frequency in dbSNP | Use gnomAD via `gnomad-database` directly for population frequencies | | `root.iter("DocumentSummary")` returns 0 with `rettype="xml"` | EFetch XML root is na - Network accessline 624
| `global_mafs` empty | Variant has no aggregated population frequency in dbSNP | Use gnomAD via `gnomad-database` directly for population frequencies | | `root.iter("DocumentSummary")` returns 0 with `rettype="xml"` | EFetch XML root is na - Network accessline 625
| `root.iter("DocumentSummary")` returns 0 with `rettype="xml"` | EFetch XML root is namespaced (`{https://www.ncbi.nlm.nih.gov/SNP/docsum}ExchangeSet`) | Use `rettype="docsum"` (no namespace) or pass the full namespaced tag to `iter()` | - Network accessline 625
| `root.iter("DocumentSummary")` returns 0 with `rettype="xml"` | EFetch XML root is namespaced (`{https://www.ncbi.nlm.nih.gov/SNP/docsum}ExchangeSet`) | Use `rettype="docsum"` (no namespace) or pass the full namespaced tag to `iter()` | - Network accessline 640
- [NCBI E-utilities for dbSNP](https://www.ncbi.nlm.nih.gov/snp/docs/entrez_help/) — E-utilities field tags and query syntax specific to dbSNP - Network accessline 640
- [NCBI E-utilities for dbSNP](https://www.ncbi.nlm.nih.gov/snp/docs/entrez_help/) — E-utilities field tags and query syntax specific to dbSNP - Network accessline 641
- [NCBI Variation Services API](https://api.ncbi.nlm.nih.gov/variation/v0/) — REST API documentation and interactive Swagger UI - Network accessline 641
- [NCBI Variation Services API](https://api.ncbi.nlm.nih.gov/variation/v0/) — REST API documentation and interactive Swagger UI - Network accessline 642
- [Sherry et al., Nucleic Acids Research 2001](https://doi.org/10.1093/nar/29.1.308) — dbSNP original description paper - Network accessline 642
- [Sherry et al., Nucleic Acids Research 2001](https://doi.org/10.1093/nar/29.1.308) — dbSNP original description paper - Network accessline 643
- [NCBI E-utilities Reference](https://www.ncbi.nlm.nih.gov/books/NBK25499/) — Full E-utilities API reference (applies to all NCBI databases) - Network accessline 643
- [NCBI E-utilities Reference](https://www.ncbi.nlm.nih.gov/books/NBK25499/) — Full E-utilities API reference (applies to all NCBI databases)
skills/genomics-bioinformatics/databases/depmap-crispr-essentiality/SKILL.md
- Network accessline 246
- [DepMap Portal](https://depmap.org/portal/) -- Primary data source for CRISPR gene effect scores, expression data, and other omics datasets across cancer cell lines - [Dempster et al. (2019) -- Chronos algorithm](https://doi.org/10.1038/s - Network accessline 246
- [DepMap Portal](https://depmap.org/portal/) -- Primary data source for CRISPR gene effect scores, expression data, and other omics datasets across cancer cell lines - [Dempster et al. (2019) -- Chronos algorithm](https://doi.org/10.1038/s - Network accessline 247
- [Dempster et al. (2019) -- Chronos algorithm](https://doi.org/10.1038/s41467-019-09612-6) -- Original paper describing the Chronos computational method for estimating gene effect from CRISPR screen data - Network accessline 247
- [Dempster et al. (2019) -- Chronos algorithm](https://doi.org/10.1038/s41467-019-09612-6) -- Original paper describing the Chronos computational method for estimating gene effect from CRISPR screen data - Network accessline 248
- [DepMap Documentation and Data Downloads](https://depmap.org/portal/download/all/) -- Detailed file format descriptions, release notes, and download links for all DepMap datasets - Network accessline 248
- [DepMap Documentation and Data Downloads](https://depmap.org/portal/download/all/) -- Detailed file format descriptions, release notes, and download links for all DepMap datasets
skills/genomics-bioinformatics/databases/ena-database/SKILL.md
- Network accessline 33
- **No authentication** required - **Large result sets**: use pagination (`limit` + `offset`) or streaming (`limit=0` for TSV download) - Portal API base: `https://www.ebi.ac.uk/ena/portal/api` - Network accessline 33
- **No authentication** required - **Large result sets**: use pagination (`limit` + `offset`) or streaming (`limit=0` for TSV download) - Portal API base: `https://www.ebi.ac.uk/ena/portal/api` - Network accessline 34
- **Large result sets**: use pagination (`limit` + `offset`) or streaming (`limit=0` for TSV download) - Portal API base: `https://www.ebi.ac.uk/ena/portal/api` - Browser API base: `https://www.ebi.ac.uk/ena/browser/api` - Network accessline 34
- **Large result sets**: use pagination (`limit` + `offset`) or streaming (`limit=0` for TSV download) - Portal API base: `https://www.ebi.ac.uk/ena/portal/api` - Browser API base: `https://www.ebi.ac.uk/ena/browser/api` - Network accessline 35
- Portal API base: `https://www.ebi.ac.uk/ena/portal/api` - Network accessline 35
- Portal API base: `https://www.ebi.ac.uk/ena/portal/api` - Network accessline 36
- Browser API base: `https://www.ebi.ac.uk/ena/browser/api` - Network accessline 36
- Browser API base: `https://www.ebi.ac.uk/ena/browser/api` - Network accessline 37
- Taxonomy API base: `https://www.ebi.ac.uk/ena/taxonomy/rest` - Network accessline 37
- Taxonomy API base: `https://www.ebi.ac.uk/ena/taxonomy/rest` - Network accessline 38
- Cross-ref API base: `https://www.ebi.ac.uk/ena/xref/rest` - Network accessline 38
- Cross-ref API base: `https://www.ebi.ac.uk/ena/xref/rest` - Network accessline 46
BASE_PORTAL = "https://www.ebi.ac.uk/ena/portal/api" - Network accessline 46
BASE_PORTAL = "https://www.ebi.ac.uk/ena/portal/api" - Network accessline 47
BASE_BROWSER = "https://www.ebi.ac.uk/ena/browser/api" - Network accessline 47
BASE_BROWSER = "https://www.ebi.ac.uk/ena/browser/api" - Network accessline 48
BASE_TAXONOMY = "https://www.ebi.ac.uk/ena/taxonomy/rest" - Network accessline 48
BASE_TAXONOMY = "https://www.ebi.ac.uk/ena/taxonomy/rest" - Network accessline 49
BASE_XREF = "https://www.ebi.ac.uk/ena/xref/rest" - Network accessline 49
BASE_XREF = "https://www.ebi.ac.uk/ena/xref/rest" - Network accessline 51
def ena_query(endpoint, params=None, base=BASE_PORTAL): """Reusable ENA API caller with rate-limit compliance.""" resp = requests.get(f"{base}/{endpoint}", params=params) - Network accessline 51
def ena_query(endpoint, params=None, base=BASE_PORTAL): """Reusable ENA API caller with rate-limit compliance.""" resp = requests.get(f"{base}/{endpoint}", params=params) - Network accessline 52
"""Reusable ENA API caller with rate-limit compliance.""" resp = requests.get(f"{base}/{endpoint}", params=params) resp.raise_for_status() - Network accessline 52
"""Reusable ENA API caller with rate-limit compliance.""" resp = requests.get(f"{base}/{endpoint}", params=params) resp.raise_for_status() - Network accessline 53
resp = requests.get(f"{base}/{endpoint}", params=params) - Network accessline 53
resp = requests.get(f"{base}/{endpoint}", params=params) - Network accessline 211
resp = ena_query( f"cram/md5/{md5}", base="https://www.ebi.ac.uk/ena/cram" - Network accessline 211
resp = ena_query( f"cram/md5/{md5}", base="https://www.ebi.ac.uk/ena/cram" - Network accessline 212
f"cram/md5/{md5}", base="https://www.ebi.ac.uk/ena/cram" ) - Network accessline 212
f"cram/md5/{md5}", base="https://www.ebi.ac.uk/ena/cram" ) - Network accessline 213
base="https://www.ebi.ac.uk/ena/cram" - Network accessline 213
base="https://www.ebi.ac.uk/ena/cram" - Network accessline 470
def ena_session(): """Create a requests session with retry logic for ENA APIs.""" session = requests.Session() - Network accessline 470
def ena_session(): """Create a requests session with retry logic for ENA APIs.""" session = requests.Session() - Network accessline 471
"""Create a requests session with retry logic for ENA APIs.""" session = requests.Session() retry = Retry( - Network accessline 471
"""Create a requests session with retry logic for ENA APIs.""" session = requests.Session() retry = Retry( - Network accessline 472
session = requests.Session() - Network accessline 472
session = requests.Session() - Network accessline 529
- ENA Portal API docs: https://www.ebi.ac.uk/ena/portal/api/doc - Network accessline 529
- ENA Portal API docs: https://www.ebi.ac.uk/ena/portal/api/doc - Network accessline 530
- ENA Browser API docs: https://www.ebi.ac.uk/ena/browser/api/doc - Network accessline 530
- ENA Browser API docs: https://www.ebi.ac.uk/ena/browser/api/doc - Network accessline 531
- ENA Taxonomy REST API: https://www.ebi.ac.uk/ena/taxonomy/rest/ - Network accessline 531
- ENA Taxonomy REST API: https://www.ebi.ac.uk/ena/taxonomy/rest/ - Network accessline 532
- ENA Cross-Reference Service: https://www.ebi.ac.uk/ena/xref/rest/ - Network accessline 532
- ENA Cross-Reference Service: https://www.ebi.ac.uk/ena/xref/rest/ - Network accessline 533
- ENA data model guide: https://ena-docs.readthedocs.io/en/latest/submit/general-guide/data-model.html - Network accessline 533
- ENA data model guide: https://ena-docs.readthedocs.io/en/latest/submit/general-guide/data-model.html - Network accessline 534
- INSDC standards: https://www.insdc.org/ - Network accessline 534
- INSDC standards: https://www.insdc.org/
skills/genomics-bioinformatics/databases/encode-database/SKILL.md
- Access to secretsline 28
[Credential-related source omitted] - Access to secretsline 28
[Credential-related source omitted] - Network accessline 40
BASE = "https://www.encodeproject.org" - Network accessline 40
BASE = "https://www.encodeproject.org" - Network accessline 51
"limit": limit, } r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 51
"limit": limit, } r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 52
} r = requests.get(f"{BASE}/search/", params=params, timeout=30) r.raise_for_status() - Network accessline 52
} r = requests.get(f"{BASE}/search/", params=params, timeout=30) r.raise_for_status() - Network accessline 53
r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 53
r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 74
BASE = "https://www.encodeproject.org" - Network accessline 74
BASE = "https://www.encodeproject.org" - Network accessline 96
r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 96
r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 133
BASE = "https://www.encodeproject.org" - Network accessline 133
BASE = "https://www.encodeproject.org" - Network accessline 152
"limit": 50, } r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 152
"limit": 50, } r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 153
} r = requests.get(f"{BASE}/search/", params=params, timeout=30) r.raise_for_status() - Network accessline 153
} r = requests.get(f"{BASE}/search/", params=params, timeout=30) r.raise_for_status() - Network accessline 154
r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 154
r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 183
"""Download an ENCODE file from its href URL.""" print(f"Downloading {download_url} → {output_path}") urllib.request.urlretrieve(download_url, output_path) - Network accessline 183
"""Download an ENCODE file from its href URL.""" print(f"Downloading {download_url} → {output_path}") urllib.request.urlretrieve(download_url, output_path) - Network accessline 184
print(f"Downloading {download_url} → {output_path}") urllib.request.urlretrieve(download_url, output_path) import os - Network accessline 184
print(f"Downloading {download_url} → {output_path}") urllib.request.urlretrieve(download_url, output_path) import os - Network accessline 185
urllib.request.urlretrieve(download_url, output_path) - Network accessline 185
urllib.request.urlretrieve(download_url, output_path) - Network accessline 203
SCREEN_BASE = "https://api.encodeproject.org/screen" - Network accessline 203
SCREEN_BASE = "https://api.encodeproject.org/screen" - Network accessline 217
"limit": limit, } r = requests.post(f"{SCREEN_BASE}/search/", json=payload, timeout=30) - Network accessline 217
"limit": limit, } r = requests.post(f"{SCREEN_BASE}/search/", json=payload, timeout=30) - Network accessline 218
} r = requests.post(f"{SCREEN_BASE}/search/", json=payload, timeout=30) r.raise_for_status() - Network accessline 218
} r = requests.post(f"{SCREEN_BASE}/search/", json=payload, timeout=30) r.raise_for_status() - Network accessline 219
r = requests.post(f"{SCREEN_BASE}/search/", json=payload, timeout=30) - Network accessline 219
r = requests.post(f"{SCREEN_BASE}/search/", json=payload, timeout=30) - Network accessline 256
SCREEN_BASE = "https://api.encodeproject.org/screen" - Network accessline 256
SCREEN_BASE = "https://api.encodeproject.org/screen" - Network accessline 257
ENCODE_BASE = "https://www.encodeproject.org" - Network accessline 257
ENCODE_BASE = "https://www.encodeproject.org" - Network accessline 263
""" # Step 1: resolve gene to genomic coordinates via ENCODE gene search r = requests.get( - Network accessline 263
""" # Step 1: resolve gene to genomic coordinates via ENCODE gene search r = requests.get( - Network accessline 264
# Step 1: resolve gene to genomic coordinates via ENCODE gene search r = requests.get( f"{ENCODE_BASE}/search/", - Network accessline 264
# Step 1: resolve gene to genomic coordinates via ENCODE gene search r = requests.get( f"{ENCODE_BASE}/search/", - Network accessline 265
r = requests.get( - Network accessline 265
r = requests.get( - Network accessline 302
BASE = "https://www.encodeproject.org" - Network accessline 302
BASE = "https://www.encodeproject.org" - Network accessline 319
r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 319
r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 349
BASE = "https://www.encodeproject.org" - Network accessline 349
BASE = "https://www.encodeproject.org" - Network accessline 363
"limit": limit, } r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 363
"limit": limit, } r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 364
} r = requests.get(f"{BASE}/search/", params=params, timeout=30) r.raise_for_status() - Network accessline 364
} r = requests.get(f"{BASE}/search/", params=params, timeout=30) r.raise_for_status() - Network accessline 365
r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 365
r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 394
BASE = "https://www.encodeproject.org" - Network accessline 394
BASE = "https://www.encodeproject.org" - Network accessline 399
Returns: dict with file accession, download URL, and file metadata. """ r = requests.get(f"{BASE}/experiments/{accession}/?format=json", timeout=30) - Network accessline 399
Returns: dict with file accession, download URL, and file metadata. """ r = requests.get(f"{BASE}/experiments/{accession}/?format=json", timeout=30) - Network accessline 400
""" r = requests.get(f"{BASE}/experiments/{accession}/?format=json", timeout=30) r.raise_for_status() - Network accessline 400
""" r = requests.get(f"{BASE}/experiments/{accession}/?format=json", timeout=30) r.raise_for_status() - Network accessline 401
r = requests.get(f"{BASE}/experiments/{accession}/?format=json", timeout=30) - Network accessline 401
r = requests.get(f"{BASE}/experiments/{accession}/?format=json", timeout=30) - Network accessline 486
BASE = "https://www.encodeproject.org" - Network accessline 486
BASE = "https://www.encodeproject.org" - Network accessline 499
"limit": 5, } r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 499
"limit": 5, } r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 500
} r = requests.get(f"{BASE}/search/", params=params, timeout=30) r.raise_for_status() - Network accessline 500
} r = requests.get(f"{BASE}/search/", params=params, timeout=30) r.raise_for_status() - Network accessline 501
r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 501
r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 515
# Step 3: download and parse BED r2 = requests.get(peak["download_url"], timeout=120, stream=True) - Network accessline 515
# Step 3: download and parse BED r2 = requests.get(peak["download_url"], timeout=120, stream=True) - Network accessline 516
r2 = requests.get(peak["download_url"], timeout=120, stream=True) - Network accessline 516
r2 = requests.get(peak["download_url"], timeout=120, stream=True) - Network accessline 548
SCREEN_BASE = "https://api.encodeproject.org/screen" - Network accessline 548
SCREEN_BASE = "https://api.encodeproject.org/screen" - Network accessline 552
payload = {"assembly": assembly, "coord_chrom": chrom, "coord_start": start, "coord_end": end, "limit": 1000} r = requests.post(f"{SCREEN_BASE}/search/", json=payload, timeout=30) - Network accessline 552
payload = {"assembly": assembly, "coord_chrom": chrom, "coord_start": start, "coord_end": end, "limit": 1000} r = requests.post(f"{SCREEN_BASE}/search/", json=payload, timeout=30) - Network accessline 553
"coord_start": start, "coord_end": end, "limit": 1000} r = requests.post(f"{SCREEN_BASE}/search/", json=payload, timeout=30) r.raise_for_status() - Network accessline 553
"coord_start": start, "coord_end": end, "limit": 1000} r = requests.post(f"{SCREEN_BASE}/search/", json=payload, timeout=30) r.raise_for_status() - Network accessline 554
r = requests.post(f"{SCREEN_BASE}/search/", json=payload, timeout=30) - Network accessline 554
r = requests.post(f"{SCREEN_BASE}/search/", json=payload, timeout=30) - Network accessline 618
BASE = "https://www.encodeproject.org" - Network accessline 618
BASE = "https://www.encodeproject.org" - Network accessline 632
"limit": max_experiments, } r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 632
"limit": max_experiments, } r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 633
} r = requests.get(f"{BASE}/search/", params=params, timeout=30) r.raise_for_status() - Network accessline 633
} r = requests.get(f"{BASE}/search/", params=params, timeout=30) r.raise_for_status() - Network accessline 634
r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 634
r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 703
BASE = "https://www.encodeproject.org" - Network accessline 703
BASE = "https://www.encodeproject.org" - Network accessline 705
r = requests.get( - Network accessline 705
r = requests.get( - Network accessline 726
BASE = "https://www.encodeproject.org" - Network accessline 726
BASE = "https://www.encodeproject.org" - Network accessline 728
def get_experiment_metadata(accession): r = requests.get(f"{BASE}/experiments/{accession}/?format=json", timeout=30) - Network accessline 728
def get_experiment_metadata(accession): r = requests.get(f"{BASE}/experiments/{accession}/?format=json", timeout=30) - Network accessline 729
r = requests.get(f"{BASE}/experiments/{accession}/?format=json", timeout=30) - Network accessline 729
r = requests.get(f"{BASE}/experiments/{accession}/?format=json", timeout=30) - Network accessline 751
BASE = "https://www.encodeproject.org" - Network accessline 751
BASE = "https://www.encodeproject.org" - Network accessline 763
"limit": 10, } r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 763
"limit": 10, } r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 764
} r = requests.get(f"{BASE}/search/", params=params, timeout=30) r.raise_for_status() - Network accessline 764
} r = requests.get(f"{BASE}/search/", params=params, timeout=30) r.raise_for_status() - Network accessline 765
r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 765
r = requests.get(f"{BASE}/search/", params=params, timeout=30) - Network accessline 780
| Problem | Cause | Solution | |---------|-------|----------| | `HTTP 404` on experiment endpoint | Accession does not exist or was revoked | Verify accession on https://www.encodeproject.org; check `status` field in search results | - Network accessline 780
| Problem | Cause | Solution | |---------|-------|----------| | `HTTP 404` on experiment endpoint | Accession does not exist or was revoked | Verify accession on https://www.encodeproject.org; check `status` field in search results | - Network accessline 781
|---------|-------|----------| | `HTTP 404` on experiment endpoint | Accession does not exist or was revoked | Verify accession on https://www.encodeproject.org; check `status` field in search results | | Empty `@graph` in search results | - Network accessline 781
|---------|-------|----------| | `HTTP 404` on experiment endpoint | Accession does not exist or was revoked | Verify accession on https://www.encodeproject.org; check `status` field in search results | | Empty `@graph` in search results | - Network accessline 782
| `HTTP 404` on experiment endpoint | Accession does not exist or was revoked | Verify accession on https://www.encodeproject.org; check `status` field in search results | | Empty `@graph` in search results | Overly restrictive filters or n - Network accessline 782
| `HTTP 404` on experiment endpoint | Accession does not exist or was revoked | Verify accession on https://www.encodeproject.org; check `status` field in search results | | Empty `@graph` in search results | Overly restrictive filters or n - Network accessline 799
- [ENCODE Portal REST API Documentation](https://www.encodeproject.org/help/rest-api/) — Full API reference with query examples - Network accessline 799
- [ENCODE Portal REST API Documentation](https://www.encodeproject.org/help/rest-api/) — Full API reference with query examples - Network accessline 800
- [ENCODE SCREEN Browser](https://screen.encodeproject.org/) — Interactive cCRE browser and documentation - Network accessline 800
- [ENCODE SCREEN Browser](https://screen.encodeproject.org/) — Interactive cCRE browser and documentation - Network accessline 801
- ENCODE Project Consortium. "An integrated encyclopedia of DNA elements in the human genome." *Nature* 489: 57–74 (2012). https://doi.org/10.1038/nature11247 - Network accessline 801
- ENCODE Project Consortium. "An integrated encyclopedia of DNA elements in the human genome." *Nature* 489: 57–74 (2012). https://doi.org/10.1038/nature11247 - Network accessline 802
- ENCODE Project Consortium et al. "Perspectives on ENCODE." *Nature* 583: 693–698 (2020). https://doi.org/10.1038/s41586-020-2449-8 - Network accessline 802
- ENCODE Project Consortium et al. "Perspectives on ENCODE." *Nature* 583: 693–698 (2020). https://doi.org/10.1038/s41586-020-2449-8 - Network accessline 803
- Moore JE et al. "Expanded encyclopaedias of DNA elements in the human and mouse genomes." *Nature* 583: 699–710 (2020). https://doi.org/10.1038/s41586-020-2493-4 - Network accessline 803
- Moore JE et al. "Expanded encyclopaedias of DNA elements in the human and mouse genomes." *Nature* 583: 699–710 (2020). https://doi.org/10.1038/s41586-020-2493-4
skills/genomics-bioinformatics/databases/ensembl-database/SKILL.md
- Network accessline 40
BASE = "https://rest.ensembl.org" - Network accessline 40
BASE = "https://rest.ensembl.org" - Network accessline 43
def ensembl_get(endpoint, params=None): r = requests.get(f"{BASE}{endpoint}", headers=HEADERS, params=params) - Network accessline 43
def ensembl_get(endpoint, params=None): r = requests.get(f"{BASE}{endpoint}", headers=HEADERS, params=params) - Network accessline 44
r = requests.get(f"{BASE}{endpoint}", headers=HEADERS, params=params) - Network accessline 44
r = requests.get(f"{BASE}{endpoint}", headers=HEADERS, params=params) - Network accessline 63
BASE = "https://rest.ensembl.org" - Network accessline 63
BASE = "https://rest.ensembl.org" - Network accessline 66
# By gene symbol r = requests.get( - Network accessline 66
# By gene symbol r = requests.get( - Network accessline 67
r = requests.get( - Network accessline 67
r = requests.get( - Network accessline 80
# By stable ID (works for genes, transcripts, proteins) r = requests.get( - Network accessline 80
# By stable ID (works for genes, transcripts, proteins) r = requests.get( - Network accessline 81
r = requests.get( - Network accessline 81
r = requests.get( - Network accessline 97
BASE = "https://rest.ensembl.org" - Network accessline 97
BASE = "https://rest.ensembl.org" - Network accessline 100
# Batch lookup by symbols symbols = ["BRCA1", "BRCA2", "TP53", "EGFR", "MYC"] r = requests.post( - Network accessline 100
# Batch lookup by symbols symbols = ["BRCA1", "BRCA2", "TP53", "EGFR", "MYC"] r = requests.post( - Network accessline 101
symbols = ["BRCA1", "BRCA2", "TP53", "EGFR", "MYC"] r = requests.post( f"{BASE}/lookup/symbol/homo_sapiens", - Network accessline 101
symbols = ["BRCA1", "BRCA2", "TP53", "EGFR", "MYC"] r = requests.post( f"{BASE}/lookup/symbol/homo_sapiens", - Network accessline 102
r = requests.post( - Network accessline 102
r = requests.post( - Network accessline 120
BASE = "https://rest.ensembl.org" - Network accessline 120
BASE = "https://rest.ensembl.org" - Network accessline 123
# Protein sequence for canonical transcript r = requests.get( - Network accessline 123
# Protein sequence for canonical transcript r = requests.get( - Network accessline 124
r = requests.get( - Network accessline 124
r = requests.get( - Network accessline 134
# Genomic region sequence HEADERS_JSON = {"Content-Type": "application/json"} r = requests.get( - Network accessline 134
# Genomic region sequence HEADERS_JSON = {"Content-Type": "application/json"} r = requests.get( - Network accessline 135
HEADERS_JSON = {"Content-Type": "application/json"} r = requests.get( f"{BASE}/sequence/region/human/17:43044295..43125364", - Network accessline 135
HEADERS_JSON = {"Content-Type": "application/json"} r = requests.get( f"{BASE}/sequence/region/human/17:43044295..43125364", - Network accessline 136
r = requests.get( - Network accessline 136
r = requests.get( - Network accessline 152
BASE = "https://rest.ensembl.org" - Network accessline 152
BASE = "https://rest.ensembl.org" - Network accessline 155
# All xrefs for a gene r = requests.get( - Network accessline 155
# All xrefs for a gene r = requests.get( - Network accessline 156
r = requests.get( - Network accessline 156
r = requests.get( - Network accessline 180
BASE = "https://rest.ensembl.org" - Network accessline 180
BASE = "https://rest.ensembl.org" - Network accessline 183
# Annotate a list of hgvs notations variants = ["17:g.43094692C>T", "13:g.32929387C>T"] r = requests.post( - Network accessline 183
# Annotate a list of hgvs notations variants = ["17:g.43094692C>T", "13:g.32929387C>T"] r = requests.post( - Network accessline 184
variants = ["17:g.43094692C>T", "13:g.32929387C>T"] r = requests.post( f"{BASE}/vep/human/hgvs", - Network accessline 184
variants = ["17:g.43094692C>T", "13:g.32929387C>T"] r = requests.post( f"{BASE}/vep/human/hgvs", - Network accessline 185
r = requests.post( - Network accessline 185
r = requests.post( - Network accessline 197
# Annotate by rsID r = requests.get( - Network accessline 197
# Annotate by rsID r = requests.get( - Network accessline 198
r = requests.get( - Network accessline 198
r = requests.get( - Network accessline 214
BASE = "https://rest.ensembl.org" - Network accessline 214
BASE = "https://rest.ensembl.org" - Network accessline 217
# Regulatory features in BRCA1 region r = requests.get( - Network accessline 217
# Regulatory features in BRCA1 region r = requests.get( - Network accessline 218
r = requests.get( - Network accessline 218
r = requests.get( - Network accessline 236
BASE = "https://rest.ensembl.org" - Network accessline 236
BASE = "https://rest.ensembl.org" - Network accessline 239
# Get mouse ortholog for human TP53 r = requests.get( - Network accessline 239
# Get mouse ortholog for human TP53 r = requests.get( - Network accessline 240
r = requests.get( - Network accessline 240
r = requests.get( - Network accessline 270
BASE = "https://rest.ensembl.org" - Network accessline 270
BASE = "https://rest.ensembl.org" - Network accessline 273
def batch_lookup(symbols, species="homo_sapiens"): r = requests.post( - Network accessline 273
def batch_lookup(symbols, species="homo_sapiens"): r = requests.post( - Network accessline 274
r = requests.post( - Network accessline 274
r = requests.post( - Network accessline 313
BASE = "https://rest.ensembl.org" - Network accessline 313
BASE = "https://rest.ensembl.org" - Network accessline 323
# Annotate in batches of 200 def vep_batch(hgvs_batch): r = requests.post( - Network accessline 323
# Annotate in batches of 200 def vep_batch(hgvs_batch): r = requests.post( - Network accessline 324
def vep_batch(hgvs_batch): r = requests.post( f"{BASE}/vep/human/hgvs", - Network accessline 324
def vep_batch(hgvs_batch): r = requests.post( f"{BASE}/vep/human/hgvs", - Network accessline 325
r = requests.post( - Network accessline 325
r = requests.post( - Network accessline 388
BASE = "https://rest.ensembl.org" - Network accessline 388
BASE = "https://rest.ensembl.org" - Network accessline 391
symbols = ["EGFR", "KRAS", "BRAF", "PIK3CA", "PTEN", "AKT1", "MYC", "RB1"] r = requests.post( - Network accessline 391
symbols = ["EGFR", "KRAS", "BRAF", "PIK3CA", "PTEN", "AKT1", "MYC", "RB1"] r = requests.post( - Network accessline 392
r = requests.post( - Network accessline 392
r = requests.post( - Network accessline 412
BASE = "https://rest.ensembl.org" - Network accessline 412
BASE = "https://rest.ensembl.org" - Network accessline 415
chrom, start, end = "17", 43044295, 43125364 r = requests.get( - Network accessline 415
chrom, start, end = "17", 43044295, 43125364 r = requests.get( - Network accessline 416
r = requests.get( - Network accessline 416
r = requests.get( - Network accessline 437
BASE = "https://rest.ensembl.org" - Network accessline 437
BASE = "https://rest.ensembl.org" - Network accessline 440
r = requests.get(f"{BASE}/info/species", headers=HEADERS) - Network accessline 440
r = requests.get(f"{BASE}/info/species", headers=HEADERS) - Network accessline 469
- [Ensembl REST API documentation](https://rest.ensembl.org) — Interactive API explorer and endpoint reference - Network accessline 469
- [Ensembl REST API documentation](https://rest.ensembl.org) — Interactive API explorer and endpoint reference - Network accessline 470
- [Ensembl Help & Documentation](https://www.ensembl.org/info/docs/api/rest/rest_access.html) — REST API overview - Network accessline 470
- [Ensembl Help & Documentation](https://www.ensembl.org/info/docs/api/rest/rest_access.html) — REST API overview - Network accessline 471
- [Ensembl stable IDs guide](https://www.ensembl.org/info/genome/stable_ids/index.html) — ID versioning policy - Network accessline 471
- [Ensembl stable IDs guide](https://www.ensembl.org/info/genome/stable_ids/index.html) — ID versioning policy - Network accessline 472
- [VEP documentation](https://www.ensembl.org/info/docs/tools/vep/index.html) — Variant Effect Predictor full reference - Network accessline 472
- [VEP documentation](https://www.ensembl.org/info/docs/tools/vep/index.html) — Variant Effect Predictor full reference
skills/genomics-bioinformatics/databases/gene-database/SKILL.md
- Network accessline 39
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 39
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 40
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 40
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 42
def gene_search(query, retmax=5): r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 42
def gene_search(query, retmax=5): r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 43
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 43
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 63
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 63
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 64
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 64
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 66
# Exact symbol match for human gene r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 66
# Exact symbol match for human gene r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 67
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 67
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 75
# Search by function keyword r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 75
# Search by function keyword r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 76
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 76
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 90
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 90
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 91
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 91
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 93
def esummary_gene(gene_ids): r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 93
def esummary_gene(gene_ids): r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 94
r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 94
r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 119
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 119
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 120
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 120
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 122
def efetch_gene_xml(gene_id): r = requests.get(f"{BASE}/efetch.fcgi", - Network accessline 122
def efetch_gene_xml(gene_id): r = requests.get(f"{BASE}/efetch.fcgi", - Network accessline 123
r = requests.get(f"{BASE}/efetch.fcgi", - Network accessline 123
r = requests.get(f"{BASE}/efetch.fcgi", - Network accessline 135
# XML for RefSeq accession extraction r = requests.get(f"{BASE}/efetch.fcgi", - Network accessline 135
# XML for RefSeq accession extraction r = requests.get(f"{BASE}/efetch.fcgi", - Network accessline 136
r = requests.get(f"{BASE}/efetch.fcgi", - Network accessline 136
r = requests.get(f"{BASE}/efetch.fcgi", - Network accessline 157
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 157
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 158
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 158
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 162
mapping = {} for sym in symbols: r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 162
mapping = {} for sym in symbols: r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 163
for sym in symbols: r = requests.get(f"{BASE}/esearch.fcgi", params={"db": "gene", "email": EMAIL, "retmode": "json", - Network accessline 163
for sym in symbols: r = requests.get(f"{BASE}/esearch.fcgi", params={"db": "gene", "email": EMAIL, "retmode": "json", - Network accessline 164
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 164
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 186
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 186
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 187
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 187
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 189
r = requests.get(f"{BASE}/efetch.fcgi", - Network accessline 189
r = requests.get(f"{BASE}/efetch.fcgi", - Network accessline 215
DATASETS_BASE = "https://api.ncbi.nlm.nih.gov/datasets/v2" - Network accessline 215
DATASETS_BASE = "https://api.ncbi.nlm.nih.gov/datasets/v2" - Network accessline 223
ids = taxon_filter if isinstance(taxon_filter, (list, tuple)) else [taxon_filter] params["taxon_filter"] = [str(t) for t in ids] r = requests.get(f"{DATASETS_BASE}/gene/id/{gene_id}/orthologs", - Network accessline 223
ids = taxon_filter if isinstance(taxon_filter, (list, tuple)) else [taxon_filter] params["taxon_filter"] = [str(t) for t in ids] r = requests.get(f"{DATASETS_BASE}/gene/id/{gene_id}/orthologs", - Network accessline 224
params["taxon_filter"] = [str(t) for t in ids] r = requests.get(f"{DATASETS_BASE}/gene/id/{gene_id}/orthologs", params=params, timeout=30) - Network accessline 224
params["taxon_filter"] = [str(t) for t in ids] r = requests.get(f"{DATASETS_BASE}/gene/id/{gene_id}/orthologs", params=params, timeout=30) - Network accessline 225
r = requests.get(f"{DATASETS_BASE}/gene/id/{gene_id}/orthologs", - Network accessline 225
r = requests.get(f"{DATASETS_BASE}/gene/id/{gene_id}/orthologs", - Network accessline 263
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 263
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 264
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 264
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 266
def search_gene(sym, organism="Homo sapiens"): r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 266
def search_gene(sym, organism="Homo sapiens"): r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 267
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 267
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 273
def batch_summary(gene_ids): r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 273
def batch_summary(gene_ids): r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 274
r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 274
r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 316
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 316
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 317
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 317
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 319
keyword = "DNA mismatch repair" r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 319
keyword = "DNA mismatch repair" r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 320
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 320
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 327
# Fetch summaries r2 = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 327
# Fetch summaries r2 = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 328
r2 = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 328
r2 = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 363
[Credential-related source omitted] - Network accessline 363
[Credential-related source omitted] - Access to secretsline 363
[Credential-related source omitted] - Access to secretsline 363
[Credential-related source omitted] - Network accessline 379
r = requests.get( - Network accessline 379
r = requests.get( - Network accessline 380
"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi", - Network accessline 380
"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi", - Network accessline 397
# P53 is an alias for TP53 r = requests.get( - Network accessline 397
# P53 is an alias for TP53 r = requests.get( - Network accessline 398
r = requests.get( - Network accessline 398
r = requests.get( - Network accessline 399
"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi", - Network accessline 399
"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi", - Network accessline 405
r2 = requests.post("https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi", - Network accessline 405
r2 = requests.post("https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi", - Network accessline 423
r = requests.get( - Network accessline 423
r = requests.get( - Network accessline 424
"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi", - Network accessline 424
"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi", - Network accessline 454
- [NCBI Gene database](https://www.ncbi.nlm.nih.gov/gene/) — Official homepage and search interface - Network accessline 454
- [NCBI Gene database](https://www.ncbi.nlm.nih.gov/gene/) — Official homepage and search interface - Network accessline 455
- [E-utilities documentation](https://www.ncbi.nlm.nih.gov/books/NBK25499/) — Complete API reference for ESearch, ESummary, EFetch - Network accessline 455
- [E-utilities documentation](https://www.ncbi.nlm.nih.gov/books/NBK25499/) — Complete API reference for ESearch, ESummary, EFetch - Network accessline 456
- [NCBI Gene field tags](https://www.ncbi.nlm.nih.gov/books/NBK3840/) — Field tag reference for constructing Entrez queries - Network accessline 456
- [NCBI Gene field tags](https://www.ncbi.nlm.nih.gov/books/NBK3840/) — Field tag reference for constructing Entrez queries - Network accessline 457
- [NCBI API Key registration](https://www.ncbi.nlm.nih.gov/account/) — Free registration for 10 req/s rate limit - Network accessline 457
- [NCBI API Key registration](https://www.ncbi.nlm.nih.gov/account/) — Free registration for 10 req/s rate limit - Network accessline 458
- [NCBI Datasets v2 REST API](https://www.ncbi.nlm.nih.gov/datasets/docs/v2/reference-docs/rest-api/) — Modern endpoint for orthologs, gene metadata, and taxonomy (replaces retired HomoloGene) - Network accessline 458
- [NCBI Datasets v2 REST API](https://www.ncbi.nlm.nih.gov/datasets/docs/v2/reference-docs/rest-api/) — Modern endpoint for orthologs, gene metadata, and taxonomy (replaces retired HomoloGene)
skills/genomics-bioinformatics/databases/geo-database/SKILL.md
- Network accessline 59
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 59
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 60
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 60
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 62
def geo_search(query, retmax=20): r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 62
def geo_search(query, retmax=20): r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 63
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 63
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 94
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 94
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 95
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 95
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 97
def geo_summary(uids): r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 97
def geo_summary(uids): r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 98
r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 98
r = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 104
# Get metadata for search results result = geo_search_func = lambda q: requests.get( - Network accessline 104
# Get metadata for search results result = geo_search_func = lambda q: requests.get( - Network accessline 105
result = geo_search_func = lambda q: requests.get( - Network accessline 105
result = geo_search_func = lambda q: requests.get( - Network accessline 110
uids = requests.get( - Network accessline 110
uids = requests.get( - Network accessline 206
url = f"https://ftp.ncbi.nlm.nih.gov/geo/series/{folder}/{accession}/matrix/{accession}_series_matrix.txt.gz" - Network accessline 206
url = f"https://ftp.ncbi.nlm.nih.gov/geo/series/{folder}/{accession}/matrix/{accession}_series_matrix.txt.gz" - Network accessline 208
with urllib.request.urlopen(url) as resp: - Network accessline 208
with urllib.request.urlopen(url) as resp: - Network accessline 301
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 301
EMAIL = "your@email.com" BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 302
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 302
BASE = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 304
topic = "Alzheimer disease" r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 304
topic = "Alzheimer disease" r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 305
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 305
r = requests.get(f"{BASE}/esearch.fcgi", - Network accessline 312
for i in range(0, len(uids), 20): batch = uids[i:i+20] r2 = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 312
for i in range(0, len(uids), 20): batch = uids[i:i+20] r2 = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 313
batch = uids[i:i+20] r2 = requests.post(f"{BASE}/esummary.fcgi", data={"db": "gds", "id": ",".join(batch), - Network accessline 313
batch = uids[i:i+20] r2 = requests.post(f"{BASE}/esummary.fcgi", data={"db": "gds", "id": ",".join(batch), - Network accessline 314
r2 = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 314
r2 = requests.post(f"{BASE}/esummary.fcgi", - Network accessline 426
- [GEO database home](https://www.ncbi.nlm.nih.gov/geo/) — Browse and search GEO datasets - Network accessline 426
- [GEO database home](https://www.ncbi.nlm.nih.gov/geo/) — Browse and search GEO datasets - Network accessline 427
- [GEOparse GitHub](https://github.com/guma44/GEOparse) — Python library documentation and examples - Network accessline 427
- [GEOparse GitHub](https://github.com/guma44/GEOparse) — Python library documentation and examples - Network accessline 428
- [GEO FTP server](https://ftp.ncbi.nlm.nih.gov/geo/) — Direct file access for series, samples, and platforms - Network accessline 428
- [GEO FTP server](https://ftp.ncbi.nlm.nih.gov/geo/) — Direct file access for series, samples, and platforms - Network accessline 429
- [NCBI E-utilities for GEO](https://www.ncbi.nlm.nih.gov/books/NBK25499/) — ESearch/ESummary API reference for the `gds` database - Network accessline 429
- [NCBI E-utilities for GEO](https://www.ncbi.nlm.nih.gov/books/NBK25499/) — ESearch/ESummary API reference for the `gds` database
skills/genomics-bioinformatics/databases/gget-genomic-databases/SKILL.md
- Network accessline 546
- [gget documentation](https://pachterlab.github.io/gget/) — official docs and tutorials - Network accessline 546
- [gget documentation](https://pachterlab.github.io/gget/) — official docs and tutorials - Network accessline 547
- [gget GitHub](https://github.com/pachterlab/gget) — source code, issues - Network accessline 547
- [gget GitHub](https://github.com/pachterlab/gget) — source code, issues - Network accessline 548
- Luebbert, L. & Pachter, L. (2023). Efficient querying of genomic reference databases with gget. *Bioinformatics*. https://doi.org/10.1093/bioinformatics/btac836 - Network accessline 548
- Luebbert, L. & Pachter, L. (2023). Efficient querying of genomic reference databases with gget. *Bioinformatics*. https://doi.org/10.1093/bioinformatics/btac836
skills/genomics-bioinformatics/databases/gget-genomic-databases/references/databases_workflows.md
- Network accessline 7
| Database | URL | gget Module(s) | Update Frequency | Access Method | |----------|-----|-----------------|------------------|---------------| | **Ensembl** | https://ensembl.org | `ref`, `search`, `info`, `seq` | Quarterly (major releases - Network accessline 7
| Database | URL | gget Module(s) | Update Frequency | Access Method | |----------|-----|-----------------|------------------|---------------| | **Ensembl** | https://ensembl.org | `ref`, `search`, `info`, `seq` | Quarterly (major releases - Network accessline 8
|----------|-----|-----------------|------------------|---------------| | **Ensembl** | https://ensembl.org | `ref`, `search`, `info`, `seq` | Quarterly (major releases ~112+) | REST API | | **UCSC Genome Browser** | https://genome.ucsc.edu - Network accessline 8
|----------|-----|-----------------|------------------|---------------| | **Ensembl** | https://ensembl.org | `ref`, `search`, `info`, `seq` | Quarterly (major releases ~112+) | REST API | | **UCSC Genome Browser** | https://genome.ucsc.edu - Network accessline 9
| **Ensembl** | https://ensembl.org | `ref`, `search`, `info`, `seq` | Quarterly (major releases ~112+) | REST API | - Network accessline 9
| **Ensembl** | https://ensembl.org | `ref`, `search`, `info`, `seq` | Quarterly (major releases ~112+) | REST API | - Network accessline 10
| **UCSC Genome Browser** | https://genome.ucsc.edu | `blat` | Continuous | REST API (BLAT server) | - Network accessline 10
| **UCSC Genome Browser** | https://genome.ucsc.edu | `blat` | Continuous | REST API (BLAT server) | - Network accessline 17
| Database | URL | gget Module(s) | Update Frequency | Access Method | |----------|-----|-----------------|------------------|---------------| | **UniProt** | https://uniprot.org | `info` | Monthly | REST API (via Ensembl cross-refs) | - Network accessline 17
| Database | URL | gget Module(s) | Update Frequency | Access Method | |----------|-----|-----------------|------------------|---------------| | **UniProt** | https://uniprot.org | `info` | Monthly | REST API (via Ensembl cross-refs) | - Network accessline 18
|----------|-----|-----------------|------------------|---------------| | **UniProt** | https://uniprot.org | `info` | Monthly | REST API (via Ensembl cross-refs) | | **NCBI Gene** | https://ncbi.nlm.nih.gov/gene | `info` | Continuous | RES - Network accessline 18
|----------|-----|-----------------|------------------|---------------| | **UniProt** | https://uniprot.org | `info` | Monthly | REST API (via Ensembl cross-refs) | | **NCBI Gene** | https://ncbi.nlm.nih.gov/gene | `info` | Continuous | RES - Network accessline 19
| **UniProt** | https://uniprot.org | `info` | Monthly | REST API (via Ensembl cross-refs) | - Network accessline 19
| **UniProt** | https://uniprot.org | `info` | Monthly | REST API (via Ensembl cross-refs) | - Network accessline 20
| **NCBI Gene** | https://ncbi.nlm.nih.gov/gene | `info` | Continuous | REST API (via Ensembl cross-refs) | - Network accessline 20
| **NCBI Gene** | https://ncbi.nlm.nih.gov/gene | `info` | Continuous | REST API (via Ensembl cross-refs) | - Network accessline 21
| **RCSB PDB** | https://rcsb.org | `pdb` | Weekly (Wed) | REST API + FTP | | **AlphaFold DB** | https://alphafold.ebi.ac.uk | `alphafold` | Major releases | Local model (ColabFold/OpenMM) | | **ELM** | http://elm.eu.org | `elm` | ~Annually - Network accessline 21
| **RCSB PDB** | https://rcsb.org | `pdb` | Weekly (Wed) | REST API + FTP | | **AlphaFold DB** | https://alphafold.ebi.ac.uk | `alphafold` | Major releases | Local model (ColabFold/OpenMM) | | **ELM** | http://elm.eu.org | `elm` | ~Annually - Network accessline 22
| **AlphaFold DB** | https://alphafold.ebi.ac.uk | `alphafold` | Major releases | Local model (ColabFold/OpenMM) | - Network accessline 22
| **AlphaFold DB** | https://alphafold.ebi.ac.uk | `alphafold` | Major releases | Local model (ColabFold/OpenMM) | - Network accessline 23
| **ELM** | http://elm.eu.org | `elm` | ~Annually | Local download (`gget setup elm`) | - Network accessline 23
| **ELM** | http://elm.eu.org | `elm` | ~Annually | Local download (`gget setup elm`) | - Network accessline 32
| Database | URL | gget Module(s) | Update Frequency | Access Method | |----------|-----|-----------------|------------------|---------------| | **NCBI BLAST** | https://blast.ncbi.nlm.nih.gov | `blast` | Continuous | REST API (remote queue - Network accessline 32
| Database | URL | gget Module(s) | Update Frequency | Access Method | |----------|-----|-----------------|------------------|---------------| | **NCBI BLAST** | https://blast.ncbi.nlm.nih.gov | `blast` | Continuous | REST API (remote queue - Network accessline 33
|----------|-----|-----------------|------------------|---------------| | **NCBI BLAST** | https://blast.ncbi.nlm.nih.gov | `blast` | Continuous | REST API (remote queue) | | **DIAMOND** (local) | https://github.com/bbuchfink/diamond | `dia - Network accessline 33
|----------|-----|-----------------|------------------|---------------| | **NCBI BLAST** | https://blast.ncbi.nlm.nih.gov | `blast` | Continuous | REST API (remote queue) | | **DIAMOND** (local) | https://github.com/bbuchfink/diamond | `dia - Network accessline 34
| **NCBI BLAST** | https://blast.ncbi.nlm.nih.gov | `blast` | Continuous | REST API (remote queue) | - Network accessline 34
| **NCBI BLAST** | https://blast.ncbi.nlm.nih.gov | `blast` | Continuous | REST API (remote queue) | - Network accessline 35
| **DIAMOND** (local) | https://github.com/bbuchfink/diamond | `diamond` | User-managed | Local binary | - Network accessline 35
| **DIAMOND** (local) | https://github.com/bbuchfink/diamond | `diamond` | User-managed | Local binary | - Network accessline 36
| **Muscle5** (local) | https://drive5.com/muscle5 | `muscle` | Stable | Local binary | - Network accessline 36
| **Muscle5** (local) | https://drive5.com/muscle5 | `muscle` | Stable | Local binary | - Network accessline 44
| Database | URL | gget Module(s) | Update Frequency | Access Method | |----------|-----|-----------------|------------------|---------------| | **ARCHS4** | https://maayanlab.cloud/archs4 | `archs4` | ~Quarterly | REST API | - Network accessline 44
| Database | URL | gget Module(s) | Update Frequency | Access Method | |----------|-----|-----------------|------------------|---------------| | **ARCHS4** | https://maayanlab.cloud/archs4 | `archs4` | ~Quarterly | REST API | - Network accessline 45
|----------|-----|-----------------|------------------|---------------| | **ARCHS4** | https://maayanlab.cloud/archs4 | `archs4` | ~Quarterly | REST API | | **CZ CELLxGENE Census** | https://cellxgene.cziscience.com | `cellxgene` | Monthly - Network accessline 45
|----------|-----|-----------------|------------------|---------------| | **ARCHS4** | https://maayanlab.cloud/archs4 | `archs4` | ~Quarterly | REST API | | **CZ CELLxGENE Census** | https://cellxgene.cziscience.com | `cellxgene` | Monthly - Network accessline 46
| **ARCHS4** | https://maayanlab.cloud/archs4 | `archs4` | ~Quarterly | REST API | | **CZ CELLxGENE Census** | https://cellxgene.cziscience.com | `cellxgene` | Monthly snapshots | Local download via `cellxgene-census` | | **Bgee** | https:/ - Network accessline 46
| **ARCHS4** | https://maayanlab.cloud/archs4 | `archs4` | ~Quarterly | REST API | | **CZ CELLxGENE Census** | https://cellxgene.cziscience.com | `cellxgene` | Monthly snapshots | Local download via `cellxgene-census` | | **Bgee** | https:/ - Network accessline 47
| **CZ CELLxGENE Census** | https://cellxgene.cziscience.com | `cellxgene` | Monthly snapshots | Local download via `cellxgene-census` | - Network accessline 47
| **CZ CELLxGENE Census** | https://cellxgene.cziscience.com | `cellxgene` | Monthly snapshots | Local download via `cellxgene-census` | - Network accessline 48
| **Bgee** | https://bgee.org | `bgee` | ~Annually | REST API | - Network accessline 48
| **Bgee** | https://bgee.org | `bgee` | ~Annually | REST API | - Network accessline 56
| Database | URL | gget Module(s) | Update Frequency | Access Method | |----------|-----|-----------------|------------------|---------------| | **Enrichr** | https://maayanlab.cloud/Enrichr | `enrichr` | ~Annually per library | REST API | - Network accessline 56
| Database | URL | gget Module(s) | Update Frequency | Access Method | |----------|-----|-----------------|------------------|---------------| | **Enrichr** | https://maayanlab.cloud/Enrichr | `enrichr` | ~Annually per library | REST API | - Network accessline 58
| **Enrichr** | https://maayanlab.cloud/Enrichr | `enrichr` | ~Annually per library | REST API | - Network accessline 58
| **Enrichr** | https://maayanlab.cloud/Enrichr | `enrichr` | ~Annually per library | REST API | - Network accessline 64
| Database | URL | gget Module(s) | Update Frequency | Access Method | |----------|-----|-----------------|------------------|---------------| | **OpenTargets** | https://platform.opentargets.org | `opentargets` | ~Bimonthly | REST API (Gra - Network accessline 64
| Database | URL | gget Module(s) | Update Frequency | Access Method | |----------|-----|-----------------|------------------|---------------| | **OpenTargets** | https://platform.opentargets.org | `opentargets` | ~Bimonthly | REST API (Gra - Network accessline 65
|----------|-----|-----------------|------------------|---------------| | **OpenTargets** | https://platform.opentargets.org | `opentargets` | ~Bimonthly | REST API (GraphQL) | | **cBioPortal** | https://www.cbioportal.org | `cbio` | Contin - Network accessline 65
|----------|-----|-----------------|------------------|---------------| | **OpenTargets** | https://platform.opentargets.org | `opentargets` | ~Bimonthly | REST API (GraphQL) | | **cBioPortal** | https://www.cbioportal.org | `cbio` | Contin - Network accessline 66
| **OpenTargets** | https://platform.opentargets.org | `opentargets` | ~Bimonthly | REST API (GraphQL) | | **cBioPortal** | https://www.cbioportal.org | `cbio` | Continuous | REST API | | **COSMIC** | https://cancer.sanger.ac.uk/cosmic | `c - Network accessline 66
| **OpenTargets** | https://platform.opentargets.org | `opentargets` | ~Bimonthly | REST API (GraphQL) | | **cBioPortal** | https://www.cbioportal.org | `cbio` | Continuous | REST API | | **COSMIC** | https://cancer.sanger.ac.uk/cosmic | `c - Network accessline 67
| **cBioPortal** | https://www.cbioportal.org | `cbio` | Continuous | REST API | - Network accessline 67
| **cBioPortal** | https://www.cbioportal.org | `cbio` | Continuous | REST API | - Network accessline 68
| **COSMIC** | https://cancer.sanger.ac.uk/cosmic | `cosmic` | ~Quarterly | Local TSV download (requires account) | - Network accessline 68
| **COSMIC** | https://cancer.sanger.ac.uk/cosmic | `cosmic` | ~Quarterly | Local TSV download (requires account) | - Network accessline 70
- **OpenTargets**: Comprehensive target-disease-drug platform. Seven resource types: `diseases`, `drugs`, `tractability`, `pharmacogenetics`, `expression`, `depmap`, `interactions`. Input: Ensembl gene ID. Returns scored associations. No au - Network accessline 70
- **OpenTargets**: Comprehensive target-disease-drug platform. Seven resource types: `diseases`, `drugs`, `tractability`, `pharmacogenetics`, `expression`, `depmap`, `interactions`. Input: Ensembl gene ID. Returns scored associations. No au - Network accessline 72
[Credential-related source omitted] - Network accessline 72
[Credential-related source omitted] - Network accessline 76
| Database | URL | gget Module(s) | Update Frequency | Access Method | |----------|-----|-----------------|------------------|---------------| | **OpenAI** | https://openai.com | `gpt` | N/A | REST API (requires API key) | - Network accessline 76
| Database | URL | gget Module(s) | Update Frequency | Access Method | |----------|-----|-----------------|------------------|---------------| | **OpenAI** | https://openai.com | `gpt` | N/A | REST API (requires API key) | - Network accessline 78
| **OpenAI** | https://openai.com | `gpt` | N/A | REST API (requires API key) | - Network accessline 78
| **OpenAI** | https://openai.com | `gpt` | N/A | REST API (requires API key) | - Network accessline 287
1. **Ensembl schema changes**: Gene IDs are stable across releases but annotations change. If `gget.info()` returns unexpected columns, update gget: `pip install --upgrade gget`. 2. **CELLxGENE Census API changes**: The Census API evolves r - Network accessline 287
1. **Ensembl schema changes**: Gene IDs are stable across releases but annotations change. If `gget.info()` returns unexpected columns, update gget: `pip install --upgrade gget`. 2. **CELLxGENE Census API changes**: The Census API evolves r - Network accessline 288
2. **CELLxGENE Census API changes**: The Census API evolves rapidly. Pin `census_version` to a specific date. If queries fail after a gget update, check the [Census changelog](https://chanzuckerberg.github.io/cellxgene-census/). - Network accessline 288
2. **CELLxGENE Census API changes**: The Census API evolves rapidly. Pin `census_version` to a specific date. If queries fail after a gget update, check the [Census changelog](https://chanzuckerberg.github.io/cellxgene-census/). - Network accessline 322
1. **gget itself**: Luebbert, L. & Pachter, L. (2023). Efficient querying of genomic reference databases with gget. *Bioinformatics*. https://doi.org/10.1093/bioinformatics/btac836 - Network accessline 322
1. **gget itself**: Luebbert, L. & Pachter, L. (2023). Efficient querying of genomic reference databases with gget. *Bioinformatics*. https://doi.org/10.1093/bioinformatics/btac836
skills/genomics-bioinformatics/databases/gnomad-database/SKILL.md
- Network accessline 11
The Genome Aggregation Database (gnomAD) is a resource of aggregated exome and genome sequencing data from 730,000+ individuals. It provides population variant frequencies stratified by 9 ancestry groups, gene-level constraint scores (pLI, - Network accessline 11
The Genome Aggregation Database (gnomAD) is a resource of aggregated exome and genome sequencing data from 730,000+ individuals. It provides population variant frequencies stratified by 9 ancestry groups, gene-level constraint scores (pLI, - Network accessline 41
GNOMAD_API = "https://gnomad.broadinstitute.org/api" - Network accessline 41
GNOMAD_API = "https://gnomad.broadinstitute.org/api" - Network accessline 44
"""Execute a gnomAD GraphQL query and return the data payload.""" payload = {"query": query, "variables": variables or {}} r = requests.post(GNOMAD_API, json=payload, timeout=30) - Network accessline 44
"""Execute a gnomAD GraphQL query and return the data payload.""" payload = {"query": query, "variables": variables or {}} r = requests.post(GNOMAD_API, json=payload, timeout=30) - Network accessline 45
payload = {"query": query, "variables": variables or {}} r = requests.post(GNOMAD_API, json=payload, timeout=30) r.raise_for_status() - Network accessline 45
payload = {"query": query, "variables": variables or {}} r = requests.post(GNOMAD_API, json=payload, timeout=30) r.raise_for_status() - Network accessline 46
r = requests.post(GNOMAD_API, json=payload, timeout=30) - Network accessline 46
r = requests.post(GNOMAD_API, json=payload, timeout=30) - Network accessline 78
GNOMAD_API = "https://gnomad.broadinstitute.org/api" - Network accessline 78
GNOMAD_API = "https://gnomad.broadinstitute.org/api" - Network accessline 80
def gnomad_query(query, variables=None): r = requests.post(GNOMAD_API, json={"query": query, "variables": variables or {}}, timeout=30) - Network accessline 80
def gnomad_query(query, variables=None): r = requests.post(GNOMAD_API, json={"query": query, "variables": variables or {}}, timeout=30) - Network accessline 81
r = requests.post(GNOMAD_API, json={"query": query, "variables": variables or {}}, timeout=30) - Network accessline 81
r = requests.post(GNOMAD_API, json={"query": query, "variables": variables or {}}, timeout=30) - Network accessline 409
GNOMAD_API = "https://gnomad.broadinstitute.org/api" - Network accessline 409
GNOMAD_API = "https://gnomad.broadinstitute.org/api" - Network accessline 411
def gnomad_query(query, variables=None): r = requests.post(GNOMAD_API, json={"query": query, "variables": variables or {}}, timeout=30) - Network accessline 411
def gnomad_query(query, variables=None): r = requests.post(GNOMAD_API, json={"query": query, "variables": variables or {}}, timeout=30) - Network accessline 412
r = requests.post(GNOMAD_API, json={"query": query, "variables": variables or {}}, timeout=30) - Network accessline 412
r = requests.post(GNOMAD_API, json={"query": query, "variables": variables or {}}, timeout=30) - Network accessline 483
GNOMAD_API = "https://gnomad.broadinstitute.org/api" - Network accessline 483
GNOMAD_API = "https://gnomad.broadinstitute.org/api" - Network accessline 485
def gnomad_query(query, variables=None): r = requests.post(GNOMAD_API, json={"query": query, "variables": variables or {}}, timeout=30) - Network accessline 485
def gnomad_query(query, variables=None): r = requests.post(GNOMAD_API, json={"query": query, "variables": variables or {}}, timeout=30) - Network accessline 486
r = requests.post(GNOMAD_API, json={"query": query, "variables": variables or {}}, timeout=30) - Network accessline 486
r = requests.post(GNOMAD_API, json={"query": query, "variables": variables or {}}, timeout=30) - Network accessline 542
GNOMAD_API = "https://gnomad.broadinstitute.org/api" - Network accessline 542
GNOMAD_API = "https://gnomad.broadinstitute.org/api" - Network accessline 544
def gnomad_query(query, variables=None): r = requests.post(GNOMAD_API, json={"query": query, "variables": variables or {}}, timeout=30) - Network accessline 544
def gnomad_query(query, variables=None): r = requests.post(GNOMAD_API, json={"query": query, "variables": variables or {}}, timeout=30) - Network accessline 545
r = requests.post(GNOMAD_API, json={"query": query, "variables": variables or {}}, timeout=30) - Network accessline 545
r = requests.post(GNOMAD_API, json={"query": query, "variables": variables or {}}, timeout=30) - Network accessline 627
GNOMAD_API = "https://gnomad.broadinstitute.org/api" - Network accessline 627
GNOMAD_API = "https://gnomad.broadinstitute.org/api" - Network accessline 635
} """ r = requests.post(GNOMAD_API, json={"query": query, - Network accessline 635
} """ r = requests.post(GNOMAD_API, json={"query": query, - Network accessline 636
""" r = requests.post(GNOMAD_API, json={"query": query, "variables": {"variantId": variant_id, "dataset": dataset}}, - Network accessline 636
""" r = requests.post(GNOMAD_API, json={"query": query, "variables": {"variantId": variant_id, "dataset": dataset}}, - Network accessline 637
r = requests.post(GNOMAD_API, json={"query": query, - Network accessline 637
r = requests.post(GNOMAD_API, json={"query": query, - Network accessline 661
GNOMAD_API = "https://gnomad.broadinstitute.org/api" - Network accessline 661
GNOMAD_API = "https://gnomad.broadinstitute.org/api" - Network accessline 669
} """ r = requests.post(GNOMAD_API, json={"query": query, - Network accessline 669
} """ r = requests.post(GNOMAD_API, json={"query": query, - Network accessline 670
""" r = requests.post(GNOMAD_API, json={"query": query, "variables": {"gene_symbol": gene_symbol, "reference_genome": reference_genome}}, - Network accessline 670
""" r = requests.post(GNOMAD_API, json={"query": query, "variables": {"gene_symbol": gene_symbol, "reference_genome": reference_genome}}, - Network accessline 671
r = requests.post(GNOMAD_API, json={"query": query, - Network accessline 671
r = requests.post(GNOMAD_API, json={"query": query, - Network accessline 698
GNOMAD_API = "https://gnomad.broadinstitute.org/api" - Network accessline 698
GNOMAD_API = "https://gnomad.broadinstitute.org/api" - Network accessline 709
} """ r = requests.post(GNOMAD_API, json={"query": query, - Network accessline 709
} """ r = requests.post(GNOMAD_API, json={"query": query, - Network accessline 710
""" r = requests.post(GNOMAD_API, json={"query": query, "variables": {"gene_symbol": gene_symbol, "reference_genome": "GRCh38", "dataset": dataset}}, - Network accessline 710
""" r = requests.post(GNOMAD_API, json={"query": query, "variables": {"gene_symbol": gene_symbol, "reference_genome": "GRCh38", "dataset": dataset}}, - Network accessline 711
r = requests.post(GNOMAD_API, json={"query": query, - Network accessline 711
r = requests.post(GNOMAD_API, json={"query": query, - Network accessline 754
- [gnomAD GraphQL API](https://gnomad.broadinstitute.org/api) — Interactive GraphQL explorer and endpoint documentation - Network accessline 754
- [gnomAD GraphQL API](https://gnomad.broadinstitute.org/api) — Interactive GraphQL explorer and endpoint documentation - Network accessline 755
- [Karczewski et al., Nature 2020](https://doi.org/10.1038/s41586-020-2308-7) — gnomAD v2.1 flagship paper (constraint metrics, LoF analysis) - Network accessline 755
- [Karczewski et al., Nature 2020](https://doi.org/10.1038/s41586-020-2308-7) — gnomAD v2.1 flagship paper (constraint metrics, LoF analysis) - Network accessline 756
- [gnomAD Help & FAQ](https://gnomad.broadinstitute.org/help) — Data model, ancestry definitions, FAF95 explanation - Network accessline 756
- [gnomAD Help & FAQ](https://gnomad.broadinstitute.org/help) — Data model, ancestry definitions, FAF95 explanation - Network accessline 757
- [gnomAD v4 blog post](https://gnomad.broadinstitute.org/news/2023-11-gnomad-v4-0/) — gnomAD v4 release notes and dataset composition - Network accessline 757
- [gnomAD v4 blog post](https://gnomad.broadinstitute.org/news/2023-11-gnomad-v4-0/) — gnomAD v4 release notes and dataset composition
skills/genomics-bioinformatics/databases/gwas-database/SKILL.md
- Network accessline 34
- **No authentication** required -- fully open access - **Rate limits**: no official limit, but add `time.sleep(0.2)` between requests to be courteous - **Base URL**: `https://www.ebi.ac.uk/gwas/rest/api` - Network accessline 34
- **No authentication** required -- fully open access - **Rate limits**: no official limit, but add `time.sleep(0.2)` between requests to be courteous - **Base URL**: `https://www.ebi.ac.uk/gwas/rest/api` - Network accessline 35
- **Rate limits**: no official limit, but add `time.sleep(0.2)` between requests to be courteous - **Base URL**: `https://www.ebi.ac.uk/gwas/rest/api` - **Response format**: HAL+JSON with `_embedded` data and `_links` for pagination - Network accessline 35
- **Rate limits**: no official limit, but add `time.sleep(0.2)` between requests to be courteous - **Base URL**: `https://www.ebi.ac.uk/gwas/rest/api` - **Response format**: HAL+JSON with `_embedded` data and `_links` for pagination - Network accessline 36
- **Base URL**: `https://www.ebi.ac.uk/gwas/rest/api` - Network accessline 36
- **Base URL**: `https://www.ebi.ac.uk/gwas/rest/api` - Network accessline 46
BASE = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 46
BASE = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 49
"""GWAS Catalog REST API helper with rate limiting and pagination support.""" url = f"{BASE}/{endpoint}" resp = requests.get(url, params=params or {}) - Network accessline 49
"""GWAS Catalog REST API helper with rate limiting and pagination support.""" url = f"{BASE}/{endpoint}" resp = requests.get(url, params=params or {}) - Network accessline 50
url = f"{BASE}/{endpoint}" resp = requests.get(url, params=params or {}) resp.raise_for_status() - Network accessline 50
url = f"{BASE}/{endpoint}" resp = requests.get(url, params=params or {}) resp.raise_for_status() - Network accessline 51
resp = requests.get(url, params=params or {}) - Network accessline 51
resp = requests.get(url, params=params or {}) - Network accessline 131
if not href: return [] r = requests.get(href, timeout=15) - Network accessline 131
if not href: return [] r = requests.get(href, timeout=15) - Network accessline 132
return [] r = requests.get(href, timeout=15) if not r.ok: - Network accessline 132
return [] r = requests.get(href, timeout=15) if not r.ok: - Network accessline 133
r = requests.get(href, timeout=15) - Network accessline 133
r = requests.get(href, timeout=15) - Network accessline 218
# (`studies/{acc}/summaryStatistics` returns 404). Use the GWAS Catalog FTP # directly — paths are predictable by study accession: ftp_base = "http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics" - Network accessline 218
# (`studies/{acc}/summaryStatistics` returns 404). Use the GWAS Catalog FTP # directly — paths are predictable by study accession: ftp_base = "http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics" - Network accessline 219
# directly — paths are predictable by study accession: ftp_base = "http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics" acc = studies[0]["accessionId"] - Network accessline 219
# directly — paths are predictable by study accession: ftp_base = "http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics" acc = studies[0]["accessionId"] - Network accessline 220
ftp_base = "http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics" - Network accessline 220
ftp_base = "http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics" - Network accessline 231
study_id = "GCST006867" # Example study ftp_base = "http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics" # Actual paths vary by study; check the study page for the download link - Network accessline 231
study_id = "GCST006867" # Example study ftp_base = "http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics" # Actual paths vary by study; check the study page for the download link - Network accessline 232
ftp_base = "http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics" # Actual paths vary by study; check the study page for the download link # Example: ftp_base/{study_id}/{study_id}.tsv.gz - Network accessline 232
ftp_base = "http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics" # Actual paths vary by study; check the study page for the download link # Example: ftp_base/{study_id}/{study_id}.tsv.gz - Network accessline 235
url = f"{ftp_base}/{study_id}" print(f"Summary stats FTP directory: {url}") # Use requests.get() or urllib to download the .tsv.gz file - Network accessline 235
url = f"{ftp_base}/{study_id}" print(f"Summary stats FTP directory: {url}") # Use requests.get() or urllib to download the .tsv.gz file - Network accessline 237
# Use requests.get() or urllib to download the .tsv.gz file - Network accessline 237
# Use requests.get() or urllib to download the .tsv.gz file - Network accessline 313
BASE = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 313
BASE = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 315
def gwas_get(endpoint, params=None): url = f"{BASE}/{endpoint}" resp = requests.get(url, params=params or {}) - Network accessline 315
def gwas_get(endpoint, params=None): url = f"{BASE}/{endpoint}" resp = requests.get(url, params=params or {}) - Network accessline 316
url = f"{BASE}/{endpoint}" resp = requests.get(url, params=params or {}) resp.raise_for_status() - Network accessline 316
url = f"{BASE}/{endpoint}" resp = requests.get(url, params=params or {}) resp.raise_for_status() - Network accessline 317
resp = requests.get(url, params=params or {}) - Network accessline 317
resp = requests.get(url, params=params or {}) - Network accessline 361
BASE = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 361
BASE = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 363
def gwas_get(endpoint, params=None): url = f"{BASE}/{endpoint}" resp = requests.get(url, params=params or {}) - Network accessline 363
def gwas_get(endpoint, params=None): url = f"{BASE}/{endpoint}" resp = requests.get(url, params=params or {}) - Network accessline 364
url = f"{BASE}/{endpoint}" resp = requests.get(url, params=params or {}) resp.raise_for_status() - Network accessline 364
url = f"{BASE}/{endpoint}" resp = requests.get(url, params=params or {}) resp.raise_for_status() - Network accessline 365
resp = requests.get(url, params=params or {}) - Network accessline 365
resp = requests.get(url, params=params or {}) - Network accessline 382
if href in href_cache: return href_cache[href] r = requests.get(href, timeout=15) - Network accessline 382
if href in href_cache: return href_cache[href] r = requests.get(href, timeout=15) - Network accessline 383
return href_cache[href] r = requests.get(href, timeout=15) href_cache[href] = (r.json().get("_embedded", {}).get("efoTraits", [])) if r.ok else [] - Network accessline 383
return href_cache[href] r = requests.get(href, timeout=15) href_cache[href] = (r.json().get("_embedded", {}).get("efoTraits", [])) if r.ok else [] - Network accessline 384
r = requests.get(href, timeout=15) - Network accessline 384
r = requests.get(href, timeout=15) - Network accessline 415
# Summary statistics as a CHR/BP/P table (simulated here for demonstration). # In practice: download from GWAS Catalog FTP, e.g. # url = "http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCSTXXXXXX/..." - Network accessline 415
# Summary statistics as a CHR/BP/P table (simulated here for demonstration). # In practice: download from GWAS Catalog FTP, e.g. # url = "http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCSTXXXXXX/..." - Network accessline 416
# In practice: download from GWAS Catalog FTP, e.g. # url = "http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCSTXXXXXX/..." # import gzip, requests; data = gzip.decompress(requests.get(url).content) - Network accessline 416
# In practice: download from GWAS Catalog FTP, e.g. # url = "http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCSTXXXXXX/..." # import gzip, requests; data = gzip.decompress(requests.get(url).content) - Network accessline 417
# url = "http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCSTXXXXXX/..." # import gzip, requests; data = gzip.decompress(requests.get(url).content) np.random.seed(42) - Network accessline 417
# url = "http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCSTXXXXXX/..." # import gzip, requests; data = gzip.decompress(requests.get(url).content) np.random.seed(42) - Network accessline 418
# import gzip, requests; data = gzip.decompress(requests.get(url).content) - Network accessline 418
# import gzip, requests; data = gzip.decompress(requests.get(url).content) - Network accessline 472
# Step 1: Get EFO/MONDO shortForm from GWAS Catalog # (the old `findByDescription` endpoint was removed; use `findByEfoTrait`) BASE = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 472
# Step 1: Get EFO/MONDO shortForm from GWAS Catalog # (the old `findByDescription` endpoint was removed; use `findByEfoTrait`) BASE = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 473
# (the old `findByDescription` endpoint was removed; use `findByEfoTrait`) BASE = "https://www.ebi.ac.uk/gwas/rest/api" traits = requests.get(f"{BASE}/efoTraits/search/findByEfoTrait", - Network accessline 473
# (the old `findByDescription` endpoint was removed; use `findByEfoTrait`) BASE = "https://www.ebi.ac.uk/gwas/rest/api" traits = requests.get(f"{BASE}/efoTraits/search/findByEfoTrait", - Network accessline 474
BASE = "https://www.ebi.ac.uk/gwas/rest/api" traits = requests.get(f"{BASE}/efoTraits/search/findByEfoTrait", params={"trait": "coronary artery disease"}).json() - Network accessline 474
BASE = "https://www.ebi.ac.uk/gwas/rest/api" traits = requests.get(f"{BASE}/efoTraits/search/findByEfoTrait", params={"trait": "coronary artery disease"}).json() - Network accessline 475
traits = requests.get(f"{BASE}/efoTraits/search/findByEfoTrait", - Network accessline 475
traits = requests.get(f"{BASE}/efoTraits/search/findByEfoTrait", - Network accessline 480
# Step 2: Query PGS Catalog for scores using same EFO pgs_url = f"https://www.pgscatalog.org/rest/score/search?trait_id={efo_id}" pgs_data = requests.get(pgs_url).json() - Network accessline 480
# Step 2: Query PGS Catalog for scores using same EFO pgs_url = f"https://www.pgscatalog.org/rest/score/search?trait_id={efo_id}" pgs_data = requests.get(pgs_url).json() - Network accessline 481
pgs_url = f"https://www.pgscatalog.org/rest/score/search?trait_id={efo_id}" pgs_data = requests.get(pgs_url).json() print(f"PGS scores for {efo_id}: {pgs_data.get('count', 0)}") - Network accessline 481
pgs_url = f"https://www.pgscatalog.org/rest/score/search?trait_id={efo_id}" pgs_data = requests.get(pgs_url).json() print(f"PGS scores for {efo_id}: {pgs_data.get('count', 0)}") - Network accessline 482
pgs_data = requests.get(pgs_url).json() - Network accessline 482
pgs_data = requests.get(pgs_url).json() - Network accessline 495
BASE = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 495
BASE = "https://www.ebi.ac.uk/gwas/rest/api" - Network accessline 497
# Query region: chr9:21,900,000-22,200,000 (CDKN2A/2B locus) data = requests.get(f"{BASE}/singleNucleotidePolymorphisms/search/findByChromBpLocationRange", - Network accessline 497
# Query region: chr9:21,900,000-22,200,000 (CDKN2A/2B locus) data = requests.get(f"{BASE}/singleNucleotidePolymorphisms/search/findByChromBpLocationRange", - Network accessline 498
data = requests.get(f"{BASE}/singleNucleotidePolymorphisms/search/findByChromBpLocationRange", - Network accessline 498
data = requests.get(f"{BASE}/singleNucleotidePolymorphisms/search/findByChromBpLocationRange", - Network accessline 508
rs = v["rsId"] try: a_data = requests.get(f"{BASE}/singleNucleotidePolymorphisms/{rs}/associations", - Network accessline 508
rs = v["rsId"] try: a_data = requests.get(f"{BASE}/singleNucleotidePolymorphisms/{rs}/associations", - Network accessline 509
try: a_data = requests.get(f"{BASE}/singleNucleotidePolymorphisms/{rs}/associations", params={"size": 50}).json() - Network accessline 509
try: a_data = requests.get(f"{BASE}/singleNucleotidePolymorphisms/{rs}/associations", params={"size": 50}).json() - Network accessline 510
a_data = requests.get(f"{BASE}/singleNucleotidePolymorphisms/{rs}/associations", - Network accessline 510
a_data = requests.get(f"{BASE}/singleNucleotidePolymorphisms/{rs}/associations", - Network accessline 532
BASE = "https://www.ebi.ac.uk/gwas/rest/api" data = requests.get(f"{BASE}/singleNucleotidePolymorphisms/rs1801282/associations", - Network accessline 532
BASE = "https://www.ebi.ac.uk/gwas/rest/api" data = requests.get(f"{BASE}/singleNucleotidePolymorphisms/rs1801282/associations", - Network accessline 533
data = requests.get(f"{BASE}/singleNucleotidePolymorphisms/rs1801282/associations", - Network accessline 533
data = requests.get(f"{BASE}/singleNucleotidePolymorphisms/rs1801282/associations", - Network accessline 562
| `404 Not Found` | Invalid GCST, rs ID, or EFO ID | Verify identifier exists via search endpoint first | | Empty `_embedded` | No results for query | Broaden search terms or check spelling; try partial match | | `ConnectionError` / timeout - Network accessline 562
| `404 Not Found` | Invalid GCST, rs ID, or EFO ID | Verify identifier exists via search endpoint first | | Empty `_embedded` | No results for query | Broaden search terms or check spelling; try partial match | | `ConnectionError` / timeout - Network accessline 563
| Empty `_embedded` | No results for query | Broaden search terms or check spelling; try partial match | | `ConnectionError` / timeout | EBI server temporarily down | Retry with exponential backoff; check https://www.ebi.ac.uk/gwas/status | - Network accessline 563
| Empty `_embedded` | No results for query | Broaden search terms or check spelling; try partial match | | `ConnectionError` / timeout | EBI server temporarily down | Retry with exponential backoff; check https://www.ebi.ac.uk/gwas/status | - Network accessline 564
| `ConnectionError` / timeout | EBI server temporarily down | Retry with exponential backoff; check https://www.ebi.ac.uk/gwas/status | - Network accessline 564
| `ConnectionError` / timeout | EBI server temporarily down | Retry with exponential backoff; check https://www.ebi.ac.uk/gwas/status | - Network accessline 591
- [GWAS Catalog website](https://www.ebi.ac.uk/gwas/) -- main portal for browsing and searching - Network accessline 591
- [GWAS Catalog website](https://www.ebi.ac.uk/gwas/) -- main portal for browsing and searching - Network accessline 592
- [GWAS Catalog REST API documentation](https://www.ebi.ac.uk/gwas/rest/docs/api) -- official API reference - Network accessline 592
- [GWAS Catalog REST API documentation](https://www.ebi.ac.uk/gwas/rest/docs/api) -- official API reference - Network accessline 593
- [GWAS Catalog GitHub](https://github.com/EBISPOT/goci) -- source code and issue tracker - Network accessline 593
- [GWAS Catalog GitHub](https://github.com/EBISPOT/goci) -- source code and issue tracker - Network accessline 594
- [Summary Statistics FTP](http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/) -- bulk summary statistics downloads - Network accessline 594
- [Summary Statistics FTP](http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/) -- bulk summary statistics downloads - Network accessline 595
- [PGS Catalog](https://www.pgscatalog.org/) -- polygenic score database using GWAS data - Network accessline 595
- [PGS Catalog](https://www.pgscatalog.org/) -- polygenic score database using GWAS data
skills/genomics-bioinformatics/databases/gwas-database/references/api_endpoints.md
skills/genomics-bioinformatics/databases/jaspar-database/SKILL.md
- Network accessline 11
JASPAR is a curated, open-access database of transcription factor (TF) binding profiles represented as position frequency matrices (PFMs). The 2024 release contains 1,209 profiles in the CORE vertebrate collection, covering 783 TFs with exp - Network accessline 11
JASPAR is a curated, open-access database of transcription factor (TF) binding profiles represented as position frequency matrices (PFMs). The 2024 release contains 1,209 profiles in the CORE vertebrate collection, covering 783 TFs with exp - Network accessline 42
JASPAR_API = "https://jaspar.elixir.no/api/v1" - Network accessline 42
JASPAR_API = "https://jaspar.elixir.no/api/v1" - Network accessline 44
# Search for CTCF profile in the CORE vertebrate collection r = requests.get(f"{JASPAR_API}/matrix/", params={ - Network accessline 44
# Search for CTCF profile in the CORE vertebrate collection r = requests.get(f"{JASPAR_API}/matrix/", params={ - Network accessline 45
r = requests.get(f"{JASPAR_API}/matrix/", params={ - Network accessline 45
r = requests.get(f"{JASPAR_API}/matrix/", params={ - Network accessline 70
JASPAR_API = "https://jaspar.elixir.no/api/v1" - Network accessline 70
JASPAR_API = "https://jaspar.elixir.no/api/v1" - Network accessline 84
url = f"{JASPAR_API}/matrix/" while url: r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) - Network accessline 84
url = f"{JASPAR_API}/matrix/" while url: r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) - Network accessline 85
while url: r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) r.raise_for_status() - Network accessline 85
while url: r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) r.raise_for_status() - Network accessline 86
r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) - Network accessline 86
r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) - Network accessline 108
JASPAR_API = "https://jaspar.elixir.no/api/v1" - Network accessline 108
JASPAR_API = "https://jaspar.elixir.no/api/v1" - Network accessline 110
def get_matrix(matrix_id): """Return full matrix record for a JASPAR ID (e.g. 'MA0139.1').""" r = requests.get(f"{JASPAR_API}/matrix/{matrix_id}/", params={"format": "json"}, timeout=15) - Network accessline 110
def get_matrix(matrix_id): """Return full matrix record for a JASPAR ID (e.g. 'MA0139.1').""" r = requests.get(f"{JASPAR_API}/matrix/{matrix_id}/", params={"format": "json"}, timeout=15) - Network accessline 111
"""Return full matrix record for a JASPAR ID (e.g. 'MA0139.1').""" r = requests.get(f"{JASPAR_API}/matrix/{matrix_id}/", params={"format": "json"}, timeout=15) r.raise_for_status() - Network accessline 111
"""Return full matrix record for a JASPAR ID (e.g. 'MA0139.1').""" r = requests.get(f"{JASPAR_API}/matrix/{matrix_id}/", params={"format": "json"}, timeout=15) r.raise_for_status() - Network accessline 112
r = requests.get(f"{JASPAR_API}/matrix/{matrix_id}/", params={"format": "json"}, timeout=15) - Network accessline 112
r = requests.get(f"{JASPAR_API}/matrix/{matrix_id}/", params={"format": "json"}, timeout=15) - Network accessline 139
JASPAR_API = "https://jaspar.elixir.no/api/v1" - Network accessline 139
JASPAR_API = "https://jaspar.elixir.no/api/v1" - Network accessline 158
r = requests.get(f"{JASPAR_API}/matrix/MA0139.1/", params={"format": "json"}, timeout=15) - Network accessline 158
r = requests.get(f"{JASPAR_API}/matrix/MA0139.1/", params={"format": "json"}, timeout=15) - Network accessline 177
JASPAR_API = "https://jaspar.elixir.no/api/v1" - Network accessline 177
JASPAR_API = "https://jaspar.elixir.no/api/v1" - Network accessline 209
# Fetch CTCF matrix and scan a synthetic CTCF-like sequence r = requests.get(f"{JASPAR_API}/matrix/MA0139.1/", params={"format": "json"}, timeout=15) - Network accessline 209
# Fetch CTCF matrix and scan a synthetic CTCF-like sequence r = requests.get(f"{JASPAR_API}/matrix/MA0139.1/", params={"format": "json"}, timeout=15) - Network accessline 210
r = requests.get(f"{JASPAR_API}/matrix/MA0139.1/", params={"format": "json"}, timeout=15) - Network accessline 210
r = requests.get(f"{JASPAR_API}/matrix/MA0139.1/", params={"format": "json"}, timeout=15) - Network accessline 231
JASPAR_API = "https://jaspar.elixir.no/api/v1" - Network accessline 231
JASPAR_API = "https://jaspar.elixir.no/api/v1" - Network accessline 246
url = f"{JASPAR_API}/matrix/" while url: r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) - Network accessline 246
url = f"{JASPAR_API}/matrix/" while url: r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) - Network accessline 247
while url: r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) r.raise_for_status() - Network accessline 247
while url: r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) r.raise_for_status() - Network accessline 248
r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) - Network accessline 248
r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) - Network accessline 277
JASPAR_API = "https://jaspar.elixir.no/api/v1" - Network accessline 277
JASPAR_API = "https://jaspar.elixir.no/api/v1" - Network accessline 285
url = f"{JASPAR_API}/matrix/" while url: r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) - Network accessline 285
url = f"{JASPAR_API}/matrix/" while url: r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) - Network accessline 286
while url: r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) r.raise_for_status() - Network accessline 286
while url: r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) r.raise_for_status() - Network accessline 287
r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) - Network accessline 287
r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) - Network accessline 349
import requests r = requests.get("https://jaspar.elixir.no/api/v1/matrix/MA0139.1/", - Network accessline 349
import requests r = requests.get("https://jaspar.elixir.no/api/v1/matrix/MA0139.1/", - Network accessline 350
r = requests.get("https://jaspar.elixir.no/api/v1/matrix/MA0139.1/", - Network accessline 350
r = requests.get("https://jaspar.elixir.no/api/v1/matrix/MA0139.1/", - Network accessline 369
JASPAR_API = "https://jaspar.elixir.no/api/v1" - Network accessline 369
JASPAR_API = "https://jaspar.elixir.no/api/v1" - Network accessline 373
results, url = [], f"{JASPAR_API}/matrix/" while url: r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) - Network accessline 373
results, url = [], f"{JASPAR_API}/matrix/" while url: r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) - Network accessline 374
while url: r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) r.raise_for_status() - Network accessline 374
while url: r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) r.raise_for_status() - Network accessline 375
r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) - Network accessline 375
r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) - Network accessline 428
JASPAR_API = "https://jaspar.elixir.no/api/v1" - Network accessline 428
JASPAR_API = "https://jaspar.elixir.no/api/v1" - Network accessline 432
def fetch_pwm(matrix_id, pseudocount=0.8): r = requests.get(f"{JASPAR_API}/matrix/{matrix_id}/", params={"format": "json"}, timeout=15) - Network accessline 432
def fetch_pwm(matrix_id, pseudocount=0.8): r = requests.get(f"{JASPAR_API}/matrix/{matrix_id}/", params={"format": "json"}, timeout=15) - Network accessline 433
r = requests.get(f"{JASPAR_API}/matrix/{matrix_id}/", params={"format": "json"}, timeout=15) - Network accessline 433
r = requests.get(f"{JASPAR_API}/matrix/{matrix_id}/", params={"format": "json"}, timeout=15) - Network accessline 498
JASPAR_API = "https://jaspar.elixir.no/api/v1" - Network accessline 498
JASPAR_API = "https://jaspar.elixir.no/api/v1" - Network accessline 502
results, url = [], f"{JASPAR_API}/matrix/" while url: r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) - Network accessline 502
results, url = [], f"{JASPAR_API}/matrix/" while url: r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) - Network accessline 503
while url: r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) r.raise_for_status() - Network accessline 503
while url: r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) r.raise_for_status() - Network accessline 504
r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) - Network accessline 504
r = requests.get(url, params=params if url == f"{JASPAR_API}/matrix/" else None, timeout=15) - Network accessline 573
JASPAR_API = "https://jaspar.elixir.no/api/v1" - Network accessline 573
JASPAR_API = "https://jaspar.elixir.no/api/v1" - Network accessline 575
def get_all_versions(tf_name, collection="CORE"): r = requests.get(f"{JASPAR_API}/matrix/", params={ - Network accessline 575
def get_all_versions(tf_name, collection="CORE"): r = requests.get(f"{JASPAR_API}/matrix/", params={ - Network accessline 576
r = requests.get(f"{JASPAR_API}/matrix/", params={ - Network accessline 576
r = requests.get(f"{JASPAR_API}/matrix/", params={ - Network accessline 600
JASPAR_API = "https://jaspar.elixir.no/api/v1" - Network accessline 600
JASPAR_API = "https://jaspar.elixir.no/api/v1" - Network accessline 602
def get_jaspar_format(matrix_id): """Return matrix as JASPAR flat-file string.""" r = requests.get(f"{JASPAR_API}/matrix/{matrix_id}/", params={"format": "jaspar"}, timeout=15) - Network accessline 602
def get_jaspar_format(matrix_id): """Return matrix as JASPAR flat-file string.""" r = requests.get(f"{JASPAR_API}/matrix/{matrix_id}/", params={"format": "jaspar"}, timeout=15) - Network accessline 603
"""Return matrix as JASPAR flat-file string.""" r = requests.get(f"{JASPAR_API}/matrix/{matrix_id}/", params={"format": "jaspar"}, timeout=15) r.raise_for_status() - Network accessline 603
"""Return matrix as JASPAR flat-file string.""" r = requests.get(f"{JASPAR_API}/matrix/{matrix_id}/", params={"format": "jaspar"}, timeout=15) r.raise_for_status() - Network accessline 604
r = requests.get(f"{JASPAR_API}/matrix/{matrix_id}/", params={"format": "jaspar"}, timeout=15) - Network accessline 604
r = requests.get(f"{JASPAR_API}/matrix/{matrix_id}/", params={"format": "jaspar"}, timeout=15) - Network accessline 669
- [JASPAR REST API v1 documentation](https://jaspar.elixir.no/api/v1/) — interactive browser and endpoint reference - Network accessline 669
- [JASPAR REST API v1 documentation](https://jaspar.elixir.no/api/v1/) — interactive browser and endpoint reference - Network accessline 670
- [Castro-Mondragon et al., Nucleic Acids Research 2022](https://doi.org/10.1093/nar/gkab1113) — JASPAR 2022 flagship paper describing collection structure and validation - Network accessline 670
- [Castro-Mondragon et al., Nucleic Acids Research 2022](https://doi.org/10.1093/nar/gkab1113) — JASPAR 2022 flagship paper describing collection structure and validation - Network accessline 671
- [pyJASPAR GitHub repository](https://github.com/asntech/pyjaspar) — Python library wrapping the JASPAR API with BioPython motif objects - Network accessline 671
- [pyJASPAR GitHub repository](https://github.com/asntech/pyjaspar) — Python library wrapping the JASPAR API with BioPython motif objects - Network accessline 672
- [Fornes et al., Nucleic Acids Research 2020](https://doi.org/10.1093/nar/gkz1001) — JASPAR 2020 paper introducing the CORE 2020 collection - Network accessline 672
- [Fornes et al., Nucleic Acids Research 2020](https://doi.org/10.1093/nar/gkz1001) — JASPAR 2020 paper introducing the CORE 2020 collection
skills/genomics-bioinformatics/databases/kegg-database/SKILL.md
- Network accessline 20
- Cross-referencing compounds, reactions, enzymes, and pathways - For **Python-native multi-database queries** (KEGG + UniProt + Ensembl in one script), prefer `bioservices` instead - For **pathway visualization**, use KEGG Mapper (https:// - Network accessline 20
- Cross-referencing compounds, reactions, enzymes, and pathways - For **Python-native multi-database queries** (KEGG + UniProt + Ensembl in one script), prefer `bioservices` instead - For **pathway visualization**, use KEGG Mapper (https:// - Network accessline 22
- For **pathway visualization**, use KEGG Mapper (https://www.kegg.jp/kegg/mapper/) directly - Network accessline 22
- For **pathway visualization**, use KEGG Mapper (https://www.kegg.jp/kegg/mapper/) directly - Network accessline 32
- **Max 10 entries** per `get`/`list`/`conv`/`link`/`ddi` call (image/kgml/json: 1 entry only) - **No explicit rate limit**, but add `time.sleep(0.5)` between batch requests to avoid server-side throttling - Base URL: `https://rest.kegg.jp/ - Network accessline 32
- **Max 10 entries** per `get`/`list`/`conv`/`link`/`ddi` call (image/kgml/json: 1 entry only) - **No explicit rate limit**, but add `time.sleep(0.5)` between batch requests to avoid server-side throttling - Base URL: `https://rest.kegg.jp/ - Network accessline 34
- Base URL: `https://rest.kegg.jp/` - Network accessline 34
- Base URL: `https://rest.kegg.jp/` - Network accessline 42
BASE = "https://rest.kegg.jp" - Network accessline 42
BASE = "https://rest.kegg.jp" - Network accessline 45
"""Generic KEGG REST API caller.""" url = f"{BASE}/{operation}/{'/'.join(args)}" resp = requests.get(url) - Network accessline 45
"""Generic KEGG REST API caller.""" url = f"{BASE}/{operation}/{'/'.join(args)}" resp = requests.get(url) - Network accessline 46
url = f"{BASE}/{operation}/{'/'.join(args)}" resp = requests.get(url) resp.raise_for_status() - Network accessline 46
url = f"{BASE}/{operation}/{'/'.join(args)}" resp = requests.get(url) resp.raise_for_status() - Network accessline 47
resp = requests.get(url) - Network accessline 47
resp = requests.get(url) - Network accessline 72
BASE = "https://rest.kegg.jp" - Network accessline 72
BASE = "https://rest.kegg.jp" - Network accessline 74
# Database-level info info = requests.get(f"{BASE}/info/pathway").text - Network accessline 74
# Database-level info info = requests.get(f"{BASE}/info/pathway").text - Network accessline 75
info = requests.get(f"{BASE}/info/pathway").text - Network accessline 75
info = requests.get(f"{BASE}/info/pathway").text - Network accessline 82
# Organism-level info hsa_info = requests.get(f"{BASE}/info/hsa").text - Network accessline 82
# Organism-level info hsa_info = requests.get(f"{BASE}/info/hsa").text - Network accessline 83
hsa_info = requests.get(f"{BASE}/info/hsa").text - Network accessline 83
hsa_info = requests.get(f"{BASE}/info/hsa").text - Network accessline 96
BASE = "https://rest.kegg.jp" - Network accessline 96
BASE = "https://rest.kegg.jp" - Network accessline 98
# All human pathways hsa_pathways = requests.get(f"{BASE}/list/pathway/hsa").text - Network accessline 98
# All human pathways hsa_pathways = requests.get(f"{BASE}/list/pathway/hsa").text - Network accessline 99
hsa_pathways = requests.get(f"{BASE}/list/pathway/hsa").text - Network accessline 99
hsa_pathways = requests.get(f"{BASE}/list/pathway/hsa").text - Network accessline 106
# Specific entries (max 10, joined with +) genes = requests.get(f"{BASE}/list/hsa:10458+hsa:10459").text - Network accessline 106
# Specific entries (max 10, joined with +) genes = requests.get(f"{BASE}/list/hsa:10458+hsa:10459").text - Network accessline 107
genes = requests.get(f"{BASE}/list/hsa:10458+hsa:10459").text - Network accessline 107
genes = requests.get(f"{BASE}/list/hsa:10458+hsa:10459").text - Network accessline 121
BASE = "https://rest.kegg.jp" - Network accessline 121
BASE = "https://rest.kegg.jp" - Network accessline 123
# Keyword search in genes results = requests.get(f"{BASE}/find/genes/p53").text - Network accessline 123
# Keyword search in genes results = requests.get(f"{BASE}/find/genes/p53").text - Network accessline 124
results = requests.get(f"{BASE}/find/genes/p53").text - Network accessline 124
results = requests.get(f"{BASE}/find/genes/p53").text - Network accessline 128
# Chemical formula search (exact match) compounds = requests.get(f"{BASE}/find/compound/C7H10N4O2/formula").text - Network accessline 128
# Chemical formula search (exact match) compounds = requests.get(f"{BASE}/find/compound/C7H10N4O2/formula").text - Network accessline 129
compounds = requests.get(f"{BASE}/find/compound/C7H10N4O2/formula").text - Network accessline 129
compounds = requests.get(f"{BASE}/find/compound/C7H10N4O2/formula").text - Network accessline 133
# Molecular weight range search drugs = requests.get(f"{BASE}/find/drug/300-310/exact_mass").text - Network accessline 133
# Molecular weight range search drugs = requests.get(f"{BASE}/find/drug/300-310/exact_mass").text - Network accessline 134
drugs = requests.get(f"{BASE}/find/drug/300-310/exact_mass").text - Network accessline 134
drugs = requests.get(f"{BASE}/find/drug/300-310/exact_mass").text - Network accessline 148
BASE = "https://rest.kegg.jp" - Network accessline 148
BASE = "https://rest.kegg.jp" - Network accessline 150
# Full pathway entry (text format) pathway = requests.get(f"{BASE}/get/hsa00010").text - Network accessline 150
# Full pathway entry (text format) pathway = requests.get(f"{BASE}/get/hsa00010").text - Network accessline 151
pathway = requests.get(f"{BASE}/get/hsa00010").text - Network accessline 151
pathway = requests.get(f"{BASE}/get/hsa00010").text - Network accessline 155
# Multiple entries (max 10, joined with +) genes = requests.get(f"{BASE}/get/hsa:10458+hsa:10459").text - Network accessline 155
# Multiple entries (max 10, joined with +) genes = requests.get(f"{BASE}/get/hsa:10458+hsa:10459").text - Network accessline 156
genes = requests.get(f"{BASE}/get/hsa:10458+hsa:10459").text - Network accessline 156
genes = requests.get(f"{BASE}/get/hsa:10458+hsa:10459").text - Network accessline 158
# Protein sequence (FASTA) fasta = requests.get(f"{BASE}/get/hsa:10458/aaseq").text - Network accessline 158
# Protein sequence (FASTA) fasta = requests.get(f"{BASE}/get/hsa:10458/aaseq").text - Network accessline 159
fasta = requests.get(f"{BASE}/get/hsa:10458/aaseq").text - Network accessline 159
fasta = requests.get(f"{BASE}/get/hsa:10458/aaseq").text - Network accessline 163
# Compound structure (MOL format) mol = requests.get(f"{BASE}/get/cpd:C00002/mol").text # ATP - Network accessline 163
# Compound structure (MOL format) mol = requests.get(f"{BASE}/get/cpd:C00002/mol").text # ATP - Network accessline 164
mol = requests.get(f"{BASE}/get/cpd:C00002/mol").text # ATP - Network accessline 164
mol = requests.get(f"{BASE}/get/cpd:C00002/mol").text # ATP - Network accessline 166
# Pathway image (PNG, single entry only) img_resp = requests.get(f"{BASE}/get/hsa05130/image") - Network accessline 166
# Pathway image (PNG, single entry only) img_resp = requests.get(f"{BASE}/get/hsa05130/image") - Network accessline 167
img_resp = requests.get(f"{BASE}/get/hsa05130/image") - Network accessline 167
img_resp = requests.get(f"{BASE}/get/hsa05130/image") - Network accessline 183
BASE = "https://rest.kegg.jp" - Network accessline 183
BASE = "https://rest.kegg.jp" - Network accessline 185
# KEGG gene → NCBI Gene ID (specific gene) ncbi = requests.get(f"{BASE}/conv/ncbi-geneid/hsa:10458").text - Network accessline 185
# KEGG gene → NCBI Gene ID (specific gene) ncbi = requests.get(f"{BASE}/conv/ncbi-geneid/hsa:10458").text - Network accessline 186
ncbi = requests.get(f"{BASE}/conv/ncbi-geneid/hsa:10458").text - Network accessline 186
ncbi = requests.get(f"{BASE}/conv/ncbi-geneid/hsa:10458").text - Network accessline 191
# KEGG gene → UniProt uniprot = requests.get(f"{BASE}/conv/uniprot/hsa:10458").text - Network accessline 191
# KEGG gene → UniProt uniprot = requests.get(f"{BASE}/conv/uniprot/hsa:10458").text - Network accessline 192
uniprot = requests.get(f"{BASE}/conv/uniprot/hsa:10458").text - Network accessline 192
uniprot = requests.get(f"{BASE}/conv/uniprot/hsa:10458").text - Network accessline 196
# Bulk conversion: all human genes → NCBI Gene IDs all_conv = requests.get(f"{BASE}/conv/ncbi-geneid/hsa").text - Network accessline 196
# Bulk conversion: all human genes → NCBI Gene IDs all_conv = requests.get(f"{BASE}/conv/ncbi-geneid/hsa").text - Network accessline 197
all_conv = requests.get(f"{BASE}/conv/ncbi-geneid/hsa").text - Network accessline 197
all_conv = requests.get(f"{BASE}/conv/ncbi-geneid/hsa").text - Network accessline 201
# Reverse: NCBI Gene ID → KEGG reverse = requests.get(f"{BASE}/conv/hsa/ncbi-geneid:7157").text - Network accessline 201
# Reverse: NCBI Gene ID → KEGG reverse = requests.get(f"{BASE}/conv/hsa/ncbi-geneid:7157").text - Network accessline 202
reverse = requests.get(f"{BASE}/conv/hsa/ncbi-geneid:7157").text - Network accessline 202
reverse = requests.get(f"{BASE}/conv/hsa/ncbi-geneid:7157").text - Network accessline 216
BASE = "https://rest.kegg.jp" - Network accessline 216
BASE = "https://rest.kegg.jp" - Network accessline 218
# Genes in glycolysis pathway genes = requests.get(f"{BASE}/link/genes/hsa00010").text - Network accessline 218
# Genes in glycolysis pathway genes = requests.get(f"{BASE}/link/genes/hsa00010").text - Network accessline 219
genes = requests.get(f"{BASE}/link/genes/hsa00010").text - Network accessline 219
genes = requests.get(f"{BASE}/link/genes/hsa00010").text - Network accessline 224
# Pathways containing a specific gene pathways = requests.get(f"{BASE}/link/pathway/hsa:7157").text # TP53 - Network accessline 224
# Pathways containing a specific gene pathways = requests.get(f"{BASE}/link/pathway/hsa:7157").text # TP53 - Network accessline 225
pathways = requests.get(f"{BASE}/link/pathway/hsa:7157").text # TP53 - Network accessline 225
pathways = requests.get(f"{BASE}/link/pathway/hsa:7157").text # TP53 - Network accessline 229
# Compounds in a pathway compounds = requests.get(f"{BASE}/link/compound/hsa00010").text - Network accessline 229
# Compounds in a pathway compounds = requests.get(f"{BASE}/link/compound/hsa00010").text - Network accessline 230
compounds = requests.get(f"{BASE}/link/compound/hsa00010").text - Network accessline 230
compounds = requests.get(f"{BASE}/link/compound/hsa00010").text - Network accessline 233
# Map genes to KO (orthology) groups ko = requests.get(f"{BASE}/link/ko/hsa:10458").text - Network accessline 233
# Map genes to KO (orthology) groups ko = requests.get(f"{BASE}/link/ko/hsa:10458").text - Network accessline 234
ko = requests.get(f"{BASE}/link/ko/hsa:10458").text - Network accessline 234
ko = requests.get(f"{BASE}/link/ko/hsa:10458").text - Network accessline 247
BASE = "https://rest.kegg.jp" - Network accessline 247
BASE = "https://rest.kegg.jp" - Network accessline 249
# Single drug — all known interactions interactions = requests.get(f"{BASE}/ddi/D00001").text - Network accessline 249
# Single drug — all known interactions interactions = requests.get(f"{BASE}/ddi/D00001").text - Network accessline 250
interactions = requests.get(f"{BASE}/ddi/D00001").text - Network accessline 250
interactions = requests.get(f"{BASE}/ddi/D00001").text - Network accessline 253
# Pairwise check (max 10 drugs, joined with +) pair = requests.get(f"{BASE}/ddi/D00001+D00002+D00003").text - Network accessline 253
# Pairwise check (max 10 drugs, joined with +) pair = requests.get(f"{BASE}/ddi/D00001+D00002+D00003").text - Network accessline 254
pair = requests.get(f"{BASE}/ddi/D00001+D00002+D00003").text - Network accessline 254
pair = requests.get(f"{BASE}/ddi/D00001+D00002+D00003").text - Network accessline 294
BASE = "https://rest.kegg.jp" - Network accessline 294
BASE = "https://rest.kegg.jp" - Network accessline 296
# Step 1: Find gene by keyword results = requests.get(f"{BASE}/find/genes/BRCA1+homo+sapiens").text - Network accessline 296
# Step 1: Find gene by keyword results = requests.get(f"{BASE}/find/genes/BRCA1+homo+sapiens").text - Network accessline 297
results = requests.get(f"{BASE}/find/genes/BRCA1+homo+sapiens").text - Network accessline 297
results = requests.get(f"{BASE}/find/genes/BRCA1+homo+sapiens").text - Network accessline 303
# Step 2: Get pathways linked to BRCA1 pathways = requests.get(f"{BASE}/link/pathway/hsa:672").text - Network accessline 303
# Step 2: Get pathways linked to BRCA1 pathways = requests.get(f"{BASE}/link/pathway/hsa:672").text - Network accessline 304
pathways = requests.get(f"{BASE}/link/pathway/hsa:672").text - Network accessline 304
pathways = requests.get(f"{BASE}/link/pathway/hsa:672").text - Network accessline 309
# Step 3: Get pathway names for pid in pathway_ids[:5]: info = requests.get(f"{BASE}/get/{pid}").text - Network accessline 309
# Step 3: Get pathway names for pid in pathway_ids[:5]: info = requests.get(f"{BASE}/get/{pid}").text - Network accessline 310
for pid in pathway_ids[:5]: info = requests.get(f"{BASE}/get/{pid}").text # Extract NAME field - Network accessline 310
for pid in pathway_ids[:5]: info = requests.get(f"{BASE}/get/{pid}").text # Extract NAME field - Network accessline 311
info = requests.get(f"{BASE}/get/{pid}").text - Network accessline 311
info = requests.get(f"{BASE}/get/{pid}").text - Network accessline 328
BASE = "https://rest.kegg.jp" - Network accessline 328
BASE = "https://rest.kegg.jp" - Network accessline 330
# Step 1: List all human pathways pathways_text = requests.get(f"{BASE}/list/pathway/hsa").text - Network accessline 330
# Step 1: List all human pathways pathways_text = requests.get(f"{BASE}/list/pathway/hsa").text - Network accessline 331
pathways_text = requests.get(f"{BASE}/list/pathway/hsa").text - Network accessline 331
pathways_text = requests.get(f"{BASE}/list/pathway/hsa").text - Network accessline 340
gene_sets = {} for pid in list(pathways.keys())[:3]: genes_text = requests.get(f"{BASE}/link/genes/{pid}").text - Network accessline 340
gene_sets = {} for pid in list(pathways.keys())[:3]: genes_text = requests.get(f"{BASE}/link/genes/{pid}").text - Network accessline 341
for pid in list(pathways.keys())[:3]: genes_text = requests.get(f"{BASE}/link/genes/{pid}").text gene_ids = [line.split("\t")[1] for line in genes_text.strip().split("\n") if line] - Network accessline 341
for pid in list(pathways.keys())[:3]: genes_text = requests.get(f"{BASE}/link/genes/{pid}").text gene_ids = [line.split("\t")[1] for line in genes_text.strip().split("\n") if line] - Network accessline 342
genes_text = requests.get(f"{BASE}/link/genes/{pid}").text - Network accessline 342
genes_text = requests.get(f"{BASE}/link/genes/{pid}").text - Network accessline 360
BASE = "https://rest.kegg.jp" - Network accessline 360
BASE = "https://rest.kegg.jp" - Network accessline 362
# Step 1: Search for compound results = requests.get(f"{BASE}/find/compound/glucose").text - Network accessline 362
# Step 1: Search for compound results = requests.get(f"{BASE}/find/compound/glucose").text - Network accessline 363
results = requests.get(f"{BASE}/find/compound/glucose").text - Network accessline 363
results = requests.get(f"{BASE}/find/compound/glucose").text - Network accessline 369
# Step 2: Find reactions involving glucose (C00031) reactions = requests.get(f"{BASE}/link/reaction/cpd:C00031").text - Network accessline 369
# Step 2: Find reactions involving glucose (C00031) reactions = requests.get(f"{BASE}/link/reaction/cpd:C00031").text - Network accessline 370
reactions = requests.get(f"{BASE}/link/reaction/cpd:C00031").text - Network accessline 370
reactions = requests.get(f"{BASE}/link/reaction/cpd:C00031").text - Network accessline 375
# Step 3: Find pathways for a specific reaction pathways = requests.get(f"{BASE}/link/pathway/rn:R00299").text - Network accessline 375
# Step 3: Find pathways for a specific reaction pathways = requests.get(f"{BASE}/link/pathway/rn:R00299").text - Network accessline 376
pathways = requests.get(f"{BASE}/link/pathway/rn:R00299").text - Network accessline 376
pathways = requests.get(f"{BASE}/link/pathway/rn:R00299").text - Network accessline 381
# Step 4: Get pathway detail detail = requests.get(f"{BASE}/get/map00010").text - Network accessline 381
# Step 4: Get pathway detail detail = requests.get(f"{BASE}/get/map00010").text - Network accessline 382
detail = requests.get(f"{BASE}/get/map00010").text - Network accessline 382
detail = requests.get(f"{BASE}/get/map00010").text - Network accessline 395
BASE = "https://rest.kegg.jp" - Network accessline 395
BASE = "https://rest.kegg.jp" - Network accessline 400
uniprot = requests.get(f"{BASE}/conv/uniprot/{gene}").text.strip() - Network accessline 400
uniprot = requests.get(f"{BASE}/conv/uniprot/{gene}").text.strip() - Network accessline 404
ncbi = requests.get(f"{BASE}/conv/ncbi-geneid/{gene}").text.strip() - Network accessline 404
ncbi = requests.get(f"{BASE}/conv/ncbi-geneid/{gene}").text.strip() - Network accessline 408
# Step 2: Get protein sequence from KEGG fasta = requests.get(f"{BASE}/get/{gene}/aaseq").text - Network accessline 408
# Step 2: Get protein sequence from KEGG fasta = requests.get(f"{BASE}/get/{gene}/aaseq").text - Network accessline 409
fasta = requests.get(f"{BASE}/get/{gene}/aaseq").text - Network accessline 409
fasta = requests.get(f"{BASE}/get/{gene}/aaseq").text - Network accessline 413
# Step 3: Convert compounds to PubChem CIDs cpd_conv = requests.get(f"{BASE}/conv/pubchem/cpd:C00002").text.strip() # ATP - Network accessline 413
# Step 3: Convert compounds to PubChem CIDs cpd_conv = requests.get(f"{BASE}/conv/pubchem/cpd:C00002").text.strip() # ATP - Network accessline 414
cpd_conv = requests.get(f"{BASE}/conv/pubchem/cpd:C00002").text.strip() # ATP - Network accessline 414
cpd_conv = requests.get(f"{BASE}/conv/pubchem/cpd:C00002").text.strip() # ATP - Network accessline 462
import requests pathway = requests.get("https://rest.kegg.jp/get/hsa00010").text - Network accessline 462
import requests pathway = requests.get("https://rest.kegg.jp/get/hsa00010").text - Network accessline 463
pathway = requests.get("https://rest.kegg.jp/get/hsa00010").text - Network accessline 463
pathway = requests.get("https://rest.kegg.jp/get/hsa00010").text - Network accessline 475
BASE = "https://rest.kegg.jp" - Network accessline 475
BASE = "https://rest.kegg.jp" - Network accessline 480
for org, name in organisms.items(): genes = requests.get(f"{BASE}/link/genes/{org}{pathway}").text - Network accessline 480
for org, name in organisms.items(): genes = requests.get(f"{BASE}/link/genes/{org}{pathway}").text - Network accessline 481
genes = requests.get(f"{BASE}/link/genes/{org}{pathway}").text - Network accessline 481
genes = requests.get(f"{BASE}/link/genes/{org}{pathway}").text - Network accessline 496
BASE = "https://rest.kegg.jp" - Network accessline 496
BASE = "https://rest.kegg.jp" - Network accessline 498
# Get all human gene-pathway links links = requests.get(f"{BASE}/link/pathway/hsa").text - Network accessline 498
# Get all human gene-pathway links links = requests.get(f"{BASE}/link/pathway/hsa").text - Network accessline 499
links = requests.get(f"{BASE}/link/pathway/hsa").text - Network accessline 499
links = requests.get(f"{BASE}/link/pathway/hsa").text - Network accessline 535
- [KEGG REST API documentation](https://www.kegg.jp/kegg/rest/keggapi.html) — official API specification - Network accessline 535
- [KEGG REST API documentation](https://www.kegg.jp/kegg/rest/keggapi.html) — official API specification - Network accessline 536
- [KEGG website](https://www.kegg.jp/) — pathway browser, KEGG Mapper, BlastKOALA - Network accessline 536
- [KEGG website](https://www.kegg.jp/) — pathway browser, KEGG Mapper, BlastKOALA - Network accessline 537
- [KEGG organism codes](https://www.kegg.jp/kegg/catalog/org_list.html) — full list of 3-4 letter organism codes - Network accessline 537
- [KEGG organism codes](https://www.kegg.jp/kegg/catalog/org_list.html) — full list of 3-4 letter organism codes
skills/genomics-bioinformatics/databases/monarch-database/SKILL.md
- Network accessline 11
The Monarch Initiative integrates disease-phenotype-gene relationships from 30+ biomedical databases (OMIM, Orphanet, ClinVar, MGI, ZFIN, Reactome) into a unified knowledge graph. The REST API at `https://api.monarchinitiative.org/v3/api` p - Network accessline 11
The Monarch Initiative integrates disease-phenotype-gene relationships from 30+ biomedical databases (OMIM, Orphanet, ClinVar, MGI, ZFIN, Reactome) into a unified knowledge graph. The REST API at `https://api.monarchinitiative.org/v3/api` p - Network accessline 40
MONARCH_API = "https://api.monarchinitiative.org/v3/api" - Network accessline 40
MONARCH_API = "https://api.monarchinitiative.org/v3/api" - Network accessline 42
def monarch_get(endpoint: str, params: dict = None) -> dict: """GET request to Monarch API; raises on HTTP errors.""" r = requests.get(f"{MONARCH_API}{endpoint}", params=params, timeout=30) - Network accessline 42
def monarch_get(endpoint: str, params: dict = None) -> dict: """GET request to Monarch API; raises on HTTP errors.""" r = requests.get(f"{MONARCH_API}{endpoint}", params=params, timeout=30) - Network accessline 43
"""GET request to Monarch API; raises on HTTP errors.""" r = requests.get(f"{MONARCH_API}{endpoint}", params=params, timeout=30) r.raise_for_status() - Network accessline 43
"""GET request to Monarch API; raises on HTTP errors.""" r = requests.get(f"{MONARCH_API}{endpoint}", params=params, timeout=30) r.raise_for_status() - Network accessline 44
r = requests.get(f"{MONARCH_API}{endpoint}", params=params, timeout=30) - Network accessline 44
r = requests.get(f"{MONARCH_API}{endpoint}", params=params, timeout=30) - Network accessline 73
MONARCH_API = "https://api.monarchinitiative.org/v3/api" - Network accessline 73
MONARCH_API = "https://api.monarchinitiative.org/v3/api" - Network accessline 75
def monarch_get(endpoint, params=None): r = requests.get(f"{MONARCH_API}{endpoint}", params=params, timeout=30) - Network accessline 75
def monarch_get(endpoint, params=None): r = requests.get(f"{MONARCH_API}{endpoint}", params=params, timeout=30) - Network accessline 76
r = requests.get(f"{MONARCH_API}{endpoint}", params=params, timeout=30) - Network accessline 76
r = requests.get(f"{MONARCH_API}{endpoint}", params=params, timeout=30) - Network accessline 344
MONARCH_API = "https://api.monarchinitiative.org/v3/api" - Network accessline 344
MONARCH_API = "https://api.monarchinitiative.org/v3/api" - Network accessline 346
def monarch_get(endpoint, params=None): r = requests.get(f"{MONARCH_API}{endpoint}", params=params, timeout=30) - Network accessline 346
def monarch_get(endpoint, params=None): r = requests.get(f"{MONARCH_API}{endpoint}", params=params, timeout=30) - Network accessline 347
r = requests.get(f"{MONARCH_API}{endpoint}", params=params, timeout=30) - Network accessline 347
r = requests.get(f"{MONARCH_API}{endpoint}", params=params, timeout=30) - Network accessline 395
MONARCH_API = "https://api.monarchinitiative.org/v3/api" - Network accessline 395
MONARCH_API = "https://api.monarchinitiative.org/v3/api" - Network accessline 397
def monarch_get(endpoint, params=None): r = requests.get(f"{MONARCH_API}{endpoint}", params=params, timeout=30) - Network accessline 397
def monarch_get(endpoint, params=None): r = requests.get(f"{MONARCH_API}{endpoint}", params=params, timeout=30) - Network accessline 398
r = requests.get(f"{MONARCH_API}{endpoint}", params=params, timeout=30) - Network accessline 398
r = requests.get(f"{MONARCH_API}{endpoint}", params=params, timeout=30) - Network accessline 455
MONARCH_API = "https://api.monarchinitiative.org/v3/api" - Network accessline 455
MONARCH_API = "https://api.monarchinitiative.org/v3/api" - Network accessline 457
def monarch_get(endpoint, params=None): r = requests.get(f"{MONARCH_API}{endpoint}", params=params, timeout=30) - Network accessline 457
def monarch_get(endpoint, params=None): r = requests.get(f"{MONARCH_API}{endpoint}", params=params, timeout=30) - Network accessline 458
r = requests.get(f"{MONARCH_API}{endpoint}", params=params, timeout=30) - Network accessline 458
r = requests.get(f"{MONARCH_API}{endpoint}", params=params, timeout=30) - Network accessline 535
MONARCH_API = "https://api.monarchinitiative.org/v3/api" - Network accessline 535
MONARCH_API = "https://api.monarchinitiative.org/v3/api" - Network accessline 537
def resolve_disease(name: str, top_n: int = 5) -> list: """Search for disease name and return top MONDO ID candidates.""" r = requests.get(f"{MONARCH_API}/search", - Network accessline 537
def resolve_disease(name: str, top_n: int = 5) -> list: """Search for disease name and return top MONDO ID candidates.""" r = requests.get(f"{MONARCH_API}/search", - Network accessline 538
"""Search for disease name and return top MONDO ID candidates.""" r = requests.get(f"{MONARCH_API}/search", params={"q": name, "category": "biolink:Disease", "limit": top_n}, - Network accessline 538
"""Search for disease name and return top MONDO ID candidates.""" r = requests.get(f"{MONARCH_API}/search", params={"q": name, "category": "biolink:Disease", "limit": top_n}, - Network accessline 539
r = requests.get(f"{MONARCH_API}/search", - Network accessline 539
r = requests.get(f"{MONARCH_API}/search", - Network accessline 561
MONARCH_API = "https://api.monarchinitiative.org/v3/api" - Network accessline 561
MONARCH_API = "https://api.monarchinitiative.org/v3/api" - Network accessline 563
def get_causal_genes(mondo_id): r = requests.get(f"{MONARCH_API}/association/all", - Network accessline 563
def get_causal_genes(mondo_id): r = requests.get(f"{MONARCH_API}/association/all", - Network accessline 564
r = requests.get(f"{MONARCH_API}/association/all", - Network accessline 564
r = requests.get(f"{MONARCH_API}/association/all", - Network accessline 594
MONARCH_API = "https://api.monarchinitiative.org/v3/api" - Network accessline 594
MONARCH_API = "https://api.monarchinitiative.org/v3/api" - Network accessline 596
def get_hp_profile(mondo_id, limit=500): """Return list of HP term IDs for a disease.""" r = requests.get(f"{MONARCH_API}/association/all", - Network accessline 596
def get_hp_profile(mondo_id, limit=500): """Return list of HP term IDs for a disease.""" r = requests.get(f"{MONARCH_API}/association/all", - Network accessline 597
"""Return list of HP term IDs for a disease.""" r = requests.get(f"{MONARCH_API}/association/all", params={"subject": mondo_id, - Network accessline 597
"""Return list of HP term IDs for a disease.""" r = requests.get(f"{MONARCH_API}/association/all", params={"subject": mondo_id, - Network accessline 598
r = requests.get(f"{MONARCH_API}/association/all", - Network accessline 598
r = requests.get(f"{MONARCH_API}/association/all", - Network accessline 640
- [Monarch Initiative API v3](https://api.monarchinitiative.org/v3/api) — Interactive Swagger/OpenAPI documentation - Network accessline 640
- [Monarch Initiative API v3](https://api.monarchinitiative.org/v3/api) — Interactive Swagger/OpenAPI documentation - Network accessline 641
- [Monarch Initiative](https://monarchinitiative.org/) — Main portal with disease-gene-phenotype knowledge graph browser - Network accessline 641
- [Monarch Initiative](https://monarchinitiative.org/) — Main portal with disease-gene-phenotype knowledge graph browser - Network accessline 642
- [Mungall et al., Genome Biology 2017](https://doi.org/10.1186/s13059-017-1223-7) — Monarch Integration methodology paper - Network accessline 642
- [Mungall et al., Genome Biology 2017](https://doi.org/10.1186/s13059-017-1223-7) — Monarch Integration methodology paper - Network accessline 643
- [Mondo Disease Ontology](https://mondo.monarchinitiative.org/) — MONDO ID reference and ontology browser - Network accessline 643
- [Mondo Disease Ontology](https://mondo.monarchinitiative.org/) — MONDO ID reference and ontology browser - Network accessline 644
- [Human Phenotype Ontology](https://hpo.jax.org/) — HPO term browser and clinical applications - Network accessline 644
- [Human Phenotype Ontology](https://hpo.jax.org/) — HPO term browser and clinical applications
skills/genomics-bioinformatics/databases/mouse-phenome-database/SKILL.md
- Network accessline 11
The Mouse Phenome Database (MPD), maintained at the Jackson Laboratory, catalogs standardized phenotype measurements across inbred, recombinant inbred (e.g., BXD), and Collaborative Cross / Diversity Outbred mouse panels. It aggregates 520+ - Network accessline 11
The Mouse Phenome Database (MPD), maintained at the Jackson Laboratory, catalogs standardized phenotype measurements across inbred, recombinant inbred (e.g., BXD), and Collaborative Cross / Diversity Outbred mouse panels. It aggregates 520+ - Network accessline 41
MPD = "https://phenome.jax.org/api" - Network accessline 41
MPD = "https://phenome.jax.org/api" - Network accessline 43
# 1) Pick a project (Jaxwest1 — cardiovascular phenotyping on inbred panel) r = requests.get(f"{MPD}/projects/Jaxwest1/strains", timeout=30) - Network accessline 43
# 1) Pick a project (Jaxwest1 — cardiovascular phenotyping on inbred panel) r = requests.get(f"{MPD}/projects/Jaxwest1/strains", timeout=30) - Network accessline 44
r = requests.get(f"{MPD}/projects/Jaxwest1/strains", timeout=30) - Network accessline 44
r = requests.get(f"{MPD}/projects/Jaxwest1/strains", timeout=30) - Network accessline 48
# 2) Discover its measures r = requests.get(f"{MPD}/pheno/measureinfo/Jaxwest1", timeout=30) - Network accessline 48
# 2) Discover its measures r = requests.get(f"{MPD}/pheno/measureinfo/Jaxwest1", timeout=30) - Network accessline 49
r = requests.get(f"{MPD}/pheno/measureinfo/Jaxwest1", timeout=30) - Network accessline 49
r = requests.get(f"{MPD}/pheno/measureinfo/Jaxwest1", timeout=30) - Network accessline 54
# 3) Pull strain means for heart rate (varname=HR, measnum=15101) r = requests.get(f"{MPD}/pheno/strainmeans/15101", timeout=30) - Network accessline 54
# 3) Pull strain means for heart rate (varname=HR, measnum=15101) r = requests.get(f"{MPD}/pheno/strainmeans/15101", timeout=30) - Network accessline 55
r = requests.get(f"{MPD}/pheno/strainmeans/15101", timeout=30) - Network accessline 55
r = requests.get(f"{MPD}/pheno/strainmeans/15101", timeout=30) - Network accessline 72
MPD = "https://phenome.jax.org/api" - Network accessline 72
MPD = "https://phenome.jax.org/api" - Network accessline 74
# List allowed panel symbols (e.g., BXD, CC, DO) filters = requests.get(f"{MPD}/project_filters/panelsym", timeout=30).json() - Network accessline 74
# List allowed panel symbols (e.g., BXD, CC, DO) filters = requests.get(f"{MPD}/project_filters/panelsym", timeout=30).json() - Network accessline 75
filters = requests.get(f"{MPD}/project_filters/panelsym", timeout=30).json() - Network accessline 75
filters = requests.get(f"{MPD}/project_filters/panelsym", timeout=30).json() - Network accessline 78
# All projects in the BXD recombinant inbred panel r = requests.get(f"{MPD}/projects", params={"panelsym": "BXD"}, timeout=30) - Network accessline 78
# All projects in the BXD recombinant inbred panel r = requests.get(f"{MPD}/projects", params={"panelsym": "BXD"}, timeout=30) - Network accessline 79
r = requests.get(f"{MPD}/projects", params={"panelsym": "BXD"}, timeout=30) - Network accessline 79
r = requests.get(f"{MPD}/projects", params={"panelsym": "BXD"}, timeout=30) - Network accessline 94
# Filter by MPD sector — komp, pheno, qtla, snp, onestrain, phenoarchive r = requests.get(f"{MPD}/projects", params={"mpdsector": "qtla"}, timeout=30) - Network accessline 94
# Filter by MPD sector — komp, pheno, qtla, snp, onestrain, phenoarchive r = requests.get(f"{MPD}/projects", params={"mpdsector": "qtla"}, timeout=30) - Network accessline 95
r = requests.get(f"{MPD}/projects", params={"mpdsector": "qtla"}, timeout=30) - Network accessline 95
r = requests.get(f"{MPD}/projects", params={"mpdsector": "qtla"}, timeout=30) - Network accessline 109
MPD = "https://phenome.jax.org/api" - Network accessline 109
MPD = "https://phenome.jax.org/api" - Network accessline 111
# Full per-animal dataset as CSV (default). Use json=yes for JSON. r = requests.get(f"{MPD}/projects/Jaxwest1/dataset", timeout=60) - Network accessline 111
# Full per-animal dataset as CSV (default). Use json=yes for JSON. r = requests.get(f"{MPD}/projects/Jaxwest1/dataset", timeout=60) - Network accessline 112
r = requests.get(f"{MPD}/projects/Jaxwest1/dataset", timeout=60) - Network accessline 112
r = requests.get(f"{MPD}/projects/Jaxwest1/dataset", timeout=60) - Network accessline 120
# Strains tested in a project + publication list strains = requests.get(f"{MPD}/projects/Jaxwest1/strains", timeout=30).json() - Network accessline 120
# Strains tested in a project + publication list strains = requests.get(f"{MPD}/projects/Jaxwest1/strains", timeout=30).json() - Network accessline 121
strains = requests.get(f"{MPD}/projects/Jaxwest1/strains", timeout=30).json() - Network accessline 121
strains = requests.get(f"{MPD}/projects/Jaxwest1/strains", timeout=30).json() - Network accessline 126
pubs = requests.get(f"{MPD}/projects/Jaxwest1/publications", timeout=30).json() - Network accessline 126
pubs = requests.get(f"{MPD}/projects/Jaxwest1/publications", timeout=30).json() - Network accessline 137
MPD = "https://phenome.jax.org/api" - Network accessline 137
MPD = "https://phenome.jax.org/api" - Network accessline 139
# All measures in the Jaxwest1 cardiovascular project r = requests.get(f"{MPD}/pheno/measureinfo/Jaxwest1", timeout=30) - Network accessline 139
# All measures in the Jaxwest1 cardiovascular project r = requests.get(f"{MPD}/pheno/measureinfo/Jaxwest1", timeout=30) - Network accessline 140
r = requests.get(f"{MPD}/pheno/measureinfo/Jaxwest1", timeout=30) - Network accessline 140
r = requests.get(f"{MPD}/pheno/measureinfo/Jaxwest1", timeout=30) - Network accessline 155
# Single-measure metadata lookup (protocol + dimensional details) r = requests.get(f"{MPD}/pheno/measureinfo/15101", timeout=30).json() - Network accessline 155
# Single-measure metadata lookup (protocol + dimensional details) r = requests.get(f"{MPD}/pheno/measureinfo/15101", timeout=30).json() - Network accessline 156
r = requests.get(f"{MPD}/pheno/measureinfo/15101", timeout=30).json() - Network accessline 156
r = requests.get(f"{MPD}/pheno/measureinfo/15101", timeout=30).json() - Network accessline 172
MPD = "https://phenome.jax.org/api" - Network accessline 172
MPD = "https://phenome.jax.org/api" - Network accessline 174
# Strain means for one measure (heart rate, measnum=15101 from Jaxwest1) r = requests.get(f"{MPD}/pheno/strainmeans/15101", timeout=30) - Network accessline 174
# Strain means for one measure (heart rate, measnum=15101 from Jaxwest1) r = requests.get(f"{MPD}/pheno/strainmeans/15101", timeout=30) - Network accessline 175
r = requests.get(f"{MPD}/pheno/strainmeans/15101", timeout=30) - Network accessline 175
r = requests.get(f"{MPD}/pheno/strainmeans/15101", timeout=30) - Network accessline 185
# Optional: model-adjusted means (lsmeans) account for covariates fit in MPD's models. # Use lsmeans when comparing strains across cohorts within a project. r = requests.get(f"{MPD}/pheno/lsmeans/Jaxwest1", timeout=30).json() - Network accessline 185
# Optional: model-adjusted means (lsmeans) account for covariates fit in MPD's models. # Use lsmeans when comparing strains across cohorts within a project. r = requests.get(f"{MPD}/pheno/lsmeans/Jaxwest1", timeout=30).json() - Network accessline 186
# Use lsmeans when comparing strains across cohorts within a project. r = requests.get(f"{MPD}/pheno/lsmeans/Jaxwest1", timeout=30).json() print("LS-mean measures available for Jaxwest1:", r.get("ls_measures", [])[:10]) - Network accessline 186
# Use lsmeans when comparing strains across cohorts within a project. r = requests.get(f"{MPD}/pheno/lsmeans/Jaxwest1", timeout=30).json() print("LS-mean measures available for Jaxwest1:", r.get("ls_measures", [])[:10]) - Network accessline 187
r = requests.get(f"{MPD}/pheno/lsmeans/Jaxwest1", timeout=30).json() - Network accessline 187
r = requests.get(f"{MPD}/pheno/lsmeans/Jaxwest1", timeout=30).json() - Network accessline 198
MPD = "https://phenome.jax.org/api" - Network accessline 198
MPD = "https://phenome.jax.org/api" - Network accessline 200
# Per-animal heart rate values r = requests.get(f"{MPD}/pheno/animalvals/15101", timeout=30) - Network accessline 200
# Per-animal heart rate values r = requests.get(f"{MPD}/pheno/animalvals/15101", timeout=30) - Network accessline 201
r = requests.get(f"{MPD}/pheno/animalvals/15101", timeout=30) - Network accessline 201
r = requests.get(f"{MPD}/pheno/animalvals/15101", timeout=30) - Network accessline 220
MPD = "https://phenome.jax.org/api" - Network accessline 220
MPD = "https://phenome.jax.org/api" - Network accessline 222
# MP:0001262 = "decreased body weight" r = requests.get(f"{MPD}/pheno/measures_by_ontology/MP:0001262", - Network accessline 222
# MP:0001262 = "decreased body weight" r = requests.get(f"{MPD}/pheno/measures_by_ontology/MP:0001262", - Network accessline 223
r = requests.get(f"{MPD}/pheno/measures_by_ontology/MP:0001262", - Network accessline 223
r = requests.get(f"{MPD}/pheno/measures_by_ontology/MP:0001262", - Network accessline 241
MPD = "https://phenome.jax.org/api" - Network accessline 241
MPD = "https://phenome.jax.org/api" - Network accessline 243
# Validate C57BL/6J — note `requests` URL-encodes the slash automatically in params r = requests.get(f"{MPD}/straininfo", params={"name": "C57BL/6J"}, timeout=30).json() - Network accessline 243
# Validate C57BL/6J — note `requests` URL-encodes the slash automatically in params r = requests.get(f"{MPD}/straininfo", params={"name": "C57BL/6J"}, timeout=30).json() - Network accessline 244
r = requests.get(f"{MPD}/straininfo", params={"name": "C57BL/6J"}, timeout=30).json() - Network accessline 244
r = requests.get(f"{MPD}/straininfo", params={"name": "C57BL/6J"}, timeout=30).json() - Network accessline 259
MPD = "https://phenome.jax.org/api" - Network accessline 259
MPD = "https://phenome.jax.org/api" - Network accessline 261
r = requests.get(f"{MPD}/geneinfo/Lep", timeout=30).json() - Network accessline 261
r = requests.get(f"{MPD}/geneinfo/Lep", timeout=30).json() - Network accessline 322
MPD = "https://phenome.jax.org/api" - Network accessline 322
MPD = "https://phenome.jax.org/api" - Network accessline 324
# 1) Find candidate projects whose name/description hints at the trait projects = requests.get(f"{MPD}/projects", timeout=30).json()["projects"] - Network accessline 324
# 1) Find candidate projects whose name/description hints at the trait projects = requests.get(f"{MPD}/projects", timeout=30).json()["projects"] - Network accessline 325
projects = requests.get(f"{MPD}/projects", timeout=30).json()["projects"] - Network accessline 325
projects = requests.get(f"{MPD}/projects", timeout=30).json()["projects"] - Network accessline 333
# 2) Inspect measures for the chosen project projsym = "Jaxwest1" mi = requests.get(f"{MPD}/pheno/measureinfo/{projsym}", timeout=30).json()["measures_info"] - Network accessline 333
# 2) Inspect measures for the chosen project projsym = "Jaxwest1" mi = requests.get(f"{MPD}/pheno/measureinfo/{projsym}", timeout=30).json()["measures_info"] - Network accessline 334
projsym = "Jaxwest1" mi = requests.get(f"{MPD}/pheno/measureinfo/{projsym}", timeout=30).json()["measures_info"] hr = next(m for m in mi if m["varname"] == "HR") - Network accessline 334
projsym = "Jaxwest1" mi = requests.get(f"{MPD}/pheno/measureinfo/{projsym}", timeout=30).json()["measures_info"] hr = next(m for m in mi if m["varname"] == "HR") - Network accessline 335
mi = requests.get(f"{MPD}/pheno/measureinfo/{projsym}", timeout=30).json()["measures_info"] - Network accessline 335
mi = requests.get(f"{MPD}/pheno/measureinfo/{projsym}", timeout=30).json()["measures_info"] - Network accessline 339
# 3) Pull strain means, plot male strains ranked sm = pd.DataFrame(requests.get(f"{MPD}/pheno/strainmeans/{hr['measnum']}", timeout=30).json()["strainmeans"]) - Network accessline 339
# 3) Pull strain means, plot male strains ranked sm = pd.DataFrame(requests.get(f"{MPD}/pheno/strainmeans/{hr['measnum']}", timeout=30).json()["strainmeans"]) - Network accessline 340
sm = pd.DataFrame(requests.get(f"{MPD}/pheno/strainmeans/{hr['measnum']}", timeout=30).json()["strainmeans"]) - Network accessline 340
sm = pd.DataFrame(requests.get(f"{MPD}/pheno/strainmeans/{hr['measnum']}", timeout=30).json()["strainmeans"]) - Network accessline 355
MPD = "https://phenome.jax.org/api" - Network accessline 355
MPD = "https://phenome.jax.org/api" - Network accessline 357
measnum = 15101 # heart rate in Jaxwest1 mi = requests.get(f"{MPD}/pheno/measureinfo/{measnum}", timeout=30).json()["measures_info"][0] - Network accessline 357
measnum = 15101 # heart rate in Jaxwest1 mi = requests.get(f"{MPD}/pheno/measureinfo/{measnum}", timeout=30).json()["measures_info"][0] - Network accessline 358
mi = requests.get(f"{MPD}/pheno/measureinfo/{measnum}", timeout=30).json()["measures_info"][0] - Network accessline 358
mi = requests.get(f"{MPD}/pheno/measureinfo/{measnum}", timeout=30).json()["measures_info"][0] - Network accessline 359
ad = pd.DataFrame(requests.get(f"{MPD}/pheno/animalvals/{measnum}", timeout=30).json()["animaldata"]) - Network accessline 359
ad = pd.DataFrame(requests.get(f"{MPD}/pheno/animalvals/{measnum}", timeout=30).json()["animaldata"]) - Network accessline 378
MPD = "https://phenome.jax.org/api" - Network accessline 378
MPD = "https://phenome.jax.org/api" - Network accessline 381
# Pull all strain means for the project in one call sm = pd.DataFrame(requests.get(f"{MPD}/pheno/strainmeans/{projsym}", timeout=30).json()["strainmeans"]) - Network accessline 381
# Pull all strain means for the project in one call sm = pd.DataFrame(requests.get(f"{MPD}/pheno/strainmeans/{projsym}", timeout=30).json()["strainmeans"]) - Network accessline 382
sm = pd.DataFrame(requests.get(f"{MPD}/pheno/strainmeans/{projsym}", timeout=30).json()["strainmeans"]) - Network accessline 382
sm = pd.DataFrame(requests.get(f"{MPD}/pheno/strainmeans/{projsym}", timeout=30).json()["strainmeans"]) - Network accessline 434
MPD = "https://phenome.jax.org/api" - Network accessline 434
MPD = "https://phenome.jax.org/api" - Network accessline 436
def find_projects(keyword): """Search project titles for a keyword (case-insensitive).""" projects = requests.get(f"{MPD}/projects", timeout=30).json()["projects"] - Network accessline 436
def find_projects(keyword): """Search project titles for a keyword (case-insensitive).""" projects = requests.get(f"{MPD}/projects", timeout=30).json()["projects"] - Network accessline 437
"""Search project titles for a keyword (case-insensitive).""" projects = requests.get(f"{MPD}/projects", timeout=30).json()["projects"] kw = keyword.lower() - Network accessline 437
"""Search project titles for a keyword (case-insensitive).""" projects = requests.get(f"{MPD}/projects", timeout=30).json()["projects"] kw = keyword.lower() - Network accessline 438
projects = requests.get(f"{MPD}/projects", timeout=30).json()["projects"] - Network accessline 438
projects = requests.get(f"{MPD}/projects", timeout=30).json()["projects"] - Network accessline 457
MPD = "https://phenome.jax.org/api" - Network accessline 457
MPD = "https://phenome.jax.org/api" - Network accessline 459
def load_project_dataset(projsym): """Fetch /projects/{projsym}/dataset CSV directly into a DataFrame.""" r = requests.get(f"{MPD}/projects/{projsym}/dataset", timeout=120) - Network accessline 459
def load_project_dataset(projsym): """Fetch /projects/{projsym}/dataset CSV directly into a DataFrame.""" r = requests.get(f"{MPD}/projects/{projsym}/dataset", timeout=120) - Network accessline 460
"""Fetch /projects/{projsym}/dataset CSV directly into a DataFrame.""" r = requests.get(f"{MPD}/projects/{projsym}/dataset", timeout=120) r.raise_for_status() - Network accessline 460
"""Fetch /projects/{projsym}/dataset CSV directly into a DataFrame.""" r = requests.get(f"{MPD}/projects/{projsym}/dataset", timeout=120) r.raise_for_status() - Network accessline 461
r = requests.get(f"{MPD}/projects/{projsym}/dataset", timeout=120) - Network accessline 461
r = requests.get(f"{MPD}/projects/{projsym}/dataset", timeout=120) - Network accessline 475
MPD = "https://phenome.jax.org/api" - Network accessline 475
MPD = "https://phenome.jax.org/api" - Network accessline 477
# Multiple measnums in one call (comma-separated selector) selector = "15101,15102,15103" # HR, QRS, PR from Jaxwest1 sm = pd.DataFrame(requests.get(f"{MPD}/pheno/strainmeans/{selector}", timeout=30).json()["strainmeans"]) - Network accessline 477
# Multiple measnums in one call (comma-separated selector) selector = "15101,15102,15103" # HR, QRS, PR from Jaxwest1 sm = pd.DataFrame(requests.get(f"{MPD}/pheno/strainmeans/{selector}", timeout=30).json()["strainmeans"]) - Network accessline 478
selector = "15101,15102,15103" # HR, QRS, PR from Jaxwest1 sm = pd.DataFrame(requests.get(f"{MPD}/pheno/strainmeans/{selector}", timeout=30).json()["strainmeans"]) wide = (sm[sm["sex"] == "m"] - Network accessline 478
selector = "15101,15102,15103" # HR, QRS, PR from Jaxwest1 sm = pd.DataFrame(requests.get(f"{MPD}/pheno/strainmeans/{selector}", timeout=30).json()["strainmeans"]) wide = (sm[sm["sex"] == "m"] - Network accessline 479
sm = pd.DataFrame(requests.get(f"{MPD}/pheno/strainmeans/{selector}", timeout=30).json()["strainmeans"]) - Network accessline 479
sm = pd.DataFrame(requests.get(f"{MPD}/pheno/strainmeans/{selector}", timeout=30).json()["strainmeans"]) - Network accessline 491
MPD = "https://phenome.jax.org/api" - Network accessline 491
MPD = "https://phenome.jax.org/api" - Network accessline 493
def gene_window(symbol, flank_kb=100): r = requests.get(f"{MPD}/geneinfo/{symbol}", timeout=30).json() - Network accessline 493
def gene_window(symbol, flank_kb=100): r = requests.get(f"{MPD}/geneinfo/{symbol}", timeout=30).json() - Network accessline 494
r = requests.get(f"{MPD}/geneinfo/{symbol}", timeout=30).json() - Network accessline 494
r = requests.get(f"{MPD}/geneinfo/{symbol}", timeout=30).json() - Network accessline 520
| Strain name returns empty `mpdinfo` from `/straininfo` | Non-canonical name (e.g., `B6`, `C57Bl/6`) | Use exact JAX nomenclature (`C57BL/6J`); try `stocknum=` lookup if you have the JAX stock number | | `/pheno/measures_by_ontology/{term} - Network accessline 520
| Strain name returns empty `mpdinfo` from `/straininfo` | Non-canonical name (e.g., `B6`, `C57Bl/6`) | Use exact JAX nomenclature (`C57BL/6J`); try `stocknum=` lookup if you have the JAX stock number | | `/pheno/measures_by_ontology/{term} - Network accessline 522
| `HTTP 5xx` intermittently on large CSV pulls | MPD's per-project datasets can be tens of MB | Increase `timeout=120`; for very large projects use `csv=yes` + stream with `requests.get(..., stream=True)` | - Network accessline 522
| `HTTP 5xx` intermittently on large CSV pulls | MPD's per-project datasets can be tens of MB | Increase `timeout=120`; for very large projects use `csv=yes` + stream with `requests.get(..., stream=True)` | - Network accessline 533
- [Mouse Phenome Database portal](https://phenome.jax.org/) — interactive dataset browser and download UI - Network accessline 533
- [Mouse Phenome Database portal](https://phenome.jax.org/) — interactive dataset browser and download UI - Network accessline 534
- [MPD REST API documentation](https://phenome.jax.org/about/api) — authoritative endpoint reference (the one used to build this skill) - Network accessline 534
- [MPD REST API documentation](https://phenome.jax.org/about/api) — authoritative endpoint reference (the one used to build this skill) - Network accessline 535
- [Bogue et al., *Mammalian Genome* 2020](https://doi.org/10.1007/s00335-020-09839-9) — MPD overview paper (architecture, data content, strain coverage) - Network accessline 535
- [Bogue et al., *Mammalian Genome* 2020](https://doi.org/10.1007/s00335-020-09839-9) — MPD overview paper (architecture, data content, strain coverage) - Network accessline 536
- [Jackson Laboratory Inbred Strain Catalog](https://www.jax.org/inbred-strains) — canonical strain nomenclature and stock numbers - Network accessline 536
- [Jackson Laboratory Inbred Strain Catalog](https://www.jax.org/inbred-strains) — canonical strain nomenclature and stock numbers - Network accessline 537
- [Mammalian Phenotype Ontology](https://www.informatics.jax.org/vocab/mp_ontology) — MP term browser (used by `/pheno/measures_by_ontology/{term}`) - Network accessline 537
- [Mammalian Phenotype Ontology](https://www.informatics.jax.org/vocab/mp_ontology) — MP term browser (used by `/pheno/measures_by_ontology/{term}`) - Network accessline 538
- [MPD Data Use Policy](https://phenome.jax.org/about/datause) — CC-BY-4.0 license terms and citation requirements - Network accessline 538
- [MPD Data Use Policy](https://phenome.jax.org/about/datause) — CC-BY-4.0 license terms and citation requirements
skills/genomics-bioinformatics/databases/quickgo-database/SKILL.md
- Network accessline 11
QuickGO is the EBI's Gene Ontology annotation browser and REST API. It provides programmatic access to the GO ontology (terms, synonyms, hierarchies) and to the manually curated and electronic GO annotations for proteins across all species. - Network accessline 11
QuickGO is the EBI's Gene Ontology annotation browser and REST API. It provides programmatic access to the GO ontology (terms, synonyms, hierarchies) and to the manually curated and electronic GO annotations for proteins across all species. - Network accessline 41
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 41
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 45
url = f"{QUICKGO_BASE}/{endpoint}" headers = {"Accept": "application/json"} r = requests.get(url, params=params, headers=headers, timeout=30) - Network accessline 45
url = f"{QUICKGO_BASE}/{endpoint}" headers = {"Accept": "application/json"} r = requests.get(url, params=params, headers=headers, timeout=30) - Network accessline 46
headers = {"Accept": "application/json"} r = requests.get(url, params=params, headers=headers, timeout=30) r.raise_for_status() - Network accessline 46
headers = {"Accept": "application/json"} r = requests.get(url, params=params, headers=headers, timeout=30) r.raise_for_status() - Network accessline 47
r = requests.get(url, params=params, headers=headers, timeout=30) - Network accessline 47
r = requests.get(url, params=params, headers=headers, timeout=30) - Network accessline 73
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 73
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 76
"""Retrieve metadata for a single GO term by ID.""" headers = {"Accept": "application/json"} r = requests.get( - Network accessline 76
"""Retrieve metadata for a single GO term by ID.""" headers = {"Accept": "application/json"} r = requests.get( - Network accessline 77
headers = {"Accept": "application/json"} r = requests.get( f"{QUICKGO_BASE}/ontology/go/terms/{go_id}", - Network accessline 77
headers = {"Accept": "application/json"} r = requests.get( f"{QUICKGO_BASE}/ontology/go/terms/{go_id}", - Network accessline 78
r = requests.get( - Network accessline 78
r = requests.get( - Network accessline 99
go_ids = ["GO:0006915", "GO:0005515", "GO:0016020"] ids_param = ",".join(go_ids) r = requests.get( - Network accessline 99
go_ids = ["GO:0006915", "GO:0005515", "GO:0016020"] ids_param = ",".join(go_ids) r = requests.get( - Network accessline 100
ids_param = ",".join(go_ids) r = requests.get( f"{QUICKGO_BASE}/ontology/go/terms/{ids_param}", - Network accessline 100
ids_param = ",".join(go_ids) r = requests.get( f"{QUICKGO_BASE}/ontology/go/terms/{ids_param}", - Network accessline 101
r = requests.get( - Network accessline 101
r = requests.get( - Network accessline 120
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 120
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 131
params["evidenceCode"] = ",".join(evidence_codes) headers = {"Accept": "application/json"} r = requests.get( - Network accessline 131
params["evidenceCode"] = ",".join(evidence_codes) headers = {"Accept": "application/json"} r = requests.get( - Network accessline 132
headers = {"Accept": "application/json"} r = requests.get( f"{QUICKGO_BASE}/annotation/search", - Network accessline 132
headers = {"Accept": "application/json"} r = requests.get( f"{QUICKGO_BASE}/annotation/search", - Network accessline 133
r = requests.get( - Network accessline 133
r = requests.get( - Network accessline 160
"page": 1, } r = requests.get( - Network accessline 160
"page": 1, } r = requests.get( - Network accessline 161
} r = requests.get( f"{QUICKGO_BASE}/annotation/search", - Network accessline 161
} r = requests.get( f"{QUICKGO_BASE}/annotation/search", - Network accessline 162
r = requests.get( - Network accessline 162
r = requests.get( - Network accessline 183
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 183
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 185
def get_ancestors(go_id: str, relations: str = "is_a,part_of") -> list: """Return ancestor GO IDs for a term via the ontology hierarchy.""" r = requests.get( - Network accessline 185
def get_ancestors(go_id: str, relations: str = "is_a,part_of") -> list: """Return ancestor GO IDs for a term via the ontology hierarchy.""" r = requests.get( - Network accessline 186
"""Return ancestor GO IDs for a term via the ontology hierarchy.""" r = requests.get( f"{QUICKGO_BASE}/ontology/go/terms/{go_id}/ancestors", - Network accessline 186
"""Return ancestor GO IDs for a term via the ontology hierarchy.""" r = requests.get( f"{QUICKGO_BASE}/ontology/go/terms/{go_id}/ancestors", - Network accessline 187
r = requests.get( - Network accessline 187
r = requests.get( - Network accessline 197
def get_descendants(go_id: str, relations: str = "is_a,part_of") -> list: """Return descendant GO IDs for a term via the ontology hierarchy.""" r = requests.get( - Network accessline 197
def get_descendants(go_id: str, relations: str = "is_a,part_of") -> list: """Return descendant GO IDs for a term via the ontology hierarchy.""" r = requests.get( - Network accessline 198
"""Return descendant GO IDs for a term via the ontology hierarchy.""" r = requests.get( f"{QUICKGO_BASE}/ontology/go/terms/{go_id}/descendants", - Network accessline 198
"""Return descendant GO IDs for a term via the ontology hierarchy.""" r = requests.get( f"{QUICKGO_BASE}/ontology/go/terms/{go_id}/descendants", - Network accessline 199
r = requests.get( - Network accessline 199
r = requests.get( - Network accessline 226
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 226
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 228
def search_go_terms(query: str, limit: int = 20) -> list: """Search GO terms by keyword; returns list of term dicts.""" r = requests.get( - Network accessline 228
def search_go_terms(query: str, limit: int = 20) -> list: """Search GO terms by keyword; returns list of term dicts.""" r = requests.get( - Network accessline 229
"""Search GO terms by keyword; returns list of term dicts.""" r = requests.get( f"{QUICKGO_BASE}/ontology/go/search", - Network accessline 229
"""Search GO terms by keyword; returns list of term dicts.""" r = requests.get( f"{QUICKGO_BASE}/ontology/go/search", - Network accessline 230
r = requests.get( - Network accessline 230
r = requests.get( - Network accessline 256
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 256
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 263
"page": 1, } r = requests.get( - Network accessline 263
"page": 1, } r = requests.get( - Network accessline 264
} r = requests.get( f"{QUICKGO_BASE}/annotation/search", - Network accessline 264
} r = requests.get( f"{QUICKGO_BASE}/annotation/search", - Network accessline 265
r = requests.get( - Network accessline 265
r = requests.get( - Network accessline 300
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 300
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 303
"""Resolve a list of GO IDs → {id: {name, aspect, definition}} in one call.""" ids_param = ",".join(go_ids) r = requests.get( - Network accessline 303
"""Resolve a list of GO IDs → {id: {name, aspect, definition}} in one call.""" ids_param = ",".join(go_ids) r = requests.get( - Network accessline 304
ids_param = ",".join(go_ids) r = requests.get( f"{QUICKGO_BASE}/ontology/go/terms/{ids_param}", - Network accessline 304
ids_param = ",".join(go_ids) r = requests.get( f"{QUICKGO_BASE}/ontology/go/terms/{ids_param}", - Network accessline 305
r = requests.get( - Network accessline 305
r = requests.get( - Network accessline 367
page_size: int = 100) -> list: """Retrieve all annotation pages for a GO term + taxon combination.""" QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 367
page_size: int = 100) -> list: """Retrieve all annotation pages for a GO term + taxon combination.""" QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 368
"""Retrieve all annotation pages for a GO term + taxon combination.""" QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" all_results = [] - Network accessline 368
"""Retrieve all annotation pages for a GO term + taxon combination.""" QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" all_results = [] - Network accessline 369
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 369
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 374
"evidenceCode": evidence_codes, "limit": page_size, "page": page} r = requests.get(f"{QUICKGO_BASE}/annotation/search", - Network accessline 374
"evidenceCode": evidence_codes, "limit": page_size, "page": page} r = requests.get(f"{QUICKGO_BASE}/annotation/search", - Network accessline 375
"page": page} r = requests.get(f"{QUICKGO_BASE}/annotation/search", params=params, headers={"Accept": "application/json"}, - Network accessline 375
"page": page} r = requests.get(f"{QUICKGO_BASE}/annotation/search", params=params, headers={"Accept": "application/json"}, - Network accessline 376
r = requests.get(f"{QUICKGO_BASE}/annotation/search", - Network accessline 376
r = requests.get(f"{QUICKGO_BASE}/annotation/search", - Network accessline 402
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 402
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 408
params = {"geneProductId": f"UniProtKB:{uniprot_id}", "limit": page_size, "page": page} r = requests.get(f"{QUICKGO_BASE}/annotation/search", - Network accessline 408
params = {"geneProductId": f"UniProtKB:{uniprot_id}", "limit": page_size, "page": page} r = requests.get(f"{QUICKGO_BASE}/annotation/search", - Network accessline 409
"limit": page_size, "page": page} r = requests.get(f"{QUICKGO_BASE}/annotation/search", params=params, headers={"Accept": "application/json"}, - Network accessline 409
"limit": page_size, "page": page} r = requests.get(f"{QUICKGO_BASE}/annotation/search", params=params, headers={"Accept": "application/json"}, - Network accessline 410
r = requests.get(f"{QUICKGO_BASE}/annotation/search", - Network accessline 410
r = requests.get(f"{QUICKGO_BASE}/annotation/search", - Network accessline 457
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 457
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 459
def quickgo_get(endpoint, params=None): r = requests.get(f"{QUICKGO_BASE}/{endpoint}", - Network accessline 459
def quickgo_get(endpoint, params=None): r = requests.get(f"{QUICKGO_BASE}/{endpoint}", - Network accessline 460
r = requests.get(f"{QUICKGO_BASE}/{endpoint}", - Network accessline 460
r = requests.get(f"{QUICKGO_BASE}/{endpoint}", - Network accessline 513
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 513
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 525
"evidenceCode": "EXP,IDA,IPI,IMP,IGI,IEP", "limit": 200, "page": 1} r = requests.get(f"{QUICKGO_BASE}/annotation/search", - Network accessline 525
"evidenceCode": "EXP,IDA,IPI,IMP,IGI,IEP", "limit": 200, "page": 1} r = requests.get(f"{QUICKGO_BASE}/annotation/search", - Network accessline 526
"limit": 200, "page": 1} r = requests.get(f"{QUICKGO_BASE}/annotation/search", params=params, headers={"Accept": "application/json"}, - Network accessline 526
"limit": 200, "page": 1} r = requests.get(f"{QUICKGO_BASE}/annotation/search", params=params, headers={"Accept": "application/json"}, - Network accessline 527
r = requests.get(f"{QUICKGO_BASE}/annotation/search", - Network accessline 527
r = requests.get(f"{QUICKGO_BASE}/annotation/search", - Network accessline 584
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 584
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 587
"""Return {go_id: name} for a list of GO IDs (single batch call).""" ids_str = ",".join(go_ids) r = requests.get( - Network accessline 587
"""Return {go_id: name} for a list of GO IDs (single batch call).""" ids_str = ",".join(go_ids) r = requests.get( - Network accessline 588
ids_str = ",".join(go_ids) r = requests.get( f"{QUICKGO_BASE}/ontology/go/terms/{ids_str}", - Network accessline 588
ids_str = ",".join(go_ids) r = requests.get( f"{QUICKGO_BASE}/ontology/go/terms/{ids_str}", - Network accessline 589
r = requests.get( - Network accessline 589
r = requests.get( - Network accessline 612
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 612
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 616
results, page = [], 1 while True: r = requests.get( - Network accessline 616
results, page = [], 1 while True: r = requests.get( - Network accessline 617
while True: r = requests.get( f"{QUICKGO_BASE}/annotation/search", - Network accessline 617
while True: r = requests.get( f"{QUICKGO_BASE}/annotation/search", - Network accessline 618
r = requests.get( - Network accessline 618
r = requests.get( - Network accessline 648
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 648
QUICKGO_BASE = "https://www.ebi.ac.uk/QuickGO/services" - Network accessline 653
def annotation_is_experimental(uniprot_id: str, go_id: str) -> bool: """Return True if any experimental annotation exists for protein + GO term.""" r = requests.get( - Network accessline 653
def annotation_is_experimental(uniprot_id: str, go_id: str) -> bool: """Return True if any experimental annotation exists for protein + GO term.""" r = requests.get( - Network accessline 654
"""Return True if any experimental annotation exists for protein + GO term.""" r = requests.get( f"{QUICKGO_BASE}/annotation/search", - Network accessline 654
"""Return True if any experimental annotation exists for protein + GO term.""" r = requests.get( f"{QUICKGO_BASE}/annotation/search", - Network accessline 655
r = requests.get( - Network accessline 655
r = requests.get( - Network accessline 690
- [QuickGO API documentation](https://www.ebi.ac.uk/QuickGO/api/index.html) — Full Swagger/OpenAPI endpoint reference - Network accessline 690
- [QuickGO API documentation](https://www.ebi.ac.uk/QuickGO/api/index.html) — Full Swagger/OpenAPI endpoint reference - Network accessline 691
- [Binns et al., Bioinformatics 2009](https://doi.org/10.1093/bioinformatics/btp536) — QuickGO: a web-based tool for Gene Ontology searching - Network accessline 691
- [Binns et al., Bioinformatics 2009](https://doi.org/10.1093/bioinformatics/btp536) — QuickGO: a web-based tool for Gene Ontology searching - Network accessline 692
- [Gene Ontology Consortium](https://geneontology.org/) — GO evidence codes, ontology files, and annotation downloads - Network accessline 692
- [Gene Ontology Consortium](https://geneontology.org/) — GO evidence codes, ontology files, and annotation downloads - Network accessline 693
- [Ashburner et al., Nature Genetics 2000](https://doi.org/10.1038/75556) — Original Gene Ontology paper - Network accessline 693
- [Ashburner et al., Nature Genetics 2000](https://doi.org/10.1038/75556) — Original Gene Ontology paper
skills/genomics-bioinformatics/databases/regulomedb-database/SKILL.md
- Network accessline 11
RegulomeDB integrates large-scale functional genomics data (ENCODE, Roadmap Epigenomics) to score genetic variants for regulatory potential. Each variant receives a ranking from 1a (highest regulatory confidence: eQTL + TF + DNase + motif + - Network accessline 11
RegulomeDB integrates large-scale functional genomics data (ENCODE, Roadmap Epigenomics) to score genetic variants for regulatory potential. Each variant receives a ranking from 1a (highest regulatory confidence: eQTL + TF + DNase + motif + - Network accessline 39
BASE = "https://regulomedb.org" - Network accessline 39
BASE = "https://regulomedb.org" - Network accessline 41
def regulome_score(variant, genome="GRCh38"): """Score a single variant (rsID or chr:pos-pos) via the GET /regulome-search/ endpoint.""" r = requests.get( - Network accessline 41
def regulome_score(variant, genome="GRCh38"): """Score a single variant (rsID or chr:pos-pos) via the GET /regulome-search/ endpoint.""" r = requests.get( - Network accessline 42
"""Score a single variant (rsID or chr:pos-pos) via the GET /regulome-search/ endpoint.""" r = requests.get( f"{BASE}/regulome-search/", - Network accessline 42
"""Score a single variant (rsID or chr:pos-pos) via the GET /regulome-search/ endpoint.""" r = requests.get( f"{BASE}/regulome-search/", - Network accessline 43
r = requests.get( - Network accessline 43
r = requests.get( - Network accessline 75
BASE = "https://regulomedb.org" - Network accessline 75
BASE = "https://regulomedb.org" - Network accessline 77
def score_variant(variant, genome="GRCh38"): """Return the regulome_score block and resolved coordinates.""" r = requests.get( - Network accessline 77
def score_variant(variant, genome="GRCh38"): """Return the regulome_score block and resolved coordinates.""" r = requests.get( - Network accessline 78
"""Return the regulome_score block and resolved coordinates.""" r = requests.get( f"{BASE}/regulome-search/", - Network accessline 78
"""Return the regulome_score block and resolved coordinates.""" r = requests.get( f"{BASE}/regulome-search/", - Network accessline 79
r = requests.get( - Network accessline 79
r = requests.get( - Network accessline 113
BASE = "https://regulomedb.org" - Network accessline 113
BASE = "https://regulomedb.org" - Network accessline 115
def scan_region(chrom, start, end, genome="GRCh38", limit=200): """List variants in a region with their resolved positions and overlapping rsIDs.""" r = requests.get( - Network accessline 115
def scan_region(chrom, start, end, genome="GRCh38", limit=200): """List variants in a region with their resolved positions and overlapping rsIDs.""" r = requests.get( - Network accessline 116
"""List variants in a region with their resolved positions and overlapping rsIDs.""" r = requests.get( f"{BASE}/regulome-search/", - Network accessline 116
"""List variants in a region with their resolved positions and overlapping rsIDs.""" r = requests.get( f"{BASE}/regulome-search/", - Network accessline 117
r = requests.get( - Network accessline 117
r = requests.get( - Network accessline 144
BASE = "https://regulomedb.org" - Network accessline 144
BASE = "https://regulomedb.org" - Network accessline 146
def evidence_rows(variant, genome="GRCh38"): r = requests.get( - Network accessline 146
def evidence_rows(variant, genome="GRCh38"): r = requests.get( - Network accessline 147
r = requests.get( - Network accessline 147
r = requests.get( - Network accessline 180
BASE = "https://regulomedb.org" - Network accessline 180
BASE = "https://regulomedb.org" - Network accessline 182
def tf_binding(variant, genome="GRCh38"): r = requests.get(f"{BASE}/regulome-search/", - Network accessline 182
def tf_binding(variant, genome="GRCh38"): r = requests.get(f"{BASE}/regulome-search/", - Network accessline 183
r = requests.get(f"{BASE}/regulome-search/", - Network accessline 183
r = requests.get(f"{BASE}/regulome-search/", - Network accessline 211
BASE = "https://regulomedb.org" - Network accessline 211
BASE = "https://regulomedb.org" - Network accessline 213
def tissue_scores(variant, genome="GRCh38", top_n=10): r = requests.get(f"{BASE}/regulome-search/", - Network accessline 213
def tissue_scores(variant, genome="GRCh38", top_n=10): r = requests.get(f"{BASE}/regulome-search/", - Network accessline 214
r = requests.get(f"{BASE}/regulome-search/", - Network accessline 214
r = requests.get(f"{BASE}/regulome-search/", - Network accessline 233
BASE = "https://regulomedb.org" - Network accessline 233
BASE = "https://regulomedb.org" - Network accessline 235
def nearby(variant, genome="GRCh38"): r = requests.get(f"{BASE}/regulome-search/", - Network accessline 235
def nearby(variant, genome="GRCh38"): r = requests.get(f"{BASE}/regulome-search/", - Network accessline 236
r = requests.get(f"{BASE}/regulome-search/", - Network accessline 236
r = requests.get(f"{BASE}/regulome-search/", - Network accessline 307
BASE = "https://regulomedb.org" - Network accessline 307
BASE = "https://regulomedb.org" - Network accessline 312
records = [] for snp in gwas_snps: r = requests.get(f"{BASE}/regulome-search/", - Network accessline 312
records = [] for snp in gwas_snps: r = requests.get(f"{BASE}/regulome-search/", - Network accessline 313
for snp in gwas_snps: r = requests.get(f"{BASE}/regulome-search/", params={"regions": snp, "genome": "GRCh38", "format": "json"}, - Network accessline 313
for snp in gwas_snps: r = requests.get(f"{BASE}/regulome-search/", params={"regions": snp, "genome": "GRCh38", "format": "json"}, - Network accessline 314
r = requests.get(f"{BASE}/regulome-search/", - Network accessline 314
r = requests.get(f"{BASE}/regulome-search/", - Network accessline 355
BASE = "https://regulomedb.org" - Network accessline 355
BASE = "https://regulomedb.org" - Network accessline 357
def locus_profile(region, genome="GRCh38"): r = requests.get(f"{BASE}/regulome-search/", - Network accessline 357
def locus_profile(region, genome="GRCh38"): r = requests.get(f"{BASE}/regulome-search/", - Network accessline 358
r = requests.get(f"{BASE}/regulome-search/", - Network accessline 358
r = requests.get(f"{BASE}/regulome-search/", - Network accessline 413
def quick_score(variant, genome="GRCh38"): r = requests.get("https://regulomedb.org/regulome-search/", - Network accessline 413
def quick_score(variant, genome="GRCh38"): r = requests.get("https://regulomedb.org/regulome-search/", - Network accessline 414
r = requests.get("https://regulomedb.org/regulome-search/", - Network accessline 414
r = requests.get("https://regulomedb.org/regulome-search/", - Network accessline 433
keep = [] for v in variants: r = requests.get("https://regulomedb.org/regulome-search/", - Network accessline 433
keep = [] for v in variants: r = requests.get("https://regulomedb.org/regulome-search/", - Network accessline 434
for v in variants: r = requests.get("https://regulomedb.org/regulome-search/", params={"regions": v, "genome": genome, "format": "json"}, - Network accessline 434
for v in variants: r = requests.get("https://regulomedb.org/regulome-search/", params={"regions": v, "genome": genome, "format": "json"}, - Network accessline 435
r = requests.get("https://regulomedb.org/regulome-search/", - Network accessline 435
r = requests.get("https://regulomedb.org/regulome-search/", - Network accessline 456
rows = [] for v in variants: r = requests.get("https://regulomedb.org/regulome-search/", - Network accessline 456
rows = [] for v in variants: r = requests.get("https://regulomedb.org/regulome-search/", - Network accessline 457
for v in variants: r = requests.get("https://regulomedb.org/regulome-search/", params={"regions": v, "genome": genome, "format": "json"}, - Network accessline 457
for v in variants: r = requests.get("https://regulomedb.org/regulome-search/", params={"regions": v, "genome": genome, "format": "json"}, - Network accessline 458
r = requests.get("https://regulomedb.org/regulome-search/", - Network accessline 458
r = requests.get("https://regulomedb.org/regulome-search/", - Network accessline 500
- [RegulomeDB Official Help](https://regulomedb.org/regulome-help/) — Documentation and scoring schema - Network accessline 500
- [RegulomeDB Official Help](https://regulomedb.org/regulome-help/) — Documentation and scoring schema - Network accessline 501
- [RegulomeDB search endpoint](https://regulomedb.org/regulome-search/) — Main REST API entry point (GET only) - Network accessline 501
- [RegulomeDB search endpoint](https://regulomedb.org/regulome-search/) — Main REST API entry point (GET only) - Network accessline 502
- Boyle AP et al. "Annotation of functional variation in personal genomes using RegulomeDB." *Genome Research* 22(9): 1790–1797 (2012). https://doi.org/10.1101/gr.137323.112 - Network accessline 502
- Boyle AP et al. "Annotation of functional variation in personal genomes using RegulomeDB." *Genome Research* 22(9): 1790–1797 (2012). https://doi.org/10.1101/gr.137323.112 - Network accessline 503
- Dong S et al. "Annotating genomic variants and their functional effects using RegulomeDB 2.0." *Bioinformatics* 35(24): 5341–5343 (2019). https://doi.org/10.1093/bioinformatics/btz560 - Network accessline 503
- Dong S et al. "Annotating genomic variants and their functional effects using RegulomeDB 2.0." *Bioinformatics* 35(24): 5341–5343 (2019). https://doi.org/10.1093/bioinformatics/btz560
skills/genomics-bioinformatics/databases/remap-database/SKILL.md
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ReMap 2022 is an integrative database of transcription factor (TF), cofactor, and chromatin regulator binding sites derived from uniformly reprocessed ChIP-seq experiments. The 2022 release catalogs 165 million non-redundant peaks from 8,11 - Network accessline 11
ReMap 2022 is an integrative database of transcription factor (TF), cofactor, and chromatin regulator binding sites derived from uniformly reprocessed ChIP-seq experiments. The 2022 release catalogs 165 million non-redundant peaks from 8,11 - Network accessline 41
REMAP_API = "https://remap2022.univ-amu.fr/api/v1" - Network accessline 41
REMAP_API = "https://remap2022.univ-amu.fr/api/v1" - Network accessline 43
# Query TF peaks overlapping a genomic region r = requests.get(f"{REMAP_API}/peaks/overlap/", params={ - Network accessline 43
# Query TF peaks overlapping a genomic region r = requests.get(f"{REMAP_API}/peaks/overlap/", params={ - Network accessline 44
r = requests.get(f"{REMAP_API}/peaks/overlap/", params={ - Network accessline 44
r = requests.get(f"{REMAP_API}/peaks/overlap/", params={ - Network accessline 67
REMAP_API = "https://remap2022.univ-amu.fr/api/v1" - Network accessline 67
REMAP_API = "https://remap2022.univ-amu.fr/api/v1" - Network accessline 69
def query_region(chrom, start, end, assembly="hg38", timeout=30): """Return all ReMap peaks overlapping [chrom:start-end].""" r = requests.get(f"{REMAP_API}/peaks/overlap/", params={ - Network accessline 69
def query_region(chrom, start, end, assembly="hg38", timeout=30): """Return all ReMap peaks overlapping [chrom:start-end].""" r = requests.get(f"{REMAP_API}/peaks/overlap/", params={ - Network accessline 70
"""Return all ReMap peaks overlapping [chrom:start-end].""" r = requests.get(f"{REMAP_API}/peaks/overlap/", params={ "chr": chrom, "start": start, "end": end, "assembly": assembly - Network accessline 70
"""Return all ReMap peaks overlapping [chrom:start-end].""" r = requests.get(f"{REMAP_API}/peaks/overlap/", params={ "chr": chrom, "start": start, "end": end, "assembly": assembly - Network accessline 71
r = requests.get(f"{REMAP_API}/peaks/overlap/", params={ - Network accessline 71
r = requests.get(f"{REMAP_API}/peaks/overlap/", params={ - Network accessline 104
# Fallback: if API is unavailable, use a locally downloaded BED file # Download from: https://remap2022.univ-amu.fr/download_page - Network accessline 104
# Fallback: if API is unavailable, use a locally downloaded BED file # Download from: https://remap2022.univ-amu.fr/download_page - Network accessline 105
# Download from: https://remap2022.univ-amu.fr/download_page - Network accessline 105
# Download from: https://remap2022.univ-amu.fr/download_page - Network accessline 131
REMAP_API = "https://remap2022.univ-amu.fr/api/v1" - Network accessline 131
REMAP_API = "https://remap2022.univ-amu.fr/api/v1" - Network accessline 133
def query_gene_peaks(gene_name, assembly="hg38", timeout=30): """Return all ReMap peaks near a gene TSS.""" r = requests.get(f"{REMAP_API}/peaks/gene/", params={ - Network accessline 133
def query_gene_peaks(gene_name, assembly="hg38", timeout=30): """Return all ReMap peaks near a gene TSS.""" r = requests.get(f"{REMAP_API}/peaks/gene/", params={ - Network accessline 134
"""Return all ReMap peaks near a gene TSS.""" r = requests.get(f"{REMAP_API}/peaks/gene/", params={ "gene": gene_name, "assembly": assembly - Network accessline 134
"""Return all ReMap peaks near a gene TSS.""" r = requests.get(f"{REMAP_API}/peaks/gene/", params={ "gene": gene_name, "assembly": assembly - Network accessline 135
r = requests.get(f"{REMAP_API}/peaks/gene/", params={ - Network accessline 135
r = requests.get(f"{REMAP_API}/peaks/gene/", params={ - Network accessline 170
REMAP_API = "https://remap2022.univ-amu.fr/api/v1" - Network accessline 170
REMAP_API = "https://remap2022.univ-amu.fr/api/v1" - Network accessline 172
def list_tfs(assembly="hg38", timeout=30): """Return all TFs in ReMap for the given assembly with statistics.""" r = requests.get(f"{REMAP_API}/tfbs/list/", params={"assembly": assembly}, timeout=timeout) - Network accessline 172
def list_tfs(assembly="hg38", timeout=30): """Return all TFs in ReMap for the given assembly with statistics.""" r = requests.get(f"{REMAP_API}/tfbs/list/", params={"assembly": assembly}, timeout=timeout) - Network accessline 173
"""Return all TFs in ReMap for the given assembly with statistics.""" r = requests.get(f"{REMAP_API}/tfbs/list/", params={"assembly": assembly}, timeout=timeout) r.raise_for_status() - Network accessline 173
"""Return all TFs in ReMap for the given assembly with statistics.""" r = requests.get(f"{REMAP_API}/tfbs/list/", params={"assembly": assembly}, timeout=timeout) r.raise_for_status() - Network accessline 174
r = requests.get(f"{REMAP_API}/tfbs/list/", params={"assembly": assembly}, timeout=timeout) - Network accessline 174
r = requests.get(f"{REMAP_API}/tfbs/list/", params={"assembly": assembly}, timeout=timeout) - Network accessline 178
def get_database_stats(assembly="hg38", timeout=30): """Return overall database statistics for the assembly.""" r = requests.get(f"{REMAP_API}/stats/", params={"assembly": assembly}, timeout=timeout) - Network accessline 178
def get_database_stats(assembly="hg38", timeout=30): """Return overall database statistics for the assembly.""" r = requests.get(f"{REMAP_API}/stats/", params={"assembly": assembly}, timeout=timeout) - Network accessline 179
"""Return overall database statistics for the assembly.""" r = requests.get(f"{REMAP_API}/stats/", params={"assembly": assembly}, timeout=timeout) r.raise_for_status() - Network accessline 179
"""Return overall database statistics for the assembly.""" r = requests.get(f"{REMAP_API}/stats/", params={"assembly": assembly}, timeout=timeout) r.raise_for_status() - Network accessline 180
r = requests.get(f"{REMAP_API}/stats/", params={"assembly": assembly}, timeout=timeout) - Network accessline 180
r = requests.get(f"{REMAP_API}/stats/", params={"assembly": assembly}, timeout=timeout) - Network accessline 215
REMAP_API = "https://remap2022.univ-amu.fr/api/v1" - Network accessline 215
REMAP_API = "https://remap2022.univ-amu.fr/api/v1" - Network accessline 217
def query_tf_peaks(tf_name, assembly="hg38", timeout=30): """Return all ChIP-seq peaks for a TF across all cell types.""" r = requests.get(f"{REMAP_API}/tfbs/name/", params={ - Network accessline 217
def query_tf_peaks(tf_name, assembly="hg38", timeout=30): """Return all ChIP-seq peaks for a TF across all cell types.""" r = requests.get(f"{REMAP_API}/tfbs/name/", params={ - Network accessline 218
"""Return all ChIP-seq peaks for a TF across all cell types.""" r = requests.get(f"{REMAP_API}/tfbs/name/", params={ "name": tf_name, "assembly": assembly - Network accessline 218
"""Return all ChIP-seq peaks for a TF across all cell types.""" r = requests.get(f"{REMAP_API}/tfbs/name/", params={ "name": tf_name, "assembly": assembly - Network accessline 219
r = requests.get(f"{REMAP_API}/tfbs/name/", params={ - Network accessline 219
r = requests.get(f"{REMAP_API}/tfbs/name/", params={ - Network accessline 258
REMAP_API = "https://remap2022.univ-amu.fr/api/v1" - Network accessline 258
REMAP_API = "https://remap2022.univ-amu.fr/api/v1" - Network accessline 260
def get_biotypes(assembly="hg38", timeout=30): """List all regulatory biotype categories available.""" r = requests.get(f"{REMAP_API}/biotypes/", params={"assembly": assembly}, timeout=timeout) - Network accessline 260
def get_biotypes(assembly="hg38", timeout=30): """List all regulatory biotype categories available.""" r = requests.get(f"{REMAP_API}/biotypes/", params={"assembly": assembly}, timeout=timeout) - Network accessline 261
"""List all regulatory biotype categories available.""" r = requests.get(f"{REMAP_API}/biotypes/", params={"assembly": assembly}, timeout=timeout) r.raise_for_status() - Network accessline 261
"""List all regulatory biotype categories available.""" r = requests.get(f"{REMAP_API}/biotypes/", params={"assembly": assembly}, timeout=timeout) r.raise_for_status() - Network accessline 262
r = requests.get(f"{REMAP_API}/biotypes/", params={"assembly": assembly}, timeout=timeout) - Network accessline 262
r = requests.get(f"{REMAP_API}/biotypes/", params={"assembly": assembly}, timeout=timeout) - Network accessline 266
def query_tf_by_biotype(tf_name, biotype, assembly="hg38", timeout=30): """Retrieve TF peaks filtered by regulatory biotype.""" r = requests.get(f"{REMAP_API}/peaks/biotype/", params={ - Network accessline 266
def query_tf_by_biotype(tf_name, biotype, assembly="hg38", timeout=30): """Retrieve TF peaks filtered by regulatory biotype.""" r = requests.get(f"{REMAP_API}/peaks/biotype/", params={ - Network accessline 267
"""Retrieve TF peaks filtered by regulatory biotype.""" r = requests.get(f"{REMAP_API}/peaks/biotype/", params={ "name": tf_name, "biotype": biotype, "assembly": assembly - Network accessline 267
"""Retrieve TF peaks filtered by regulatory biotype.""" r = requests.get(f"{REMAP_API}/peaks/biotype/", params={ "name": tf_name, "biotype": biotype, "assembly": assembly - Network accessline 268
r = requests.get(f"{REMAP_API}/peaks/biotype/", params={ - Network accessline 268
r = requests.get(f"{REMAP_API}/peaks/biotype/", params={ - Network accessline 285
def query_tf_peaks(tf_name, assembly="hg38", timeout=30): r = requests.get(f"https://remap2022.univ-amu.fr/api/v1/tfbs/name/", - Network accessline 285
def query_tf_peaks(tf_name, assembly="hg38", timeout=30): r = requests.get(f"https://remap2022.univ-amu.fr/api/v1/tfbs/name/", - Network accessline 286
r = requests.get(f"https://remap2022.univ-amu.fr/api/v1/tfbs/name/", - Network accessline 286
r = requests.get(f"https://remap2022.univ-amu.fr/api/v1/tfbs/name/", - Network accessline 346
When the REST API is unavailable or for offline bulk analysis, ReMap provides pre-built BED files at `https://remap2022.univ-amu.fr/download_page`. Key files: - Network accessline 346
When the REST API is unavailable or for offline bulk analysis, ReMap provides pre-built BED files at `https://remap2022.univ-amu.fr/download_page`. Key files: - Network accessline 391
REMAP_API = "https://remap2022.univ-amu.fr/api/v1" - Network accessline 391
REMAP_API = "https://remap2022.univ-amu.fr/api/v1" - Network accessline 393
def query_region(chrom, start, end, assembly="hg38", timeout=30): r = requests.get(f"{REMAP_API}/peaks/overlap/", params={ - Network accessline 393
def query_region(chrom, start, end, assembly="hg38", timeout=30): r = requests.get(f"{REMAP_API}/peaks/overlap/", params={ - Network accessline 394
r = requests.get(f"{REMAP_API}/peaks/overlap/", params={ - Network accessline 394
r = requests.get(f"{REMAP_API}/peaks/overlap/", params={ - Network accessline 447
REMAP_API = "https://remap2022.univ-amu.fr/api/v1" - Network accessline 447
REMAP_API = "https://remap2022.univ-amu.fr/api/v1" - Network accessline 449
def query_gene_peaks(gene_name, assembly="hg38", timeout=30): try: r = requests.get(f"{REMAP_API}/peaks/gene/", params={ - Network accessline 449
def query_gene_peaks(gene_name, assembly="hg38", timeout=30): try: r = requests.get(f"{REMAP_API}/peaks/gene/", params={ - Network accessline 450
try: r = requests.get(f"{REMAP_API}/peaks/gene/", params={ "gene": gene_name, "assembly": assembly - Network accessline 450
try: r = requests.get(f"{REMAP_API}/peaks/gene/", params={ "gene": gene_name, "assembly": assembly - Network accessline 451
r = requests.get(f"{REMAP_API}/peaks/gene/", params={ - Network accessline 451
r = requests.get(f"{REMAP_API}/peaks/gene/", params={ - Network accessline 495
# ReMap provides per-TF BED files. For large-scale offline analysis: REMAP_DOWNLOAD_BASE = "https://remap2022.univ-amu.fr/storage/remap2022/hg38/MACS2" - Network accessline 495
# ReMap provides per-TF BED files. For large-scale offline analysis: REMAP_DOWNLOAD_BASE = "https://remap2022.univ-amu.fr/storage/remap2022/hg38/MACS2" - Network accessline 496
REMAP_DOWNLOAD_BASE = "https://remap2022.univ-amu.fr/storage/remap2022/hg38/MACS2" - Network accessline 496
REMAP_DOWNLOAD_BASE = "https://remap2022.univ-amu.fr/storage/remap2022/hg38/MACS2" - Network accessline 504
url = f"{REMAP_DOWNLOAD_BASE}/{filename}" print(f"Attempting download: {url}") r = requests.get(url, stream=True, timeout=60) - Network accessline 504
url = f"{REMAP_DOWNLOAD_BASE}/{filename}" print(f"Attempting download: {url}") r = requests.get(url, stream=True, timeout=60) - Network accessline 505
print(f"Attempting download: {url}") r = requests.get(url, stream=True, timeout=60) if r.status_code == 200: - Network accessline 505
print(f"Attempting download: {url}") r = requests.get(url, stream=True, timeout=60) if r.status_code == 200: - Network accessline 506
r = requests.get(url, stream=True, timeout=60) - Network accessline 506
r = requests.get(url, stream=True, timeout=60) - Network accessline 585
REMAP_API = "https://remap2022.univ-amu.fr/api/v1" - Network accessline 585
REMAP_API = "https://remap2022.univ-amu.fr/api/v1" - Network accessline 587
def tfs_at_snp(chrom, pos, window=500, assembly="hg38"): """Find TFs with ChIP-seq peaks overlapping a SNP position ± window bp.""" r = requests.get(f"{REMAP_API}/peaks/overlap/", params={ - Network accessline 587
def tfs_at_snp(chrom, pos, window=500, assembly="hg38"): """Find TFs with ChIP-seq peaks overlapping a SNP position ± window bp.""" r = requests.get(f"{REMAP_API}/peaks/overlap/", params={ - Network accessline 588
"""Find TFs with ChIP-seq peaks overlapping a SNP position ± window bp.""" r = requests.get(f"{REMAP_API}/peaks/overlap/", params={ "chr": chrom, "start": pos - window, "end": pos + window, - Network accessline 588
"""Find TFs with ChIP-seq peaks overlapping a SNP position ± window bp.""" r = requests.get(f"{REMAP_API}/peaks/overlap/", params={ "chr": chrom, "start": pos - window, "end": pos + window, - Network accessline 589
r = requests.get(f"{REMAP_API}/peaks/overlap/", params={ - Network accessline 589
r = requests.get(f"{REMAP_API}/peaks/overlap/", params={ - Network accessline 616
REMAP_API = "https://remap2022.univ-amu.fr/api/v1" - Network accessline 616
REMAP_API = "https://remap2022.univ-amu.fr/api/v1" - Network accessline 618
def get_gene_tfs(gene, assembly="hg38"): try: r = requests.get(f"{REMAP_API}/peaks/gene/", params={"gene": gene, "assembly": assembly}, timeout=30) - Network accessline 618
def get_gene_tfs(gene, assembly="hg38"): try: r = requests.get(f"{REMAP_API}/peaks/gene/", params={"gene": gene, "assembly": assembly}, timeout=30) - Network accessline 619
try: r = requests.get(f"{REMAP_API}/peaks/gene/", params={"gene": gene, "assembly": assembly}, timeout=30) r.raise_for_status() - Network accessline 619
try: r = requests.get(f"{REMAP_API}/peaks/gene/", params={"gene": gene, "assembly": assembly}, timeout=30) r.raise_for_status() - Network accessline 620
r = requests.get(f"{REMAP_API}/peaks/gene/", params={"gene": gene, "assembly": assembly}, timeout=30) - Network accessline 620
r = requests.get(f"{REMAP_API}/peaks/gene/", params={"gene": gene, "assembly": assembly}, timeout=30) - Network accessline 650
REMAP_API = "https://remap2022.univ-amu.fr/api/v1" - Network accessline 650
REMAP_API = "https://remap2022.univ-amu.fr/api/v1" - Network accessline 652
def export_region_as_bed(chrom, start, end, outfile, assembly="hg38"): """Query ReMap region and save as 6-column BED file.""" r = requests.get(f"{REMAP_API}/peaks/overlap/", params={ - Network accessline 652
def export_region_as_bed(chrom, start, end, outfile, assembly="hg38"): """Query ReMap region and save as 6-column BED file.""" r = requests.get(f"{REMAP_API}/peaks/overlap/", params={ - Network accessline 653
"""Query ReMap region and save as 6-column BED file.""" r = requests.get(f"{REMAP_API}/peaks/overlap/", params={ "chr": chrom, "start": start, "end": end, "assembly": assembly - Network accessline 653
"""Query ReMap region and save as 6-column BED file.""" r = requests.get(f"{REMAP_API}/peaks/overlap/", params={ "chr": chrom, "start": start, "end": end, "assembly": assembly - Network accessline 654
r = requests.get(f"{REMAP_API}/peaks/overlap/", params={ - Network accessline 654
r = requests.get(f"{REMAP_API}/peaks/overlap/", params={ - Network accessline 678
| Problem | Cause | Solution | |---------|-------|----------| | `404 Not Found` from API | Endpoint path changed or unavailable | Check `https://remap2022.univ-amu.fr/api/` for current endpoint list; fall back to BED download | - Network accessline 678
| Problem | Cause | Solution | |---------|-------|----------| | `404 Not Found` from API | Endpoint path changed or unavailable | Check `https://remap2022.univ-amu.fr/api/` for current endpoint list; fall back to BED download | - Network accessline 679
|---------|-------|----------| | `404 Not Found` from API | Endpoint path changed or unavailable | Check `https://remap2022.univ-amu.fr/api/` for current endpoint list; fall back to BED download | | Empty JSON list `[]` from region query | - Network accessline 679
|---------|-------|----------| | `404 Not Found` from API | Endpoint path changed or unavailable | Check `https://remap2022.univ-amu.fr/api/` for current endpoint list; fall back to BED download | | Empty JSON list `[]` from region query | - Network accessline 680
| `404 Not Found` from API | Endpoint path changed or unavailable | Check `https://remap2022.univ-amu.fr/api/` for current endpoint list; fall back to BED download | - Network accessline 680
| `404 Not Found` from API | Endpoint path changed or unavailable | Check `https://remap2022.univ-amu.fr/api/` for current endpoint list; fall back to BED download | - Network accessline 683
| `requests.exceptions.Timeout` | Large region or slow server | Increase `timeout=60`; for regions >1 Mb use BED file download instead | | `name` field has only one component | Incomplete metadata in ReMap for that experiment | Guard with ` - Network accessline 683
| `requests.exceptions.Timeout` | Large region or slow server | Increase `timeout=60`; for regions >1 Mb use BED file download instead | | `name` field has only one component | Incomplete metadata in ReMap for that experiment | Guard with ` - Network accessline 684
| `name` field has only one component | Incomplete metadata in ReMap for that experiment | Guard with `parts[n] if len(parts) > n else "unknown"` | | BED download 404 | Per-TF files use exact ReMap TF naming | Check TF name case and spellin - Network accessline 684
| `name` field has only one component | Incomplete metadata in ReMap for that experiment | Guard with `parts[n] if len(parts) > n else "unknown"` | | BED download 404 | Per-TF files use exact ReMap TF naming | Check TF name case and spellin - Network accessline 685
| BED download 404 | Per-TF files use exact ReMap TF naming | Check TF name case and spelling at `https://remap2022.univ-amu.fr/download_page` | - Network accessline 685
| BED download 404 | Per-TF files use exact ReMap TF naming | Check TF name case and spelling at `https://remap2022.univ-amu.fr/download_page` | - Network accessline 698
- [ReMap 2022 API documentation](https://remap2022.univ-amu.fr/api/) — REST API endpoint reference and interactive explorer - [Hammal et al., Nucleic Acids Research 2022](https://doi.org/10.1093/nar/gkab996) — ReMap 2022 paper describing th - Network accessline 698
- [ReMap 2022 API documentation](https://remap2022.univ-amu.fr/api/) — REST API endpoint reference and interactive explorer - [Hammal et al., Nucleic Acids Research 2022](https://doi.org/10.1093/nar/gkab996) — ReMap 2022 paper describing th - Network accessline 699
- [Hammal et al., Nucleic Acids Research 2022](https://doi.org/10.1093/nar/gkab996) — ReMap 2022 paper describing the 2022 release (165M peaks, 1,210 TFs) - [ReMap portal and download page](https://remap2022.univ-amu.fr/) — web browser, dow - Network accessline 699
- [Hammal et al., Nucleic Acids Research 2022](https://doi.org/10.1093/nar/gkab996) — ReMap 2022 paper describing the 2022 release (165M peaks, 1,210 TFs) - [ReMap portal and download page](https://remap2022.univ-amu.fr/) — web browser, dow - Network accessline 700
- [ReMap portal and download page](https://remap2022.univ-amu.fr/) — web browser, download page for BED files and cis-regulatory modules - Network accessline 700
- [ReMap portal and download page](https://remap2022.univ-amu.fr/) — web browser, download page for BED files and cis-regulatory modules - Network accessline 701
- [Chèneby et al., Nucleic Acids Research 2020](https://doi.org/10.1093/nar/gkz945) — ReMap 2020 paper describing the reprocessing pipeline and quality control methodology - Network accessline 701
- [Chèneby et al., Nucleic Acids Research 2020](https://doi.org/10.1093/nar/gkz945) — ReMap 2020 paper describing the reprocessing pipeline and quality control methodology
skills/genomics-bioinformatics/databases/ucsc-genome-browser/SKILL.md
- Network accessline 11
The UCSC Genome Browser REST API at `https://api.genome.ucsc.edu/` provides programmatic access to genome sequences, annotation tracks, and hub data for 100+ assemblies including hg38, mm39, and dm6. The API is free, requires no authenticat - Network accessline 11
The UCSC Genome Browser REST API at `https://api.genome.ucsc.edu/` provides programmatic access to genome sequences, annotation tracks, and hub data for 100+ assemblies including hg38, mm39, and dm6. The API is free, requires no authenticat - Network accessline 40
BASE = "https://api.genome.ucsc.edu" - Network accessline 40
BASE = "https://api.genome.ucsc.edu" - Network accessline 42
def get_sequence(genome, chrom, start, end): """Fetch DNA sequence for a genomic region (0-based, half-open).""" r = requests.get(f"{BASE}/getData/sequence", - Network accessline 42
def get_sequence(genome, chrom, start, end): """Fetch DNA sequence for a genomic region (0-based, half-open).""" r = requests.get(f"{BASE}/getData/sequence", - Network accessline 43
"""Fetch DNA sequence for a genomic region (0-based, half-open).""" r = requests.get(f"{BASE}/getData/sequence", params={"genome": genome, "chrom": chrom, - Network accessline 43
"""Fetch DNA sequence for a genomic region (0-based, half-open).""" r = requests.get(f"{BASE}/getData/sequence", params={"genome": genome, "chrom": chrom, - Network accessline 44
r = requests.get(f"{BASE}/getData/sequence", - Network accessline 44
r = requests.get(f"{BASE}/getData/sequence", - Network accessline 67
BASE = "https://api.genome.ucsc.edu" - Network accessline 67
BASE = "https://api.genome.ucsc.edu" - Network accessline 69
def get_sequence(genome, chrom, start, end): """Return DNA sequence string for the given region.""" r = requests.get(f"{BASE}/getData/sequence", - Network accessline 69
def get_sequence(genome, chrom, start, end): """Return DNA sequence string for the given region.""" r = requests.get(f"{BASE}/getData/sequence", - Network accessline 70
"""Return DNA sequence string for the given region.""" r = requests.get(f"{BASE}/getData/sequence", params={"genome": genome, "chrom": chrom, - Network accessline 70
"""Return DNA sequence string for the given region.""" r = requests.get(f"{BASE}/getData/sequence", params={"genome": genome, "chrom": chrom, - Network accessline 71
r = requests.get(f"{BASE}/getData/sequence", - Network accessline 71
r = requests.get(f"{BASE}/getData/sequence", - Network accessline 104
BASE = "https://api.genome.ucsc.edu" - Network accessline 104
BASE = "https://api.genome.ucsc.edu" - Network accessline 106
def get_track_data(genome, track, chrom, start, end): """Fetch annotation records from a UCSC track for a region.""" r = requests.get(f"{BASE}/getData/track", - Network accessline 106
def get_track_data(genome, track, chrom, start, end): """Fetch annotation records from a UCSC track for a region.""" r = requests.get(f"{BASE}/getData/track", - Network accessline 107
"""Fetch annotation records from a UCSC track for a region.""" r = requests.get(f"{BASE}/getData/track", params={"genome": genome, "track": track, - Network accessline 107
"""Fetch annotation records from a UCSC track for a region.""" r = requests.get(f"{BASE}/getData/track", params={"genome": genome, "track": track, - Network accessline 108
r = requests.get(f"{BASE}/getData/track", - Network accessline 108
r = requests.get(f"{BASE}/getData/track", - Network accessline 139
BASE = "https://api.genome.ucsc.edu" - Network accessline 139
BASE = "https://api.genome.ucsc.edu" - Network accessline 141
def list_tracks(genome): """Return a dict of {track_name: track_metadata} for a genome assembly.""" r = requests.get(f"{BASE}/list/tracks", params={"genome": genome}) - Network accessline 141
def list_tracks(genome): """Return a dict of {track_name: track_metadata} for a genome assembly.""" r = requests.get(f"{BASE}/list/tracks", params={"genome": genome}) - Network accessline 142
"""Return a dict of {track_name: track_metadata} for a genome assembly.""" r = requests.get(f"{BASE}/list/tracks", params={"genome": genome}) r.raise_for_status() - Network accessline 142
"""Return a dict of {track_name: track_metadata} for a genome assembly.""" r = requests.get(f"{BASE}/list/tracks", params={"genome": genome}) r.raise_for_status() - Network accessline 143
r = requests.get(f"{BASE}/list/tracks", params={"genome": genome}) - Network accessline 143
r = requests.get(f"{BASE}/list/tracks", params={"genome": genome}) - Network accessline 163
BASE = "https://api.genome.ucsc.edu" - Network accessline 163
BASE = "https://api.genome.ucsc.edu" - Network accessline 165
def get_chrom_sizes(genome): """Return {chrom: size_in_bp} for a genome assembly.""" r = requests.get(f"{BASE}/list/chromosomes", params={"genome": genome}) - Network accessline 165
def get_chrom_sizes(genome): """Return {chrom: size_in_bp} for a genome assembly.""" r = requests.get(f"{BASE}/list/chromosomes", params={"genome": genome}) - Network accessline 166
"""Return {chrom: size_in_bp} for a genome assembly.""" r = requests.get(f"{BASE}/list/chromosomes", params={"genome": genome}) r.raise_for_status() - Network accessline 166
"""Return {chrom: size_in_bp} for a genome assembly.""" r = requests.get(f"{BASE}/list/chromosomes", params={"genome": genome}) r.raise_for_status() - Network accessline 167
r = requests.get(f"{BASE}/list/chromosomes", params={"genome": genome}) - Network accessline 167
r = requests.get(f"{BASE}/list/chromosomes", params={"genome": genome}) - Network accessline 189
BASE = "https://api.genome.ucsc.edu" - Network accessline 189
BASE = "https://api.genome.ucsc.edu" - Network accessline 191
def get_refgene(genome, chrom, start, end): """Retrieve RefSeq gene annotations for a region.""" r = requests.get(f"{BASE}/getData/track", - Network accessline 191
def get_refgene(genome, chrom, start, end): """Retrieve RefSeq gene annotations for a region.""" r = requests.get(f"{BASE}/getData/track", - Network accessline 192
"""Retrieve RefSeq gene annotations for a region.""" r = requests.get(f"{BASE}/getData/track", params={"genome": genome, "track": "refGene", - Network accessline 192
"""Retrieve RefSeq gene annotations for a region.""" r = requests.get(f"{BASE}/getData/track", params={"genome": genome, "track": "refGene", - Network accessline 193
r = requests.get(f"{BASE}/getData/track", - Network accessline 193
r = requests.get(f"{BASE}/getData/track", - Network accessline 231
BASE = "https://api.genome.ucsc.edu" - Network accessline 231
BASE = "https://api.genome.ucsc.edu" - Network accessline 233
def get_conservation(genome, track, chrom, start, end): """Retrieve per-base conservation scores from a bigWig track.""" r = requests.get(f"{BASE}/getData/track", - Network accessline 233
def get_conservation(genome, track, chrom, start, end): """Retrieve per-base conservation scores from a bigWig track.""" r = requests.get(f"{BASE}/getData/track", - Network accessline 234
"""Retrieve per-base conservation scores from a bigWig track.""" r = requests.get(f"{BASE}/getData/track", params={"genome": genome, "track": track, - Network accessline 234
"""Retrieve per-base conservation scores from a bigWig track.""" r = requests.get(f"{BASE}/getData/track", params={"genome": genome, "track": track, - Network accessline 235
r = requests.get(f"{BASE}/getData/track", - Network accessline 235
r = requests.get(f"{BASE}/getData/track", - Network accessline 259
BASE = "https://api.genome.ucsc.edu" - Network accessline 259
BASE = "https://api.genome.ucsc.edu" - Network accessline 261
def list_ucsc_genomes(): """Return all UCSC-hosted genome assemblies.""" r = requests.get(f"{BASE}/list/ucscGenomes") - Network accessline 261
def list_ucsc_genomes(): """Return all UCSC-hosted genome assemblies.""" r = requests.get(f"{BASE}/list/ucscGenomes") - Network accessline 262
"""Return all UCSC-hosted genome assemblies.""" r = requests.get(f"{BASE}/list/ucscGenomes") r.raise_for_status() - Network accessline 262
"""Return all UCSC-hosted genome assemblies.""" r = requests.get(f"{BASE}/list/ucscGenomes") r.raise_for_status() - Network accessline 263
r = requests.get(f"{BASE}/list/ucscGenomes") - Network accessline 263
r = requests.get(f"{BASE}/list/ucscGenomes") - Network accessline 305
BASE = "https://api.genome.ucsc.edu" - Network accessline 305
BASE = "https://api.genome.ucsc.edu" - Network accessline 309
def get_refgene(genome, chrom, start, end): r = requests.get(f"{BASE}/getData/track", - Network accessline 309
def get_refgene(genome, chrom, start, end): r = requests.get(f"{BASE}/getData/track", - Network accessline 310
r = requests.get(f"{BASE}/getData/track", - Network accessline 310
r = requests.get(f"{BASE}/getData/track", - Network accessline 316
def get_sequence(genome, chrom, start, end): r = requests.get(f"{BASE}/getData/sequence", - Network accessline 316
def get_sequence(genome, chrom, start, end): r = requests.get(f"{BASE}/getData/sequence", - Network accessline 317
r = requests.get(f"{BASE}/getData/sequence", - Network accessline 317
r = requests.get(f"{BASE}/getData/sequence", - Network accessline 374
BASE = "https://api.genome.ucsc.edu" - Network accessline 374
BASE = "https://api.genome.ucsc.edu" - Network accessline 376
def get_refgene(genome, chrom, start, end): r = requests.get(f"{BASE}/getData/track", - Network accessline 376
def get_refgene(genome, chrom, start, end): r = requests.get(f"{BASE}/getData/track", - Network accessline 377
r = requests.get(f"{BASE}/getData/track", - Network accessline 377
r = requests.get(f"{BASE}/getData/track", - Network accessline 441
BASE = "https://api.genome.ucsc.edu" - Network accessline 441
BASE = "https://api.genome.ucsc.edu" - Network accessline 443
def get_conservation(genome, track, chrom, start, end): r = requests.get(f"{BASE}/getData/track", - Network accessline 443
def get_conservation(genome, track, chrom, start, end): r = requests.get(f"{BASE}/getData/track", - Network accessline 444
r = requests.get(f"{BASE}/getData/track", - Network accessline 444
r = requests.get(f"{BASE}/getData/track", - Network accessline 512
r = requests.get("https://api.genome.ucsc.edu/list/ucscGenomes") - Network accessline 512
r = requests.get("https://api.genome.ucsc.edu/list/ucscGenomes") - Network accessline 530
BASE = "https://api.genome.ucsc.edu" - Network accessline 530
BASE = "https://api.genome.ucsc.edu" - Network accessline 532
r = requests.get(f"{BASE}/getData/track", - Network accessline 532
r = requests.get(f"{BASE}/getData/track", - Network accessline 552
seq = requests.get( - Network accessline 552
seq = requests.get( - Network accessline 553
"https://api.genome.ucsc.edu/getData/sequence", - Network accessline 553
"https://api.genome.ucsc.edu/getData/sequence", - Network accessline 569
sizes = requests.get("https://api.genome.ucsc.edu/list/chromosomes", - Network accessline 569
sizes = requests.get("https://api.genome.ucsc.edu/list/chromosomes", - Network accessline 588
| Track query returns empty list | No features in the region for that track | Confirm track exists with `list/tracks`; widen the query window | | `KeyError` on track response | Response key differs from track parameter | Use `.get(track, da - Network accessline 588
| Track query returns empty list | No features in the region for that track | Confirm track exists with `list/tracks`; widen the query window | | `KeyError` on track response | Response key differs from track parameter | Use `.get(track, da - Network accessline 589
| `KeyError` on track response | Response key differs from track parameter | Use `.get(track, data.get("data", []))` to handle variant key names | | `ConnectionError` or timeout | Network issue or server load | Retry with `requests.Session( - Network accessline 589
| `KeyError` on track response | Response key differs from track parameter | Use `.get(track, data.get("data", []))` to handle variant key names | | `ConnectionError` or timeout | Network issue or server load | Retry with `requests.Session( - Network accessline 590
| `ConnectionError` or timeout | Network issue or server load | Retry with `requests.Session()` and set `timeout=30`; add `time.sleep(1)` | - Network accessline 590
| `ConnectionError` or timeout | Network issue or server load | Retry with `requests.Session()` and set `timeout=30`; add `time.sleep(1)` | - Network accessline 604
- [UCSC REST API documentation](https://genome.ucsc.edu/goldenPath/help/api.html) — full endpoint reference, parameters, and response formats - Network accessline 604
- [UCSC REST API documentation](https://genome.ucsc.edu/goldenPath/help/api.html) — full endpoint reference, parameters, and response formats - Network accessline 605
- [UCSC API base URL](https://api.genome.ucsc.edu/) — interactive endpoint explorer - Network accessline 605
- [UCSC API base URL](https://api.genome.ucsc.edu/) — interactive endpoint explorer - Network accessline 606
- [Kent WJ et al. (2002) Genome Res 12:996–1006](https://doi.org/10.1101/gr.229102) — original UCSC Genome Browser paper - Network accessline 606
- [Kent WJ et al. (2002) Genome Res 12:996–1006](https://doi.org/10.1101/gr.229102) — original UCSC Genome Browser paper - Network accessline 607
- [UCSC Track Database](https://genome.ucsc.edu/cgi-bin/hgTables) — Table Browser to explore track names and schemas - Network accessline 607
- [UCSC Track Database](https://genome.ucsc.edu/cgi-bin/hgTables) — Table Browser to explore track names and schemas
skills/genomics-bioinformatics/etetoolkit/SKILL.md
- Network accessline 574
- [ETE3 documentation](http://etetoolkit.org/docs/latest/tutorial/) — official tutorial covering all modules - Network accessline 574
- [ETE3 documentation](http://etetoolkit.org/docs/latest/tutorial/) — official tutorial covering all modules - Network accessline 575
- [ETE3 API reference](http://etetoolkit.org/docs/latest/reference/) — complete class and method reference - Network accessline 575
- [ETE3 API reference](http://etetoolkit.org/docs/latest/reference/) — complete class and method reference - Network accessline 576
- [ETE3 GitHub repository](https://github.com/etetoolkit/ete) — source, issue tracker, and example notebooks - Network accessline 576
- [ETE3 GitHub repository](https://github.com/etetoolkit/ete) — source, issue tracker, and example notebooks - Network accessline 577
- [Huerta-Cepas J, Serra F, Bork P (2016) ETE 3: Reconstruction, Analysis, and Visualization of Phylogenomic Data. *Mol Biol Evol* 33:1635-1638](https://doi.org/10.1093/molbev/msw046) — primary citation for ETE3 - Network accessline 577
- [Huerta-Cepas J, Serra F, Bork P (2016) ETE 3: Reconstruction, Analysis, and Visualization of Phylogenomic Data. *Mol Biol Evol* 33:1635-1638](https://doi.org/10.1093/molbev/msw046) — primary citation for ETE3
skills/genomics-bioinformatics/homer-motif-analysis/SKILL.md
- Network accessline 658
- [HOMER official documentation](http://homer.ucsd.edu/homer/) — comprehensive guide to all HOMER commands, parameters, and output formats - Network accessline 658
- [HOMER official documentation](http://homer.ucsd.edu/homer/) — comprehensive guide to all HOMER commands, parameters, and output formats - Network accessline 659
- Heinz S et al. (2010) "Simple Combinations of Lineage-Determining Transcription Factors Prime cis-Regulatory Elements Required for Macrophage and B Cell Identities." *Molecular Cell* 38(4):576–589. [DOI:10.1016/j.molcel.2010.05.004](https - Network accessline 659
- Heinz S et al. (2010) "Simple Combinations of Lineage-Determining Transcription Factors Prime cis-Regulatory Elements Required for Macrophage and B Cell Identities." *Molecular Cell* 38(4):576–589. [DOI:10.1016/j.molcel.2010.05.004](https - Network accessline 660
- [HOMER GitHub: samtools/homer](https://github.com/samtools/homer) — source code and issue tracker - Network accessline 660
- [HOMER GitHub: samtools/homer](https://github.com/samtools/homer) — source code and issue tracker - Network accessline 661
- [JASPAR 2024 database](https://jaspar.elixir.no/) — curated TF binding motif database used by HOMER for known motif enrichment - Network accessline 661
- [JASPAR 2024 database](https://jaspar.elixir.no/) — curated TF binding motif database used by HOMER for known motif enrichment
skills/genomics-bioinformatics/interval-ops/bedtools-genomic-intervals/SKILL.md
- Network accessline 442
- [bedtools documentation](https://bedtools.readthedocs.io/) — command reference and examples - Network accessline 442
- [bedtools documentation](https://bedtools.readthedocs.io/) — command reference and examples - Network accessline 443
- [GitHub: arq5x/bedtools2](https://github.com/arq5x/bedtools2) — source, releases, issue tracker - Network accessline 443
- [GitHub: arq5x/bedtools2](https://github.com/arq5x/bedtools2) — source, releases, issue tracker - Network accessline 444
- Quinlan & Hall (2010) "BEDTools: a flexible suite of utilities for comparing genomic features" — [Bioinformatics 26(6)](https://doi.org/10.1093/bioinformatics/btq033) - Network accessline 444
- Quinlan & Hall (2010) "BEDTools: a flexible suite of utilities for comparing genomic features" — [Bioinformatics 26(6)](https://doi.org/10.1093/bioinformatics/btq033) - Network accessline 445
- [ENCODE blacklist regions](https://github.com/Boyle-Lab/Blacklist) — curated problematic genomic regions - Network accessline 445
- [ENCODE blacklist regions](https://github.com/Boyle-Lab/Blacklist) — curated problematic genomic regions
skills/genomics-bioinformatics/interval-ops/deeptools-ngs-analysis/SKILL.md
- Network accessline 432
- [deepTools documentation](https://deeptools.readthedocs.io/) — official user guide and tool reference - Network accessline 432
- [deepTools documentation](https://deeptools.readthedocs.io/) — official user guide and tool reference - Network accessline 433
- [deepTools Galaxy](https://deeptools.ie-freiburg.mpg.de/) — web-based interface - Network accessline 433
- [deepTools Galaxy](https://deeptools.ie-freiburg.mpg.de/) — web-based interface - Network accessline 434
- Ramirez et al. (2016) "deepTools2: a next generation web server for deep-sequencing data analysis" — [Nucleic Acids Research](https://doi.org/10.1093/nar/gkw257) - Network accessline 434
- Ramirez et al. (2016) "deepTools2: a next generation web server for deep-sequencing data analysis" — [Nucleic Acids Research](https://doi.org/10.1093/nar/gkw257)
skills/genomics-bioinformatics/interval-ops/geniml/SKILL.md
- Network accessline 461
- [Geniml documentation](https://geniml.databio.org/) — official docs and tutorials - Network accessline 461
- [Geniml documentation](https://geniml.databio.org/) — official docs and tutorials - Network accessline 462
- [Geniml GitHub repository](https://github.com/databio/geniml) — source code and examples - Network accessline 462
- [Geniml GitHub repository](https://github.com/databio/geniml) — source code and examples - Network accessline 463
- [PyPI: geniml](https://pypi.org/project/geniml/) — installation and version info - Network accessline 463
- [PyPI: geniml](https://pypi.org/project/geniml/) — installation and version info - Network accessline 464
- [HuggingFace: databio region2vec models](https://huggingface.co/databio) — pre-trained region2vec model collection - Network accessline 464
- [HuggingFace: databio region2vec models](https://huggingface.co/databio) — pre-trained region2vec model collection
skills/genomics-bioinformatics/interval-ops/gtars/SKILL.md
- Network accessline 449
- [GTARS GitHub repository](https://github.com/databio/gtars) — source, issue tracker, and changelog - Network accessline 449
- [GTARS GitHub repository](https://github.com/databio/gtars) — source, issue tracker, and changelog - Network accessline 450
- [PyPI: gtars](https://pypi.org/project/gtars/) — installation instructions and version history - Network accessline 450
- [PyPI: gtars](https://pypi.org/project/gtars/) — installation instructions and version history - Network accessline 451
- [Geniml documentation](https://geniml.databio.org/) — ecosystem context; GTARS is the preprocessing engine for geniml - Network accessline 451
- [Geniml documentation](https://geniml.databio.org/) — ecosystem context; GTARS is the preprocessing engine for geniml
skills/genomics-bioinformatics/macs3-peak-calling/SKILL.md
- Network accessline 403
- [MACS3 GitHub: macs3-project/MACS](https://github.com/macs3-project/MACS) — source code, documentation, and changelog - Network accessline 403
- [MACS3 GitHub: macs3-project/MACS](https://github.com/macs3-project/MACS) — source code, documentation, and changelog - Network accessline 404
- Zhang Y et al. (2008) "Model-based Analysis of ChIP-Seq (MACS)" — *Genome Biology* 9:R137. [DOI:10.1186/gb-2008-9-9-r137](https://doi.org/10.1186/gb-2008-9-9-r137) - Network accessline 404
- Zhang Y et al. (2008) "Model-based Analysis of ChIP-Seq (MACS)" — *Genome Biology* 9:R137. [DOI:10.1186/gb-2008-9-9-r137](https://doi.org/10.1186/gb-2008-9-9-r137) - Network accessline 405
- [ENCODE ATAC-seq pipeline](https://www.encodeproject.org/atac-seq/) — ENCODE standardized ATAC-seq workflow using MACS3 - Network accessline 405
- [ENCODE ATAC-seq pipeline](https://www.encodeproject.org/atac-seq/) — ENCODE standardized ATAC-seq workflow using MACS3 - Network accessline 406
- [IDR framework](https://github.com/nboley/idr) — irreproducible discovery rate for reproducible peak calls - Network accessline 406
- [IDR framework](https://github.com/nboley/idr) — irreproducible discovery rate for reproducible peak calls
skills/genomics-bioinformatics/qc/busco-status-interpretation/SKILL.md
- Network accessline 280
- [BUSCO User Guide](https://busco.ezlab.org/busco_userguide.html) -- Official documentation covering installation, usage modes, lineage datasets, and interpretation guidelines - Network accessline 280
- [BUSCO User Guide](https://busco.ezlab.org/busco_userguide.html) -- Official documentation covering installation, usage modes, lineage datasets, and interpretation guidelines - Network accessline 281
- [Manni et al. (2021) "BUSCO Update"](https://doi.org/10.1093/molbev/msab199) -- The BUSCO v5 paper describing the current framework, metaeuk integration, and auto-lineage selection (Molecular Biology and Evolution) - Network accessline 281
- [Manni et al. (2021) "BUSCO Update"](https://doi.org/10.1093/molbev/msab199) -- The BUSCO v5 paper describing the current framework, metaeuk integration, and auto-lineage selection (Molecular Biology and Evolution) - Network accessline 282
- [OrthoDB](https://www.orthodb.org/) -- The underlying database of orthologs that BUSCO uses; useful for understanding lineage dataset composition and ortholog definitions - Network accessline 282
- [OrthoDB](https://www.orthodb.org/) -- The underlying database of orthologs that BUSCO uses; useful for understanding lineage dataset composition and ortholog definitions - Network accessline 283
- [Simao et al. (2015) "BUSCO"](https://doi.org/10.1093/bioinformatics/btv351) -- The original BUSCO paper establishing the completeness assessment framework (Bioinformatics) - Network accessline 283
- [Simao et al. (2015) "BUSCO"](https://doi.org/10.1093/bioinformatics/btv351) -- The original BUSCO paper establishing the completeness assessment framework (Bioinformatics)
skills/genomics-bioinformatics/qc/fastp-fastq-preprocessing/SKILL.md
- Network accessline 34
# Or download pre-compiled binary (Linux) wget https://github.com/OpenGene/fastp/releases/download/v0.24.0/fastp - Network accessline 34
# Or download pre-compiled binary (Linux) wget https://github.com/OpenGene/fastp/releases/download/v0.24.0/fastp - Network accessline 35
wget https://github.com/OpenGene/fastp/releases/download/v0.24.0/fastp - Network accessline 35
wget https://github.com/OpenGene/fastp/releases/download/v0.24.0/fastp - Network accessline 442
- [fastp GitHub: OpenGene/fastp](https://github.com/OpenGene/fastp) — source code, changelog, and usage guide - Network accessline 442
- [fastp GitHub: OpenGene/fastp](https://github.com/OpenGene/fastp) — source code, changelog, and usage guide - Network accessline 443
- Chen S et al. (2018) "fastp: an ultra-fast all-in-one FASTQ preprocessor" — *Bioinformatics* 34(17):i884-i890. [DOI:10.1093/bioinformatics/bty560](https://doi.org/10.1093/bioinformatics/bty560) - Network accessline 443
- Chen S et al. (2018) "fastp: an ultra-fast all-in-one FASTQ preprocessor" — *Bioinformatics* 34(17):i884-i890. [DOI:10.1093/bioinformatics/bty560](https://doi.org/10.1093/bioinformatics/bty560) - Network accessline 444
- [fastp documentation](https://github.com/OpenGene/fastp#readme) — full parameter reference and recipes - Network accessline 444
- [fastp documentation](https://github.com/OpenGene/fastp#readme) — full parameter reference and recipes - Network accessline 445
- [MultiQC fastp module](https://multiqc.info/modules/fastp/) — aggregating fastp JSON reports across samples - Network accessline 445
- [MultiQC fastp module](https://multiqc.info/modules/fastp/) — aggregating fastp JSON reports across samples
skills/genomics-bioinformatics/qc/multiqc-qc-reports/SKILL.md
- Network accessline 322
- [MultiQC documentation](https://multiqc.info/docs/) — official user guide and module list - Network accessline 322
- [MultiQC documentation](https://multiqc.info/docs/) — official user guide and module list - Network accessline 323
- [GitHub: ewels/MultiQC](https://github.com/ewels/MultiQC) — source code and community modules - Network accessline 323
- [GitHub: ewels/MultiQC](https://github.com/ewels/MultiQC) — source code and community modules - Network accessline 324
- Ewels et al. (2016) "MultiQC: summarize analysis results for multiple tools and samples in a single report" — [Bioinformatics 32(19)](https://doi.org/10.1093/bioinformatics/btw354) - Network accessline 324
- Ewels et al. (2016) "MultiQC: summarize analysis results for multiple tools and samples in a single report" — [Bioinformatics 32(19)](https://doi.org/10.1093/bioinformatics/btw354) - Network accessline 325
- [MultiQC supported tools](https://multiqc.info/modules/) — full list of 150+ supported modules - Network accessline 325
- [MultiQC supported tools](https://multiqc.info/modules/) — full list of 150+ supported modules
skills/genomics-bioinformatics/rnaseq/deseq2-differential-expression/SKILL.md
- Network accessline 545
- [DESeq2 Bioconductor page](https://bioconductor.org/packages/DESeq2) — Package home, vignette, and reference manual - Network accessline 545
- [DESeq2 Bioconductor page](https://bioconductor.org/packages/DESeq2) — Package home, vignette, and reference manual - Network accessline 546
- [DESeq2 vignette](https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html) — Comprehensive worked example covering all design types - Network accessline 546
- [DESeq2 vignette](https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html) — Comprehensive worked example covering all design types - Network accessline 547
- [Love et al. (2014) Genome Biology](https://doi.org/10.1186/s13059-014-0550-8) — Original DESeq2 paper: moderated estimation of fold change and dispersion for RNA-seq data - Network accessline 547
- [Love et al. (2014) Genome Biology](https://doi.org/10.1186/s13059-014-0550-8) — Original DESeq2 paper: moderated estimation of fold change and dispersion for RNA-seq data - Network accessline 548
- [Zhu et al. (2019) Bioinformatics](https://doi.org/10.1093/bioinformatics/bty895) — apeglm LFC shrinkage for DESeq2 - Network accessline 548
- [Zhu et al. (2019) Bioinformatics](https://doi.org/10.1093/bioinformatics/bty895) — apeglm LFC shrinkage for DESeq2 - Network accessline 549
- [tximeta Bioconductor page](https://bioconductor.org/packages/tximeta) — Salmon import with transcript-level metadata - Network accessline 549
- [tximeta Bioconductor page](https://bioconductor.org/packages/tximeta) — Salmon import with transcript-level metadata - Network accessline 550
- [EnhancedVolcano Bioconductor page](https://bioconductor.org/packages/EnhancedVolcano) — Publication-quality volcano plots for DE results - Network accessline 550
- [EnhancedVolcano Bioconductor page](https://bioconductor.org/packages/EnhancedVolcano) — Publication-quality volcano plots for DE results
skills/genomics-bioinformatics/rnaseq/featurecounts-rna-counting/SKILL.md
- Dangerous shell operationline 39
sudo apt-get install subread - Dangerous shell operationline 39
sudo apt-get install subread - Network accessline 120
# Download GENCODE GTF (same version used for STAR indexing) wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_47/gencode.v47.primary_assembly.annotation.gtf.gz - Network accessline 120
# Download GENCODE GTF (same version used for STAR indexing) wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_47/gencode.v47.primary_assembly.annotation.gtf.gz - Network accessline 121
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_47/gencode.v47.primary_assembly.annotation.gtf.gz - Network accessline 121
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_47/gencode.v47.primary_assembly.annotation.gtf.gz - Network accessline 374
- [Subread documentation](https://subread.sourceforge.net/) — featureCounts user guide and parameter reference - Network accessline 374
- [Subread documentation](https://subread.sourceforge.net/) — featureCounts user guide and parameter reference - Network accessline 375
- Liao Y, Smyth GK, Shi W (2014) "featureCounts: an efficient general purpose program for assigning sequence reads to genomic features" — *Bioinformatics* 30(7):923-930. [DOI:10.1093/bioinformatics/btt656](https://doi.org/10.1093/bioinforma - Network accessline 375
- Liao Y, Smyth GK, Shi W (2014) "featureCounts: an efficient general purpose program for assigning sequence reads to genomic features" — *Bioinformatics* 30(7):923-930. [DOI:10.1093/bioinformatics/btt656](https://doi.org/10.1093/bioinforma - Network accessline 376
- [Subread GitHub: ShiLab-Bioinformatics/subread](https://github.com/ShiLab-Bioinformatics/subread) — source code and releases - Network accessline 376
- [Subread GitHub: ShiLab-Bioinformatics/subread](https://github.com/ShiLab-Bioinformatics/subread) — source code and releases - Network accessline 377
- [RNAseq123 workflow](https://bioconductor.org/packages/RNAseq123/) — Bioconductor RNA-seq workflow using featureCounts → edgeR - Network accessline 377
- [RNAseq123 workflow](https://bioconductor.org/packages/RNAseq123/) — Bioconductor RNA-seq workflow using featureCounts → edgeR
skills/genomics-bioinformatics/rnaseq/gseapy-gene-enrichment/SKILL.md
- Network accessline 398
| Problem | Cause | Solution | |---------|-------|----------| | `ConnectionError` in `enrichr` | No internet or Enrichr API down | Check https://maayanlab.cloud/Enrichr/; use local gene sets with `gene_sets="path/to/gmt"` | - Network accessline 398
| Problem | Cause | Solution | |---------|-------|----------| | `ConnectionError` in `enrichr` | No internet or Enrichr API down | Check https://maayanlab.cloud/Enrichr/; use local gene sets with `gene_sets="path/to/gmt"` | - Network accessline 399
|---------|-------|----------| | `ConnectionError` in `enrichr` | No internet or Enrichr API down | Check https://maayanlab.cloud/Enrichr/; use local gene sets with `gene_sets="path/to/gmt"` | | No significant terms returned | Gene list too - Network accessline 399
|---------|-------|----------| | `ConnectionError` in `enrichr` | No internet or Enrichr API down | Check https://maayanlab.cloud/Enrichr/; use local gene sets with `gene_sets="path/to/gmt"` | | No significant terms returned | Gene list too - Network accessline 400
| `ConnectionError` in `enrichr` | No internet or Enrichr API down | Check https://maayanlab.cloud/Enrichr/; use local gene sets with `gene_sets="path/to/gmt"` | - Network accessline 400
| `ConnectionError` in `enrichr` | No internet or Enrichr API down | Check https://maayanlab.cloud/Enrichr/; use local gene sets with `gene_sets="path/to/gmt"` | - Network accessline 411
- [GSEApy documentation](https://gseapy.readthedocs.io/) — official usage guide and API reference - Network accessline 411
- [GSEApy documentation](https://gseapy.readthedocs.io/) — official usage guide and API reference - Network accessline 412
- [GSEApy GitHub: zqfang/GSEApy](https://github.com/zqfang/GSEApy) — source code and examples - Network accessline 412
- [GSEApy GitHub: zqfang/GSEApy](https://github.com/zqfang/GSEApy) — source code and examples - Network accessline 413
- Fang Z et al. (2023) "GSEApy: a comprehensive package for performing gene set enrichment analysis in Python" — *Bioinformatics* 39(1):btac757. [DOI:10.1093/bioinformatics/btac757](https://doi.org/10.1093/bioinformatics/btac757) - Network accessline 413
- Fang Z et al. (2023) "GSEApy: a comprehensive package for performing gene set enrichment analysis in Python" — *Bioinformatics* 39(1):btac757. [DOI:10.1093/bioinformatics/btac757](https://doi.org/10.1093/bioinformatics/btac757) - Network accessline 414
- Subramanian A et al. (2005) "Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles" — *PNAS* 102(43):15545-15550. [DOI:10.1073/pnas.0506580102](https://doi.org/10.1073/pnas.0506580102) - Network accessline 414
- Subramanian A et al. (2005) "Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles" — *PNAS* 102(43):15545-15550. [DOI:10.1073/pnas.0506580102](https://doi.org/10.1073/pnas.0506580102) - Network accessline 415
- [Enrichr gene set databases](https://maayanlab.cloud/Enrichr/) — full list of 200+ available gene set libraries - Network accessline 415
- [Enrichr gene set databases](https://maayanlab.cloud/Enrichr/) — full list of 200+ available gene set libraries
skills/genomics-bioinformatics/rnaseq/pydeseq2-differential-expression/SKILL.md
- Network accessline 396
- [PyDESeq2 Documentation](https://pydeseq2.readthedocs.io/) — Official API reference and tutorials - Network accessline 396
- [PyDESeq2 Documentation](https://pydeseq2.readthedocs.io/) — Official API reference and tutorials - Network accessline 397
- [Muzellec et al. (2023)](https://doi.org/10.1093/bioinformatics/btad547) — PyDESeq2: a Python package for bulk differential expression analysis with DESeq2. Bioinformatics. - Network accessline 397
- [Muzellec et al. (2023)](https://doi.org/10.1093/bioinformatics/btad547) — PyDESeq2: a Python package for bulk differential expression analysis with DESeq2. Bioinformatics. - Network accessline 398
- [Love et al. (2014)](https://doi.org/10.1186/s13059-014-0550-8) — Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology. - Network accessline 398
- [Love et al. (2014)](https://doi.org/10.1186/s13059-014-0550-8) — Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology. - Network accessline 399
- [DESeq2 Vignette (Bioconductor)](https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html) — Comprehensive R DESeq2 guide with design formula examples - Network accessline 399
- [DESeq2 Vignette (Bioconductor)](https://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html) — Comprehensive R DESeq2 guide with design formula examples
skills/genomics-bioinformatics/rnaseq/salmon-rna-quantification/SKILL.md
- Network accessline 39
# Or download pre-compiled binary wget https://github.com/COMBINE-lab/salmon/releases/download/v1.10.0/salmon-1.10.0_linux_x86_64.tar.gz - Network accessline 39
# Or download pre-compiled binary wget https://github.com/COMBINE-lab/salmon/releases/download/v1.10.0/salmon-1.10.0_linux_x86_64.tar.gz - Network accessline 40
wget https://github.com/COMBINE-lab/salmon/releases/download/v1.10.0/salmon-1.10.0_linux_x86_64.tar.gz - Network accessline 40
wget https://github.com/COMBINE-lab/salmon/releases/download/v1.10.0/salmon-1.10.0_linux_x86_64.tar.gz - Network accessline 137
# Human transcriptome from GENCODE (recommended) wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_47/gencode.v47.transcripts.fa.gz - Network accessline 137
# Human transcriptome from GENCODE (recommended) wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_47/gencode.v47.transcripts.fa.gz - Network accessline 138
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_47/gencode.v47.transcripts.fa.gz - Network accessline 138
wget https://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_47/gencode.v47.transcripts.fa.gz - Network accessline 432
- [Salmon documentation](https://salmon.readthedocs.io/) — official usage guide and FAQ - Network accessline 432
- [Salmon documentation](https://salmon.readthedocs.io/) — official usage guide and FAQ - Network accessline 433
- [Salmon GitHub: COMBINE-lab/salmon](https://github.com/COMBINE-lab/salmon) — source, releases, and issues - Network accessline 433
- [Salmon GitHub: COMBINE-lab/salmon](https://github.com/COMBINE-lab/salmon) — source, releases, and issues - Network accessline 434
- Patro R et al. (2017) "Salmon provides fast and bias-aware quantification of transcript expression" — *Nature Methods* 14:417-419. [DOI:10.1038/nmeth.4197](https://doi.org/10.1038/nmeth.4197) - Network accessline 434
- Patro R et al. (2017) "Salmon provides fast and bias-aware quantification of transcript expression" — *Nature Methods* 14:417-419. [DOI:10.1038/nmeth.4197](https://doi.org/10.1038/nmeth.4197) - Network accessline 435
- [tximeta R package](https://bioconductor.org/packages/tximeta/) — recommended for importing Salmon output into R/Bioconductor with full provenance - Network accessline 435
- [tximeta R package](https://bioconductor.org/packages/tximeta/) — recommended for importing Salmon output into R/Bioconductor with full provenance
skills/genomics-bioinformatics/scikit-bio/SKILL.md
- Network accessline 460
- scikit-bio documentation: https://scikit.bio/docs/latest/ - Network accessline 460
- scikit-bio documentation: https://scikit.bio/docs/latest/ - Network accessline 461
- GitHub: https://github.com/scikit-bio/scikit-bio - Network accessline 461
- GitHub: https://github.com/scikit-bio/scikit-bio - Network accessline 462
- QIIME 2 forum: https://forum.qiime2.org - Network accessline 462
- QIIME 2 forum: https://forum.qiime2.org
skills/genomics-bioinformatics/single-cell/anndata-data-structure/SKILL.md
- Network accessline 545
- [AnnData documentation](https://anndata.readthedocs.io/) -- official API reference and tutorials - Network accessline 545
- [AnnData documentation](https://anndata.readthedocs.io/) -- official API reference and tutorials - Network accessline 546
- [scverse ecosystem](https://scverse.org/) -- coordinated single-cell analysis tools - Network accessline 546
- [scverse ecosystem](https://scverse.org/) -- coordinated single-cell analysis tools - Network accessline 547
- [AnnData GitHub](https://github.com/scverse/anndata) -- source code and issue tracker - Network accessline 547
- [AnnData GitHub](https://github.com/scverse/anndata) -- source code and issue tracker - Network accessline 548
- Virshup et al. (2024) "anndata: Access and store annotated data matrices" -- [JOSS](https://doi.org/10.21105/joss.04371) - Network accessline 548
- Virshup et al. (2024) "anndata: Access and store annotated data matrices" -- [JOSS](https://doi.org/10.21105/joss.04371)
skills/genomics-bioinformatics/single-cell/cell-type-annotation/SKILL.md
- Network accessline 380
- [CellTypist documentation and model zoo](https://www.celltypist.org/) — Official documentation, pre-trained model list with tissue annotations, and tutorials for automated cell type annotation with majority voting - Network accessline 380
- [CellTypist documentation and model zoo](https://www.celltypist.org/) — Official documentation, pre-trained model list with tissue annotations, and tutorials for automated cell type annotation with majority voting - Network accessline 381
- [popV GitHub repository](https://github.com/YosefLab/popV) — Installation, usage examples, and reference to the Luecken et al. 2024 Nature Methods benchmarking paper - Network accessline 381
- [popV GitHub repository](https://github.com/YosefLab/popV) — Installation, usage examples, and reference to the Luecken et al. 2024 Nature Methods benchmarking paper - Network accessline 382
- [Human Cell Atlas Data Portal](https://www.humancellatlas.org/) — Curated single-cell reference atlases for dozens of tissues across development and disease; source of reference data for label transfer - Network accessline 382
- [Human Cell Atlas Data Portal](https://www.humancellatlas.org/) — Curated single-cell reference atlases for dozens of tissues across development and disease; source of reference data for label transfer - Network accessline 383
- [Dominguez Conde et al. Science 2022](https://doi.org/10.1126/science.abl5197) — CellTypist paper describing the pan-immune atlas and the logistic regression classifier; cite when using CellTypist - Network accessline 383
- [Dominguez Conde et al. Science 2022](https://doi.org/10.1126/science.abl5197) — CellTypist paper describing the pan-immune atlas and the logistic regression classifier; cite when using CellTypist - Network accessline 384
- [Luecken et al. Nature Methods 2024](https://doi.org/10.1038/s41592-024-02249-2) — popV benchmarking study comparing 10 annotation methods across 8 datasets; provides empirical guidance on method selection - Network accessline 384
- [Luecken et al. Nature Methods 2024](https://doi.org/10.1038/s41592-024-02249-2) — popV benchmarking study comparing 10 annotation methods across 8 datasets; provides empirical guidance on method selection - Network accessline 385
- [Scanpy documentation: Clustering and annotation](https://scanpy.readthedocs.io/en/stable/tutorials/basics/clustering.html) — Official Scanpy tutorial for the full QC→preprocessing→clustering→annotation workflow - Network accessline 385
- [Scanpy documentation: Clustering and annotation](https://scanpy.readthedocs.io/en/stable/tutorials/basics/clustering.html) — Official Scanpy tutorial for the full QC→preprocessing→clustering→annotation workflow - Network accessline 386
- [Best Practices for Single-Cell Analysis (Heumos et al., Nature Reviews Genetics 2023)](https://doi.org/10.1038/s41576-023-00586-w) — Community best-practices review covering all steps of scRNA-seq analysis including annotation; recommend - Network accessline 386
- [Best Practices for Single-Cell Analysis (Heumos et al., Nature Reviews Genetics 2023)](https://doi.org/10.1038/s41576-023-00586-w) — Community best-practices review covering all steps of scRNA-seq analysis including annotation; recommend
skills/genomics-bioinformatics/single-cell/celltypist-cell-annotation/SKILL.md
- Network accessline 467
- [CellTypist documentation](https://celltypist.readthedocs.io/) — official API reference, model descriptions, and tutorials - Network accessline 467
- [CellTypist documentation](https://celltypist.readthedocs.io/) — official API reference, model descriptions, and tutorials - Network accessline 468
- [GitHub: Teichlab/celltypist](https://github.com/Teichlab/celltypist) — source code and issue tracker - Network accessline 468
- [GitHub: Teichlab/celltypist](https://github.com/Teichlab/celltypist) — source code and issue tracker - Network accessline 469
- [Dominguez Conde et al., Science 2022](https://doi.org/10.1126/science.abl5197) — "Cross-tissue immune cell analysis reveals tissue-specific features in humans", original CellTypist paper - Network accessline 469
- [Dominguez Conde et al., Science 2022](https://doi.org/10.1126/science.abl5197) — "Cross-tissue immune cell analysis reveals tissue-specific features in humans", original CellTypist paper - Network accessline 470
- [CellTypist model portal](https://www.celltypist.org/models) — interactive model browser with cell type hierarchies and training dataset details - Network accessline 470
- [CellTypist model portal](https://www.celltypist.org/models) — interactive model browser with cell type hierarchies and training dataset details
skills/genomics-bioinformatics/single-cell/cellxgene-census/SKILL.md
- Network accessline 389
- [CELLxGENE Census documentation](https://chanzuckerberg.github.io/cellxgene-census/) — official API reference - Network accessline 389
- [CELLxGENE Census documentation](https://chanzuckerberg.github.io/cellxgene-census/) — official API reference - Network accessline 390
- [CELLxGENE Discover](https://cellxgene.cziscience.com/) — web browser for Census data - Network accessline 390
- [CELLxGENE Discover](https://cellxgene.cziscience.com/) — web browser for Census data - Network accessline 391
- [TileDB-SOMA](https://github.com/single-cell-data/TileDB-SOMA) — underlying data access layer - Network accessline 391
- [TileDB-SOMA](https://github.com/single-cell-data/TileDB-SOMA) — underlying data access layer - Network accessline 392
- CZI (2023) "CZ CELLxGENE Discover: A single-cell data platform for scalable exploration, analysis and modeling of aggregated data" — [bioRxiv](https://doi.org/10.1101/2023.10.30.563174) - Network accessline 392
- CZI (2023) "CZ CELLxGENE Discover: A single-cell data platform for scalable exploration, analysis and modeling of aggregated data" — [bioRxiv](https://doi.org/10.1101/2023.10.30.563174)
skills/genomics-bioinformatics/single-cell/harmony-batch-correction/SKILL.md
- Network accessline 500
- [Harmony paper — Korsunsky et al., Nature Methods 2019](https://doi.org/10.1038/s41592-019-0619-0) — original algorithm, benchmarks against MNN, Seurat, BBKNN, Scanorama - Network accessline 500
- [Harmony paper — Korsunsky et al., Nature Methods 2019](https://doi.org/10.1038/s41592-019-0619-0) — original algorithm, benchmarks against MNN, Seurat, BBKNN, Scanorama - Network accessline 501
- [harmonypy (Python) — GitHub](https://github.com/slowkow/harmonypy) — Python port by Kamil Slowikowski - Network accessline 501
- [harmonypy (Python) — GitHub](https://github.com/slowkow/harmonypy) — Python port by Kamil Slowikowski - Network accessline 502
- [harmony (R) — GitHub](https://github.com/immunogenomics/harmony) — original R implementation by Immunogenomics Lab - Network accessline 502
- [harmony (R) — GitHub](https://github.com/immunogenomics/harmony) — original R implementation by Immunogenomics Lab - Network accessline 503
- [Harmony documentation — Broad Institute](https://portals.broadinstitute.org/harmony/) — tutorials and parameter guidance - Network accessline 503
- [Harmony documentation — Broad Institute](https://portals.broadinstitute.org/harmony/) — tutorials and parameter guidance - Network accessline 504
- [Scanpy external API — harmony_integrate](https://scanpy.readthedocs.io/en/stable/generated/scanpy.external.pp.harmony_integrate.html) — scanpy wrapper documentation - Network accessline 504
- [Scanpy external API — harmony_integrate](https://scanpy.readthedocs.io/en/stable/generated/scanpy.external.pp.harmony_integrate.html) — scanpy wrapper documentation - Network accessline 505
- [Single-cell best practices — batch correction chapter](https://www.sc-best-practices.org/preprocessing_visualization/batch_correction.html) — benchmarks and when to use each method - Network accessline 505
- [Single-cell best practices — batch correction chapter](https://www.sc-best-practices.org/preprocessing_visualization/batch_correction.html) — benchmarks and when to use each method
skills/genomics-bioinformatics/single-cell/popv-cell-annotation/SKILL.md
- Network accessline 603
- [GitHub: YosefLab/popV](https://github.com/YosefLab/popV) — official source code, installation instructions, and example notebooks - Network accessline 603
- [GitHub: YosefLab/popV](https://github.com/YosefLab/popV) — official source code, installation instructions, and example notebooks - Network accessline 604
- [popV documentation](https://popv.readthedocs.io/) — API reference and tutorials - Network accessline 604
- [popV documentation](https://popv.readthedocs.io/) — API reference and tutorials - Network accessline 605
- [Ergen et al., bioRxiv 2023](https://doi.org/10.1101/2023.06.15.545239) — "Population-level integration of single-cell datasets enables multi-scale analysis across samples", original popV preprint - Network accessline 605
- [Ergen et al., bioRxiv 2023](https://doi.org/10.1101/2023.06.15.545239) — "Population-level integration of single-cell datasets enables multi-scale analysis across samples", original popV preprint - Network accessline 606
- [ONCLASS paper — Wang et al., Nature Methods 2021](https://doi.org/10.1038/s41592-021-01080-9) — ontology-aware cell type classification underlying the ONCLASS method in popV - Network accessline 606
- [ONCLASS paper — Wang et al., Nature Methods 2021](https://doi.org/10.1038/s41592-021-01080-9) — ontology-aware cell type classification underlying the ONCLASS method in popV
skills/genomics-bioinformatics/single-cell/scanpy-scrna-seq/SKILL.md
- Network accessline 470
- [Scanpy documentation](https://scanpy.readthedocs.io/) — Official API reference and tutorials (BSD-3) - Network accessline 470
- [Scanpy documentation](https://scanpy.readthedocs.io/) — Official API reference and tutorials (BSD-3) - Network accessline 471
- [Scanpy PBMC3k tutorial](https://scanpy-tutorials.readthedocs.io/en/latest/pbmc3k.html) — Canonical walkthrough (BSD-3) - Network accessline 471
- [Scanpy PBMC3k tutorial](https://scanpy-tutorials.readthedocs.io/en/latest/pbmc3k.html) — Canonical walkthrough (BSD-3) - Network accessline 472
- [Galaxy Training: Clustering 3K PBMCs with Scanpy](https://training.galaxyproject.org/training-material/topics/single-cell/) — Step-by-step tutorial (CC-BY) - Network accessline 472
- [Galaxy Training: Clustering 3K PBMCs with Scanpy](https://training.galaxyproject.org/training-material/topics/single-cell/) — Step-by-step tutorial (CC-BY) - Network accessline 473
- [Luecken & Theis (2019)](https://doi.org/10.15252/msb.20188746) — "Current best practices in single-cell RNA-seq analysis: a tutorial", *Mol Syst Biol* - Network accessline 473
- [Luecken & Theis (2019)](https://doi.org/10.15252/msb.20188746) — "Current best practices in single-cell RNA-seq analysis: a tutorial", *Mol Syst Biol* - Network accessline 474
- [Heumos et al. (2023)](https://doi.org/10.1038/s41576-023-00586-w) — "Best practices for single-cell analysis across modalities", *Nat Rev Genet* - Network accessline 474
- [Heumos et al. (2023)](https://doi.org/10.1038/s41576-023-00586-w) — "Best practices for single-cell analysis across modalities", *Nat Rev Genet* - Network accessline 475
- [scverse ecosystem](https://scverse.org/) — Related tools: anndata, scvi-tools, squidpy, cellrank - Network accessline 475
- [scverse ecosystem](https://scverse.org/) — Related tools: anndata, scvi-tools, squidpy, cellrank
skills/genomics-bioinformatics/single-cell/scvi-tools-single-cell/SKILL.md
- Network accessline 655
- [scvi-tools documentation](https://docs.scvi-tools.org/) — official API reference, tutorials, and model guides - Network accessline 655
- [scvi-tools documentation](https://docs.scvi-tools.org/) — official API reference, tutorials, and model guides - Network accessline 656
- [scvi-tools GitHub](https://github.com/scverse/scvi-tools) — source code, issue tracker, and changelog - Network accessline 656
- [scvi-tools GitHub](https://github.com/scverse/scvi-tools) — source code, issue tracker, and changelog - Network accessline 657
- Lopez et al. (2018) "Deep generative modeling for single-cell transcriptomics" — [Nature Methods 15:1053–1058](https://doi.org/10.1038/s41592-018-0229-2) — original scVI paper - Network accessline 657
- Lopez et al. (2018) "Deep generative modeling for single-cell transcriptomics" — [Nature Methods 15:1053–1058](https://doi.org/10.1038/s41592-018-0229-2) — original scVI paper - Network accessline 658
- Xu et al. (2021) "Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models" — [Molecular Systems Biology 17:e9620](https://doi.org/10.15252/msb.20209620) — scANVI paper - Network accessline 658
- Xu et al. (2021) "Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models" — [Molecular Systems Biology 17:e9620](https://doi.org/10.15252/msb.20209620) — scANVI paper - Network accessline 659
- Gayoso et al. (2022) "A Python library for probabilistic analysis of single-cell omics data" — [Nature Biotechnology 40:163–166](https://doi.org/10.1038/s41587-021-01206-w) — scvi-tools library paper - Network accessline 659
- Gayoso et al. (2022) "A Python library for probabilistic analysis of single-cell omics data" — [Nature Biotechnology 40:163–166](https://doi.org/10.1038/s41587-021-01206-w) — scvi-tools library paper
skills/genomics-bioinformatics/spatial-transcriptomics/multimodal-omics-integration/SKILL.md
- Network accessline 79
CACHE = Path("data_cache/CytAssist_FFPE_Protein_Expression_Human_Tonsil") BASE = ("https://cf.10xgenomics.com/samples/spatial-exp/2.1.0/CytAssist_FFPE_Protein_Expression_Human_Tonsil/" - Network accessline 79
CACHE = Path("data_cache/CytAssist_FFPE_Protein_Expression_Human_Tonsil") BASE = ("https://cf.10xgenomics.com/samples/spatial-exp/2.1.0/CytAssist_FFPE_Protein_Expression_Human_Tonsil/" - Network accessline 80
BASE = ("https://cf.10xgenomics.com/samples/spatial-exp/2.1.0/CytAssist_FFPE_Protein_Expression_Human_Tonsil/" - Network accessline 80
BASE = ("https://cf.10xgenomics.com/samples/spatial-exp/2.1.0/CytAssist_FFPE_Protein_Expression_Human_Tonsil/" - Network accessline 83
for name in ["filtered_feature_bc_matrix.h5", "spatial.tar.gz"]: if not (CACHE / name).exists(): urllib.request.urlretrieve(BASE + name, CACHE / name) # ~37 MB + ~34 MB - Network accessline 83
for name in ["filtered_feature_bc_matrix.h5", "spatial.tar.gz"]: if not (CACHE / name).exists(): urllib.request.urlretrieve(BASE + name, CACHE / name) # ~37 MB + ~34 MB - Network accessline 84
if not (CACHE / name).exists(): urllib.request.urlretrieve(BASE + name, CACHE / name) # ~37 MB + ~34 MB if not (CACHE / "spatial").exists(): - Network accessline 84
if not (CACHE / name).exists(): urllib.request.urlretrieve(BASE + name, CACHE / name) # ~37 MB + ~34 MB if not (CACHE / "spatial").exists(): - Network accessline 85
urllib.request.urlretrieve(BASE + name, CACHE / name) # ~37 MB + ~34 MB - Network accessline 85
urllib.request.urlretrieve(BASE + name, CACHE / name) # ~37 MB + ~34 MB - Network accessline 156
if not path.exists(): path.parent.mkdir(parents=True, exist_ok=True) urllib.request.urlretrieve("https://exampledata.scverse.org/scvi-tools/pbmc_10k_preprocessed.h5mu", path) - Network accessline 156
if not path.exists(): path.parent.mkdir(parents=True, exist_ok=True) urllib.request.urlretrieve("https://exampledata.scverse.org/scvi-tools/pbmc_10k_preprocessed.h5mu", path) - Network accessline 157
path.parent.mkdir(parents=True, exist_ok=True) urllib.request.urlretrieve("https://exampledata.scverse.org/scvi-tools/pbmc_10k_preprocessed.h5mu", path) mdata = md.read_h5mu(path) - Network accessline 157
path.parent.mkdir(parents=True, exist_ok=True) urllib.request.urlretrieve("https://exampledata.scverse.org/scvi-tools/pbmc_10k_preprocessed.h5mu", path) mdata = md.read_h5mu(path) - Network accessline 158
urllib.request.urlretrieve("https://exampledata.scverse.org/scvi-tools/pbmc_10k_preprocessed.h5mu", path) - Network accessline 158
urllib.request.urlretrieve("https://exampledata.scverse.org/scvi-tools/pbmc_10k_preprocessed.h5mu", path) - Network accessline 388
- Gayoso et al. (2021) totalVI, Nature Methods: https://doi.org/10.1038/s41592-020-01050-x - Network accessline 388
- Gayoso et al. (2021) totalVI, Nature Methods: https://doi.org/10.1038/s41592-020-01050-x - Network accessline 389
- Ashuach et al. (2023) MultiVI, Nature Methods: https://doi.org/10.1038/s41592-023-01909-9 - Network accessline 389
- Ashuach et al. (2023) MultiVI, Nature Methods: https://doi.org/10.1038/s41592-023-01909-9 - Network accessline 390
- Polanski et al. (2019) BBKNN, Bioinformatics: https://doi.org/10.1093/bioinformatics/btz625 - Network accessline 390
- Polanski et al. (2019) BBKNN, Bioinformatics: https://doi.org/10.1093/bioinformatics/btz625 - Network accessline 391
- scvi-tools documentation: https://docs.scvi-tools.org/en/stable/ - Network accessline 391
- scvi-tools documentation: https://docs.scvi-tools.org/en/stable/ - Network accessline 392
- scanpy ingest: https://scanpy.readthedocs.io/en/stable/generated/scanpy.tl.ingest.html - Network accessline 392
- scanpy ingest: https://scanpy.readthedocs.io/en/stable/generated/scanpy.tl.ingest.html
skills/genomics-bioinformatics/spatial-transcriptomics/spatial-domain-detection/SKILL.md
- Network accessline 47
# stack; a conda add can swap it and bare pip can shadow it. pixi add --pypi scanpy SpaGCN GraphST pixi add --pypi "utag @ git+https://github.com/ElementoLab/utag.git@main" - Network accessline 47
# stack; a conda add can swap it and bare pip can shadow it. pixi add --pypi scanpy SpaGCN GraphST pixi add --pypi "utag @ git+https://github.com/ElementoLab/utag.git@main" - Network accessline 49
pixi add --pypi "utag @ git+https://github.com/ElementoLab/utag.git@main" - Network accessline 49
pixi add --pypi "utag @ git+https://github.com/ElementoLab/utag.git@main" - Network accessline 76
CACHE = Path("data_cache/V1_Mouse_Brain_Sagittal_Anterior") BASE = ("https://cf.10xgenomics.com/samples/spatial-exp/1.1.0/V1_Mouse_Brain_Sagittal_Anterior/" - Network accessline 76
CACHE = Path("data_cache/V1_Mouse_Brain_Sagittal_Anterior") BASE = ("https://cf.10xgenomics.com/samples/spatial-exp/1.1.0/V1_Mouse_Brain_Sagittal_Anterior/" - Network accessline 77
BASE = ("https://cf.10xgenomics.com/samples/spatial-exp/1.1.0/V1_Mouse_Brain_Sagittal_Anterior/" - Network accessline 77
BASE = ("https://cf.10xgenomics.com/samples/spatial-exp/1.1.0/V1_Mouse_Brain_Sagittal_Anterior/" - Network accessline 80
for name in ["filtered_feature_bc_matrix.h5", "spatial.tar.gz"]: if not (CACHE / name).exists(): urllib.request.urlretrieve(BASE + name, CACHE / name) # ~20 MB + ~9 MB - Network accessline 80
for name in ["filtered_feature_bc_matrix.h5", "spatial.tar.gz"]: if not (CACHE / name).exists(): urllib.request.urlretrieve(BASE + name, CACHE / name) # ~20 MB + ~9 MB - Network accessline 81
if not (CACHE / name).exists(): urllib.request.urlretrieve(BASE + name, CACHE / name) # ~20 MB + ~9 MB if not (CACHE / "spatial").exists(): - Network accessline 81
if not (CACHE / name).exists(): urllib.request.urlretrieve(BASE + name, CACHE / name) # ~20 MB + ~9 MB if not (CACHE / "spatial").exists(): - Network accessline 82
urllib.request.urlretrieve(BASE + name, CACHE / name) # ~20 MB + ~9 MB - Network accessline 82
urllib.request.urlretrieve(BASE + name, CACHE / name) # ~20 MB + ~9 MB - Network accessline 394
- Hu et al. (2021) SpaGCN, Nature Methods: https://doi.org/10.1038/s41592-021-01255-8 - Network accessline 394
- Hu et al. (2021) SpaGCN, Nature Methods: https://doi.org/10.1038/s41592-021-01255-8 - Network accessline 395
- Long et al. (2023) GraphST, Nature Communications: https://doi.org/10.1038/s41467-023-36796-3 - Network accessline 395
- Long et al. (2023) GraphST, Nature Communications: https://doi.org/10.1038/s41467-023-36796-3 - Network accessline 396
- Kim et al. (2022) UTAG, Nature Methods: https://doi.org/10.1038/s41592-022-01657-2 - Network accessline 396
- Kim et al. (2022) UTAG, Nature Methods: https://doi.org/10.1038/s41592-022-01657-2 - Network accessline 397
- SpaGCN source: https://github.com/jianhuupenn/SpaGCN - Network accessline 397
- SpaGCN source: https://github.com/jianhuupenn/SpaGCN - Network accessline 398
- GraphST source: https://github.com/JinmiaoChenLab/GraphST - Network accessline 398
- GraphST source: https://github.com/JinmiaoChenLab/GraphST - Network accessline 399
- UTAG source: https://github.com/ElementoLab/utag - Network accessline 399
- UTAG source: https://github.com/ElementoLab/utag
skills/genomics-bioinformatics/spatial-transcriptomics/spatial-mapping-tangram/SKILL.md
- Network accessline 737
- Biancalani et al. (2021) Tangram, Nature Methods: https://doi.org/10.1038/s41592-021-01264-7 - Network accessline 737
- Biancalani et al. (2021) Tangram, Nature Methods: https://doi.org/10.1038/s41592-021-01264-7 - Network accessline 738
- Tangram source and tutorials: https://github.com/broadinstitute/Tangram - Network accessline 738
- Tangram source and tutorials: https://github.com/broadinstitute/Tangram - Network accessline 739
- Tangram documentation: https://tangram-sc.readthedocs.io/ - Network accessline 739
- Tangram documentation: https://tangram-sc.readthedocs.io/ - Network accessline 740
- squidpy + Tangram tutorial (demo data used here): https://squidpy.readthedocs.io/en/stable/notebooks/tutorials/tutorial_tangram.html - Network accessline 740
- squidpy + Tangram tutorial (demo data used here): https://squidpy.readthedocs.io/en/stable/notebooks/tutorials/tutorial_tangram.html
skills/genomics-bioinformatics/spatial-transcriptomics/spatial-panel-design/SKILL.md
- Network accessline 189
PANGLAO = "ref_cache/PanglaoDB_Augmented_2021.txt" if not os.path.exists(PANGLAO): url = "https://maayanlab.cloud/Enrichr/geneSetLibrary?mode=text&libraryName=PanglaoDB_Augmented_2021" - Network accessline 189
PANGLAO = "ref_cache/PanglaoDB_Augmented_2021.txt" if not os.path.exists(PANGLAO): url = "https://maayanlab.cloud/Enrichr/geneSetLibrary?mode=text&libraryName=PanglaoDB_Augmented_2021" - Network accessline 190
if not os.path.exists(PANGLAO): url = "https://maayanlab.cloud/Enrichr/geneSetLibrary?mode=text&libraryName=PanglaoDB_Augmented_2021" open(PANGLAO, "w").write(requests.get(url, timeout=300).text) - Network accessline 190
if not os.path.exists(PANGLAO): url = "https://maayanlab.cloud/Enrichr/geneSetLibrary?mode=text&libraryName=PanglaoDB_Augmented_2021" open(PANGLAO, "w").write(requests.get(url, timeout=300).text) - Network accessline 191
url = "https://maayanlab.cloud/Enrichr/geneSetLibrary?mode=text&libraryName=PanglaoDB_Augmented_2021" open(PANGLAO, "w").write(requests.get(url, timeout=300).text) panglao = {} - Network accessline 191
url = "https://maayanlab.cloud/Enrichr/geneSetLibrary?mode=text&libraryName=PanglaoDB_Augmented_2021" open(PANGLAO, "w").write(requests.get(url, timeout=300).text) panglao = {} - Network accessline 192
open(PANGLAO, "w").write(requests.get(url, timeout=300).text) - Network accessline 192
open(PANGLAO, "w").write(requests.get(url, timeout=300).text) - Network accessline 228
CM = f"ref_cache/Cell_marker_{species}.xlsx" if not os.path.exists(CM): url = f"http://117.50.127.228/CellMarker/CellMarker_download_files/file/Cell_marker_{species}.xlsx" - Network accessline 228
CM = f"ref_cache/Cell_marker_{species}.xlsx" if not os.path.exists(CM): url = f"http://117.50.127.228/CellMarker/CellMarker_download_files/file/Cell_marker_{species}.xlsx" - Network accessline 229
if not os.path.exists(CM): url = f"http://117.50.127.228/CellMarker/CellMarker_download_files/file/Cell_marker_{species}.xlsx" open(CM, "wb").write(requests.get(url, timeout=600).content) - Network accessline 229
if not os.path.exists(CM): url = f"http://117.50.127.228/CellMarker/CellMarker_download_files/file/Cell_marker_{species}.xlsx" open(CM, "wb").write(requests.get(url, timeout=600).content) - Network accessline 230
url = f"http://117.50.127.228/CellMarker/CellMarker_download_files/file/Cell_marker_{species}.xlsx" open(CM, "wb").write(requests.get(url, timeout=600).content) cm = pd.read_excel(CM) - Network accessline 230
url = f"http://117.50.127.228/CellMarker/CellMarker_download_files/file/Cell_marker_{species}.xlsx" open(CM, "wb").write(requests.get(url, timeout=600).content) cm = pd.read_excel(CM) - Network accessline 231
open(CM, "wb").write(requests.get(url, timeout=600).content) - Network accessline 231
open(CM, "wb").write(requests.get(url, timeout=600).content) - Network accessline 408
- Franzen et al. (2019) PanglaoDB, Database: https://doi.org/10.1093/database/baz046 - Network accessline 408
- Franzen et al. (2019) PanglaoDB, Database: https://doi.org/10.1093/database/baz046 - Network accessline 409
- Hu et al. (2022) CellMarker 2.0, Nucleic Acids Research: https://doi.org/10.1093/nar/gkac947 - Network accessline 409
- Hu et al. (2022) CellMarker 2.0, Nucleic Acids Research: https://doi.org/10.1093/nar/gkac947 - Network accessline 410
- Enrichr libraries (PanglaoDB_Augmented_2021): https://maayanlab.cloud/Enrichr/ - Network accessline 410
- Enrichr libraries (PanglaoDB_Augmented_2021): https://maayanlab.cloud/Enrichr/ - Network accessline 411
- CZ CELLxGENE Census documentation: https://chanzuckerberg.github.io/cellxgene-census/ - Network accessline 411
- CZ CELLxGENE Census documentation: https://chanzuckerberg.github.io/cellxgene-census/
skills/genomics-bioinformatics/spatial-transcriptomics/spatial-transcriptomics-analysis-squidpy/SKILL.md
- Network accessline 352
- Palla et al. (2022) Squidpy, Nature Methods: https://doi.org/10.1038/s41592-021-01358-2 - Network accessline 352
- Palla et al. (2022) Squidpy, Nature Methods: https://doi.org/10.1038/s41592-021-01358-2 - Network accessline 353
- squidpy documentation: https://squidpy.readthedocs.io/en/stable/ - Network accessline 353
- squidpy documentation: https://squidpy.readthedocs.io/en/stable/ - Network accessline 354
- squidpy ligand-receptor test: https://squidpy.readthedocs.io/en/stable/api/squidpy.gr.ligrec.html - Network accessline 354
- squidpy ligand-receptor test: https://squidpy.readthedocs.io/en/stable/api/squidpy.gr.ligrec.html - Network accessline 355
- squidpy Ripley's statistics: https://squidpy.readthedocs.io/en/stable/api/squidpy.gr.ripley.html - Network accessline 355
- squidpy Ripley's statistics: https://squidpy.readthedocs.io/en/stable/api/squidpy.gr.ripley.html - Network accessline 356
- OmniPath: https://omnipathdb.org/ - Network accessline 356
- OmniPath: https://omnipathdb.org/
skills/genomics-bioinformatics/spatial-transcriptomics/spatial-transcriptomics-cell-cell-communication/SKILL.md
- Network accessline 540
EUTILS = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi" - Network accessline 540
EUTILS = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi" - Network accessline 544
a, b = ligand.split("_")[0], receptor.split("_")[0] # first subunit for complexes term = f"{a}[tiab] AND {b}[tiab]" + (f" AND ({CONTEXT})" if CONTEXT else "") r = requests.get(EUTILS, params={"db": "pubmed", "term": term, "retmode - Network accessline 544
a, b = ligand.split("_")[0], receptor.split("_")[0] # first subunit for complexes term = f"{a}[tiab] AND {b}[tiab]" + (f" AND ({CONTEXT})" if CONTEXT else "") r = requests.get(EUTILS, params={"db": "pubmed", "term": term, "retmode - Network accessline 545
term = f"{a}[tiab] AND {b}[tiab]" + (f" AND ({CONTEXT})" if CONTEXT else "") r = requests.get(EUTILS, params={"db": "pubmed", "term": term, "retmode": "json", "retmax": 0}, timeout=30) r.raise_for_status(); time.sleep(0.4) - Network accessline 545
term = f"{a}[tiab] AND {b}[tiab]" + (f" AND ({CONTEXT})" if CONTEXT else "") r = requests.get(EUTILS, params={"db": "pubmed", "term": term, "retmode": "json", "retmax": 0}, timeout=30) r.raise_for_status(); time.sleep(0.4) - Network accessline 546
r = requests.get(EUTILS, params={"db": "pubmed", "term": term, "retmode": "json", "retmax": 0}, timeout=30) - Network accessline 546
r = requests.get(EUTILS, params={"db": "pubmed", "term": term, "retmode": "json", "retmax": 0}, timeout=30) - Network accessline 701
- Dimitrov et al. (2024) LIANA+, Nature Cell Biology: https://doi.org/10.1038/s41556-024-01469-w - Network accessline 701
- Dimitrov et al. (2024) LIANA+, Nature Cell Biology: https://doi.org/10.1038/s41556-024-01469-w - Network accessline 702
- LIANA+ documentation: https://liana.readthedocs.io/ - Network accessline 702
- LIANA+ documentation: https://liana.readthedocs.io/ - Network accessline 703
- Troule et al. (2025) CellPhoneDB v5, Nature Protocols: https://doi.org/10.1038/s41596-024-01137-1 - Network accessline 703
- Troule et al. (2025) CellPhoneDB v5, Nature Protocols: https://doi.org/10.1038/s41596-024-01137-1 - Network accessline 704
- Palla et al. (2022) Squidpy, Nature Methods: https://doi.org/10.1038/s41592-021-01358-2 - Network accessline 704
- Palla et al. (2022) Squidpy, Nature Methods: https://doi.org/10.1038/s41592-021-01358-2 - Network accessline 705
- squidpy documentation: https://squidpy.readthedocs.io/en/stable/ - Network accessline 705
- squidpy documentation: https://squidpy.readthedocs.io/en/stable/
skills/genomics-bioinformatics/spatial-transcriptomics/spatial-transcriptomics-cell-type-annotation/SKILL.md
- Network accessline 200
datasets = requests.get("https://api.cellxgene.cziscience.com/curation/v1/datasets", timeout=120).json() - Network accessline 200
datasets = requests.get("https://api.cellxgene.cziscience.com/curation/v1/datasets", timeout=120).json() - Access to secretsline 302
[Credential-related source omitted] - Access to secretsline 302
[Credential-related source omitted] - Network accessline 560
- CZ CELLxGENE Census documentation: https://chanzuckerberg.github.io/cellxgene-census/ - Network accessline 560
- CZ CELLxGENE Census documentation: https://chanzuckerberg.github.io/cellxgene-census/ - Network accessline 561
- CELLxGENE Discover Curation API: https://api.cellxgene.cziscience.com/curation/ui/ - Network accessline 561
- CELLxGENE Discover Curation API: https://api.cellxgene.cziscience.com/curation/ui/ - Network accessline 562
- Korsunsky et al. (2019) Harmony, Nature Methods: https://doi.org/10.1038/s41592-019-0619-0 - Network accessline 562
- Korsunsky et al. (2019) Harmony, Nature Methods: https://doi.org/10.1038/s41592-019-0619-0 - Network accessline 563
- harmonypy: https://github.com/slowkow/harmonypy - Network accessline 563
- harmonypy: https://github.com/slowkow/harmonypy - Network accessline 564
- squidpy readers for Vizgen and NanoString: https://squidpy.readthedocs.io/en/stable/api/squidpy.read.vizgen.html - Network accessline 564
- squidpy readers for Vizgen and NanoString: https://squidpy.readthedocs.io/en/stable/api/squidpy.read.vizgen.html
skills/genomics-bioinformatics/spatial-transcriptomics/spatial-transcriptomics-deconvolution-cell2location/SKILL.md
- Network accessline 352
- Kleshchevnikov et al. (2022) cell2location, Nature Biotechnology: https://doi.org/10.1038/s41587-021-01139-4 - Network accessline 352
- Kleshchevnikov et al. (2022) cell2location, Nature Biotechnology: https://doi.org/10.1038/s41587-021-01139-4 - Network accessline 353
- cell2location documentation: https://cell2location.readthedocs.io/en/latest/ - Network accessline 353
- cell2location documentation: https://cell2location.readthedocs.io/en/latest/ - Network accessline 354
- cell2location tutorial (hyperparameter guidance): https://cell2location.readthedocs.io/en/latest/notebooks/cell2location_tutorial.html - Network accessline 354
- cell2location tutorial (hyperparameter guidance): https://cell2location.readthedocs.io/en/latest/notebooks/cell2location_tutorial.html - Network accessline 355
- cell2location source: https://github.com/BayraktarLab/cell2location - Network accessline 355
- cell2location source: https://github.com/BayraktarLab/cell2location - Network accessline 356
- squidpy Visium reader: https://squidpy.readthedocs.io/en/stable/api/squidpy.read.visium.html - Network accessline 356
- squidpy Visium reader: https://squidpy.readthedocs.io/en/stable/api/squidpy.read.visium.html
skills/genomics-bioinformatics/spatial-transcriptomics/trajectory-inference/SKILL.md
- Network accessline 333
- Wolf et al. (2019) PAGA, Genome Biology: https://doi.org/10.1186/s13059-019-1663-x - Network accessline 333
- Wolf et al. (2019) PAGA, Genome Biology: https://doi.org/10.1186/s13059-019-1663-x - Network accessline 334
- Haghverdi et al. (2016) Diffusion pseudotime, Nature Methods: https://doi.org/10.1038/nmeth.3971 - Network accessline 334
- Haghverdi et al. (2016) Diffusion pseudotime, Nature Methods: https://doi.org/10.1038/nmeth.3971 - Network accessline 335
- Bergen et al. (2020) scVelo, Nature Biotechnology: https://doi.org/10.1038/s41587-020-0591-3 - Network accessline 335
- Bergen et al. (2020) scVelo, Nature Biotechnology: https://doi.org/10.1038/s41587-020-0591-3 - Network accessline 336
- Weiler et al. (2024) CellRank 2, Nature Methods: https://doi.org/10.1038/s41592-024-02303-9 - Network accessline 336
- Weiler et al. (2024) CellRank 2, Nature Methods: https://doi.org/10.1038/s41592-024-02303-9 - Network accessline 337
- scVelo documentation: https://scvelo.readthedocs.io/en/stable/ - Network accessline 337
- scVelo documentation: https://scvelo.readthedocs.io/en/stable/ - Network accessline 338
- CellRank documentation: https://cellrank.readthedocs.io/en/latest/ - Network accessline 338
- CellRank documentation: https://cellrank.readthedocs.io/en/latest/
skills/genomics-bioinformatics/variant/bcftools-variant-manipulation/SKILL.md
- Network accessline 502
- [bcftools documentation](https://samtools.github.io/bcftools/bcftools.html) — complete command reference - Network accessline 502
- [bcftools documentation](https://samtools.github.io/bcftools/bcftools.html) — complete command reference - Network accessline 503
- [GitHub: samtools/bcftools](https://github.com/samtools/bcftools) — source code, releases, issue tracker - Network accessline 503
- [GitHub: samtools/bcftools](https://github.com/samtools/bcftools) — source code, releases, issue tracker - Network accessline 504
- Danecek et al. (2021) "Twelve years of SAMtools and BCFtools" — [GigaScience 10(2)](https://doi.org/10.1093/gigascience/giab008) - Network accessline 504
- Danecek et al. (2021) "Twelve years of SAMtools and BCFtools" — [GigaScience 10(2)](https://doi.org/10.1093/gigascience/giab008) - Network accessline 505
- [VCF file format specification](https://samtools.github.io/hts-specs/VCFv4.3.pdf) — field definitions and encoding rules - Network accessline 505
- [VCF file format specification](https://samtools.github.io/hts-specs/VCFv4.3.pdf) — field definitions and encoding rules
skills/genomics-bioinformatics/variant/cnvkit-copy-number/SKILL.md
- Network accessline 636
- [CNVkit documentation](https://cnvkit.readthedocs.io) — full CLI and API reference, algorithm details, and tutorials - Network accessline 636
- [CNVkit documentation](https://cnvkit.readthedocs.io) — full CLI and API reference, algorithm details, and tutorials - Network accessline 637
- [Talevich E et al. (2016) PLoS Comput Biol 12(4): e1004873](https://doi.org/10.1371/journal.pcbi.1004873) — CNVkit original paper with algorithm description and benchmarks - Network accessline 637
- [Talevich E et al. (2016) PLoS Comput Biol 12(4): e1004873](https://doi.org/10.1371/journal.pcbi.1004873) — CNVkit original paper with algorithm description and benchmarks - Network accessline 638
- [CNVkit GitHub: etal/cnvkit](https://github.com/etal/cnvkit) — source code, issue tracker, and example datasets - Network accessline 638
- [CNVkit GitHub: etal/cnvkit](https://github.com/etal/cnvkit) — source code, issue tracker, and example datasets - Network accessline 639
- [UCSC Genome Browser data files](https://hgdownload.soe.ucsc.edu/goldenPath/hg38/database/) — refFlat and access BED files used in CNVkit preprocessing - Network accessline 639
- [UCSC Genome Browser data files](https://hgdownload.soe.ucsc.edu/goldenPath/hg38/database/) — refFlat and access BED files used in CNVkit preprocessing
skills/genomics-bioinformatics/variant/gatk-variant-calling/SKILL.md
- Network accessline 32
# Install GATK4 wget https://github.com/broadinstitute/gatk/releases/download/4.6.0.0/gatk-4.6.0.0.zip - Network accessline 32
# Install GATK4 wget https://github.com/broadinstitute/gatk/releases/download/4.6.0.0/gatk-4.6.0.0.zip - Network accessline 33
wget https://github.com/broadinstitute/gatk/releases/download/4.6.0.0/gatk-4.6.0.0.zip - Network accessline 33
wget https://github.com/broadinstitute/gatk/releases/download/4.6.0.0/gatk-4.6.0.0.zip - Dangerous shell operationline 379
| GenomicsDB import fails on existing path | GenomicsDB workspace already exists | Delete existing workspace: `rm -rf genomicsdb/chr1` before re-running | - Dangerous shell operationline 379
| GenomicsDB import fails on existing path | GenomicsDB workspace already exists | Delete existing workspace: `rm -rf genomicsdb/chr1` before re-running | - Network accessline 385
- [GATK documentation](https://gatk.broadinstitute.org/hc/en-us) — official pipeline guides and tool documentation - Network accessline 385
- [GATK documentation](https://gatk.broadinstitute.org/hc/en-us) — official pipeline guides and tool documentation - Network accessline 386
- [GATK Best Practices for germline short variants](https://gatk.broadinstitute.org/hc/en-us/articles/360035535932) — step-by-step protocol - Network accessline 386
- [GATK Best Practices for germline short variants](https://gatk.broadinstitute.org/hc/en-us/articles/360035535932) — step-by-step protocol - Network accessline 387
- [GATK GitHub: broadinstitute/gatk](https://github.com/broadinstitute/gatk) — source code, releases, and resource bundle - Network accessline 387
- [GATK GitHub: broadinstitute/gatk](https://github.com/broadinstitute/gatk) — source code, releases, and resource bundle
skills/genomics-bioinformatics/variant/plink2-gwas-analysis/SKILL.md
- Network accessline 36
else # Download PLINK2 pre-compiled binary (Linux) wget https://s3.amazonaws.com/plink2-assets/alpha6/plink2_linux_avx2_20241112.zip - Network accessline 36
else # Download PLINK2 pre-compiled binary (Linux) wget https://s3.amazonaws.com/plink2-assets/alpha6/plink2_linux_avx2_20241112.zip - Network accessline 37
# Download PLINK2 pre-compiled binary (Linux) wget https://s3.amazonaws.com/plink2-assets/alpha6/plink2_linux_avx2_20241112.zip unzip plink2_linux_avx2_20241112.zip - Network accessline 37
# Download PLINK2 pre-compiled binary (Linux) wget https://s3.amazonaws.com/plink2-assets/alpha6/plink2_linux_avx2_20241112.zip unzip plink2_linux_avx2_20241112.zip - Network accessline 38
wget https://s3.amazonaws.com/plink2-assets/alpha6/plink2_linux_avx2_20241112.zip - Network accessline 38
wget https://s3.amazonaws.com/plink2-assets/alpha6/plink2_linux_avx2_20241112.zip - Network accessline 43
# macOS # wget https://s3.amazonaws.com/plink2-assets/alpha6/plink2_mac_20241112.zip - Network accessline 43
# macOS # wget https://s3.amazonaws.com/plink2-assets/alpha6/plink2_mac_20241112.zip - Network accessline 44
# wget https://s3.amazonaws.com/plink2-assets/alpha6/plink2_mac_20241112.zip - Network accessline 44
# wget https://s3.amazonaws.com/plink2-assets/alpha6/plink2_mac_20241112.zip - Network accessline 376
- [PLINK2 documentation](https://www.cog-genomics.org/plink/2.0/) — official command reference and tutorials - Network accessline 376
- [PLINK2 documentation](https://www.cog-genomics.org/plink/2.0/) — official command reference and tutorials - Network accessline 377
- [PLINK2 GitHub: chrchang/plink-ng](https://github.com/chrchang/plink-ng) — source code and releases - Network accessline 377
- [PLINK2 GitHub: chrchang/plink-ng](https://github.com/chrchang/plink-ng) — source code and releases - Network accessline 378
- Chang CC et al. (2015) "Second-generation PLINK: rising to the challenge of larger and richer datasets" — *GigaScience* 4:7. [DOI:10.1186/s13742-015-0047-8](https://doi.org/10.1186/s13742-015-0047-8) - Network accessline 378
- Chang CC et al. (2015) "Second-generation PLINK: rising to the challenge of larger and richer datasets" — *GigaScience* 4:7. [DOI:10.1186/s13742-015-0047-8](https://doi.org/10.1186/s13742-015-0047-8) - Network accessline 379
- [GWAS best practices (Marees et al. 2018)](https://doi.org/10.1002/mpr.1608) — comprehensive QC and analysis guide - Network accessline 379
- [GWAS best practices (Marees et al. 2018)](https://doi.org/10.1002/mpr.1608) — comprehensive QC and analysis guide
skills/genomics-bioinformatics/variant/snpeff-variant-annotation/SKILL.md
- Network accessline 34
# Download SnpEff JAR wget https://snpeff.blob.core.windows.net/versions/snpEff_latest_core.zip - Network accessline 34
# Download SnpEff JAR wget https://snpeff.blob.core.windows.net/versions/snpEff_latest_core.zip - Network accessline 35
wget https://snpeff.blob.core.windows.net/versions/snpEff_latest_core.zip - Network accessline 35
wget https://snpeff.blob.core.windows.net/versions/snpEff_latest_core.zip - Network accessline 197
# Download reference databases (one-time setup) # ClinVar (GRCh38) wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz - Network accessline 197
# Download reference databases (one-time setup) # ClinVar (GRCh38) wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz - Network accessline 198
# ClinVar (GRCh38) 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 198
# ClinVar (GRCh38) 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 199
wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz - Network accessline 199
wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz - Network accessline 200
wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz.tbi - Network accessline 200
wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz.tbi - Network accessline 202
# dbSNP (GRCh38, b156 or later) wget https://ftp.ncbi.nlm.nih.gov/snp/latest_release/VCF/GCF_000001405.40.gz - Network accessline 202
# dbSNP (GRCh38, b156 or later) wget https://ftp.ncbi.nlm.nih.gov/snp/latest_release/VCF/GCF_000001405.40.gz - Network accessline 203
wget https://ftp.ncbi.nlm.nih.gov/snp/latest_release/VCF/GCF_000001405.40.gz - Network accessline 203
wget https://ftp.ncbi.nlm.nih.gov/snp/latest_release/VCF/GCF_000001405.40.gz - Network accessline 204
wget https://ftp.ncbi.nlm.nih.gov/snp/latest_release/VCF/GCF_000001405.40.gz.tbi - Network accessline 204
wget https://ftp.ncbi.nlm.nih.gov/snp/latest_release/VCF/GCF_000001405.40.gz.tbi - Network accessline 499
- [SnpEff Documentation](https://pcingola.github.io/SnpEff/) — comprehensive usage guide, ANN field specification, filter syntax - Network accessline 499
- [SnpEff Documentation](https://pcingola.github.io/SnpEff/) — comprehensive usage guide, ANN field specification, filter syntax - Network accessline 500
- [SnpEff GitHub Repository](https://github.com/pcingola/SnpEff) — source code, issue tracker, release notes - Network accessline 500
- [SnpEff GitHub Repository](https://github.com/pcingola/SnpEff) — source code, issue tracker, release notes - Network accessline 501
- Cingolani P, et al. (2012) "A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118." *Fly* 6(2):80–92. doi:[10.4161/fly.19695](https://doi - Network accessline 501
- Cingolani P, et al. (2012) "A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118." *Fly* 6(2):80–92. doi:[10.4161/fly.19695](https://doi - Network accessline 502
- [ClinVar VCF FTP](https://ftp.ncbi.nlm.nih.gov/pub/clinvar/) — ClinVar VCF files by assembly - Network accessline 502
- [ClinVar VCF FTP](https://ftp.ncbi.nlm.nih.gov/pub/clinvar/) — ClinVar VCF files by assembly - Network accessline 503
- [cyvcf2 Documentation](https://brentp.github.io/cyvcf2/) — Python VCF parsing library used in Step 6 - Network accessline 503
- [cyvcf2 Documentation](https://brentp.github.io/cyvcf2/) — Python VCF parsing library used in Step 6
skills/genomics-bioinformatics/variant/vcf-variant-filtering/SKILL.md
- Network accessline 307
- [bcftools documentation](https://samtools.github.io/bcftools/) -- Comprehensive reference for bcftools commands, filtering expressions, and statistics output format - Network accessline 307
- [bcftools documentation](https://samtools.github.io/bcftools/) -- Comprehensive reference for bcftools commands, filtering expressions, and statistics output format - Network accessline 308
- [VCF specification (v4.3+)](https://samtools.github.io/hts-specs/VCFv4.3.pdf) -- Formal definition of VCF format including FILTER column semantics, QUAL field, and INFO annotations - Network accessline 308
- [VCF specification (v4.3+)](https://samtools.github.io/hts-specs/VCFv4.3.pdf) -- Formal definition of VCF format including FILTER column semantics, QUAL field, and INFO annotations - Network accessline 309
- [GATK Best Practices for Germline Short Variant Discovery](https://gatk.broadinstitute.org/hc/en-us/articles/360035535932-Germline-short-variant-discovery-SNPs-Indels) -- Recommended filtering strategies including VQSR and hard filtering - Network accessline 309
- [GATK Best Practices for Germline Short Variant Discovery](https://gatk.broadinstitute.org/hc/en-us/articles/360035535932-Germline-short-variant-discovery-SNPs-Indels) -- Recommended filtering strategies including VQSR and hard filtering
skills/lab-automation/benchling-integration/SKILL.md
- Network accessline 38
benchling = Benchling( url="https://your-tenant.benchling.com", - Network accessline 38
benchling = Benchling( url="https://your-tenant.benchling.com", - Network accessline 39
url="https://your-tenant.benchling.com", - Network accessline 39
url="https://your-tenant.benchling.com", - Access to secretsline 40
[Credential-related source omitted] - Access to secretsline 40
[Credential-related source omitted] - Network accessline 48
benchling = Benchling( url="https://your-tenant.benchling.com", - Network accessline 48
benchling = Benchling( url="https://your-tenant.benchling.com", - Network accessline 49
url="https://your-tenant.benchling.com", - Network accessline 49
url="https://your-tenant.benchling.com", - Access to secretsline 51
[Credential-related source omitted] - Access to secretsline 51
[Credential-related source omitted] - Access to secretsline 52
[Credential-related source omitted] - Access to secretsline 52
[Credential-related source omitted] - Network accessline 67
benchling = Benchling( url="https://your-tenant.benchling.com", - Network accessline 67
benchling = Benchling( url="https://your-tenant.benchling.com", - Network accessline 68
url="https://your-tenant.benchling.com", - Network accessline 68
url="https://your-tenant.benchling.com", - Access to secretsline 69
[Credential-related source omitted] - Access to secretsline 69
[Credential-related source omitted] - Network accessline 261
# Custom retry strategy benchling = Benchling( url="https://your-tenant.benchling.com", - Network accessline 261
# Custom retry strategy benchling = Benchling( url="https://your-tenant.benchling.com", - Network accessline 262
benchling = Benchling( url="https://your-tenant.benchling.com", auth_method=ApiKeyAuth(os.environ["BENCHLING_API_KEY"]), - Network accessline 262
benchling = Benchling( url="https://your-tenant.benchling.com", auth_method=ApiKeyAuth(os.environ["BENCHLING_API_KEY"]), - Network accessline 263
url="https://your-tenant.benchling.com", - Network accessline 263
url="https://your-tenant.benchling.com", - Access to secretsline 264
[Credential-related source omitted] - Access to secretsline 264
[Credential-related source omitted] - Network accessline 332
benchling = Benchling( url="https://your-tenant.benchling.com", - Network accessline 332
benchling = Benchling( url="https://your-tenant.benchling.com", - Network accessline 333
url="https://your-tenant.benchling.com", - Network accessline 333
url="https://your-tenant.benchling.com", - Access to secretsline 334
[Credential-related source omitted] - Access to secretsline 334
[Credential-related source omitted] - Network accessline 365
benchling = Benchling( url="https://your-tenant.benchling.com", - Network accessline 365
benchling = Benchling( url="https://your-tenant.benchling.com", - Network accessline 366
url="https://your-tenant.benchling.com", - Network accessline 366
url="https://your-tenant.benchling.com", - Access to secretsline 367
[Credential-related source omitted] - Access to secretsline 367
[Credential-related source omitted] - Access to secretsline 412
[Credential-related source omitted] - Access to secretsline 412
[Credential-related source omitted] - Network accessline 509
- [Benchling Python SDK documentation](https://docs.benchling.com/docs/getting-started-with-the-sdk) — official SDK guide - Network accessline 509
- [Benchling Python SDK documentation](https://docs.benchling.com/docs/getting-started-with-the-sdk) — official SDK guide - Network accessline 510
- [Benchling API reference](https://benchling.com/api/reference) — REST API endpoint documentation - Network accessline 510
- [Benchling API reference](https://benchling.com/api/reference) — REST API endpoint documentation - Network accessline 511
- [Benchling SDK PyPI](https://pypi.org/project/benchling-sdk/) — package installation and versioning - Network accessline 511
- [Benchling SDK PyPI](https://pypi.org/project/benchling-sdk/) — package installation and versioning
skills/lab-automation/opentrons-protocol-api/SKILL.md
- Network accessline 627
| Problem | Cause | Solution | |---------|-------|----------| | `LabwareNotFoundError: [labware name]` | Incorrect labware API name string | Look up exact names at [labware.opentrons.com](https://labware.opentrons.com/); names are case-sens - Network accessline 627
| Problem | Cause | Solution | |---------|-------|----------| | `LabwareNotFoundError: [labware name]` | Incorrect labware API name string | Look up exact names at [labware.opentrons.com](https://labware.opentrons.com/); names are case-sens - Network accessline 628
|---------|-------|----------| | `LabwareNotFoundError: [labware name]` | Incorrect labware API name string | Look up exact names at [labware.opentrons.com](https://labware.opentrons.com/); names are case-sensitive (e.g., `"corning_96_wellp - Network accessline 628
|---------|-------|----------| | `LabwareNotFoundError: [labware name]` | Incorrect labware API name string | Look up exact names at [labware.opentrons.com](https://labware.opentrons.com/); names are case-sensitive (e.g., `"corning_96_wellp - Network accessline 629
| `LabwareNotFoundError: [labware name]` | Incorrect labware API name string | Look up exact names at [labware.opentrons.com](https://labware.opentrons.com/); names are case-sensitive (e.g., `"corning_96_wellplate_360ul_flat"`) | - Network accessline 629
| `LabwareNotFoundError: [labware name]` | Incorrect labware API name string | Look up exact names at [labware.opentrons.com](https://labware.opentrons.com/); names are case-sensitive (e.g., `"corning_96_wellplate_360ul_flat"`) | - Network accessline 646
- [Opentrons Protocol API v2 Documentation](https://docs.opentrons.com/v2/) — official API reference covering all context methods, labware, and modules - Network accessline 646
- [Opentrons Protocol API v2 Documentation](https://docs.opentrons.com/v2/) — official API reference covering all context methods, labware, and modules - Network accessline 647
- [Opentrons Labware Library](https://labware.opentrons.com/) — searchable catalog of all supported labware with API name strings - Network accessline 647
- [Opentrons Labware Library](https://labware.opentrons.com/) — searchable catalog of all supported labware with API name strings - Network accessline 648
- [Protocol API Tutorial](https://docs.opentrons.com/v2/tutorial.html) — step-by-step guide from metadata through hardware modules - Network accessline 648
- [Protocol API Tutorial](https://docs.opentrons.com/v2/tutorial.html) — step-by-step guide from metadata through hardware modules - Network accessline 649
- [Opentrons GitHub Repository](https://github.com/Opentrons/opentrons) — source code, protocol examples, and issue tracker - Network accessline 649
- [Opentrons GitHub Repository](https://github.com/Opentrons/opentrons) — source code, protocol examples, and issue tracker - Network accessline 650
- [Opentrons Community Forum](https://discuss.opentrons.com/) — community Q&A for protocol debugging and hardware questions - Network accessline 650
- [Opentrons Community Forum](https://discuss.opentrons.com/) — community Q&A for protocol debugging and hardware questions
skills/lab-automation/protocolsio-integration/SKILL.md
- Network accessline 25
- **Python packages**: `requests`, `pandas` - **Data requirements**: protocol keywords, DOIs, or protocols.io protocol IDs - **Environment**: internet connection; public protocols: no auth needed; private: OAuth2 token from https://www.prot - Network accessline 25
- **Python packages**: `requests`, `pandas` - **Data requirements**: protocol keywords, DOIs, or protocols.io protocol IDs - **Environment**: internet connection; public protocols: no auth needed; private: OAuth2 token from https://www.prot - Network accessline 26
- **Data requirements**: protocol keywords, DOIs, or protocols.io protocol IDs - **Environment**: internet connection; public protocols: no auth needed; private: OAuth2 token from https://www.protocols.io/developers - **Rate limits**: 10 re - Network accessline 26
- **Data requirements**: protocol keywords, DOIs, or protocols.io protocol IDs - **Environment**: internet connection; public protocols: no auth needed; private: OAuth2 token from https://www.protocols.io/developers - **Rate limits**: 10 re - Network accessline 27
- **Environment**: internet connection; public protocols: no auth needed; private: OAuth2 token from https://www.protocols.io/developers - Network accessline 27
- **Environment**: internet connection; public protocols: no auth needed; private: OAuth2 token from https://www.protocols.io/developers - Network accessline 31
pip install requests pandas # For private protocol access or publishing: # Register at https://www.protocols.io/developers to obtain an API token - Network accessline 31
pip install requests pandas # For private protocol access or publishing: # Register at https://www.protocols.io/developers to obtain an API token - Network accessline 33
# Register at https://www.protocols.io/developers to obtain an API token - Network accessline 33
# Register at https://www.protocols.io/developers to obtain an API token - Network accessline 41
BASE = "https://www.protocols.io/api/v4" - Network accessline 41
BASE = "https://www.protocols.io/api/v4" - Network accessline 45
# Search for CRISPR protocols r = requests.get(f"{BASE}/protocols", - Network accessline 45
# Search for CRISPR protocols r = requests.get(f"{BASE}/protocols", - Network accessline 46
r = requests.get(f"{BASE}/protocols", - Network accessline 46
r = requests.get(f"{BASE}/protocols", - Network accessline 68
BASE = "https://www.protocols.io/api/v4" - Network accessline 68
BASE = "https://www.protocols.io/api/v4" - Network accessline 72
if category_id: params["filter[categories_ids][]"] = category_id r = requests.get(f"{BASE}/protocols", params=params) - Network accessline 72
if category_id: params["filter[categories_ids][]"] = category_id r = requests.get(f"{BASE}/protocols", params=params) - Network accessline 73
params["filter[categories_ids][]"] = category_id r = requests.get(f"{BASE}/protocols", params=params) r.raise_for_status() - Network accessline 73
params["filter[categories_ids][]"] = category_id r = requests.get(f"{BASE}/protocols", params=params) r.raise_for_status() - Network accessline 74
r = requests.get(f"{BASE}/protocols", params=params) - Network accessline 74
r = requests.get(f"{BASE}/protocols", params=params) - Network accessline 111
BASE = "https://www.protocols.io/api/v4" - Network accessline 111
BASE = "https://www.protocols.io/api/v4" - Network accessline 113
def get_protocol(protocol_id): r = requests.get(f"{BASE}/protocols/{protocol_id}") - Network accessline 113
def get_protocol(protocol_id): r = requests.get(f"{BASE}/protocols/{protocol_id}") - Network accessline 114
r = requests.get(f"{BASE}/protocols/{protocol_id}") - Network accessline 114
r = requests.get(f"{BASE}/protocols/{protocol_id}") - Network accessline 149
BASE = "https://www.protocols.io/api/v4" - Network accessline 149
BASE = "https://www.protocols.io/api/v4" - Network accessline 152
"""Retrieve protocol using its DOI.""" # URL-encode the DOI for the query r = requests.get(f"{BASE}/protocols", - Network accessline 152
"""Retrieve protocol using its DOI.""" # URL-encode the DOI for the query r = requests.get(f"{BASE}/protocols", - Network accessline 153
# URL-encode the DOI for the query r = requests.get(f"{BASE}/protocols", params={"q": doi, "page_size": 5}) - Network accessline 153
# URL-encode the DOI for the query r = requests.get(f"{BASE}/protocols", params={"q": doi, "page_size": 5}) - Network accessline 154
r = requests.get(f"{BASE}/protocols", - Network accessline 154
r = requests.get(f"{BASE}/protocols", - Network accessline 178
BASE = "https://www.protocols.io/api/v4" - Network accessline 178
BASE = "https://www.protocols.io/api/v4" - Network accessline 180
def get_reagents(protocol_id): r = requests.get(f"{BASE}/protocols/{protocol_id}") - Network accessline 180
def get_reagents(protocol_id): r = requests.get(f"{BASE}/protocols/{protocol_id}") - Network accessline 181
r = requests.get(f"{BASE}/protocols/{protocol_id}") - Network accessline 181
r = requests.get(f"{BASE}/protocols/{protocol_id}") - Network accessline 210
BASE = "https://www.protocols.io/api/v4" - Network accessline 210
BASE = "https://www.protocols.io/api/v4" - Network accessline 212
r = requests.get(f"{BASE}/categories") - Network accessline 212
r = requests.get(f"{BASE}/categories") - Network accessline 229
BASE = "https://www.protocols.io/api/v4" - Network accessline 229
BASE = "https://www.protocols.io/api/v4" - Network accessline 231
def get_protocol_versions(protocol_id): r = requests.get(f"{BASE}/protocols/{protocol_id}") - Network accessline 231
def get_protocol_versions(protocol_id): r = requests.get(f"{BASE}/protocols/{protocol_id}") - Network accessline 232
r = requests.get(f"{BASE}/protocols/{protocol_id}") - Network accessline 232
r = requests.get(f"{BASE}/protocols/{protocol_id}") - Network accessline 256
Public protocols are accessible without authentication. OAuth2 Bearer tokens are needed for: private protocols, workspace management, protocol creation/editing, and user-specific queries. Obtain tokens at https://www.protocols.io/developers - Network accessline 256
Public protocols are accessible without authentication. OAuth2 Bearer tokens are needed for: private protocols, workspace management, protocol creation/editing, and user-specific queries. Obtain tokens at https://www.protocols.io/developers - Network accessline 267
BASE = "https://www.protocols.io/api/v4" - Network accessline 267
BASE = "https://www.protocols.io/api/v4" - Network accessline 269
def search_and_rank(query, top_n=20): """Search protocols and return ranked by views + forks.""" r = requests.get(f"{BASE}/protocols", - Network accessline 269
def search_and_rank(query, top_n=20): """Search protocols and return ranked by views + forks.""" r = requests.get(f"{BASE}/protocols", - Network accessline 270
"""Search protocols and return ranked by views + forks.""" r = requests.get(f"{BASE}/protocols", params={"q": query, "page_size": top_n, "order_field": "views"}) - Network accessline 270
"""Search protocols and return ranked by views + forks.""" r = requests.get(f"{BASE}/protocols", params={"q": query, "page_size": top_n, "order_field": "views"}) - Network accessline 271
r = requests.get(f"{BASE}/protocols", - Network accessline 271
r = requests.get(f"{BASE}/protocols", - Network accessline 308
BASE = "https://www.protocols.io/api/v4" - Network accessline 308
BASE = "https://www.protocols.io/api/v4" - Network accessline 310
def extract_protocol_steps(protocol_id): """Extract structured step data from a protocol.""" r = requests.get(f"{BASE}/protocols/{protocol_id}") - Network accessline 310
def extract_protocol_steps(protocol_id): """Extract structured step data from a protocol.""" r = requests.get(f"{BASE}/protocols/{protocol_id}") - Network accessline 311
"""Extract structured step data from a protocol.""" r = requests.get(f"{BASE}/protocols/{protocol_id}") r.raise_for_status() - Network accessline 311
"""Extract structured step data from a protocol.""" r = requests.get(f"{BASE}/protocols/{protocol_id}") r.raise_for_status() - Network accessline 312
r = requests.get(f"{BASE}/protocols/{protocol_id}") - Network accessline 312
r = requests.get(f"{BASE}/protocols/{protocol_id}") - Network accessline 385
r = requests.get("https://www.protocols.io/api/v4/protocols", - Network accessline 385
r = requests.get("https://www.protocols.io/api/v4/protocols", - Network accessline 401
protocol_id = 45979 r = requests.get(f"https://www.protocols.io/api/v4/protocols/{protocol_id}") - Network accessline 401
protocol_id = 45979 r = requests.get(f"https://www.protocols.io/api/v4/protocols/{protocol_id}") - Network accessline 402
r = requests.get(f"https://www.protocols.io/api/v4/protocols/{protocol_id}") - Network accessline 402
r = requests.get(f"https://www.protocols.io/api/v4/protocols/{protocol_id}") - Network accessline 404
authors = "; ".join(a["name"] for a in p.get("creators", [])[:3]) print(f"Citation: {authors} ({p.get('created_on', '')[:4]}). ") print(f"{p.get('title')}. protocols.io. https://doi.org/{p.get('doi')}") - Network accessline 404
authors = "; ".join(a["name"] for a in p.get("creators", [])[:3]) print(f"Citation: {authors} ({p.get('created_on', '')[:4]}). ") print(f"{p.get('title')}. protocols.io. https://doi.org/{p.get('doi')}") - Network accessline 406
print(f"{p.get('title')}. protocols.io. https://doi.org/{p.get('doi')}") - Network accessline 406
print(f"{p.get('title')}. protocols.io. https://doi.org/{p.get('doi')}") - Network accessline 416
r = requests.get("https://www.protocols.io/api/v4/protocols", - Network accessline 416
r = requests.get("https://www.protocols.io/api/v4/protocols", - Network accessline 446
- [protocols.io API documentation](https://www.protocols.io/developers) — Official REST API reference - Network accessline 446
- [protocols.io API documentation](https://www.protocols.io/developers) — Official REST API reference - Network accessline 447
- [protocols.io platform](https://www.protocols.io/) — Public protocol library and search - Network accessline 447
- [protocols.io platform](https://www.protocols.io/) — Public protocol library and search - Network accessline 448
- [protocols.io citation guide](https://www.protocols.io/researcher-tools/citing-protocols) — How to cite protocols.io in publications - Network accessline 448
- [protocols.io citation guide](https://www.protocols.io/researcher-tools/citing-protocols) — How to cite protocols.io in publications - Network accessline 449
- [Teytelman et al. (2016) Nature Methods](https://doi.org/10.1038/nmeth.3999) — protocols.io platform description paper - Network accessline 449
- [Teytelman et al. (2016) Nature Methods](https://doi.org/10.1038/nmeth.3999) — protocols.io platform description paper
skills/lab-automation/pylabrobot/SKILL.md
- Network accessline 208
# With visual browser (default — opens http://localhost:2121) - Network accessline 208
# With visual browser (default — opens http://localhost:2121) - Network accessline 215
await lh.setup() # Check browser for visual confirmation before running on real hardware print("Simulator running at http://localhost:2121") - Network accessline 215
await lh.setup() # Check browser for visual confirmation before running on real hardware print("Simulator running at http://localhost:2121") - Network accessline 217
print("Simulator running at http://localhost:2121") - Network accessline 217
print("Simulator running at http://localhost:2121") - Network accessline 405
- Robot executes liquid handling operations as scripted (physical or simulated) - Simulator at `http://localhost:2121` shows animated deck with per-well volume tracking - Network accessline 405
- Robot executes liquid handling operations as scripted (physical or simulated) - Simulator at `http://localhost:2121` shows animated deck with per-well volume tracking - Network accessline 406
- Simulator at `http://localhost:2121` shows animated deck with per-well volume tracking - Network accessline 406
- Simulator at `http://localhost:2121` shows animated deck with per-well volume tracking - Network accessline 416
| Well address `KeyError` | Incorrect well label format | Use uppercase letter + integer: `"A1"`, `"H12"`, not `"a1"` or `"A01"` | | `VolumeError`: exceeds tip capacity | Requested volume larger than tip max | Use appropriate tip type; HTF_ - Network accessline 416
| Well address `KeyError` | Incorrect well label format | Use uppercase letter + integer: `"A1"`, `"H12"`, not `"a1"` or `"A01"` | | `VolumeError`: exceeds tip capacity | Requested volume larger than tip max | Use appropriate tip type; HTF_ - Network accessline 417
| `VolumeError`: exceeds tip capacity | Requested volume larger than tip max | Use appropriate tip type; HTF_L holds up to 1000 µL | | Simulator shows no movement | `open_browser=False` with no viewer | Set `open_browser=True` or open `http - Network accessline 417
| `VolumeError`: exceeds tip capacity | Requested volume larger than tip max | Use appropriate tip type; HTF_L holds up to 1000 µL | | Simulator shows no movement | `open_browser=False` with no viewer | Set `open_browser=True` or open `http - Network accessline 418
| Simulator shows no movement | `open_browser=False` with no viewer | Set `open_browser=True` or open `http://localhost:2121` manually | - Network accessline 418
| Simulator shows no movement | `open_browser=False` with no viewer | Set `open_browser=True` or open `http://localhost:2121` manually | - Network accessline 423
- [PyLabRobot Documentation](https://docs.pylabrobot.org/) — official docs covering all backends and resources - Network accessline 423
- [PyLabRobot Documentation](https://docs.pylabrobot.org/) — official docs covering all backends and resources - Network accessline 424
- [PyLabRobot GitHub](https://github.com/PyLabRobot/pylabrobot) — source code, examples, and issue tracker - Network accessline 424
- [PyLabRobot GitHub](https://github.com/PyLabRobot/pylabrobot) — source code, examples, and issue tracker - Network accessline 425
- [Azeloglu & Bhatt (2023), Cell Device — PyLabRobot paper](https://www.cell.com/device/fulltext/S2666-9986(23)00100-0) — original publication - Network accessline 425
- [Azeloglu & Bhatt (2023), Cell Device — PyLabRobot paper](https://www.cell.com/device/fulltext/S2666-9986(23)00100-0) — original publication - Network accessline 426
- [PyPI: pylabrobot](https://pypi.org/project/pylabrobot/) — installation and version history - Network accessline 426
- [PyPI: pylabrobot](https://pypi.org/project/pylabrobot/) — installation and version history
skills/lab-automation/western-blot-quantification/SKILL.md
- Network accessline 365
- Pillai-Kastoori L, Schutz-Geschwender AR, Harford JA. "A systematic approach to quantitative Western blot analysis." Anal Biochem. 2020;593:113608. https://doi.org/10.1016/j.ab.2020.113608 - Network accessline 365
- Pillai-Kastoori L, Schutz-Geschwender AR, Harford JA. "A systematic approach to quantitative Western blot analysis." Anal Biochem. 2020;593:113608. https://doi.org/10.1016/j.ab.2020.113608 - Network accessline 366
- Taylor SC, Berkelman T, Yadav G, Hammond M. "A defined methodology for reliable quantification of Western blot data." Mol Biotechnol. 2013;55(3):217-226. https://doi.org/10.1007/s12033-013-9672-6 - Network accessline 366
- Taylor SC, Berkelman T, Yadav G, Hammond M. "A defined methodology for reliable quantification of Western blot data." Mol Biotechnol. 2013;55(3):217-226. https://doi.org/10.1007/s12033-013-9672-6 - Network accessline 367
- Aldridge GM, Podrebarac DM, Greenough WT, Weiler IJ. "The use of total protein stains as loading controls: an alternative to high-abundance single-protein controls in semi-quantitative immunoblotting." J Neurosci Methods. 2008;172(2):250- - Network accessline 367
- Aldridge GM, Podrebarac DM, Greenough WT, Weiler IJ. "The use of total protein stains as loading controls: an alternative to high-abundance single-protein controls in semi-quantitative immunoblotting." J Neurosci Methods. 2008;172(2):250- - Network accessline 368
- Schneider CA, Rasband WS, Eliceiri KW. "NIH Image to ImageJ: 25 years of image analysis." Nat Methods. 2012;9(7):671-675. https://doi.org/10.1038/nmeth.2089 - Network accessline 368
- Schneider CA, Rasband WS, Eliceiri KW. "NIH Image to ImageJ: 25 years of image analysis." Nat Methods. 2012;9(7):671-675. https://doi.org/10.1038/nmeth.2089 - Network accessline 369
- ImageJ / Fiji documentation — Gel Analyzer: https://imagej.nih.gov/ij/docs/menus/analyze.html#gels - Network accessline 369
- ImageJ / Fiji documentation — Gel Analyzer: https://imagej.nih.gov/ij/docs/menus/analyze.html#gels - Network accessline 370
- LI-COR Biosciences — Western Blot Normalization Handbook: https://www.licor.com/bio/applications/quantitative_western_blots/normalization - Network accessline 370
- LI-COR Biosciences — Western Blot Normalization Handbook: https://www.licor.com/bio/applications/quantitative_western_blots/normalization - Network accessline 371
- Bio-Rad — Stain-Free Imaging Technology technical note: https://www.bio-rad.com/en-us/applications-technologies/stain-free-imaging-technology - Network accessline 371
- Bio-Rad — Stain-Free Imaging Technology technical note: https://www.bio-rad.com/en-us/applications-technologies/stain-free-imaging-technology
skills/medical-imaging/histolab-wsi-processing/SKILL.md
- Dangerous shell operationline 36
sudo apt-get install openslide-tools - Network accessline 554
- [Histolab documentation](https://histolab.readthedocs.io/) -- official API reference and tutorials - Network accessline 555
- [Histolab GitHub repository](https://github.com/histolab/histolab) -- source code and examples - Network accessline 556
- [OpenSlide](https://openslide.org/) -- underlying C library for WSI format support - Network accessline 557
- [TCGA sample data](https://portal.gdc.cancer.gov/) -- source of built-in sample datasets
skills/medical-imaging/imaging-data-commons/SKILL.md
- Network accessline 378
- [idc-index GitHub](https://github.com/ImagingDataCommons/idc-index) - Source, README, and issue tracker (authoritative API) - Network accessline 379
- [idc-index on PyPI](https://pypi.org/project/idc-index/) - Latest releases - Network accessline 380
- [learn.canceridc.dev](https://learn.canceridc.dev/) - Official IDC tutorials, notebooks, and conceptual guides - Network accessline 381
- [IDC portal](https://imaging.datacommons.cancer.gov/) - Browser UI for exploring collections and viewer URLs - Network accessline 382
- [IDC BigQuery dataset](https://console.cloud.google.com/bigquery?project=bigquery-public-data&dataset=idc_current) - Optional advanced/cross-dataset SQL (requires GCP billing)
skills/medical-imaging/nnunet-segmentation/SKILL.md
- Access to secretsline 157
[Credential-related source omitted] - Access to secretsline 234
[Credential-related source omitted] - Access to secretsline 287
[Credential-related source omitted] - Network accessline 507
# Use a downloaded pretrained model folder # Community models: https://zenodo.org/record/7783116 (abdominal organs) - Network accessline 508
# Community models: https://zenodo.org/record/7783116 (abdominal organs) - Network accessline 635
- [nnU-Net GitHub](https://github.com/MIC-DKFZ/nnUNet) — official source code, installation instructions, and documentation - Network accessline 636
- [Isensee F et al. (2021) Nature Methods 18:203–211](https://doi.org/10.1038/s41592-020-01008-z) — original nnU-Net paper: "nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation" - Network accessline 637
- [nnU-Net v2 documentation](https://github.com/MIC-DKFZ/nnUNet/tree/master/documentation) — migration guide, dataset format, inference API, custom trainers - Network accessline 638
- [Medical Segmentation Decathlon](http://medicaldecathlon.com/) — 10 benchmark datasets in nnU-Net-compatible format for training and validation - Network accessline 639
- [Zenodo: pretrained nnU-Net models](https://zenodo.org/record/7783116) — community-shared pretrained models for abdominal organs and other structures
skills/medical-imaging/omero-integration/SKILL.md
- Network accessline 421
- [OMERO Python API Documentation](https://omero-py.readthedocs.io/en/stable/) — official omero-py docs - Network accessline 422
- [OMERO Developer Documentation](https://docs.openmicroscopy.org/omero/5.6/developers/Python.html) — Python client guide - Network accessline 423
- [OMERO GitHub (ome/omero-py)](https://github.com/ome/omero-py) — source code and issue tracker - Network accessline 424
- [Linkert et al. (2010), J Cell Biol — OMERO paper](https://doi.org/10.1083/jcb.201004104) — original OMERO publication
skills/medical-imaging/pathml/SKILL.md
- Network accessline 37
# For GPU support pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu118 - Network accessline 38
pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu118 - Network accessline 271
- [PathML Documentation](https://pathml.readthedocs.io/) — official docs with tutorials - Network accessline 272
- [PathML GitHub (Dana-Farber/PathML)](https://github.com/Dana-Farber-AIOS/pathml) — source code and examples - Network accessline 273
- [Rosenthal et al. (2022), Cell Systems — PathML paper](https://doi.org/10.1016/j.cels.2022.01.004) — original publication - Network accessline 274
- [OpenSlide Documentation](https://openslide.org/) — WSI reading library underlying PathML
skills/medical-imaging/pydicom-medical-imaging/SKILL.md
- Network accessline 742
- [Pydicom documentation](https://pydicom.github.io/pydicom/stable/) -- official API reference and user guide - Network accessline 743
- [Pydicom GitHub repository](https://github.com/pydicom/pydicom) -- source code, examples, and issue tracker - Network accessline 744
- [DICOM Standard Browser](https://dicom.innolitics.com/ciods) -- interactive DICOM tag and IOD reference - Network accessline 745
- [pylibjpeg documentation](https://github.com/pydicom/pylibjpeg) -- JPEG codec plugin for pydicom
skills/medical-imaging/simpleitk-image-registration/SKILL.md
- Network accessline 875
- [SimpleITK documentation](https://simpleitk.readthedocs.io/en/master/) — complete API reference and Jupyter notebook tutorials - Network accessline 876
- [SimpleITK GitHub](https://github.com/SimpleITK/SimpleITK) — source code, examples, and issue tracker - Network accessline 877
- [SimpleITK Notebooks](https://github.com/InsightSoftwareConsortium/SimpleITK-Notebooks) — 50+ Jupyter notebooks covering registration, segmentation, and I/O - Network accessline 878
- [Lowekamp BC et al. (2013) Front Neuroinform](https://doi.org/10.3389/fninf.2013.00045) — SimpleITK original paper describing design and capabilities - Network accessline 879
- [Yaniv Z et al. (2018) Frontiers in Neuroinformatics](https://doi.org/10.3389/fninf.2018.00008) — SimpleITK for the biomedical image processing community
skills/molecular-biology/plannotate-plasmid-annotation/SKILL.md
- Network accessline 435
- [pLannotate GitHub: mmcguffi/pLannotate](https://github.com/mmcguffi/pLannotate) — source code, issue tracker, and installation instructions - Network accessline 435
- [pLannotate GitHub: mmcguffi/pLannotate](https://github.com/mmcguffi/pLannotate) — source code, issue tracker, and installation instructions - Network accessline 436
- [McGuffi M et al. (2021) Nucleic Acids Research 49(W1): W516–W522](https://doi.org/10.1093/nar/gkab374) — original pLannotate paper describing the BLAST-based annotation pipeline and curated feature database - Network accessline 436
- [McGuffi M et al. (2021) Nucleic Acids Research 49(W1): W516–W522](https://doi.org/10.1093/nar/gkab374) — original pLannotate paper describing the BLAST-based annotation pipeline and curated feature database - Network accessline 437
- [pLannotate PyPI: plannotate](https://pypi.org/project/plannotate/) — package metadata, version history, and pip install info - Network accessline 437
- [pLannotate PyPI: plannotate](https://pypi.org/project/plannotate/) — package metadata, version history, and pip install info - Network accessline 438
- [Addgene plasmid repository](https://www.addgene.org/) — primary source for the curated feature library used by pLannotate - Network accessline 438
- [Addgene plasmid repository](https://www.addgene.org/) — primary source for the curated feature library used by pLannotate - Network accessline 439
- [BioPython SeqIO documentation](https://biopython.org/wiki/SeqIO) — parsing and manipulating GenBank output files - Network accessline 439
- [BioPython SeqIO documentation](https://biopython.org/wiki/SeqIO) — parsing and manipulating GenBank output files
skills/molecular-biology/sgrna-design-guide/SKILL.md
- Network accessline 27
### If you use validated sgRNAs from our database (Option 1): - **Database Source**: Addgene (https://www.addgene.org) - Network accessline 27
### If you use validated sgRNAs from our database (Option 1): - **Database Source**: Addgene (https://www.addgene.org) - Network accessline 28
- **Database Source**: Addgene (https://www.addgene.org) - Network accessline 28
- **Database Source**: Addgene (https://www.addgene.org) - Network accessline 30
- **Acknowledgment**: "Validated sgRNA sequences obtained from Addgene (https://www.addgene.org/crispr/reference/grna-sequence/)" - Network accessline 30
- **Acknowledgment**: "Validated sgRNA sequences obtained from Addgene (https://www.addgene.org/crispr/reference/grna-sequence/)" - Network accessline 295
# Copy the URL from download_links.txt, then: wget [PASTE_URL_HERE] - Network accessline 295
# Copy the URL from download_links.txt, then: wget [PASTE_URL_HERE] - Network accessline 296
wget [PASTE_URL_HERE] - Network accessline 296
wget [PASTE_URL_HERE] - Network accessline 473
# Download human GRCh38 SpCas9 knockout dataset wget https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/\ - Network accessline 473
# Download human GRCh38 SpCas9 knockout dataset wget https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/\ - Network accessline 474
wget https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/\ - Network accessline 474
wget https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/\ - Network accessline 509
- Addgene CRISPR sgRNA reference sequences: https://www.addgene.org/crispr/reference/grna-sequence/ - Network accessline 509
- Addgene CRISPR sgRNA reference sequences: https://www.addgene.org/crispr/reference/grna-sequence/ - Network accessline 510
- CRISPick (Broad Institute GPP) pre-computed sgRNA designs: https://portals.broadinstitute.org/gppx/crispick/public - Network accessline 510
- CRISPick (Broad Institute GPP) pre-computed sgRNA designs: https://portals.broadinstitute.org/gppx/crispick/public - Network accessline 511
- Sanson KR, Hanna RE, Hegde M, et al. "Optimized libraries for CRISPR-Cas9 genetic screens with multiple modalities." Nat Commun. 2018;9(1):5416. PMID: 30575746. https://doi.org/10.1038/s41467-018-07901-8 - Network accessline 511
- Sanson KR, Hanna RE, Hegde M, et al. "Optimized libraries for CRISPR-Cas9 genetic screens with multiple modalities." Nat Commun. 2018;9(1):5416. PMID: 30575746. https://doi.org/10.1038/s41467-018-07901-8 - Network accessline 512
- DeWeirdt PC, Sanson KR, Sangree AK, et al. "Optimization of AsCas12a for combinatorial genetic screens in human cells." Nat Biotechnol. 2021;39(1):94-104. PMID: 32661438. https://doi.org/10.1038/s41587-020-0600-6 - Network accessline 512
- DeWeirdt PC, Sanson KR, Sangree AK, et al. "Optimization of AsCas12a for combinatorial genetic screens in human cells." Nat Biotechnol. 2021;39(1):94-104. PMID: 32661438. https://doi.org/10.1038/s41587-020-0600-6 - Network accessline 513
- Doench JG, Fusi N, Sullender M, et al. "Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9." Nat Biotechnol. 2016;34(2):184-191. https://doi.org/10.1038/nbt.3437 - Network accessline 513
- Doench JG, Fusi N, Sullender M, et al. "Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9." Nat Biotechnol. 2016;34(2):184-191. https://doi.org/10.1038/nbt.3437 - Network accessline 514
- Ran FA, Hsu PD, Wright J, et al. "Genome engineering using the CRISPR-Cas9 system." Nat Protoc. 2013;8(11):2281-2308. PMID: 24336569. https://doi.org/10.1038/nprot.2013.143 - Network accessline 514
- Ran FA, Hsu PD, Wright J, et al. "Genome engineering using the CRISPR-Cas9 system." Nat Protoc. 2013;8(11):2281-2308. PMID: 24336569. https://doi.org/10.1038/nprot.2013.143 - Network accessline 515
- Zetsche B, Heidenreich M, Mohanraju P, et al. "Multiplex gene editing by CRISPR-Cpf1 using a single crRNA array." Nat Biotechnol. 2017;35(1):31-34. https://doi.org/10.1038/nbt.3737 - Network accessline 515
- Zetsche B, Heidenreich M, Mohanraju P, et al. "Multiplex gene editing by CRISPR-Cpf1 using a single crRNA array." Nat Biotechnol. 2017;35(1):31-34. https://doi.org/10.1038/nbt.3737
skills/molecular-biology/sgrna-design-guide/resource/CRISPick_download_links.txt
- Network accessline 1
https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/sgRNA_design_8030_ICSASG_v2_SpyoCas9_CRISPRa_RS3seq-Chen2013_NCBI_20230421.txt.gz - Network accessline 1
https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/sgRNA_design_8030_ICSASG_v2_SpyoCas9_CRISPRa_RS3seq-Chen2013_NCBI_20230421.txt.gz - Network accessline 2
https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/sgRNA_design_8030_ICSASG_v2_SpyoCas9_CRISPRa_RS3seq-Chen2013_NCBI_20230421.summary.txt - Network accessline 2
https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/sgRNA_design_8030_ICSASG_v2_SpyoCas9_CRISPRa_RS3seq-Chen2013_NCBI_20230421.summary.txt - Network accessline 3
https://portals.broadinstitute.org/gppx/public/sgrna_design/api/downloads/sgRNA_design_8030_ICSASG_v2_SpyoCas9_CRISPRko_RS3seq-Chen2013+RS3target_NCBI_20230421.txt.gz - Network accessline 3
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skills/molecular-biology/sgrna-design-guide/resource/addgene_grna_sequences.csv
- Network accessline 1
Application,Cas9 Species,Depositor,Plasmid ID,PubMed ID,Target Gene,Target Sequence,Target Species cut,S. pyogenes,Goncalves,"<a href=""/58252/"">58252</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24870050/"">24870050</a>",AAVS1,GGGG - Network accessline 1
Application,Cas9 Species,Depositor,Plasmid ID,PubMed ID,Target Gene,Target Sequence,Target Species cut,S. pyogenes,Goncalves,"<a href=""/58252/"">58252</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24870050/"">24870050</a>",AAVS1,GGGG - Network accessline 2
cut,S. pyogenes,Goncalves,"<a href=""/58252/"">58252</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24870050/"">24870050</a>",AAVS1,GGGGCCACTAGGGACAGGAT,H. sapiens - Network accessline 2
cut,S. pyogenes,Goncalves,"<a href=""/58252/"">58252</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24870050/"">24870050</a>",AAVS1,GGGGCCACTAGGGACAGGAT,H. sapiens - Network accessline 3
cut,S. pyogenes,Church,"<a href=""/41818/"">41818</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23287722/"">23287722</a>",AAVS1,GGGGCCACTAGGGACAGGAT,H. sapiens - Network accessline 3
cut,S. pyogenes,Church,"<a href=""/41818/"">41818</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23287722/"">23287722</a>",AAVS1,GGGGCCACTAGGGACAGGAT,H. sapiens - Network accessline 4
cut,S. pyogenes,Sabatini,"<a href=""/50662/"">50662</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24336569/"">24336569</a>",AAVS1,GGGGCCACTAGGGACAGGAT,H. sapiens - Network accessline 4
cut,S. pyogenes,Sabatini,"<a href=""/50662/"">50662</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24336569/"">24336569</a>",AAVS1,GGGGCCACTAGGGACAGGAT,H. sapiens - Network accessline 5
cut,S. pyogenes,Sabatini,"<a href=""/70661/"">70661</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",AAVS1,GGGGCCACTAGGGACAGGAT,H. sapiens - Network accessline 5
cut,S. pyogenes,Sabatini,"<a href=""/70661/"">70661</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",AAVS1,GGGGCCACTAGGGACAGGAT,H. sapiens - Network accessline 6
cut,S. pyogenes,Church,"<a href=""/41817/"">41817</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23287722/"">23287722</a>",AAVS1,GTCCCCTCCACCCCACAGTG,H. sapiens - Network accessline 6
cut,S. pyogenes,Church,"<a href=""/41817/"">41817</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23287722/"">23287722</a>",AAVS1,GTCCCCTCCACCCCACAGTG,H. sapiens - Network accessline 7
cut,S. pyogenes,Kanemaki,"<a href=""/72833/"">72833</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/27052166/"">27052166</a>",AAVS1,GGGGCCACTAGGGACAGGAT,H. sapiens - Network accessline 7
cut,S. pyogenes,Kanemaki,"<a href=""/72833/"">72833</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/27052166/"">27052166</a>",AAVS1,GGGGCCACTAGGGACAGGAT,H. sapiens - Network accessline 8
cut,S. pyogenes,Corn,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26789497/"">26789497</a>",AAVS1,TGTCCCTAGTGGCCCCACTG,H. sapiens - Network accessline 8
cut,S. pyogenes,Corn,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26789497/"">26789497</a>",AAVS1,TGTCCCTAGTGGCCCCACTG,H. sapiens - Network accessline 9
cut,S. pyogenes,Corn,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26789497/"">26789497</a>",AAVS1,ACAGTGGGGCCACTAGGGAC,H. sapiens - Network accessline 9
cut,S. pyogenes,Corn,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26789497/"">26789497</a>",AAVS1,ACAGTGGGGCCACTAGGGAC,H. sapiens - Network accessline 10
RNA targeting,S. pyogenes,Yeo,"<a href=""/74705/"">74705</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26997482/"">26997482</a>",ACTB,UGGAGCGAGCAUCCCCCAAA,H. sapiens - Network accessline 10
RNA targeting,S. pyogenes,Yeo,"<a href=""/74705/"">74705</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26997482/"">26997482</a>",ACTB,UGGAGCGAGCAUCCCCCAAA,H. sapiens - Network accessline 11
cut,S. pyogenes,Jiang,"<a href=""/62552/"">62552</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25739462/"">25739462</a>",actII-orf4,ATTACCAGGGACCGGAGTTC,S. coelicolor - Network accessline 11
cut,S. pyogenes,Jiang,"<a href=""/62552/"">62552</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25739462/"">25739462</a>",actII-orf4,ATTACCAGGGACCGGAGTTC,S. coelicolor - Network accessline 12
cut,S. pyogenes,Zaratiegui,"<a href=""/52225/"">52225</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25352017/"">25352017</a>",ade6+,TCTATTGTTCAGATGCCTCG,S. pombe - Network accessline 12
cut,S. pyogenes,Zaratiegui,"<a href=""/52225/"">52225</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25352017/"">25352017</a>",ade6+,TCTATTGTTCAGATGCCTCG,S. pombe - Network accessline 13
cut,S. pyogenes,Zaratiegui,"<a href=""/52227/"">52227</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25352017/"">25352017</a>",ade6-L469,TCTATTGTTCAGATGCCTTG,S. pombe - Network accessline 13
cut,S. pyogenes,Zaratiegui,"<a href=""/52227/"">52227</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25352017/"">25352017</a>",ade6-L469,TCTATTGTTCAGATGCCTTG,S. pombe - Network accessline 14
cut,S. pyogenes,Zaratiegui,"<a href=""/52226/"">52226</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25352017/"">25352017</a>",ade6-M210,TCTATTGTTCAGATGCTTCG,S. pombe - Network accessline 14
cut,S. pyogenes,Zaratiegui,"<a href=""/52226/"">52226</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25352017/"">25352017</a>",ade6-M210,TCTATTGTTCAGATGCTTCG,S. pombe - Network accessline 15
cut,S. pyogenes,Ventura,"<a href=""/64071/"">64071</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25337876/"">25337876</a>",Alk and Eml,,M. musculus - Network accessline 15
cut,S. pyogenes,Ventura,"<a href=""/64071/"">64071</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25337876/"">25337876</a>",Alk and Eml,,M. musculus - Network accessline 16
nick,S. pyogenes,Shaw,"<a href=""/74374/"">74374</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26816379/"">26816379</a>",AMPK alpha 1,GGCTGTCGCCATCTTTCTCC,H. sapiens - Network accessline 16
nick,S. pyogenes,Shaw,"<a href=""/74374/"">74374</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26816379/"">26816379</a>",AMPK alpha 1,GGCTGTCGCCATCTTTCTCC,H. sapiens - Network accessline 17
nick,S. pyogenes,Shaw,"<a href=""/74375/"">74375</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26816379/"">26816379</a>",AMPK alpha 1,GAAGATCGGCCACTACATTC,H. sapiens - Network accessline 17
nick,S. pyogenes,Shaw,"<a href=""/74375/"">74375</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26816379/"">26816379</a>",AMPK alpha 1,GAAGATCGGCCACTACATTC,H. sapiens - Network accessline 18
nick,S. pyogenes,Shaw,"<a href=""/74376/"">74376</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26816379/"">26816379</a>",AMPK alpha 2,GTCAGCCATCTTCGGCGCGCG,H. sapiens - Network accessline 18
nick,S. pyogenes,Shaw,"<a href=""/74376/"">74376</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26816379/"">26816379</a>",AMPK alpha 2,GTCAGCCATCTTCGGCGCGCG,H. sapiens - Network accessline 19
nick,S. pyogenes,Shaw,"<a href=""/74377/"">74377</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26816379/"">26816379</a>",AMPK alpha 2,GAAGATCGGACACTACGTGC,H. sapiens - Network accessline 19
nick,S. pyogenes,Shaw,"<a href=""/74377/"">74377</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26816379/"">26816379</a>",AMPK alpha 2,GAAGATCGGACACTACGTGC,H. sapiens - Network accessline 20
cut,S. pyogenes,Sabatini,"<a href=""/70660/"">70660</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>","Amplicon, JAK2",GAGGCATATTCTTCTCCTGG,H. sapiens - Network accessline 20
cut,S. pyogenes,Sabatini,"<a href=""/70660/"">70660</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>","Amplicon, JAK2",GAGGCATATTCTTCTCCTGG,H. sapiens - Network accessline 21
cut,S. pyogenes,Voytas,"<a href=""/70019/"">70019</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26541286/"">26541286</a>",ANT1,TGTCGTTTATAATTTGTAGA,S. lycopersicum - Network accessline 21
cut,S. pyogenes,Voytas,"<a href=""/70019/"">70019</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26541286/"">26541286</a>",ANT1,TGTCGTTTATAATTTGTAGA,S. lycopersicum - Network accessline 22
cut,S. pyogenes,Voytas,"<a href=""/70018/"">70018</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26541286/"">26541286</a>",ANT1,ACAATTTAATACACCTTTT,S. lycopersicum - Network accessline 22
cut,S. pyogenes,Voytas,"<a href=""/70018/"">70018</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26541286/"">26541286</a>",ANT1,ACAATTTAATACACCTTTT,S. lycopersicum - Network accessline 23
cut,S. pyogenes,Kim,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26479191/"">26479191</a>",AOC,CAAAAGACTGTCAATTCCCT,N. attenuata - Network accessline 23
cut,S. pyogenes,Kim,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26479191/"">26479191</a>",AOC,CAAAAGACTGTCAATTCCCT,N. attenuata - Network accessline 24
cut/nick,S. pyogenes,Yamamoto,"<a href=""/58780/"">58780</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24954249/"">24954249</a>",APC,GAAGCGGGCAAAGGGGCGAC,H. sapiens - Network accessline 24
cut/nick,S. pyogenes,Yamamoto,"<a href=""/58780/"">58780</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24954249/"">24954249</a>",APC,GAAGCGGGCAAAGGGGCGAC,H. sapiens - Network accessline 25
cut,S. pyogenes,Joung,"<a href=""/42241/"">42241</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360964/"">23360964</a>",apoea,GGATGAGCCAAGAAGCCGCT,D. rerio - Network accessline 25
cut,S. pyogenes,Joung,"<a href=""/42241/"">42241</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360964/"">23360964</a>",apoea,GGATGAGCCAAGAAGCCGCT,D. rerio - Network accessline 26
activate,S. pyogenes,Sato,"<a href=""/64131/"">64131</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",ASCL1,TGGATGGAGAGTTTGCAAGGAGC,H. sapiens - Network accessline 26
activate,S. pyogenes,Sato,"<a href=""/64131/"">64131</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",ASCL1,TGGATGGAGAGTTTGCAAGGAGC,H. sapiens - Network accessline 27
activate,S. pyogenes,Sato,"<a href=""/64130/"">64130</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",ASCL1,TGGGCAGCCGCTCGCTGCAGCAG,H. sapiens - Network accessline 27
activate,S. pyogenes,Sato,"<a href=""/64130/"">64130</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",ASCL1,TGGGCAGCCGCTCGCTGCAGCAG,H. sapiens - Network accessline 28
activate,S. pyogenes,Sato,"<a href=""/64129/"">64129</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",ASCL1,TGGGGCTGGGTGTCCCATTGAAA,H. sapiens - Network accessline 28
activate,S. pyogenes,Sato,"<a href=""/64129/"">64129</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",ASCL1,TGGGGCTGGGTGTCCCATTGAAA,H. sapiens - Network accessline 29
activate,S. pyogenes,Sato,"<a href=""/64132/"">64132</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",ASCL1,TGGTGTTTATTCAGCCGGGAGTC,H. sapiens - Network accessline 29
activate,S. pyogenes,Sato,"<a href=""/64132/"">64132</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",ASCL1,TGGTGTTTATTCAGCCGGGAGTC,H. sapiens - Network accessline 30
cut,S. pyogenes,Mashimo,"<a href=""/60968/"">60968</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24967838/"">24967838</a>",Asip,ATGACAGGAGTCTAACTCAC,R. norvegicus - Network accessline 30
cut,S. pyogenes,Mashimo,"<a href=""/60968/"">60968</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24967838/"">24967838</a>",Asip,ATGACAGGAGTCTAACTCAC,R. norvegicus - Network accessline 31
tag,S. pyogenes,Mendenhall,"<a href=""/64690/"">64690</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26355004/"">26355004</a>",ATF1,TAGGAATCAAACACTTTTATTGG,H. sapiens - Network accessline 31
tag,S. pyogenes,Mendenhall,"<a href=""/64690/"">64690</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26355004/"">26355004</a>",ATF1,TAGGAATCAAACACTTTTATTGG,H. sapiens - Network accessline 32
cut,S. pyogenes,Yamamoto,"<a href=""/58779/"">58779</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24954249/"">24954249</a>",ATM,TGAATTGGGATGCTGTTTTT,H. sapiens - Network accessline 32
cut,S. pyogenes,Yamamoto,"<a href=""/58779/"">58779</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24954249/"">24954249</a>",ATM,TGAATTGGGATGCTGTTTTT,H. sapiens - Network accessline 33
cut,S. pyogenes,Mello,"<a href=""/58981/"">58981</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24879462/"">24879462</a>",avr-14,GATTGGAGAGTTAGACCACG,C. elegans - Network accessline 33
cut,S. pyogenes,Mello,"<a href=""/58981/"">58981</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24879462/"">24879462</a>",avr-14,GATTGGAGAGTTAGACCACG,C. elegans - Network accessline 34
cut,S. pyogenes,Mello,"<a href=""/58982/"">58982</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24879462/"">24879462</a>",avr-15,GTTTGCAATATAAGTCACCC,C. elegans - Network accessline 34
cut,S. pyogenes,Mello,"<a href=""/58982/"">58982</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24879462/"">24879462</a>",avr-15,GTTTGCAATATAAGTCACCC,C. elegans - Network accessline 35
cut,S. pyogenes,Yamamoto,"<a href=""/58782/"">58782</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24954249/"">24954249</a>",AXIN2,TATGTTGGTGACTTGCCTCC,H. sapiens - Network accessline 35
cut,S. pyogenes,Yamamoto,"<a href=""/58782/"">58782</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24954249/"">24954249</a>",AXIN2,TATGTTGGTGACTTGCCTCC,H. sapiens - Network accessline 36
methylation,S. pyogenes,Zoldoš,"<a href=""/71828/"">71828</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26969735/"">26969735</a>",BACH2,AATGTAGCGATTGAGAGTGTGGG,H. sapiens - Network accessline 36
methylation,S. pyogenes,Zoldoš,"<a href=""/71828/"">71828</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26969735/"">26969735</a>",BACH2,AATGTAGCGATTGAGAGTGTGGG,H. sapiens - Network accessline 37
cut,S. pyogenes,Sabatini,"<a href=""/70658/"">70658</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",BCR/ABL amplicon,GGCTCCCTTCAAGTGGGATG,H. sapiens - Network accessline 37
cut,S. pyogenes,Sabatini,"<a href=""/70658/"">70658</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",BCR/ABL amplicon,GGCTCCCTTCAAGTGGGATG,H. sapiens - Network accessline 38
cut,S. pyogenes,Corn,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26789497/"">26789497</a>",BFP,ATGGCGTGCAGTGCTTCAGC, - Network accessline 38
cut,S. pyogenes,Corn,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26789497/"">26789497</a>",BFP,ATGGCGTGCAGTGCTTCAGC, - Network accessline 39
cut,S. pyogenes,Kim,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26479191/"">26479191</a>",BIN2,ATCACAGTGATGCTCGTCAA,L. sativa - Network accessline 39
cut,S. pyogenes,Kim,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26479191/"">26479191</a>",BIN2,ATCACAGTGATGCTCGTCAA,L. sativa - Network accessline 40
cut,S. pyogenes,Hanna,"<a href=""/59724/"">59724</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25543152/"">25543152</a>",BLIMP1,CGGATGGGGTAAACGACCCG,H. sapiens - Network accessline 40
cut,S. pyogenes,Hanna,"<a href=""/59724/"">59724</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25543152/"">25543152</a>",BLIMP1,CGGATGGGGTAAACGACCCG,H. sapiens - Network accessline 41
cut,S. pyogenes,Kim,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26479191/"">26479191</a>",BRI1,TTTGAAAGATGGAAGCGCGG,A. thaliana - Network accessline 41
cut,S. pyogenes,Kim,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26479191/"">26479191</a>",BRI1,TTTGAAAGATGGAAGCGCGG,A. thaliana - Network accessline 42
cut,S. pyogenes,Kim,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26479191/"">26479191</a>",BRI1,TGAAACTAAACTGGTCCACA,A. thaliana - Network accessline 42
cut,S. pyogenes,Kim,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26479191/"">26479191</a>",BRI1,TGAAACTAAACTGGTCCACA,A. thaliana - Network accessline 43
interfere,S. pyogenes,Lu,"<a href=""/68892/"">68892</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26918244/"">26918244</a>",BT1754,GAAAATGGGGTGTATCCTGC,B. thetaiotaomicron - Network accessline 43
interfere,S. pyogenes,Lu,"<a href=""/68892/"">68892</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26918244/"">26918244</a>",BT1754,GAAAATGGGGTGTATCCTGC,B. thetaiotaomicron - Network accessline 44
interfere,S. pyogenes,Lu,"<a href=""/68891/"">68891</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26918244/"">26918244</a>",BT1854,ATTGAAGAACAAAAGCAGTT,B. thetaiotaomicron - Network accessline 44
interfere,S. pyogenes,Lu,"<a href=""/68891/"">68891</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26918244/"">26918244</a>",BT1854,ATTGAAGAACAAAAGCAGTT,B. thetaiotaomicron - Network accessline 45
cut,S. pyogenes,Sabatini,"<a href=""/70650/"">70650</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",C16orf80,CGATGCTGTAGAGGATGGAG,H. sapiens - Network accessline 45
cut,S. pyogenes,Sabatini,"<a href=""/70650/"">70650</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",C16orf80,CGATGCTGTAGAGGATGGAG,H. sapiens - Network accessline 46
cut,S. pyogenes,Sabatini,"<a href=""/70651/"">70651</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",C16orf80,TGTCTGAGAAGTAAACCCGT,H. sapiens - Network accessline 46
cut,S. pyogenes,Sabatini,"<a href=""/70651/"">70651</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",C16orf80,TGTCTGAGAAGTAAACCCGT,H. sapiens - Network accessline 47
cut,S. pyogenes,Sabatini,"<a href=""/70653/"">70653</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",C3orf17,GGGCCAAATGGGGTTTGTGG,H. sapiens - Network accessline 47
cut,S. pyogenes,Sabatini,"<a href=""/70653/"">70653</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",C3orf17,GGGCCAAATGGGGTTTGTGG,H. sapiens - Network accessline 48
cut,S. pyogenes,Sabatini,"<a href=""/70652/"">70652</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",C3orf17,GTGTGAGAATCCCTAAGGCG,H. sapiens - Network accessline 48
cut,S. pyogenes,Sabatini,"<a href=""/70652/"">70652</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",C3orf17,GTGTGAGAATCCCTAAGGCG,H. sapiens - Network accessline 49
cut,S. pyogenes,Sabatini,"<a href=""/70654/"">70654</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",C9orf114,CAGGCGGGCTCACCTCCGTG,H. sapiens - Network accessline 49
cut,S. pyogenes,Sabatini,"<a href=""/70654/"">70654</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",C9orf114,CAGGCGGGCTCACCTCCGTG,H. sapiens - Network accessline 50
cut,S. pyogenes,Sabatini,"<a href=""/70655/"">70655</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",C9orf114,GCGGCAGAGAAGGAGGACCG,H. sapiens - Network accessline 50
cut,S. pyogenes,Sabatini,"<a href=""/70655/"">70655</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",C9orf114,GCGGCAGAGAAGGAGGACCG,H. sapiens - Network accessline 51
cut,S. pyogenes,Church,"<a href=""/43803/"">43803</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23460208/"">23460208</a>",CAN1,GATACGTTCTCTATGGAGGA,S. cerevisiae - Network accessline 51
cut,S. pyogenes,Church,"<a href=""/43803/"">43803</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23460208/"">23460208</a>",CAN1,GATACGTTCTCTATGGAGGA,S. cerevisiae - Network accessline 52
cut,S. pyogenes,Bornhauser,"<a href=""/75164/"">75164</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/27194728/"">27194728</a>",Caspase-8,GCCTGGACTACATTCCGCAA,H. sapiens - Network accessline 52
cut,S. pyogenes,Bornhauser,"<a href=""/75164/"">75164</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/27194728/"">27194728</a>",Caspase-8,GCCTGGACTACATTCCGCAA,H. sapiens - Network accessline 53
cut,S. pyogenes,Corn,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26789497/"">26789497</a>",CCR5,TGACATCAATTATTATACAT,H. sapiens - Network accessline 53
cut,S. pyogenes,Corn,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26789497/"">26789497</a>",CCR5,TGACATCAATTATTATACAT,H. sapiens - Network accessline 54
interfere,S. pyogenes,Qi,"<a href=""/46918/"">46918</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23849981/"">23849981</a>",CD71,GGACGCGCTAGTGTGAGTGC,H. sapiens - Network accessline 54
interfere,S. pyogenes,Qi,"<a href=""/46918/"">46918</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23849981/"">23849981</a>",CD71,GGACGCGCTAGTGTGAGTGC,H. sapiens - Network accessline 55
cut,S. pyogenes,Yamamoto,"<a href=""/58781/"">58781</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24954249/"">24954249</a>",CDH1,TGACTTGCGAGGGACGCATT,H. sapiens - Network accessline 55
cut,S. pyogenes,Yamamoto,"<a href=""/58781/"">58781</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24954249/"">24954249</a>",CDH1,TGACTTGCGAGGGACGCATT,H. sapiens - Network accessline 56
activate,S. pyogenes,Vale,"<a href=""/60905/"">60905</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25307933/"">25307933</a>",CDKN1B,GAAGCCGGGACCTGGACCAG,H. sapiens - Network accessline 56
activate,S. pyogenes,Vale,"<a href=""/60905/"">60905</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25307933/"">25307933</a>",CDKN1B,GAAGCCGGGACCTGGACCAG,H. sapiens - Network accessline 57
tag,S. pyogenes,Mendenhall,"<a href=""/64036/"">64036</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26355004/"">26355004</a>",CEBPB,CTCCGGCCACTGCTAGCGCGG,H. sapiens - Network accessline 57
tag,S. pyogenes,Mendenhall,"<a href=""/64036/"">64036</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26355004/"">26355004</a>",CEBPB,CTCCGGCCACTGCTAGCGCGG,H. sapiens - Network accessline 58
tag,S. pyogenes,Mendenhall,"<a href=""/64047/"">64047</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26355004/"">26355004</a>",CEBPB,GCCGGCAGGGGGACGCGCGCGG,H. sapiens - Network accessline 58
tag,S. pyogenes,Mendenhall,"<a href=""/64047/"">64047</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26355004/"">26355004</a>",CEBPB,GCCGGCAGGGGGACGCGCGCGG,H. sapiens - Network accessline 59
cut,S. pyogenes,Ikawa,"<a href=""/50718/"">50718</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24284873/"">24284873</a>",Cent1,GGCAACGTTTGACTTCCTGA,M. musculus - Network accessline 59
cut,S. pyogenes,Ikawa,"<a href=""/50718/"">50718</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24284873/"">24284873</a>",Cent1,GGCAACGTTTGACTTCCTGA,M. musculus - Network accessline 60
cut,S. pyogenes,Yamamoto,"<a href=""/58783/"">58783</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24954249/"">24954249</a>",CFTR,TCTGTATCTATATTCATCAT,H. sapiens - Network accessline 60
cut,S. pyogenes,Yamamoto,"<a href=""/58783/"">58783</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24954249/"">24954249</a>",CFTR,TCTGTATCTATATTCATCAT,H. sapiens - Network accessline 61
tag,S. pyogenes,Mendenhall,"<a href=""/64940/"">64940</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26355004/"">26355004</a>",CREB1,GCCACAAATCAGATTAATTTGG,H. sapiens - Network accessline 61
tag,S. pyogenes,Mendenhall,"<a href=""/64940/"">64940</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26355004/"">26355004</a>",CREB1,GCCACAAATCAGATTAATTTGG,H. sapiens - Network accessline 62
tag,S. pyogenes,Mendenhall,"<a href=""/64939/"">64939</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26355004/"">26355004</a>",CREB1,GCCACAAATCAGATTAATTTGGG,H. sapiens - Network accessline 62
tag,S. pyogenes,Mendenhall,"<a href=""/64939/"">64939</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26355004/"">26355004</a>",CREB1,GCCACAAATCAGATTAATTTGGG,H. sapiens - Network accessline 63
cut,S. pyogenes,Hong,"<a href=""/68060/"">68060</a>","<a href=""http://fungalbiolbiotech.biomedcentral.com/articles/10.1186/s40694-015-0015-1"">Fungal Biology and Biotechnology 2015, 2:4</a>",csr-1,GAGTGGGAGGGTCCCGTCCT,N. crassa - Network accessline 63
cut,S. pyogenes,Hong,"<a href=""/68060/"">68060</a>","<a href=""http://fungalbiolbiotech.biomedcentral.com/articles/10.1186/s40694-015-0015-1"">Fungal Biology and Biotechnology 2015, 2:4</a>",csr-1,GAGTGGGAGGGTCCCGTCCT,N. crassa - Network accessline 64
cut,S. pyogenes,Jacks,"<a href=""/59912/"">59912</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25119044/"">25119044</a>",Ctnnb1,AGCTCCTTCCCTGAGTGGCA,M. musculus - Network accessline 64
cut,S. pyogenes,Jacks,"<a href=""/59912/"">59912</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25119044/"">25119044</a>",Ctnnb1,AGCTCCTTCCCTGAGTGGCA,M. musculus - Network accessline 65
cut,S. pyogenes,Jacks,"<a href=""/59911/"">59911</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25119044/"">25119044</a>",Ctnnb1,CTGTGGTGGTGGCACCAGAA,M. musculus - Network accessline 65
cut,S. pyogenes,Jacks,"<a href=""/59911/"">59911</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25119044/"">25119044</a>",Ctnnb1,CTGTGGTGGTGGCACCAGAA,M. musculus - Network accessline 66
interfere,S. pyogenes,Qi,"<a href=""/46917/"">46917</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23849981/"">23849981</a>",CXCR4,GCAGGTAGCAAAGTGACGCCGA,H. sapiens - Network accessline 66
interfere,S. pyogenes,Qi,"<a href=""/46917/"">46917</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23849981/"">23849981</a>",CXCR4,GCAGGTAGCAAAGTGACGCCGA,H. sapiens - Network accessline 67
cut,S. pyogenes,Corn,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26789497/"">26789497</a>",CXCR4,CCTCTTTGTCATCACGCTTC,H. sapiens - Network accessline 67
cut,S. pyogenes,Corn,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26789497/"">26789497</a>",CXCR4,CCTCTTTGTCATCACGCTTC,H. sapiens - Network accessline 68
activate,S. pyogenes,Lu,"<a href=""/64385/"">64385</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,ACAGAGCACATGCATGCCAT,S. cerevisiae - Network accessline 68
activate,S. pyogenes,Lu,"<a href=""/64385/"">64385</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,ACAGAGCACATGCATGCCAT,S. cerevisiae - Network accessline 69
activate,S. pyogenes,Lu,"<a href=""/64387/"">64387</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,ACTAATACTTTCAACATTTT,S. cerevisiae - Network accessline 69
activate,S. pyogenes,Lu,"<a href=""/64387/"">64387</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,ACTAATACTTTCAACATTTT,S. cerevisiae - Network accessline 70
activate,S. pyogenes,Lu,"<a href=""/64382/"">64382</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,ATATCGAATTCCTGCAGCCC,S. cerevisiae - Network accessline 70
activate,S. pyogenes,Lu,"<a href=""/64382/"">64382</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,ATATCGAATTCCTGCAGCCC,S. cerevisiae - Network accessline 71
activate,S. pyogenes,Lu,"<a href=""/64380/"">64380</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,ATATTCTTTCCTTATACATT,S. cerevisiae - Network accessline 71
activate,S. pyogenes,Lu,"<a href=""/64380/"">64380</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,ATATTCTTTCCTTATACATT,S. cerevisiae - Network accessline 72
activate,S. pyogenes,Lu,"<a href=""/64379/"">64379</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,CTAGATATTAAAATGTCTAA,S. cerevisiae - Network accessline 72
activate,S. pyogenes,Lu,"<a href=""/64379/"">64379</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,CTAGATATTAAAATGTCTAA,S. cerevisiae - Network accessline 73
activate,S. pyogenes,Lu,"<a href=""/64388/"">64388</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,GTTGAAAGTATTAGTTAAAG,S. cerevisiae - Network accessline 73
activate,S. pyogenes,Lu,"<a href=""/64388/"">64388</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,GTTGAAAGTATTAGTTAAAG,S. cerevisiae - Network accessline 74
activate,S. pyogenes,Lu,"<a href=""/64381/"">64381</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,TACATACAGTAGGATCCTA,S. cerevisiae - Network accessline 74
activate,S. pyogenes,Lu,"<a href=""/64381/"">64381</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,TACATACAGTAGGATCCTA,S. cerevisiae - Network accessline 75
activate,S. pyogenes,Lu,"<a href=""/64383/"">64383</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,TATAGTAATTTATGCTGCAA,S. cerevisiae - Network accessline 75
activate,S. pyogenes,Lu,"<a href=""/64383/"">64383</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,TATAGTAATTTATGCTGCAA,S. cerevisiae - Network accessline 76
activate,S. pyogenes,Lu,"<a href=""/64384/"">64384</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,TGCAAAGGTCCTAATGTATA,S. cerevisiae - Network accessline 76
activate,S. pyogenes,Lu,"<a href=""/64384/"">64384</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,TGCAAAGGTCCTAATGTATA,S. cerevisiae - Network accessline 77
activate,S. pyogenes,Lu,"<a href=""/64386/"">64386</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,TGTATGTACATACAGTACCC,S. cerevisiae - Network accessline 77
activate,S. pyogenes,Lu,"<a href=""/64386/"">64386</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,TGTATGTACATACAGTACCC,S. cerevisiae - Network accessline 78
activate,S. pyogenes,Lu,"<a href=""/64378/"">64378</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,TTACTGTATGTACATACAGT,S. cerevisiae - Network accessline 78
activate,S. pyogenes,Lu,"<a href=""/64378/"">64378</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23977949/"">23977949</a>",CYC1m promoter,TTACTGTATGTACATACAGT,S. cerevisiae - Network accessline 79
cut,S. pyogenes,Wente,"<a href=""/47932/"">47932</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23918387/"">23918387</a>",ddx19,GGCAACAGATTCGTGGGCCC,D. rerio - Network accessline 79
cut,S. pyogenes,Wente,"<a href=""/47932/"">47932</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23918387/"">23918387</a>",ddx19,GGCAACAGATTCGTGGGCCC,D. rerio - Network accessline 80
cut,S. pyogenes,Sabatini,"<a href=""/70656/"">70656</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",DDX3Y,GCAGTTTAGCGATATTGACA,H. sapiens - Network accessline 80
cut,S. pyogenes,Sabatini,"<a href=""/70656/"">70656</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",DDX3Y,GCAGTTTAGCGATATTGACA,H. sapiens - Network accessline 81
cut,S. pyogenes,Sabatini,"<a href=""/70657/"">70657</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",DDX3Y,TCTTGTTGGGGCTAAAACCA,H. sapiens - Network accessline 81
cut,S. pyogenes,Sabatini,"<a href=""/70657/"">70657</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",DDX3Y,TCTTGTTGGGGCTAAAACCA,H. sapiens - Network accessline 82
cut,S. pyogenes,Church,"<a href=""/41822/"">41822</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23287722/"">23287722</a>",DNMT3A,GAGATGATCGCCCCTTCTTC,H. sapiens - Network accessline 82
cut,S. pyogenes,Church,"<a href=""/41822/"">41822</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23287722/"">23287722</a>",DNMT3A,GAGATGATCGCCCCTTCTTC,H. sapiens - Network accessline 83
cut,S. pyogenes,Church,"<a href=""/41821/"">41821</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23287722/"">23287722</a>",DNMT3A,GCATGATGCGCGGCCCAAGG,H. sapiens - Network accessline 83
cut,S. pyogenes,Church,"<a href=""/41821/"">41821</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23287722/"">23287722</a>",DNMT3A,GCATGATGCGCGGCCCAAGG,H. sapiens - Network accessline 84
cut,S. pyogenes,Church,"<a href=""/41823/"">41823</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23287722/"">23287722</a>",DNMT3B,GAATTACTCACGCCCCAAGG,H. sapiens - Network accessline 84
cut,S. pyogenes,Church,"<a href=""/41823/"">41823</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23287722/"">23287722</a>",DNMT3B,GAATTACTCACGCCCCAAGG,H. sapiens - Network accessline 85
cut,VRER Cas9 mutant,Fire,"<a href=""/71516/"">71516</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26680661/"">26680661</a>",dpy-10,CGCTACCATAGGCACCACG,C. elegans - Network accessline 85
cut,VRER Cas9 mutant,Fire,"<a href=""/71516/"">71516</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26680661/"">26680661</a>",dpy-10,CGCTACCATAGGCACCACG,C. elegans - Network accessline 86
cut,S. pyogenes,Katic,"<a href=""/65630/"">65630</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26044730/"">26044730</a>",dpy-10,GCTACCATAGGCACCACGAG,C.elegans - Network accessline 86
cut,S. pyogenes,Katic,"<a href=""/65630/"">65630</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26044730/"">26044730</a>",dpy-10,GCTACCATAGGCACCACGAG,C.elegans - Network accessline 87
cut,S. pyogenes,Fire,"<a href=""/59933/"">59933</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25161212/"">25161212</a>",dpy-10,GCTACCATAGGCACCACGAG,C. elegans - Network accessline 87
cut,S. pyogenes,Fire,"<a href=""/59933/"">59933</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25161212/"">25161212</a>",dpy-10,GCTACCATAGGCACCACGAG,C. elegans - Network accessline 88
cut,VQR Cas9 variant,Fire,"<a href=""/71479/"">71479</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26680661/"">26680661</a>",dpy-10,TCCGCTACCATAGGCACCA,C. elegans - Network accessline 88
cut,VQR Cas9 variant,Fire,"<a href=""/71479/"">71479</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26680661/"">26680661</a>",dpy-10,TCCGCTACCATAGGCACCA,C. elegans - Network accessline 89
cut,S. pyogenes,Seydoux,"<a href=""/70047/"">70047</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26187122/"">26187122</a>",dpy‐10,GCTACCATAGGCACCACGAG,C. elegans - Network accessline 89
cut,S. pyogenes,Seydoux,"<a href=""/70047/"">70047</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26187122/"">26187122</a>",dpy‐10,GCTACCATAGGCACCACGAG,C. elegans - Network accessline 90
cut,S. pyogenes,Joung,"<a href=""/42242/"">42242</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360964/"">23360964</a>",drd3,GGAAACTACAGCCCAGCGTC,D. rerio - Network accessline 90
cut,S. pyogenes,Joung,"<a href=""/42242/"">42242</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360964/"">23360964</a>",drd3,GGAAACTACAGCCCAGCGTC,D. rerio - Network accessline 91
cut,S. pyogenes,Kim,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26479191/"">26479191</a>",DWD1,TGCATCGTCCAAGCGCACAG,O. sativa - Network accessline 91
cut,S. pyogenes,Kim,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26479191/"">26479191</a>",DWD1,TGCATCGTCCAAGCGCACAG,O. sativa - Network accessline 92
cut,S. pyogenes,Kim,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26479191/"">26479191</a>",DWD1,CTACGACGTCAGGTTCTACC,O. sativa - Network accessline 92
cut,S. pyogenes,Kim,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26479191/"">26479191</a>",DWD1,CTACGACGTCAGGTTCTACC,O. sativa - Network accessline 93
cut,S. pyogenes,Christiaen,"<a href=""/59990/"">59990</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25336740/"">25336740</a>",Ebf,GCTGAGGGTTGGACAACAGG,C. intestinalis - Network accessline 93
cut,S. pyogenes,Christiaen,"<a href=""/59990/"">59990</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25336740/"">25336740</a>",Ebf,GCTGAGGGTTGGACAACAGG,C. intestinalis - Network accessline 94
dCas9-FokI,S. pyogenes,Joung,"<a href=""/60071/"">60071</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24770325/"">24770325</a>",EGFP,"multiple, see article",A. victoria - Network accessline 94
dCas9-FokI,S. pyogenes,Joung,"<a href=""/60071/"">60071</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24770325/"">24770325</a>",EGFP,"multiple, see article",A. victoria - Network accessline 95
cut,S. pyogenes,Zhang,"<a href=""/51764/"">51764</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24336571/"">24336571</a>",EGFP,GAAGTTCGAGGGCGACACCC,A. victoria - Network accessline 95
cut,S. pyogenes,Zhang,"<a href=""/51764/"">51764</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24336571/"">24336571</a>",EGFP,GAAGTTCGAGGGCGACACCC,A. victoria - Network accessline 96
cut,S. pyogenes,Zhang,"<a href=""/51761/"">51761</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24336571/"">24336571</a>",EGFP,GAGCTGGACGGCGACGTAAA,A. victoria - Network accessline 96
cut,S. pyogenes,Zhang,"<a href=""/51761/"">51761</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24336571/"">24336571</a>",EGFP,GAGCTGGACGGCGACGTAAA,A. victoria - Network accessline 97
cut,S. pyogenes,Joung,"<a href=""/47512/"">47512</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23792628/"">23792628</a>",EGFP,GATGCCGTTCTTCTGCTTGT,A. victoria - Network accessline 97
cut,S. pyogenes,Joung,"<a href=""/47512/"">47512</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23792628/"">23792628</a>",EGFP,GATGCCGTTCTTCTGCTTGT,A. victoria - Network accessline 98
cut,S. pyogenes,Zhang,"<a href=""/51763/"">51763</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24336571/"">24336571</a>",EGFP,GGAGCGCACCATCTTCTTCA,A. victoria - Network accessline 98
cut,S. pyogenes,Zhang,"<a href=""/51763/"">51763</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24336571/"">24336571</a>",EGFP,GGAGCGCACCATCTTCTTCA,A. victoria - Network accessline 99
cut,S. pyogenes,Zhang,"<a href=""/51762/"">51762</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24336571/"">24336571</a>",EGFP,GGCCACAAGTTCAGCGTGTC,A. victoria - Network accessline 99
cut,S. pyogenes,Zhang,"<a href=""/51762/"">51762</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24336571/"">24336571</a>",EGFP,GGCCACAAGTTCAGCGTGTC,A. victoria - Network accessline 100
cut,S. pyogenes,Del Bene,"<a href=""/61051/"">61051</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24179142/"">24179142</a>",EGFP,GGCGAGGGCGATGCCACCTA,A. victoria - Network accessline 100
cut,S. pyogenes,Del Bene,"<a href=""/61051/"">61051</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24179142/"">24179142</a>",EGFP,GGCGAGGGCGATGCCACCTA,A. victoria - Network accessline 101
cut,S. pyogenes,Chen,"<a href=""/46760/"">46760</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23918387/"">23918387</a>",EGFP,GGGCACGGGCAGCTTGCCGG,A. victoria - Network accessline 101
cut,S. pyogenes,Chen,"<a href=""/46760/"">46760</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23918387/"">23918387</a>",EGFP,GGGCACGGGCAGCTTGCCGG,A. victoria - Network accessline 102
cut,S. pyogenes,Joung,"<a href=""/47511/"">47511</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23792628/"">23792628</a>",EGFP,GGGCACGGGCAGCTTGCCGG,A. victoria - Network accessline 102
cut,S. pyogenes,Joung,"<a href=""/47511/"">47511</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23792628/"">23792628</a>",EGFP,GGGCACGGGCAGCTTGCCGG,A. victoria - Network accessline 103
cut,S. pyogenes,Zhang,"<a href=""/51760/"">51760</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24336571/"">24336571</a>",EGFP,GGGCGAGGAGCTGTTCACCG,A. victoria - Network accessline 103
cut,S. pyogenes,Zhang,"<a href=""/51760/"">51760</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24336571/"">24336571</a>",EGFP,GGGCGAGGAGCTGTTCACCG,A. victoria - Network accessline 104
cut,S. pyogenes,Zhang,"<a href=""/51765/"">51765</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24336571/"">24336571</a>",EGFP,GGTGAACCGCATCGAGCTGA,A. victoria - Network accessline 104
cut,S. pyogenes,Zhang,"<a href=""/51765/"">51765</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24336571/"">24336571</a>",EGFP,GGTGAACCGCATCGAGCTGA,A. victoria - Network accessline 105
cut,S. pyogenes,Joung,"<a href=""/47513/"">47513</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23792628/"">23792628</a>",EGFP,GGTGGTGCAGATGAACTTCA,A. victoria - Network accessline 105
cut,S. pyogenes,Joung,"<a href=""/47513/"">47513</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23792628/"">23792628</a>",EGFP,GGTGGTGCAGATGAACTTCA,A. victoria - Network accessline 106
cut,S. pyogenes,Goncalves,"<a href=""/58255/"">58255</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24870050/"">24870050</a>",EGFP,TGAAGAAGATGGTGCGCTC,A. victoria - Network accessline 106
cut,S. pyogenes,Goncalves,"<a href=""/58255/"">58255</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24870050/"">24870050</a>",EGFP,TGAAGAAGATGGTGCGCTC,A. victoria - Network accessline 107
cut,S. pyogenes,Moffat,"<a href=""/74187/"">74187</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26627737/"">26627737</a>",EIF3D,TGTAGGTTGCCTCCATGGCC,H. sapiens - Network accessline 107
cut,S. pyogenes,Moffat,"<a href=""/74187/"">74187</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26627737/"">26627737</a>",EIF3D,TGTAGGTTGCCTCCATGGCC,H. sapiens - Network accessline 108
cut,S. pyogenes,Moffat,"<a href=""/74188/"">74188</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26627737/"">26627737</a>",EIF3D,AGACGACCCTGTCATCCGCA,H. sapiens - Network accessline 108
cut,S. pyogenes,Moffat,"<a href=""/74188/"">74188</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26627737/"">26627737</a>",EIF3D,AGACGACCCTGTCATCCGCA,H. sapiens - Network accessline 109
cut,S. pyogenes,Zhang,"<a href=""/42337/"">42337</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23287718/"">23287718</a>",Emx1,,H. sapiens - Network accessline 109
cut,S. pyogenes,Zhang,"<a href=""/42337/"">42337</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23287718/"">23287718</a>",Emx1,,H. sapiens - Network accessline 110
cut,S. pyogenes,Joung,"<a href=""/47508/"">47508</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23792628/"">23792628</a>",EMX1,GAGTCCGAGCAGAAGAAGAA,H. sapiens - Network accessline 110
cut,S. pyogenes,Joung,"<a href=""/47508/"">47508</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23792628/"">23792628</a>",EMX1,GAGTCCGAGCAGAAGAAGAA,H. sapiens - Network accessline 111
cut,S. pyogenes,Corn,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26789497/"">26789497</a>",EMX1,CGATGTCACCTCCAATGACT,H. sapiens - Network accessline 111
cut,S. pyogenes,Corn,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26789497/"">26789497</a>",EMX1,CGATGTCACCTCCAATGACT,H. sapiens - Network accessline 112
cut,S. pyogenes,Chen,"<a href=""/71288/"">71288</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26193878/"">26193878</a>","ETC2, TRY, and CPC",,A. thaliana - Network accessline 112
cut,S. pyogenes,Chen,"<a href=""/71288/"">71288</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26193878/"">26193878</a>","ETC2, TRY, and CPC",,A. thaliana - Network accessline 113
cut,S. pyogenes,Bornhauser,"<a href=""/75166/"">75166</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/27194728/"">27194728</a>",FADD,ACTCCCGCACACGCTCTGTC,H. sapiens - Network accessline 113
cut,S. pyogenes,Bornhauser,"<a href=""/75166/"">75166</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/27194728/"">27194728</a>",FADD,ACTCCCGCACACGCTCTGTC,H. sapiens - Network accessline 114
cut,S. pyogenes,Joung,"<a href=""/47510/"">47510</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23792628/"">23792628</a>",FANCF,GGAATCCCTTCTGCAGCACC,H. sapiens - Network accessline 114
cut,S. pyogenes,Joung,"<a href=""/47510/"">47510</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23792628/"">23792628</a>",FANCF,GGAATCCCTTCTGCAGCACC,H. sapiens - Network accessline 115
cut,S. pyogenes,Seydoux,"<a href=""/65597/"">65597</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25249454/"">25249454</a>",fbf-2,GTAGTCACGGCGATGATTA,C. elegans - Network accessline 115
cut,S. pyogenes,Seydoux,"<a href=""/65597/"">65597</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25249454/"">25249454</a>",fbf-2,GTAGTCACGGCGATGATTA,C. elegans - Network accessline 116
cut,S. pyogenes,Seydoux,"<a href=""/65591/"">65591</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25249454/"">25249454</a>",fbf-2,TAATCATCGCCGTGACTAC,C. elegans - Network accessline 116
cut,S. pyogenes,Seydoux,"<a href=""/65591/"">65591</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25249454/"">25249454</a>",fbf-2,TAATCATCGCCGTGACTAC,C. elegans - Network accessline 117
cut,S. pyogenes,Joung,"<a href=""/42244/"">42244</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360964/"">23360964</a>",fh,GGAGCGAGCGGAGCGGTACA,D. rerio - Network accessline 117
cut,S. pyogenes,Joung,"<a href=""/42244/"">42244</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360964/"">23360964</a>",fh,GGAGCGAGCGGAGCGGTACA,D. rerio - Network accessline 118
cut,S. pyogenes,Joung,"<a href=""/42243/"">42243</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360964/"">23360964</a>",fh,GGAGCGGTACATGGCGACCG,D. rerio - Network accessline 118
cut,S. pyogenes,Joung,"<a href=""/42243/"">42243</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360964/"">23360964</a>",fh,GGAGCGGTACATGGCGACCG,D. rerio - Network accessline 119
tag,S. pyogenes,Mendenhall,"<a href=""/64254/"">64254</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26355004/"">26355004</a>",GABPA,GAAAAGGATAATTGAGCCCCAGG,H. sapiens - Network accessline 119
tag,S. pyogenes,Mendenhall,"<a href=""/64254/"">64254</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26355004/"">26355004</a>",GABPA,GAAAAGGATAATTGAGCCCCAGG,H. sapiens - Network accessline 120
tag,S. pyogenes,Mendenhall,"<a href=""/64255/"">64255</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26355004/"">26355004</a>",GABPA,TTTGGAGTCTCAGAATGTCCTGG,H. sapiens - Network accessline 120
tag,S. pyogenes,Mendenhall,"<a href=""/64255/"">64255</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26355004/"">26355004</a>",GABPA,TTTGGAGTCTCAGAATGTCCTGG,H. sapiens - Network accessline 121
activate,S. pyogenes,Qi,"<a href=""/46916/"">46916</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23849981/"">23849981</a>",GAL4 UAS,GAACGACTAGTTAGGCGTGTA, - Network accessline 121
activate,S. pyogenes,Qi,"<a href=""/46916/"">46916</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23849981/"">23849981</a>",GAL4 UAS,GAACGACTAGTTAGGCGTGTA, - Network accessline 122
activate,S. pyogenes,Qi,"<a href=""/46915/"">46915</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23849981/"">23849981</a>",GAL4 UAS,GTTGGAGCACTGTCCTCCGAACGT, - Network accessline 122
activate,S. pyogenes,Qi,"<a href=""/46915/"">46915</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23849981/"">23849981</a>",GAL4 UAS,GTTGGAGCACTGTCCTCCGAACGT, - Network accessline 123
activate,S. pyogenes,Sato,"<a href=""/64158/"">64158</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",GAL4UAS,TGGGGACAGTACTCCGCTCGAGT, - Network accessline 123
activate,S. pyogenes,Sato,"<a href=""/64158/"">64158</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",GAL4UAS,TGGGGACAGTACTCCGCTCGAGT, - Network accessline 124
activate,S. pyogenes,Sato,"<a href=""/64157/"">64157</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",GAL4UAS,TGGGTCTTCGGAGGACAGTACTC, - Network accessline 124
activate,S. pyogenes,Sato,"<a href=""/64157/"">64157</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",GAL4UAS,TGGGTCTTCGGAGGACAGTACTC, - Network accessline 125
activate,S. pyogenes,Sato,"<a href=""/64159/"">64159</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",GAL4UAS,TGGTCCGTCTAGAAACTCGGTAC, - Network accessline 125
activate,S. pyogenes,Sato,"<a href=""/64159/"">64159</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",GAL4UAS,TGGTCCGTCTAGAAACTCGGTAC, - Network accessline 126
cut,S. pyogenes,Du,"<a href=""/65566/"">65566</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25849248/"">25849248</a>",gfap,GTGCGCAACACATAGCACCA,D. rerio - Network accessline 126
cut,S. pyogenes,Du,"<a href=""/65566/"">65566</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25849248/"">25849248</a>",gfap,GTGCGCAACACATAGCACCA,D. rerio - Network accessline 127
interfere,S. pyogenes,Qi,"<a href=""/46914/"">46914</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23849981/"">23849981</a>",GFP,GAATAGCTCAGAGGCCGAGG,A. victoria - Network accessline 127
interfere,S. pyogenes,Qi,"<a href=""/46914/"">46914</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23849981/"">23849981</a>",GFP,GAATAGCTCAGAGGCCGAGG,A. victoria - Network accessline 128
cut,S. pyogenes,Church,"<a href=""/41820/"">41820</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23287722/"">23287722</a>",GFP,GGAGCGCACCATCTTCTTCA,A. victoria - Network accessline 128
cut,S. pyogenes,Church,"<a href=""/41820/"">41820</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23287722/"">23287722</a>",GFP,GGAGCGCACCATCTTCTTCA,A. victoria - Network accessline 129
cut,S. pyogenes,Church,"<a href=""/41819/"">41819</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23287722/"">23287722</a>",GFP,GTGAACCGCATCGAGCTGAA,A. victoria - Network accessline 129
cut,S. pyogenes,Church,"<a href=""/41819/"">41819</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23287722/"">23287722</a>",GFP,GTGAACCGCATCGAGCTGAA,A. victoria - Network accessline 130
cut,S. pyogenes,"Guigo, Johnson","<a href=""/72619/"">72619</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26493208/"">26493208</a>",GFP,gRNA1: GAGCTGGACGGCGACGTAAA; gRNA2: CAGAACACCCCCATCGGCGA,Synthetic - Network accessline 130
cut,S. pyogenes,"Guigo, Johnson","<a href=""/72619/"">72619</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26493208/"">26493208</a>",GFP,gRNA1: GAGCTGGACGGCGACGTAAA; gRNA2: CAGAACACCCCCATCGGCGA,Synthetic - Network accessline 131
interfere,S. pyogenes,Hudson,"<a href=""/73221/"">73221</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26689101/"">26689101</a>",GFPmut3b,ACCATCTAATTCAACAAGAATT,A. victoria - Network accessline 131
interfere,S. pyogenes,Hudson,"<a href=""/73221/"">73221</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26689101/"">26689101</a>",GFPmut3b,ACCATCTAATTCAACAAGAATT,A. victoria - Network accessline 132
interfere,S. pyogenes,Hudson,"<a href=""/73224/"">73224</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26689101/"">26689101</a>",GFPmut3b,ACCATCTAATTCAACAAGAATT,synthetic - Network accessline 132
interfere,S. pyogenes,Hudson,"<a href=""/73224/"">73224</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26689101/"">26689101</a>",GFPmut3b,ACCATCTAATTCAACAAGAATT,synthetic - Network accessline 133
CRISPR-display,S. pyogenes,Rinn,"<a href=""/68422/"">68422</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26030444/"">26030444</a>",GLuc,GATCTAGATACGACTCACTAT,synthetic - Network accessline 133
CRISPR-display,S. pyogenes,Rinn,"<a href=""/68422/"">68422</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26030444/"">26030444</a>",GLuc,GATCTAGATACGACTCACTAT,synthetic - Network accessline 134
cut,S. pyogenes,Ye,"<a href=""/61833/"">61833</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24424410/"">24424410</a>",gp78,GCCCAGCCTCCGCACCTACA,H. sapiens - Network accessline 134
cut,S. pyogenes,Ye,"<a href=""/61833/"">61833</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24424410/"">24424410</a>",gp78,GCCCAGCCTCCGCACCTACA,H. sapiens - Network accessline 135
cut,S. pyogenes,Joung,"<a href=""/42245/"">42245</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360964/"">23360964</a>",gsk3b,GGGACCTGACCGGCCGCAGG,D. rerio - Network accessline 135
cut,S. pyogenes,Joung,"<a href=""/42245/"">42245</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360964/"">23360964</a>",gsk3b,GGGACCTGACCGGCCGCAGG,D. rerio - Network accessline 136
cut,S. pyogenes,Jin,"<a href=""/64333/"">64333</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25281382/"">25281382</a>",his3,ATTGCGATCTCTTTAAAGGG,S. cerevisiae - Network accessline 136
cut,S. pyogenes,Jin,"<a href=""/64333/"">64333</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25281382/"">25281382</a>",his3,ATTGCGATCTCTTTAAAGGG,S. cerevisiae - Network accessline 137
cut,S. pyogenes,Yan,"<a href=""/69539/"">69539</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26130722/"">26130722</a>",HPRT,GACTGTAAGTGAATTACTT,H. sapiens - Network accessline 137
cut,S. pyogenes,Yan,"<a href=""/69539/"">69539</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26130722/"">26130722</a>",HPRT,GACTGTAAGTGAATTACTT,H. sapiens - Network accessline 138
cut,S. pyogenes,Yamamoto,"<a href=""/58778/"">58778</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24954249/"">24954249</a>",HPRT1,CTGATAAAATCTACAGTCAT,H. sapiens - Network accessline 138
cut,S. pyogenes,Yamamoto,"<a href=""/58778/"">58778</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24954249/"">24954249</a>",HPRT1,CTGATAAAATCTACAGTCAT,H. sapiens - Network accessline 139
cut,S. pyogenes,Yamamoto,"<a href=""/58771/"">58771</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24954249/"">24954249</a>",HPRT1,TTATGCTGAGGATTTGGAAA,H. sapiens - Network accessline 139
cut,S. pyogenes,Yamamoto,"<a href=""/58771/"">58771</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24954249/"">24954249</a>",HPRT1,TTATGCTGAGGATTTGGAAA,H. sapiens - Network accessline 140
activate,S. pyogenes,Sato,"<a href=""/64142/"">64142</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",IL1RN,TGGACGCAGATAAGAACCAGTT,H. sapiens - Network accessline 140
activate,S. pyogenes,Sato,"<a href=""/64142/"">64142</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",IL1RN,TGGACGCAGATAAGAACCAGTT,H. sapiens - Network accessline 141
activate,S. pyogenes,Sato,"<a href=""/64151/"">64151</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",IL1RN,TGGCATCAAGTCAGCCATCAGC,H. sapiens - Network accessline 141
activate,S. pyogenes,Sato,"<a href=""/64151/"">64151</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",IL1RN,TGGCATCAAGTCAGCCATCAGC,H. sapiens - Network accessline 142
activate,S. pyogenes,Sato,"<a href=""/64152/"">64152</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",IL1RN,TGGGAGTCACCCTCCTGGAAAC,H. sapiens - Network accessline 142
activate,S. pyogenes,Sato,"<a href=""/64152/"">64152</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",IL1RN,TGGGAGTCACCCTCCTGGAAAC,H. sapiens - Network accessline 143
activate,S. pyogenes,Sato,"<a href=""/64140/"">64140</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",IL1RN,TGGTGTACTCTCTGAGGTGCTC,H. sapiens - Network accessline 143
activate,S. pyogenes,Sato,"<a href=""/64140/"">64140</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",IL1RN,TGGTGTACTCTCTGAGGTGCTC,H. sapiens - Network accessline 144
cut,S. pyogenes,Xue,"<a href=""/66582/"">66582</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26018130/"">26018130</a>",inverted GFP,GAGCGGCCGCTCGAGTCTAG,A. victoria - Network accessline 144
cut,S. pyogenes,Xue,"<a href=""/66582/"">66582</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26018130/"">26018130</a>",inverted GFP,GAGCGGCCGCTCGAGTCTAG,A. victoria - Network accessline 145
cut,S. pyogenes,Xue,"<a href=""/66583/"">66583</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26018130/"">26018130</a>",inverted GFP,GATATCGAATTCCTGCAGCC,A. victoria - Network accessline 145
cut,S. pyogenes,Xue,"<a href=""/66583/"">66583</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26018130/"">26018130</a>",inverted GFP,GATATCGAATTCCTGCAGCC,A. victoria - Network accessline 146
cut,S. pyogenes,Xue,"<a href=""/66581/"">66581</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26018130/"">26018130</a>",inverted GFP,GTATCGATACCGTCGACCTCG,A. victoria - Network accessline 146
cut,S. pyogenes,Xue,"<a href=""/66581/"">66581</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26018130/"">26018130</a>",inverted GFP,GTATCGATACCGTCGACCTCG,A. victoria - Network accessline 147
cut,S. pyogenes,Xue,"<a href=""/66584/"">66584</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26018130/"">26018130</a>",inverted GFP,GTTGATCCATAACTTCGTAT,A. victoria - Network accessline 147
cut,S. pyogenes,Xue,"<a href=""/66584/"">66584</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26018130/"">26018130</a>",inverted GFP,GTTGATCCATAACTTCGTAT,A. victoria - Network accessline 148
purify,S. pyogenes,Fujii,"<a href=""/61079/"">61079</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23942116/"">23942116</a>",IRF1,CCGGGGGCGCTGGGCTGTCCCGG,H. sapiens - Network accessline 148
purify,S. pyogenes,Fujii,"<a href=""/61079/"">61079</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23942116/"">23942116</a>",IRF1,CCGGGGGCGCTGGGCTGTCCCGG,H. sapiens - Network accessline 149
purify,S. pyogenes,Fujii,"<a href=""/61080/"">61080</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25051498/"">25051498</a>",IRF1,GCGCTTACACTTTAGGAGACACTC,H. sapiens - Network accessline 149
purify,S. pyogenes,Fujii,"<a href=""/61080/"">61080</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25051498/"">25051498</a>",IRF1,GCGCTTACACTTTAGGAGACACTC,H. sapiens - Network accessline 150
cut,S. pyogenes,Sabatini,"<a href=""/70679/"">70679</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",JAK2 amplicon ,GGTTTAATGGAAGAGAAGGG,H. sapiens - Network accessline 150
cut,S. pyogenes,Sabatini,"<a href=""/70679/"">70679</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26472758/"">26472758</a>",JAK2 amplicon ,GGTTTAATGGAAGAGAAGGG,H. sapiens - Network accessline 151
cut,S. pyogenes,Seydoux,"<a href=""/66085/"">66085</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25249454/"">25249454</a>",K08F4.2,AATCACTCCCTGTTTGTGT,C. elegans - Network accessline 151
cut,S. pyogenes,Seydoux,"<a href=""/66085/"">66085</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25249454/"">25249454</a>",K08F4.2,AATCACTCCCTGTTTGTGT,C. elegans - Network accessline 152
cut,S. pyogenes,Seydoux,"<a href=""/66095/"">66095</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25249454/"">25249454</a>",K08F4.2,CACGAGGTGGTATGCGCAG,C. elegans - Network accessline 152
cut,S. pyogenes,Seydoux,"<a href=""/66095/"">66095</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25249454/"">25249454</a>",K08F4.2,CACGAGGTGGTATGCGCAG,C. elegans - Network accessline 153
cut,S. pyogenes,Seydoux,"<a href=""/66092/"">66092</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25249454/"">25249454</a>",K08F4.2,CGCAGCGGTTTCCAAAATG,C. elegans - Network accessline 153
cut,S. pyogenes,Seydoux,"<a href=""/66092/"">66092</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25249454/"">25249454</a>",K08F4.2,CGCAGCGGTTTCCAAAATG,C. elegans - Network accessline 154
cut,S. pyogenes,Seydoux,"<a href=""/66087/"">66087</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25249454/"">25249454</a>",K08F4.2,GCCTTAACCCAGAATAAGA,C. elegans - Network accessline 154
cut,S. pyogenes,Seydoux,"<a href=""/66087/"">66087</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25249454/"">25249454</a>",K08F4.2,GCCTTAACCCAGAATAAGA,C. elegans - Network accessline 155
cut,S. pyogenes,Seydoux,"<a href=""/66089/"">66089</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25249454/"">25249454</a>",K08F4.2,TATTAAATGCAGATAACCT,C. elegans - Network accessline 155
cut,S. pyogenes,Seydoux,"<a href=""/66089/"">66089</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25249454/"">25249454</a>",K08F4.2,TATTAAATGCAGATAACCT,C. elegans - Network accessline 156
cut,S. pyogenes,Wolfe,"<a href=""/69238/"">69238</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26480473/"">26480473</a>",KANK3,GCATGGGTGATGTCAATGCC,H. sapiens - Network accessline 156
cut,S. pyogenes,Wolfe,"<a href=""/69238/"">69238</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26480473/"">26480473</a>",KANK3,GCATGGGTGATGTCAATGCC,H. sapiens - Network accessline 157
cut,S. pyogenes,Mashimo,"<a href=""/60969/"">60969</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24967838/"">24967838</a>",Kit-1,CATCTGTGCGGCCGTTGGCT,R. norvegicus - Network accessline 157
cut,S. pyogenes,Mashimo,"<a href=""/60969/"">60969</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24967838/"">24967838</a>",Kit-1,CATCTGTGCGGCCGTTGGCT,R. norvegicus - Network accessline 158
cut,S. pyogenes,Mashimo,"<a href=""/60970/"">60970</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24967838/"">24967838</a>",Kit-2,CTAACGTTCCAGCGCTCGTT,R. norvegicus - Network accessline 158
cut,S. pyogenes,Mashimo,"<a href=""/60970/"">60970</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24967838/"">24967838</a>",Kit-2,CTAACGTTCCAGCGCTCGTT,R. norvegicus - Network accessline 159
cut,S. pyogenes,Mashimo,"<a href=""/60971/"">60971</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24967838/"">24967838</a>",Kit-2,GTCAAGATGTCATCTTACGG,R. norvegicus - Network accessline 159
cut,S. pyogenes,Mashimo,"<a href=""/60971/"">60971</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24967838/"">24967838</a>",Kit-2,GTCAAGATGTCATCTTACGG,R. norvegicus - Network accessline 160
cut,S. pyogenes,Calarco,"<a href=""/46170/"">46170</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23817069/"">23817069</a>",klp-12,GATCCACAAGTTACAATTGG,C. elegans - Network accessline 160
cut,S. pyogenes,Calarco,"<a href=""/46170/"">46170</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23817069/"">23817069</a>",klp-12,GATCCACAAGTTACAATTGG,C. elegans - Network accessline 161
cut,S. pyogenes,Zhang,"<a href=""/60224/"">60224</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25263330/"">25263330</a>","Kras, p53, and Lkb1","multiple, see article",M. musculus - Network accessline 161
cut,S. pyogenes,Zhang,"<a href=""/60224/"">60224</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25263330/"">25263330</a>","Kras, p53, and Lkb1","multiple, see article",M. musculus - Network accessline 162
cut,S. pyogenes,Zhang,"<a href=""/60225/"">60225</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25263330/"">25263330</a>",LacZ,TGCGAATACGCCCACGCGAT,E. coli - Network accessline 162
cut,S. pyogenes,Zhang,"<a href=""/60225/"">60225</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25263330/"">25263330</a>",LacZ,TGCGAATACGCCCACGCGAT,E. coli - Network accessline 163
cut,S. pyogenes,Moffat,"<a href=""/74179/"">74179</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26627737/"">26627737</a>",LacZ,CCCGAATCTCTATCGTGCGG,E.coli - Network accessline 163
cut,S. pyogenes,Moffat,"<a href=""/74179/"">74179</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26627737/"">26627737</a>",LacZ,CCCGAATCTCTATCGTGCGG,E.coli - Network accessline 164
cut,S. pyogenes,Patron,"<a href=""/68256/"">68256</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26616834/"">26616834</a>",lC.GA4a,GTTGAGAGGGGAGCCGGTGA,B. oleracea - Network accessline 164
cut,S. pyogenes,Patron,"<a href=""/68256/"">68256</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26616834/"">26616834</a>",lC.GA4a,GTTGAGAGGGGAGCCGGTGA,B. oleracea - Network accessline 165
cut,S. pyogenes,Patron,"<a href=""/68255/"">68255</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26616834/"">26616834</a>",lC.GA4a,GTTTTCACTTGCGGCCGGAG,B. oleracea - Network accessline 165
cut,S. pyogenes,Patron,"<a href=""/68255/"">68255</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26616834/"">26616834</a>",lC.GA4a,GTTTTCACTTGCGGCCGGAG,B. oleracea - Network accessline 166
cut,S. pyogenes,Jin,"<a href=""/64332/"">64332</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25281382/"">25281382</a>",leu2,AATCACAGCCGAAGCCATTA,S. cerevisiae - Network accessline 166
cut,S. pyogenes,Jin,"<a href=""/64332/"">64332</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25281382/"">25281382</a>",leu2,AATCACAGCCGAAGCCATTA,S. cerevisiae - Network accessline 167
cut,S. pyogenes,Winslow,"<a href=""/66894/"">66894</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26178787/"">26178787</a>",Lkb1,GTGGTGGGCCGCAGTCACAA,M. musculus - Network accessline 167
cut,S. pyogenes,Winslow,"<a href=""/66894/"">66894</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26178787/"">26178787</a>",Lkb1,GTGGTGGGCCGCAGTCACAA,M. musculus - Network accessline 168
cut,S. pyogenes,Xue,"<a href=""/66586/"">66586</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26018130/"">26018130</a>",Lox-STOP-Lox,GCGTATAGCATACATTATACG,synthetic - Network accessline 168
cut,S. pyogenes,Xue,"<a href=""/66586/"">66586</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26018130/"">26018130</a>",Lox-STOP-Lox,GCGTATAGCATACATTATACG,synthetic - Network accessline 169
cut,S. pyogenes,Xue,"<a href=""/66585/"">66585</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26018130/"">26018130</a>",Lox-STOP-Lox,GCGTATAGTACACATTATACG,synthetic - Network accessline 169
cut,S. pyogenes,Xue,"<a href=""/66585/"">66585</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26018130/"">26018130</a>",Lox-STOP-Lox,GCGTATAGTACACATTATACG,synthetic - Network accessline 170
cut,S. pyogenes,Wolfe,"<a href=""/69237/"">69237</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26480473/"">26480473</a>",LRTM2,GCTGGCGGAAGACAGAGTGC,H. sapiens - Network accessline 170
cut,S. pyogenes,Wolfe,"<a href=""/69237/"">69237</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26480473/"">26480473</a>",LRTM2,GCTGGCGGAAGACAGAGTGC,H. sapiens - Network accessline 171
cut,S. pyogenes,Moffat,"<a href=""/74190/"">74190</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26627737/"">26627737</a>",Luciferase,ACAACTTTACCGACCGCGCC, - Network accessline 171
cut,S. pyogenes,Moffat,"<a href=""/74190/"">74190</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26627737/"">26627737</a>",Luciferase,ACAACTTTACCGACCGCGCC, - Network accessline 172
cut,S. pyogenes,Cate,"<a href=""/60847/"">60847</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25139909/"">25139909</a>",LYP1,CATAATAACGTCCAATAAAT,S. cerevisiae - Network accessline 172
cut,S. pyogenes,Cate,"<a href=""/60847/"">60847</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25139909/"">25139909</a>",LYP1,CATAATAACGTCCAATAAAT,S. cerevisiae - Network accessline 173
cut,S. pyogenes,"Guigo, Johnson","<a href=""/72620/"">72620</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26493208/"">26493208</a>",Malat1,gRNA1: GAACCGGTGGGGCTGCGTCA; gRNA2: GGCAGGAGAGGCCAGTTGCG,H. sapiens - Network accessline 173
cut,S. pyogenes,"Guigo, Johnson","<a href=""/72620/"">72620</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26493208/"">26493208</a>",Malat1,gRNA1: GAACCGGTGGGGCTGCGTCA; gRNA2: GGCAGGAGAGGCCAGTTGCG,H. sapiens - Network accessline 174
cut,S. pyogenes,"Guigo, Johnson","<a href=""/72621/"">72621</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26493208/"">26493208</a>",Malat1,gRNA1: GCAACTTCCATTTTCAGTCT; gRNA2: GGAAGCCTCAGCTCGCCTGA,H. sapiens - Network accessline 174
cut,S. pyogenes,"Guigo, Johnson","<a href=""/72621/"">72621</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26493208/"">26493208</a>",Malat1,gRNA1: GCAACTTCCATTTTCAGTCT; gRNA2: GGAAGCCTCAGCTCGCCTGA,H. sapiens - Network accessline 175
cut,S. pyogenes,"Guigo, Johnson","<a href=""/72622/"">72622</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26493208/"">26493208</a>",Malat1,gRNA1:GCTGGGGCTCAGTTGCGTAA; gRNA2:AGGTTTCTAAAACATGACGG,H. sapiens - Network accessline 175
cut,S. pyogenes,"Guigo, Johnson","<a href=""/72622/"">72622</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26493208/"">26493208</a>",Malat1,gRNA1:GCTGGGGCTCAGTTGCGTAA; gRNA2:AGGTTTCTAAAACATGACGG,H. sapiens - Network accessline 176
cut,S. pyogenes,"Guigo, Johnson","<a href=""/72623/"">72623</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26493208/"">26493208</a>",Malat1,gRNA1:GTTGAGATGAAGCTTCTTCA; gRNA2:TCAACCGTCCCTGCAAGGCT,H. sapiens - Network accessline 176
cut,S. pyogenes,"Guigo, Johnson","<a href=""/72623/"">72623</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26493208/"">26493208</a>",Malat1,gRNA1:GTTGAGATGAAGCTTCTTCA; gRNA2:TCAACCGTCCCTGCAAGGCT,H. sapiens - Network accessline 177
cut,S. pyogenes,"Guigo, Johnson","<a href=""/72624/"">72624</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26493208/"">26493208</a>",Malat1,gRNA1: GAAACCTCGTGTAGCTATCA; gRNA2: AATGTGAAGGACTTTCGTAA,H. sapiens - Network accessline 177
cut,S. pyogenes,"Guigo, Johnson","<a href=""/72624/"">72624</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26493208/"">26493208</a>",Malat1,gRNA1: GAAACCTCGTGTAGCTATCA; gRNA2: AATGTGAAGGACTTTCGTAA,H. sapiens - Network accessline 178
nick,S. pyogenes,Hanna,"<a href=""/61514/"">61514</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25569111/"">25569111</a>",Mettl14,GCCGCTCCCGGATCTCCTGC,M. musculus - Network accessline 178
nick,S. pyogenes,Hanna,"<a href=""/61514/"">61514</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25569111/"">25569111</a>",Mettl14,GCCGCTCCCGGATCTCCTGC,M. musculus - Network accessline 179
nick,S. pyogenes,Hanna,"<a href=""/61515/"">61515</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25569111/"">25569111</a>",Mettl14,GCGGCAGCTCCTAGCTCAGC,M. musculus - Network accessline 179
nick,S. pyogenes,Hanna,"<a href=""/61515/"">61515</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25569111/"">25569111</a>",Mettl14,GCGGCAGCTCCTAGCTCAGC,M. musculus - Network accessline 180
cut,S. pyogenes,Zhao,"<a href=""/69801/"">69801</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26293209/"">26293209</a>",mitf,CTTTCGGATATAATCCACGG,S. scrofa - Network accessline 180
cut,S. pyogenes,Zhao,"<a href=""/69801/"">69801</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26293209/"">26293209</a>",mitf,CTTTCGGATATAATCCACGG,S. scrofa - Network accessline 181
cut,S. pyogenes,Chen,"<a href=""/47931/"">47931</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23918387/"">23918387</a>",mitfa,GGTCTCTCGCAGGATGTTGC,D. rerio - Network accessline 181
cut,S. pyogenes,Chen,"<a href=""/47931/"">47931</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23918387/"">23918387</a>",mitfa,GGTCTCTCGCAGGATGTTGC,D. rerio - Network accessline 182
cut,S. pyogenes,Bleris,"<a href=""/62715/"">62715</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25527740/"">25527740</a>",mKate,ATGAGAATCAAGGCGGTCGA,synthetic - Network accessline 182
cut,S. pyogenes,Bleris,"<a href=""/62715/"">62715</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25527740/"">25527740</a>",mKate,ATGAGAATCAAGGCGGTCGA,synthetic - Network accessline 183
cut,S. pyogenes,Bornhauser,"<a href=""/75167/"">75167</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/27194728/"">27194728</a>",MLKL,ATCCCCGTGGATTCTGCTAA,H. sapiens - Network accessline 183
cut,S. pyogenes,Bornhauser,"<a href=""/75167/"">75167</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/27194728/"">27194728</a>",MLKL,ATCCCCGTGGATTCTGCTAA,H. sapiens - Network accessline 184
cut,S. pyogenes,Katsanis,"<a href=""/72890/"">72890</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26429889/"">26429889</a>",mmp21,CGGAGCTGATCACTGACA,D. rerio - Network accessline 184
cut,S. pyogenes,Katsanis,"<a href=""/72890/"">72890</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26429889/"">26429889</a>",mmp21,CGGAGCTGATCACTGACA,D. rerio - Network accessline 185
visualize,S. pyogenes,Qi,"<a href=""/51025/"">51025</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24360272/"">24360272</a>",MUC4,GTGGCGTGACCTGTGGATGCTG,H. sapiens - Network accessline 185
visualize,S. pyogenes,Qi,"<a href=""/51025/"">51025</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24360272/"">24360272</a>",MUC4,GTGGCGTGACCTGTGGATGCTG,H. sapiens - Network accessline 186
activate,S. pyogenes,Sato,"<a href=""/64138/"">64138</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",MYOD1,TGGCCTCCCTCCCTGCCCGGTAG,H. sapiens - Network accessline 186
activate,S. pyogenes,Sato,"<a href=""/64138/"">64138</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",MYOD1,TGGCCTCCCTCCCTGCCCGGTAG,H. sapiens - Network accessline 187
activate,S. pyogenes,Sato,"<a href=""/64139/"">64139</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",MYOD1,TGGGAGGTTTGGAAAGGGCGTGC,H. sapiens - Network accessline 187
activate,S. pyogenes,Sato,"<a href=""/64139/"">64139</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",MYOD1,TGGGAGGTTTGGAAAGGGCGTGC,H. sapiens - Network accessline 188
activate,S. pyogenes,Sato,"<a href=""/64136/"">64136</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",MYOD1,TGGGCCTGGGCTCCGGGGCGTTT,H. sapiens - Network accessline 188
activate,S. pyogenes,Sato,"<a href=""/64136/"">64136</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",MYOD1,TGGGCCTGGGCTCCGGGGCGTTT,H. sapiens - Network accessline 189
activate,S. pyogenes,Sato,"<a href=""/64137/"">64137</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",MYOD1,TGGGGGCCCCTGCGGCCACCCCG,H. sapiens - Network accessline 189
activate,S. pyogenes,Sato,"<a href=""/64137/"">64137</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",MYOD1,TGGGGGCCCCTGCGGCCACCCCG,H. sapiens - Network accessline 190
activate,S. pyogenes,Sato,"<a href=""/64156/"">64156</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",NANOG,TGGCATATTCCTGATTTAAAAGT,H. sapiens - Network accessline 190
activate,S. pyogenes,Sato,"<a href=""/64156/"">64156</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",NANOG,TGGCATATTCCTGATTTAAAAGT,H. sapiens - Network accessline 191
activate,S. pyogenes,Sato,"<a href=""/64153/"">64153</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",NANOG,TGGCGCCAGGAGGGGTGGGTCTA,H. sapiens - Network accessline 191
activate,S. pyogenes,Sato,"<a href=""/64153/"">64153</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",NANOG,TGGCGCCAGGAGGGGTGGGTCTA,H. sapiens - Network accessline 192
activate,S. pyogenes,Sato,"<a href=""/64154/"">64154</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",NANOG,TGGGCCTTGGTGAGACTGGTAGA,H. sapiens - Network accessline 192
activate,S. pyogenes,Sato,"<a href=""/64154/"">64154</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",NANOG,TGGGCCTTGGTGAGACTGGTAGA,H. sapiens - Network accessline 193
activate,S. pyogenes,Sato,"<a href=""/64155/"">64155</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",NANOG,TGGTGTCTTCAGGTTCTGTTGCT,H. sapiens - Network accessline 193
activate,S. pyogenes,Sato,"<a href=""/64155/"">64155</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",NANOG,TGGTGTCTTCAGGTTCTGTTGCT,H. sapiens - Network accessline 194
interfere,S. pyogenes,Lu,"<a href=""/68897/"">68897</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26918244/"">26918244</a>",NanoLuc,AGCTTACGCCACCCTGTTCC,synthetic - Network accessline 194
interfere,S. pyogenes,Lu,"<a href=""/68897/"">68897</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26918244/"">26918244</a>",NanoLuc,AGCTTACGCCACCCTGTTCC,synthetic - Network accessline 195
interfere,S. pyogenes,Lu,"<a href=""/68898/"">68898</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26918244/"">26918244</a>",NanoLuc,GACAGAACGATGCGCTGAAT,synthetic - Network accessline 195
interfere,S. pyogenes,Lu,"<a href=""/68898/"">68898</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26918244/"">26918244</a>",NanoLuc,GACAGAACGATGCGCTGAAT,synthetic - Network accessline 196
interfere,S. pyogenes,Lu,"<a href=""/68895/"">68895</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26918244/"">26918244</a>",NanoLuc,TCACGCTCACACCCAGGTTC,synthetic - Network accessline 196
interfere,S. pyogenes,Lu,"<a href=""/68895/"">68895</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26918244/"">26918244</a>",NanoLuc,TCACGCTCACACCCAGGTTC,synthetic - Network accessline 197
interfere,S. pyogenes,Lu,"<a href=""/68896/"">68896</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26918244/"">26918244</a>",NanoLuc,TTGATCCAAATTATAACCCG,synthetic - Network accessline 197
interfere,S. pyogenes,Lu,"<a href=""/68896/"">68896</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26918244/"">26918244</a>",NanoLuc,TTGATCCAAATTATAACCCG,synthetic - Network accessline 198
cut,S. pyogenes,Lu,"<a href=""/61270/"">61270</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25240928/"">25240928</a>",NDM-1,GGGCAGTCGCTTCCAACGGTTTGATCGTCA, - Network accessline 198
cut,S. pyogenes,Lu,"<a href=""/61270/"">61270</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25240928/"">25240928</a>",NDM-1,GGGCAGTCGCTTCCAACGGTTTGATCGTCA, - Network accessline 199
cut,S. pyogenes,Cepko,"<a href=""/69992/"">69992</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25155555/"">25155555</a>",near LoxP sites,CGAAGTTATATTAAGGGTTC,synthetic - Network accessline 199
cut,S. pyogenes,Cepko,"<a href=""/69992/"">69992</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25155555/"">25155555</a>",near LoxP sites,CGAAGTTATATTAAGGGTTC,synthetic - Network accessline 200
interfere,S. pyogenes,Lu,"<a href=""/68893/"">68893</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26918244/"">26918244</a>",negative control,CTGGAATGAATTGGCCTATG, - Network accessline 200
interfere,S. pyogenes,Lu,"<a href=""/68893/"">68893</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26918244/"">26918244</a>",negative control,CTGGAATGAATTGGCCTATG, - Network accessline 201
cut,S. pyogenes,Winslow,"<a href=""/66895/"">66895</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26178787/"">26178787</a>",negative control,GCGAGGTATTCGGCTCCGCG,M. musculus - Network accessline 201
cut,S. pyogenes,Winslow,"<a href=""/66895/"">66895</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26178787/"">26178787</a>",negative control,GCGAGGTATTCGGCTCCGCG,M. musculus - Network accessline 202
cut,S. pyogenes,Christiaen,"<a href=""/60006/"">60006</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25336740/"">25336740</a>",negative control,GCTTTGCTACGATCTACATT,C. intestinalis - Network accessline 202
cut,S. pyogenes,Christiaen,"<a href=""/60006/"">60006</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25336740/"">25336740</a>",negative control,GCTTTGCTACGATCTACATT,C. intestinalis - Network accessline 203
cut,S. pyogenes,Bleris,"<a href=""/64955/"">64955</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25527740/"">25527740</a>",negative control,GTCAAGGCACTCTTGCCTA,synthetic - Network accessline 203
cut,S. pyogenes,Bleris,"<a href=""/64955/"">64955</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25527740/"">25527740</a>",negative control,GTCAAGGCACTCTTGCCTA,synthetic - Network accessline 204
,S. pyogenes,Wolfe,"<a href=""/50927/"">50927</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24346702/"">24346702</a>",negative control,GTTCCGCGTTACATAACTTA,H. sapiens - Network accessline 204
,S. pyogenes,Wolfe,"<a href=""/50927/"">50927</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24346702/"">24346702</a>",negative control,GTTCCGCGTTACATAACTTA,H. sapiens - Network accessline 205
cut,S. pyogenes,Winslow,"<a href=""/67594/"">67594</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26178787/"">26178787</a>",Neomycin,TCATGGCTGATGCAATGCGG, - Network accessline 205
cut,S. pyogenes,Winslow,"<a href=""/67594/"">67594</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26178787/"">26178787</a>",Neomycin,TCATGGCTGATGCAATGCGG, - Network accessline 206
cut,S. pyogenes,Zhang,"<a href=""/60227/"">60227</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25263330/"">25263330</a>",NeuN,TCCGGTTCAGGGACCCCGAC,M. musculus - Network accessline 206
cut,S. pyogenes,Zhang,"<a href=""/60227/"">60227</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25263330/"">25263330</a>",NeuN,TCCGGTTCAGGGACCCCGAC,M. musculus - Network accessline 207
activate,S. pyogenes,Sato,"<a href=""/64161/"">64161</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",Neurog2,GTCTCTATCACTGATAGGGA,H. sapiens - Network accessline 207
activate,S. pyogenes,Sato,"<a href=""/64161/"">64161</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",Neurog2,GTCTCTATCACTGATAGGGA,H. sapiens - Network accessline 208
activate,S. pyogenes,Sato,"<a href=""/64160/"">64160</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",Neurog2,GTGAATGATGATAATACGAT,H. sapiens - Network accessline 208
activate,S. pyogenes,Sato,"<a href=""/64160/"">64160</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25619936/"">25619936</a>",Neurog2,GTGAATGATGATAATACGAT,H. sapiens - Network accessline 209
methylation,S. pyogenes,Zoldoš,"<a href=""/71830/"">71830</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26969735/"">26969735</a>",Non-targeting,GTAGGCGCGCCGCTCTCTAC,H. sapiens - Network accessline 209
methylation,S. pyogenes,Zoldoš,"<a href=""/71830/"">71830</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26969735/"">26969735</a>",Non-targeting,GTAGGCGCGCCGCTCTCTAC,H. sapiens - Network accessline 210
cut,S. pyogenes,Huangfu,"<a href=""/66989/"">66989</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26028531/"">26028531</a>",OCT4,CTCCCATGCATTCAAACTG,H. sapiens - Network accessline 210
cut,S. pyogenes,Huangfu,"<a href=""/66989/"">66989</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26028531/"">26028531</a>",OCT4,CTCCCATGCATTCAAACTG,H. sapiens - Network accessline 211
activate,S. pyogenes,Otonkoski,"<a href=""/69537/"">69537</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26352799/"">26352799</a>",OCT4,"multiple, see article",H. sapiens - Network accessline 211
activate,S. pyogenes,Otonkoski,"<a href=""/69537/"">69537</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26352799/"">26352799</a>",OCT4,"multiple, see article",H. sapiens - Network accessline 212
cut,N. meningitidis,Thomson,"<a href=""/47870/"">47870</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23940360/"">23940360</a>",OCT4,GTTGTAGCTCCCTTTCTCATTTCG,H. sapiens - Network accessline 212
cut,N. meningitidis,Thomson,"<a href=""/47870/"">47870</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23940360/"">23940360</a>",OCT4,GTTGTAGCTCCCTTTCTCATTTCG,H. sapiens - Network accessline 213
interfere,S. pyogenes,Wolfe,"<a href=""/50922/"">50922</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24346702/"">24346702</a>",Oct4A (POU5F1),GGGGCGCCAGTTGTGTCTCC,H. sapiens - Network accessline 213
interfere,S. pyogenes,Wolfe,"<a href=""/50922/"">50922</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24346702/"">24346702</a>",Oct4A (POU5F1),GGGGCGCCAGTTGTGTCTCC,H. sapiens - Network accessline 214
interfere,S. pyogenes,Wolfe,"<a href=""/50921/"">50921</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24346702/"">24346702</a>",Oct4A (POU5F1),GTGGGACTGGGGAGGGAGAG,H. sapiens - Network accessline 214
interfere,S. pyogenes,Wolfe,"<a href=""/50921/"">50921</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24346702/"">24346702</a>",Oct4A (POU5F1),GTGGGACTGGGGAGGGAGAG,H. sapiens - Network accessline 215
cut,S. pyogenes,Kim,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26479191/"">26479191</a>",P450,CATATAGTTGGGTCATGGCA,O. sativa - Network accessline 215
cut,S. pyogenes,Kim,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26479191/"">26479191</a>",P450,CATATAGTTGGGTCATGGCA,O. sativa - Network accessline 216
cut,S. pyogenes,Sherwood,"<a href=""/71483/"">71483</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26527385/"">26527385</a>",Pal1,GCTCTGTGACTAGTCACAGAG,synthetic - Network accessline 216
cut,S. pyogenes,Sherwood,"<a href=""/71483/"">71483</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26527385/"">26527385</a>",Pal1,GCTCTGTGACTAGTCACAGAG,synthetic - Network accessline 217
cut,S. pyogenes,Sherwood,"<a href=""/71484/"">71484</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26527385/"">26527385</a>",Pal7,GGCTTAGTACTAGTACTAAGC,synthetic - Network accessline 217
cut,S. pyogenes,Sherwood,"<a href=""/71484/"">71484</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26527385/"">26527385</a>",Pal7,GGCTTAGTACTAGTACTAAGC,synthetic - Network accessline 218
cut,S. pyogenes,Zhang,"<a href=""/68466/"">68466</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26145478/"">26145478</a>",PAX6,GATACTGGAGAAGCAGGGGC,H. sapiens - Network accessline 218
cut,S. pyogenes,Zhang,"<a href=""/68466/"">68466</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26145478/"">26145478</a>",PAX6,GATACTGGAGAAGCAGGGGC,H. sapiens - Network accessline 219
cut,S. pyogenes,Zhang,"<a href=""/68465/"">68465</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26145478/"">26145478</a>",PAX6,GTAGATTTTGTATGCACTGC,H. sapiens - Network accessline 219
cut,S. pyogenes,Zhang,"<a href=""/68465/"">68465</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26145478/"">26145478</a>",PAX6,GTAGATTTTGTATGCACTGC,H. sapiens - Network accessline 220
interfere,S. pyogenes,Lu,"<a href=""/68900/"">68900</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26918244/"">26918244</a>",PcfiA,AAACAAAACCTCATCAGGCA, - Network accessline 220
interfere,S. pyogenes,Lu,"<a href=""/68900/"">68900</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26918244/"">26918244</a>",PcfiA,AAACAAAACCTCATCAGGCA, - Network accessline 221
interfere,S. pyogenes,Lu,"<a href=""/68899/"">68899</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26918244/"">26918244</a>",PcfiA,GAAGCTCACTCCTTAGCACG, - Network accessline 221
interfere,S. pyogenes,Lu,"<a href=""/68899/"">68899</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26918244/"">26918244</a>",PcfiA,GAAGCTCACTCCTTAGCACG, - Network accessline 222
interfere,S. pyogenes,Lu,"<a href=""/74068/"">74068</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26829286/"">26829286</a>",pck,AGGGCGAGGCGCCGACCAAGAATA,C. glutamicum - Network accessline 222
interfere,S. pyogenes,Lu,"<a href=""/74068/"">74068</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26829286/"">26829286</a>",pck,AGGGCGAGGCGCCGACCAAGAATA,C. glutamicum - Network accessline 223
interfere,S. pyogenes,Lu,"<a href=""/74069/"">74069</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26829286/"">26829286</a>",pck,TCCAGTTCAGCAGTTCCTTATTCT,C. glutamicum - Network accessline 223
interfere,S. pyogenes,Lu,"<a href=""/74069/"">74069</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26829286/"">26829286</a>",pck,TCCAGTTCAGCAGTTCCTTATTCT,C. glutamicum - Network accessline 224
cut,S. pyogenes,Kamoun,"<a href=""/46966/"">46966</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23929340/"">23929340</a>",PDS,GCCGTTAATTTGAGAGTCCA,N. benthamiana - Network accessline 224
cut,S. pyogenes,Kamoun,"<a href=""/46966/"">46966</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23929340/"">23929340</a>",PDS,GCCGTTAATTTGAGAGTCCA,N. benthamiana - Network accessline 225
cut,S. pyogenes,Kamoun,"<a href=""/49772/"">49772</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24112467/"">24112467</a>",PDS1,"multiple, see article",N. benthamiana - Network accessline 225
cut,S. pyogenes,Kamoun,"<a href=""/49772/"">49772</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24112467/"">24112467</a>",PDS1,"multiple, see article",N. benthamiana - Network accessline 226
cut,S. pyogenes,Sheen,"<a href=""/52255/"">52255</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23929339/"">23929339</a>",PDS3,GGACTTTTGCCAGCCATGGT,A. thaliana - Network accessline 226
cut,S. pyogenes,Sheen,"<a href=""/52255/"">52255</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23929339/"">23929339</a>",PDS3,GGACTTTTGCCAGCCATGGT,A. thaliana - Network accessline 227
interfere,S. pyogenes,Lu,"<a href=""/74066/"">74066</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26829286/"">26829286</a>",pgi,TGACCGATCATTACTCAAACTTCC,C. glutamicum - Network accessline 227
interfere,S. pyogenes,Lu,"<a href=""/74066/"">74066</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26829286/"">26829286</a>",pgi,TGACCGATCATTACTCAAACTTCC,C. glutamicum - Network accessline 228
interfere,S. pyogenes,Lu,"<a href=""/74067/"">74067</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26829286/"">26829286</a>",pgi,TTGCCTGGAAGTTTGAGTAATGAT,C. glutamicum - Network accessline 228
interfere,S. pyogenes,Lu,"<a href=""/74067/"">74067</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26829286/"">26829286</a>",pgi,TTGCCTGGAAGTTTGAGTAATGAT,C. glutamicum - Network accessline 229
activate,S. pyogenes,Gersbach,"<a href=""/60719/"">60719</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25664691/"">25664691</a>",pGL3-Basic-8x-gRNA-eGFP reporter,AAAGGTCGAGAAACTGCAAA,synthetic - Network accessline 229
activate,S. pyogenes,Gersbach,"<a href=""/60719/"">60719</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25664691/"">25664691</a>",pGL3-Basic-8x-gRNA-eGFP reporter,AAAGGTCGAGAAACTGCAAA,synthetic - Network accessline 230
cut,S. pyogenes,Ward,"<a href=""/61252/"">61252</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25491644/"">25491644</a>",pha-1,ATGAATAACTTGATGAACAT,C. elegans - Network accessline 230
cut,S. pyogenes,Ward,"<a href=""/61252/"">61252</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25491644/"">25491644</a>",pha-1,ATGAATAACTTGATGAACAT,C. elegans - Network accessline 231
cut,S. pyogenes,Kim,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26479191/"">26479191</a>",PHYB,CACTAGGAGCAACACCCAA,A. thaliana - Network accessline 231
cut,S. pyogenes,Kim,"<a href=""//""></a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26479191/"">26479191</a>",PHYB,CACTAGGAGCAACACCCAA,A. thaliana - Network accessline 232
cut,S. pyogenes,Wolfe,"<a href=""/69240/"">69240</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26480473/"">26480473</a>",PLXNB2,GGGCCCAACCTAGGGCATGG,H. sapiens - Network accessline 232
cut,S. pyogenes,Wolfe,"<a href=""/69240/"">69240</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26480473/"">26480473</a>",PLXNB2,GGGCCCAACCTAGGGCATGG,H. sapiens - Network accessline 233
cut,S. pyogenes,Patron,"<a href=""/68253/"">68253</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26616834/"">26616834</a>",PM19,GCTCTCCACTCTGGGCTCTT,H. vulgare - Network accessline 233
cut,S. pyogenes,Patron,"<a href=""/68253/"">68253</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26616834/"">26616834</a>",PM19,GCTCTCCACTCTGGGCTCTT,H. vulgare - Network accessline 234
cut,S. pyogenes,Patron,"<a href=""/68254/"">68254</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26616834/"">26616834</a>",PM19,GGCTGGCGTTGGTCGTAACA,H. vulgare - Network accessline 234
cut,S. pyogenes,Patron,"<a href=""/68254/"">68254</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26616834/"">26616834</a>",PM19,GGCTGGCGTTGGTCGTAACA,H. vulgare - Network accessline 235
cut,S. pyogenes,Shaw,"<a href=""/79004/"">79004</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26944679/"">26944679</a>",Prkaa1,TTATTGTCACAGGCATATGG,M. musculus - Network accessline 235
cut,S. pyogenes,Shaw,"<a href=""/79004/"">79004</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26944679/"">26944679</a>",Prkaa1,TTATTGTCACAGGCATATGG,M. musculus - Network accessline 236
cut,S. pyogenes,Shaw,"<a href=""/79005/"">79005</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26944679/"">26944679</a>",Prkaa2,ACAGGCATATGGTTGTCCAT,M. musculus - Network accessline 236
cut,S. pyogenes,Shaw,"<a href=""/79005/"">79005</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26944679/"">26944679</a>",Prkaa2,ACAGGCATATGGTTGTCCAT,M. musculus - Network accessline 237
cut,S. pyogenes,Schmitt-Ulms,"<a href=""/61857/"">61857</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25490046/"">25490046</a>",Prnp,GGTGGAACACCGGTGGAAGC,M. musculus - Network accessline 237
cut,S. pyogenes,Schmitt-Ulms,"<a href=""/61857/"">61857</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25490046/"">25490046</a>",Prnp,GGTGGAACACCGGTGGAAGC,M. musculus - Network accessline 238
cut,S. pyogenes,Schmitt-Ulms,"<a href=""/61856/"">61856</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25490046/"">25490046</a>",Prnp,TTGGCCCCATCCACCGCCAT,M. musculus - Network accessline 238
cut,S. pyogenes,Schmitt-Ulms,"<a href=""/61856/"">61856</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25490046/"">25490046</a>",Prnp,TTGGCCCCATCCACCGCCAT,M. musculus - Network accessline 239
cut,N. meningitidis,Church,"<a href=""/48651/"">48651</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24076762/"">24076762</a>",Protospacer A,TACCATCTCAAGCTTGTTGA,Synthetic - Network accessline 239
cut,N. meningitidis,Church,"<a href=""/48651/"">48651</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24076762/"">24076762</a>",Protospacer A,TACCATCTCAAGCTTGTTGA,Synthetic - Network accessline 240
cut,S. thermophilus,Church,"<a href=""/48653/"">48653</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24076762/"">24076762</a>",Protospacer A,TACCATCTCAAGCTTGTTGA,Synthetic - Network accessline 240
cut,S. thermophilus,Church,"<a href=""/48653/"">48653</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24076762/"">24076762</a>",Protospacer A,TACCATCTCAAGCTTGTTGA,Synthetic - Network accessline 241
cut,T. denticola,Church,"<a href=""/48655/"">48655</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24076762/"">24076762</a>",Protospacer A,TACCATCTCAAGCTTGTTGA,Synthetic - Network accessline 241
cut,T. denticola,Church,"<a href=""/48655/"">48655</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24076762/"">24076762</a>",Protospacer A,TACCATCTCAAGCTTGTTGA,Synthetic - Network accessline 242
cut,N. meningitidis,Church,"<a href=""/48652/"">48652</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24076762/"">24076762</a>",Protospacer B,ACTTTAAAAGTATTCGCCAT,Synthetic - Network accessline 242
cut,N. meningitidis,Church,"<a href=""/48652/"">48652</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24076762/"">24076762</a>",Protospacer B,ACTTTAAAAGTATTCGCCAT,Synthetic - Network accessline 243
cut,S. thermophilus,Church,"<a href=""/48654/"">48654</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24076762/"">24076762</a>",Protospacer B,ACTTTAAAAGTATTCGCCAT,Synthetic - Network accessline 243
cut,S. thermophilus,Church,"<a href=""/48654/"">48654</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24076762/"">24076762</a>",Protospacer B,ACTTTAAAAGTATTCGCCAT,Synthetic - Network accessline 244
cut,T. denticola,Church,"<a href=""/48656/"">48656</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24076762/"">24076762</a>",Protospacer B,ACTTTAAAAGTATTCGCCAT,Synthetic - Network accessline 244
cut,T. denticola,Church,"<a href=""/48656/"">48656</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24076762/"">24076762</a>",Protospacer B,ACTTTAAAAGTATTCGCCAT,Synthetic - Network accessline 245
cut,S. pyogenes,Moffat,"<a href=""/74184/"">74184</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26627737/"">26627737</a>",PSMB2,ATGTTCTTGTCGCCTCCGAC,H. sapiens - Network accessline 245
cut,S. pyogenes,Moffat,"<a href=""/74184/"">74184</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26627737/"">26627737</a>",PSMB2,ATGTTCTTGTCGCCTCCGAC,H. sapiens - Network accessline 246
cut,S. pyogenes,Moffat,"<a href=""/74185/"">74185</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26627737/"">26627737</a>",PSMB2,AATATTGTCCAGATGAAGGA,H. sapiens - Network accessline 246
cut,S. pyogenes,Moffat,"<a href=""/74185/"">74185</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26627737/"">26627737</a>",PSMB2,AATATTGTCCAGATGAAGGA,H. sapiens - Network accessline 247
cut,S. pyogenes,Moffat,"<a href=""/74180/"">74180</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26627737/"">26627737</a>",PSMD1,TGTGCGCTACGGAGCTGCAA,H. sapiens - Network accessline 247
cut,S. pyogenes,Moffat,"<a href=""/74180/"">74180</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26627737/"">26627737</a>",PSMD1,TGTGCGCTACGGAGCTGCAA,H. sapiens - Network accessline 248
cut,S. pyogenes,Moffat,"<a href=""/74181/"">74181</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26627737/"">26627737</a>",PSMD1,ACCAGAGCCACAATAAGCCA,H. sapiens - Network accessline 248
cut,S. pyogenes,Moffat,"<a href=""/74181/"">74181</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26627737/"">26627737</a>",PSMD1,ACCAGAGCCACAATAAGCCA,H. sapiens - Network accessline 249
cut,S. pyogenes,Jacks,"<a href=""/59909/"">59909</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25119044/"">25119044</a>",Pten,AGATCGTTAGCAGAAACAAA,M. musculus - Network accessline 249
cut,S. pyogenes,Jacks,"<a href=""/59909/"">59909</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25119044/"">25119044</a>",Pten,AGATCGTTAGCAGAAACAAA,M. musculus - Network accessline 250
cut,S. pyogenes,Xue,"<a href=""/66588/"">66588</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26018130/"">26018130</a>",Pten,GCTGTAGTAATATCTGCTAT,M. musculus - Network accessline 250
cut,S. pyogenes,Xue,"<a href=""/66588/"">66588</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26018130/"">26018130</a>",Pten,GCTGTAGTAATATCTGCTAT,M. musculus - Network accessline 251
cut,S. pyogenes,Xue,"<a href=""/66587/"">66587</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26018130/"">26018130</a>",Pten,GTTTCATAGCGGCCACGAAGT,M. musculus - Network accessline 251
cut,S. pyogenes,Xue,"<a href=""/66587/"">66587</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26018130/"">26018130</a>",Pten,GTTTCATAGCGGCCACGAAGT,M. musculus - Network accessline 252
interfere,S. pyogenes,Lu,"<a href=""/74070/"">74070</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26829286/"">26829286</a>",pyk,AGATTGTATGTACCCTAGGCCCAG,C. glutamicum - Network accessline 252
interfere,S. pyogenes,Lu,"<a href=""/74070/"">74070</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26829286/"">26829286</a>",pyk,AGATTGTATGTACCCTAGGCCCAG,C. glutamicum - Network accessline 253
interfere,S. pyogenes,Lu,"<a href=""/74072/"">74072</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26829286/"">26829286</a>",pyk,ATTCCATCTGCACTAGCCACCGCT,C. glutamicum - Network accessline 253
interfere,S. pyogenes,Lu,"<a href=""/74072/"">74072</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26829286/"">26829286</a>",pyk,ATTCCATCTGCACTAGCCACCGCT,C. glutamicum - Network accessline 254
tag,S. pyogenes,Mendenhall,"<a href=""/64057/"">64057</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26355004/"">26355004</a>",RAD21,CCAAGGTTCCATATTATATAAGG,H. sapiens - Network accessline 254
tag,S. pyogenes,Mendenhall,"<a href=""/64057/"">64057</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26355004/"">26355004</a>",RAD21,CCAAGGTTCCATATTATATAAGG,H. sapiens - Network accessline 255
cut,S. pyogenes,Fire,"<a href=""/59927/"">59927</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25161212/"">25161212</a>",rde-1(D718),TGCCATTAACTATGTATGT,C. elegans - Network accessline 255
cut,S. pyogenes,Fire,"<a href=""/59927/"">59927</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25161212/"">25161212</a>",rde-1(D718),TGCCATTAACTATGTATGT,C. elegans - Network accessline 256
cut,S. pyogenes,Fire,"<a href=""/59928/"">59928</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25161212/"">25161212</a>",rde-1(D801),GATATTGTAGTCTATCGAGA,C. elegans - Network accessline 256
cut,S. pyogenes,Fire,"<a href=""/59928/"">59928</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25161212/"">25161212</a>",rde-1(D801),GATATTGTAGTCTATCGAGA,C. elegans - Network accessline 257
cut,S. pyogenes,Fire,"<a href=""/59929/"">59929</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25161212/"">25161212</a>",rde-1(H974),GATAAATGAGCATAATGAAC,C. elegans - Network accessline 257
cut,S. pyogenes,Fire,"<a href=""/59929/"">59929</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25161212/"">25161212</a>",rde-1(H974),GATAAATGAGCATAATGAAC,C. elegans - Network accessline 258
interfere,S. pyogenes,Lu,"<a href=""/74075/"">74075</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26829286/"">26829286</a>",rfp,AAGTTCGTATGGAAGGTTCCGTTAA, - Network accessline 258
interfere,S. pyogenes,Lu,"<a href=""/74075/"">74075</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26829286/"">26829286</a>",rfp,AAGTTCGTATGGAAGGTTCCGTTAA, - Network accessline 259
cut,S. pyogenes,Joung,"<a href=""/42246/"">42246</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360964/"">23360964</a>",rgs4,GGAGAAGGTGAAGGACACTG,D. rerio - Network accessline 259
cut,S. pyogenes,Joung,"<a href=""/42246/"">42246</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360964/"">23360964</a>",rgs4,GGAGAAGGTGAAGGACACTG,D. rerio - Network accessline 260
cut,S. pyogenes,Bornhauser,"<a href=""/75165/"">75165</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/27194728/"">27194728</a>",RIP3,TACACGAGTGATGGTTCGGT,H. sapiens - Network accessline 260
cut,S. pyogenes,Bornhauser,"<a href=""/75165/"">75165</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/27194728/"">27194728</a>",RIP3,TACACGAGTGATGGTTCGGT,H. sapiens - Network accessline 261
cut,S. pyogenes,Bornhauser,"<a href=""/75163/"">75163</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/27194728/"">27194728</a>",RIPK1,GCTCTGCTGGGAAGCGAATC,H. sapiens - Network accessline 261
cut,S. pyogenes,Bornhauser,"<a href=""/75163/"">75163</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/27194728/"">27194728</a>",RIPK1,GCTCTGCTGGGAAGCGAATC,H. sapiens - Network accessline 262
cut,S. pyogenes,Joung,"<a href=""/47509/"">47509</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23792628/"">23792628</a>",RNF2,GTCATCTTAGTCATTACCTG,H. sapiens - Network accessline 262
cut,S. pyogenes,Joung,"<a href=""/47509/"">47509</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23792628/"">23792628</a>",RNF2,GTCATCTTAGTCATTACCTG,H. sapiens - Network accessline 263
cut,S. pyogenes,Fire,"<a href=""/59930/"">59930</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25161212/"">25161212</a>",rol-6(su1006),GTGAGACGTCAACAATATGG,C. elegans - Network accessline 263
cut,S. pyogenes,Fire,"<a href=""/59930/"">59930</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25161212/"">25161212</a>",rol-6(su1006),GTGAGACGTCAACAATATGG,C. elegans - Network accessline 264
cut,S. pyogenes,Kuhn,"<a href=""/64216/"">64216</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25803306/"">25803306</a>",Rosa26,ACTCCAGTCTTTCTAGAAGA,M.musculus - Network accessline 264
cut,S. pyogenes,Kuhn,"<a href=""/64216/"">64216</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25803306/"">25803306</a>",Rosa26,ACTCCAGTCTTTCTAGAAGA,M.musculus - Network accessline 265
cut,S. pyogenes,Marraffini,"<a href=""/44505/"">44505</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360965/"">23360965</a>",rpsL,AAAAAACCGAACTCCGCGCTGCGTAAAGTA,E. coli - Network accessline 265
cut,S. pyogenes,Marraffini,"<a href=""/44505/"">44505</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360965/"">23360965</a>",rpsL,AAAAAACCGAACTCCGCGCTGCGTAAAGTA,E. coli - Network accessline 266
interfere,S. pyogenes,Voigt,"<a href=""/62285/"">62285</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25422271/"">25422271</a>",scramble,AACCCCTGATTGTATCCGCA,synthetic - Network accessline 266
interfere,S. pyogenes,Voigt,"<a href=""/62285/"">62285</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25422271/"">25422271</a>",scramble,AACCCCTGATTGTATCCGCA,synthetic - Network accessline 267
cut,S. pyogenes,Chen,"<a href=""/47930/"">47930</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23918387/"">23918387</a>",slc24a5 (golden),GGTCTCTCGCAGGATGTTGC,D. rerio - Network accessline 267
cut,S. pyogenes,Chen,"<a href=""/47930/"">47930</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23918387/"">23918387</a>",slc24a5 (golden),GGTCTCTCGCAGGATGTTGC,D. rerio - Network accessline 268
cut,S. pyogenes,Hanna,"<a href=""/59725/"">59725</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25543152/"">25543152</a>",SOX17,GCCCAGCCCGGCCATCACCG,H. sapiens - Network accessline 268
cut,S. pyogenes,Hanna,"<a href=""/59725/"">59725</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25543152/"">25543152</a>",SOX17,GCCCAGCCCGGCCATCACCG,H. sapiens - Network accessline 269
activate,S. pyogenes,Wolfe,"<a href=""/50925/"">50925</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24346702/"">24346702</a>",Sox17,GCTCCGGCTAGTTTTCCCGG,H. sapiens - Network accessline 269
activate,S. pyogenes,Wolfe,"<a href=""/50925/"">50925</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24346702/"">24346702</a>",Sox17,GCTCCGGCTAGTTTTCCCGG,H. sapiens - Network accessline 270
activate,S. pyogenes,Wolfe,"<a href=""/50924/"">50924</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24346702/"">24346702</a>",Sox17,GGAGGGGCAAGGGGCGGGCG,H. sapiens - Network accessline 270
activate,S. pyogenes,Wolfe,"<a href=""/50924/"">50924</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24346702/"">24346702</a>",Sox17,GGAGGGGCAAGGGGCGGGCG,H. sapiens - Network accessline 271
activate,S. pyogenes,Wolfe,"<a href=""/50926/"">50926</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24346702/"">24346702</a>",Sox17,GGGCAAGTACGTCGATTCCA,H. sapiens - Network accessline 271
activate,S. pyogenes,Wolfe,"<a href=""/50926/"">50926</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24346702/"">24346702</a>",Sox17,GGGCAAGTACGTCGATTCCA,H. sapiens - Network accessline 272
activate,S. pyogenes,Wolfe,"<a href=""/50923/"">50923</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24346702/"">24346702</a>",Sox17,GGGCGTGGGCCTAACGACGC,H. sapiens - Network accessline 272
activate,S. pyogenes,Wolfe,"<a href=""/50923/"">50923</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24346702/"">24346702</a>",Sox17,GGGCGTGGGCCTAACGACGC,H. sapiens - Network accessline 273
cut,S. pyogenes,Fire,"<a href=""/59935/"">59935</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25161212/"">25161212</a>",sqt-1,GGAAGGACATAGTTGTCAT,C. elegans - Network accessline 273
cut,S. pyogenes,Fire,"<a href=""/59935/"">59935</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25161212/"">25161212</a>",sqt-1,GGAAGGACATAGTTGTCAT,C. elegans - Network accessline 274
cut,S. pyogenes,Fire,"<a href=""/60212/"">60212</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25161212/"">25161212</a>",sqt-1,TGTGGAGTTGGGGTAGCGT,C. elegans - Network accessline 274
cut,S. pyogenes,Fire,"<a href=""/60212/"">60212</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25161212/"">25161212</a>",sqt-1,TGTGGAGTTGGGGTAGCGT,C. elegans - Network accessline 275
activate/interfere,S. pyogenes,Qi,"<a href=""/62338/"">62338</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25533786/"">25533786</a>",sv40 promoter,CATACTTCTGCCTGCTGGGGAGCCTG,synthetic - Network accessline 275
activate/interfere,S. pyogenes,Qi,"<a href=""/62338/"">62338</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25533786/"">25533786</a>",sv40 promoter,CATACTTCTGCCTGCTGGGGAGCCTG,synthetic - Network accessline 276
scaffold,S. pyogenes,Qi & Lim,"<a href=""/62340/"">62340</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25533786/"">25533786</a>",SV40.NT1,GAATAGCTCAGAGGCCGAGG,H. sapiens - Network accessline 276
scaffold,S. pyogenes,Qi & Lim,"<a href=""/62340/"">62340</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25533786/"">25533786</a>",SV40.NT1,GAATAGCTCAGAGGCCGAGG,H. sapiens - Network accessline 277
cut,S. pyogenes,Seydoux,"<a href=""/66100/"">66100</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25249454/"">25249454</a>",swan-2,ACAAATTGATATCCAATCA,C. elegans - Network accessline 277
cut,S. pyogenes,Seydoux,"<a href=""/66100/"">66100</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25249454/"">25249454</a>",swan-2,ACAAATTGATATCCAATCA,C. elegans - Network accessline 278
cut,S. pyogenes,Seydoux,"<a href=""/66099/"">66099</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25249454/"">25249454</a>",swan-2,TGAAGAAAGTTATACTCGA,C. elegans - Network accessline 278
cut,S. pyogenes,Seydoux,"<a href=""/66099/"">66099</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25249454/"">25249454</a>",swan-2,TGAAGAAAGTTATACTCGA,C. elegans - Network accessline 279
cut,S. pyogenes,Hanna,"<a href=""/59726/"">59726</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25543152/"">25543152</a>",T (BRACHYURY),CACGCGCAGTTCGCGCTCTG,H. sapiens - Network accessline 279
cut,S. pyogenes,Hanna,"<a href=""/59726/"">59726</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25543152/"">25543152</a>",T (BRACHYURY),CACGCGCAGTTCGCGCTCTG,H. sapiens - Network accessline 280
cut,S. pyogenes,"Guigo, Johnson","<a href=""/72627/"">72627</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26493208/"">26493208</a>",TCRC,gRNA1:GGGGAGCGGGAAAGCGGTCG; gRNA2:AACTGACCTTCAGGCCCGTA,H. sapiens - Network accessline 280
cut,S. pyogenes,"Guigo, Johnson","<a href=""/72627/"">72627</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26493208/"">26493208</a>",TCRC,gRNA1:GGGGAGCGGGAAAGCGGTCG; gRNA2:AACTGACCTTCAGGCCCGTA,H. sapiens - Network accessline 281
cut,N. meningitidis,Thomson,"<a href=""/47869/"">47869</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23940360/"">23940360</a>",tdtomato,CGAAATGAGAAAGGGAGCTACAAC,Synthetic - Network accessline 281
cut,N. meningitidis,Thomson,"<a href=""/47869/"">47869</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23940360/"">23940360</a>",tdtomato,CGAAATGAGAAAGGGAGCTACAAC,Synthetic - Network accessline 282
scaffold,S. pyogenes,Qi & Lim,"<a href=""/62320/"">62320</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25533786/"">25533786</a>",TEF,TTGATATTTAAGTTAATAAA,S. cerevisiae - Network accessline 282
scaffold,S. pyogenes,Qi & Lim,"<a href=""/62320/"">62320</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25533786/"">25533786</a>",TEF,TTGATATTTAAGTTAATAAA,S. cerevisiae - Network accessline 283
interfere,S. pyogenes,Weissman,"<a href=""/46922/"">46922</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23849981/"">23849981</a>",TEF1,TTGATATTTAAGTTAATAAA,S. cerevisiae - Network accessline 283
interfere,S. pyogenes,Weissman,"<a href=""/46922/"">46922</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23849981/"">23849981</a>",TEF1,TTGATATTTAAGTTAATAAA,S. cerevisiae - Network accessline 284
visualize,S. pyogenes,Qi,"<a href=""/51024/"">51024</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24360272/"">24360272</a>",telomeres,GTTAGGGTTAGGGTTAGGGTTA,H. sapiens - Network accessline 284
visualize,S. pyogenes,Qi,"<a href=""/51024/"">51024</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24360272/"">24360272</a>",telomeres,GTTAGGGTTAGGGTTAGGGTTA,H. sapiens - Network accessline 285
scaffold,S. pyogenes,Qi & Lim,"<a href=""/62313/"">62313</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25533786/"">25533786</a>",TET,ACTTTTCTCTATCACTGATA,S. cerevisiae - Network accessline 285
scaffold,S. pyogenes,Qi & Lim,"<a href=""/62313/"">62313</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25533786/"">25533786</a>",TET,ACTTTTCTCTATCACTGATA,S. cerevisiae - Network accessline 286
interfere,S. pyogenes,Weissman,"<a href=""/46923/"">46923</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23849981/"">23849981</a>",TET promoter,TATCAGTGATAGAGAAAAGT, - Network accessline 286
interfere,S. pyogenes,Weissman,"<a href=""/46923/"">46923</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23849981/"">23849981</a>",TET promoter,TATCAGTGATAGAGAAAAGT, - Network accessline 287
activate/interfere,S. pyogenes,Qi,"<a href=""/62327/"">62327</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25533786/"">25533786</a>",Tet3G,GTACGTTCTCTATCACTGATA,synthetic - Network accessline 287
activate/interfere,S. pyogenes,Qi,"<a href=""/62327/"">62327</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25533786/"">25533786</a>",Tet3G,GTACGTTCTCTATCACTGATA,synthetic - Network accessline 288
cut,S. pyogenes,Shaw,"<a href=""/79006/"">79006</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26944679/"">26944679</a>",Tfeb,AGCACTGTTGCCGGCCGAGG,M. musculus - Network accessline 288
cut,S. pyogenes,Shaw,"<a href=""/79006/"">79006</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26944679/"">26944679</a>",Tfeb,AGCACTGTTGCCGGCCGAGG,M. musculus - Network accessline 289
RNA targeting,S. pyogenes,Yeo,"<a href=""/74709/"">74709</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26997482/"">26997482</a>",TFRC,GCACUGACCAGAUAAGAAUG,H. sapiens - Network accessline 289
RNA targeting,S. pyogenes,Yeo,"<a href=""/74709/"">74709</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26997482/"">26997482</a>",TFRC,GCACUGACCAGAUAAGAAUG,H. sapiens - Network accessline 290
cut,S. pyogenes,"Guigo, Johnson","<a href=""/72626/"">72626</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26493208/"">26493208</a>",TFRC,"gRNA1:GCAGCCTCAGAAATACAAAA, gRNA2CGGGATATCGGGTGGCGGCT",H. sapiens - Network accessline 290
cut,S. pyogenes,"Guigo, Johnson","<a href=""/72626/"">72626</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26493208/"">26493208</a>",TFRC,"gRNA1:GCAGCCTCAGAAATACAAAA, gRNA2CGGGATATCGGGTGGCGGCT",H. sapiens - Network accessline 291
cut,S. pyogenes,Wolfe,"<a href=""/69239/"">69239</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26480473/"">26480473</a>",TGM2,CCTGGACCCAACGCCCCAGG,H. sapiens - Network accessline 291
cut,S. pyogenes,Wolfe,"<a href=""/69239/"">69239</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26480473/"">26480473</a>",TGM2,CCTGGACCCAACGCCCCAGG,H. sapiens - Network accessline 292
cut,S. pyogenes,Sibley,"<a href=""/59855/"">59855</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25480939/"">25480939</a>",TGME49_290860,ATTGGTCTGACAGCGATGTG,T. gondii - Network accessline 292
cut,S. pyogenes,Sibley,"<a href=""/59855/"">59855</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25480939/"">25480939</a>",TGME49_290860,ATTGGTCTGACAGCGATGTG,T. gondii - Network accessline 293
cut,S. pyogenes,Joung,"<a href=""/42247/"">42247</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360964/"">23360964</a>",th,GGATGCGCGTAAGGAGCGCG,D. rerio - Network accessline 293
cut,S. pyogenes,Joung,"<a href=""/42247/"">42247</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360964/"">23360964</a>",th,GGATGCGCGTAAGGAGCGCG,D. rerio - Network accessline 294
cut,S. pyogenes,Du,"<a href=""/65565/"">65565</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25849248/"">25849248</a>",th,GGGTTGTTACACTCATAAAG,D. rerio - Network accessline 294
cut,S. pyogenes,Du,"<a href=""/65565/"">65565</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25849248/"">25849248</a>",th,GGGTTGTTACACTCATAAAG,D. rerio - Network accessline 295
cut,S. pyogenes,Joung,"<a href=""/42248/"">42248</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360964/"">23360964</a>",tia1l,GGTATGTCGGGAACCTCTCC,D. rerio - Network accessline 295
cut,S. pyogenes,Joung,"<a href=""/42248/"">42248</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360964/"">23360964</a>",tia1l,GGTATGTCGGGAACCTCTCC,D. rerio - Network accessline 296
cut,S. pyogenes,Joung,"<a href=""/42249/"">42249</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360964/"">23360964</a>",tph1a,GGGAAAACACAACCGCAGCC,D. rerio - Network accessline 296
cut,S. pyogenes,Joung,"<a href=""/42249/"">42249</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23360964/"">23360964</a>",tph1a,GGGAAAACACAACCGCAGCC,D. rerio - Network accessline 297
scaffold,S. pyogenes,Qi & Lim,"<a href=""/62325/"">62325</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25533786/"">25533786</a>",TRE3G,GTACGTTCTCTATCACTGATA,H. sapiens - Network accessline 297
scaffold,S. pyogenes,Qi & Lim,"<a href=""/62325/"">62325</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25533786/"">25533786</a>",TRE3G,GTACGTTCTCTATCACTGATA,H. sapiens - Network accessline 298
cut,S. pyogenes,Jin,"<a href=""/64331/"">64331</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25281382/"">25281382</a>",trp1,GTCAATTGTTCTCTTTCTAT,S. cerevisiae - Network accessline 298
cut,S. pyogenes,Jin,"<a href=""/64331/"">64331</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25281382/"">25281382</a>",trp1,GTCAATTGTTCTCTTTCTAT,S. cerevisiae - Network accessline 299
cut,S. pyogenes,Jacks,"<a href=""/59910/"">59910</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25119044/"">25119044</a>",Trp53,CCTCGAGCTCCCTCTGAGCC,M. musculus - Network accessline 299
cut,S. pyogenes,Jacks,"<a href=""/59910/"">59910</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25119044/"">25119044</a>",Trp53,CCTCGAGCTCCCTCTGAGCC,M. musculus - Network accessline 300
cut,S. pyogenes,Wolfe,"<a href=""/69234/"">69234</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26480473/"">26480473</a>",TS2,GACCCCCTCCACCCCGCCTC,H. sapiens - Network accessline 300
cut,S. pyogenes,Wolfe,"<a href=""/69234/"">69234</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26480473/"">26480473</a>",TS2,GACCCCCTCCACCCCGCCTC,H. sapiens - Network accessline 301
cut,S. pyogenes,Wolfe,"<a href=""/69235/"">69235</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26480473/"">26480473</a>",TS3,GGTGAGTGAGTGTGTGCGTG,H. sapiens - Network accessline 301
cut,S. pyogenes,Wolfe,"<a href=""/69235/"">69235</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26480473/"">26480473</a>",TS3,GGTGAGTGAGTGTGTGCGTG,H. sapiens - Network accessline 302
cut,S. pyogenes,Wolfe,"<a href=""/69236/"">69236</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26480473/"">26480473</a>",TS4,GAGTCCGAGCAGAAGAAGAA,H. sapiens - Network accessline 302
cut,S. pyogenes,Wolfe,"<a href=""/69236/"">69236</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26480473/"">26480473</a>",TS4,GAGTCCGAGCAGAAGAAGAA,H. sapiens - Network accessline 303
cut,S. pyogenes,Goldstein,"<a href=""/47550/"">47550</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23995389/"">23995389</a>",ttTi5605 Mos1 insertion site,ATATCAGTCTGTTTCGTAA,C. elegans - Network accessline 303
cut,S. pyogenes,Goldstein,"<a href=""/47550/"">47550</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23995389/"">23995389</a>",ttTi5605 Mos1 insertion site,ATATCAGTCTGTTTCGTAA,C. elegans - Network accessline 304
cut,S. pyogenes,Chen,"<a href=""/46761/"">46761</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23918387/"">23918387</a>",tyr,CCCCAGAAGTCCTCCAGTCC,D. rerio - Network accessline 304
cut,S. pyogenes,Chen,"<a href=""/46761/"">46761</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23918387/"">23918387</a>",tyr,CCCCAGAAGTCCTCCAGTCC,D. rerio - Network accessline 305
cut,S. pyogenes,Chen,"<a href=""/64250/"">64250</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25855067/"">25855067</a>",tyr,GGACTGGAGGACTTCTGGGG,D. rerio - Network accessline 305
cut,S. pyogenes,Chen,"<a href=""/64250/"">64250</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25855067/"">25855067</a>",tyr,GGACTGGAGGACTTCTGGGG,D. rerio - Network accessline 306
cut,S. pyogenes,Mashimo,"<a href=""/60966/"">60966</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24967838/"">24967838</a>",Tyr (albino),TTTCCAGGATTATGTAATAG,R. norvegicus - Network accessline 306
cut,S. pyogenes,Mashimo,"<a href=""/60966/"">60966</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24967838/"">24967838</a>",Tyr (albino),TTTCCAGGATTATGTAATAG,R. norvegicus - Network accessline 307
cut,S. pyogenes,Mashimo,"<a href=""/60967/"">60967</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24967838/"">24967838</a>",Tyr (wild type),TTTCCAGGATTACGTAATAG,R. norvegicus - Network accessline 307
cut,S. pyogenes,Mashimo,"<a href=""/60967/"">60967</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24967838/"">24967838</a>",Tyr (wild type),TTTCCAGGATTACGTAATAG,R. norvegicus - Network accessline 308
cut,S. pyogenes,"Guigo, Johnson","<a href=""/72628/"">72628</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26493208/"">26493208</a>",UCA1,gRNA1:GGTTTCCTTTTAGATGACGG; gRNA2:TCTGAAAAGAGAGTCAGCGA,H. sapiens - Network accessline 308
cut,S. pyogenes,"Guigo, Johnson","<a href=""/72628/"">72628</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/26493208/"">26493208</a>",UCA1,gRNA1:GGTTTCCTTTTAGATGACGG; gRNA2:TCTGAAAAGAGAGTCAGCGA,H. sapiens - Network accessline 309
cut,S. pyogenes,Fire,"<a href=""/59932/"">59932</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25161212/"">25161212</a>",unc-109(n499),GGAACTCGTGTCAAAACAAC,C. elegans - Network accessline 309
cut,S. pyogenes,Fire,"<a href=""/59932/"">59932</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25161212/"">25161212</a>",unc-109(n499),GGAACTCGTGTCAAAACAAC,C. elegans - Network accessline 310
cut,S. pyogenes,Calarco,"<a href=""/46169/"">46169</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23817069/"">23817069</a>",unc-119,GAATTTTCTGAAATTAAAGA,C. elegans - Network accessline 310
cut,S. pyogenes,Calarco,"<a href=""/46169/"">46169</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23817069/"">23817069</a>",unc-119,GAATTTTCTGAAATTAAAGA,C. elegans - Network accessline 311
cut,S. pyogenes,Mello,"<a href=""/58202/"">58202</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24879462/"">24879462</a>",unc-22,GAACCCGTTGCCGAATACAC,C. elegans - Network accessline 311
cut,S. pyogenes,Mello,"<a href=""/58202/"">58202</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24879462/"">24879462</a>",unc-22,GAACCCGTTGCCGAATACAC,C. elegans - Network accessline 312
cut,S. pyogenes,Fire,"<a href=""/59931/"">59931</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25161212/"">25161212</a>",unc-58(e665),TCCACGCACATGGTCACTA,C. elegans - Network accessline 312
cut,S. pyogenes,Fire,"<a href=""/59931/"">59931</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25161212/"">25161212</a>",unc-58(e665),TCCACGCACATGGTCACTA,C. elegans - Network accessline 313
cut,S. pyogenes,Sibley,"<a href=""/54467/"">54467</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24825012/"">24825012</a>",UPRT,GGCGTCTCGATTGTGAGAGC,Toxoplasma gondii - Network accessline 313
cut,S. pyogenes,Sibley,"<a href=""/54467/"">54467</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24825012/"">24825012</a>",UPRT,GGCGTCTCGATTGTGAGAGC,Toxoplasma gondii - Network accessline 314
cut,S. pyogenes,Jin,"<a href=""/64330/"">64330</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25281382/"">25281382</a>",ura3,GAGTAAAAAATTGTACTTGG,S. cerevisiae - Network accessline 314
cut,S. pyogenes,Jin,"<a href=""/64330/"">64330</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25281382/"">25281382</a>",ura3,GAGTAAAAAATTGTACTTGG,S. cerevisiae - Network accessline 315
cut,S. pyogenes,Ye,"<a href=""/61812/"">61812</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24424410/"">24424410</a>",USP13,GCCGCCCGGCATGCCGAACA,H. sapiens - Network accessline 315
cut,S. pyogenes,Ye,"<a href=""/61812/"">61812</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24424410/"">24424410</a>",USP13,GCCGCCCGGCATGCCGAACA,H. sapiens - Network accessline 316
cut,S. pyogenes,Joung,"<a href=""/47506/"">47506</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23792628/"">23792628</a>",VEGF,GACCCCCTCCACCCCGCCTC,H. sapiens - Network accessline 316
cut,S. pyogenes,Joung,"<a href=""/47506/"">47506</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23792628/"">23792628</a>",VEGF,GACCCCCTCCACCCCGCCTC,H. sapiens - Network accessline 317
cut,S. pyogenes,Joung,"<a href=""/47505/"">47505</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23792628/"">23792628</a>",VEGF,GGGTGGGGGGAGTTTGCTCC,H. sapiens - Network accessline 317
cut,S. pyogenes,Joung,"<a href=""/47505/"">47505</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23792628/"">23792628</a>",VEGF,GGGTGGGGGGAGTTTGCTCC,H. sapiens - Network accessline 318
cut,S. pyogenes,Joung,"<a href=""/47507/"">47507</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23792628/"">23792628</a>",VEGF,GGTGAGTGAGTGTGTGCGTG,H. sapiens - Network accessline 318
cut,S. pyogenes,Joung,"<a href=""/47507/"">47507</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/23792628/"">23792628</a>",VEGF,GGTGAGTGAGTGTGTGCGTG,H. sapiens - Network accessline 319
interfere,S. pyogenes,Voigt,"<a href=""/62286/"">62286</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25422271/"">25422271</a>",VR,TGCGCTCGGTCGTTCGGCTG,synthetic - Network accessline 319
interfere,S. pyogenes,Voigt,"<a href=""/62286/"">62286</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25422271/"">25422271</a>",VR,TGCGCTCGGTCGTTCGGCTG,synthetic - Network accessline 320
nick,S. pyogenes,Yang,"<a href=""/73228/"">73228</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/27213844/"">27213844</a>",xylR,CGAGTTAGACATAATAGTGA,C. beijerinckii - Network accessline 320
nick,S. pyogenes,Yang,"<a href=""/73228/"">73228</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/27213844/"">27213844</a>",xylR,CGAGTTAGACATAATAGTGA,C. beijerinckii - Network accessline 321
cut,S. pyogenes,Ward,"<a href=""/61250/"">61250</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25491644/"">25491644</a>",Y61A9LA.1,GGATGGATGTGTAGTCAATT,C. elegans - Network accessline 321
cut,S. pyogenes,Ward,"<a href=""/61250/"">61250</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/25491644/"">25491644</a>",Y61A9LA.1,GGATGGATGTGTAGTCAATT,C. elegans - Network accessline 322
cut,S. pyogenes,Liu,"<a href=""/49331/"">49331</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24326186/"">24326186</a>",yellow,GGTTTTGGACACTGGAACCG,D. melanogaster - Network accessline 322
cut,S. pyogenes,Liu,"<a href=""/49331/"">49331</a>","<a href=""https://www.ncbi.nlm.nih.gov/pubmed/24326186/"">24326186</a>",yellow,GGTTTTGGACACTGGAACCG,D. melanogaster
skills/molecular-biology/viennarna-structure-prediction/SKILL.md
- Network accessline 545
- [ViennaRNA documentation](https://www.tbi.univie.ac.at/RNA/) — official documentation, parameter files, and Python API reference - Network accessline 545
- [ViennaRNA documentation](https://www.tbi.univie.ac.at/RNA/) — official documentation, parameter files, and Python API reference - Network accessline 546
- [Lorenz et al., Algorithms Mol Biol 2011](https://doi.org/10.1186/1748-7188-6-26) — ViennaRNA Package 2.0 paper (primary citation for structure prediction) - Network accessline 546
- [Lorenz et al., Algorithms Mol Biol 2011](https://doi.org/10.1186/1748-7188-6-26) — ViennaRNA Package 2.0 paper (primary citation for structure prediction) - Network accessline 547
- [Turner & Mathews, Nucleic Acids Res 2010](https://doi.org/10.1093/nar/gkp892) — Nearest-neighbor thermodynamic parameters used by ViennaRNA - Network accessline 547
- [Turner & Mathews, Nucleic Acids Res 2010](https://doi.org/10.1093/nar/gkp892) — Nearest-neighbor thermodynamic parameters used by ViennaRNA - Network accessline 548
- [ViennaRNA GitHub: ViennaRNA/ViennaRNA](https://github.com/ViennaRNA/ViennaRNA) — source code, issue tracker, Python tutorial notebooks - Network accessline 548
- [ViennaRNA GitHub: ViennaRNA/ViennaRNA](https://github.com/ViennaRNA/ViennaRNA) — source code, issue tracker, Python tutorial notebooks
skills/proteomics-protein-engineering/adaptyv-bio/SKILL.md
- Access to secretsline 32
[Credential-related source omitted] - Access to secretsline 32
[Credential-related source omitted] - Access to secretsline 42
[Credential-related source omitted] - Access to secretsline 42
[Credential-related source omitted] - Access to secretsline 60
[Credential-related source omitted] - Access to secretsline 60
[Credential-related source omitted] - Access to secretsline 111
[Credential-related source omitted] - Access to secretsline 111
[Credential-related source omitted] - Access to secretsline 144
[Credential-related source omitted] - Access to secretsline 144
[Credential-related source omitted] - Access to secretsline 180
[Credential-related source omitted] - Access to secretsline 180
[Credential-related source omitted] - Access to secretsline 215
[Credential-related source omitted] - Access to secretsline 215
[Credential-related source omitted] - Access to secretsline 275
[Credential-related source omitted] - Access to secretsline 275
[Credential-related source omitted] - Access to secretsline 328
[Credential-related source omitted] - Access to secretsline 328
[Credential-related source omitted] - Access to secretsline 389
[Credential-related source omitted] - Access to secretsline 389
[Credential-related source omitted] - Access to secretsline 437
[Credential-related source omitted] - Access to secretsline 437
[Credential-related source omitted] - Network accessline 453
- [Adaptyv Bio platform](https://www.adaptyvbio.com/) — service overview and documentation - Network accessline 453
- [Adaptyv Bio platform](https://www.adaptyvbio.com/) — service overview and documentation - Network accessline 454
- [Adaptyv Bio API documentation](https://docs.adaptyvbio.com/) — SDK reference and endpoint descriptions - Network accessline 454
- [Adaptyv Bio API documentation](https://docs.adaptyvbio.com/) — SDK reference and endpoint descriptions - Network accessline 455
- [Cell-free protein synthesis review: Pardee et al. (2016), Cell](https://doi.org/10.1016/j.cell.2016.08.018) — cell-free expression technology background - Network accessline 455
- [Cell-free protein synthesis review: Pardee et al. (2016), Cell](https://doi.org/10.1016/j.cell.2016.08.018) — cell-free expression technology background - Network accessline 456
- [Directed evolution review: Arnold (2018), Angewandte Chemie](https://doi.org/10.1002/anie.201708408) — Nobel lecture on directed evolution methodology - Network accessline 456
- [Directed evolution review: Arnold (2018), Angewandte Chemie](https://doi.org/10.1002/anie.201708408) — Nobel lecture on directed evolution methodology - Network accessline 457
- [Biolayer interferometry review: Sultana & Lee (2015), CMS](https://doi.org/10.1002/cmdc.201500316) — BLI measurement methodology for KD determination - Network accessline 457
- [Biolayer interferometry review: Sultana & Lee (2015), CMS](https://doi.org/10.1002/cmdc.201500316) — BLI measurement methodology for KD determination
skills/proteomics-protein-engineering/esm-protein-language-model/SKILL.md
- Network accessline 393
- [ESM GitHub](https://github.com/evolutionaryscale/esm) — source code and model weights - Network accessline 393
- [ESM GitHub](https://github.com/evolutionaryscale/esm) — source code and model weights - Network accessline 394
- [EvolutionaryScale Forge](https://forge.evolutionaryscale.ai/) — cloud inference API - Network accessline 394
- [EvolutionaryScale Forge](https://forge.evolutionaryscale.ai/) — cloud inference API - Network accessline 395
- Hayes et al. (2024) "Simulating 500 million years of evolution with a language model" — [bioRxiv](https://doi.org/10.1101/2024.07.01.600583) - Network accessline 395
- Hayes et al. (2024) "Simulating 500 million years of evolution with a language model" — [bioRxiv](https://doi.org/10.1101/2024.07.01.600583) - Network accessline 396
- Lin et al. (2023) "Evolutionary-scale prediction of atomic-level protein structure with a language model" — [Science](https://doi.org/10.1126/science.ade2574) - Network accessline 396
- Lin et al. (2023) "Evolutionary-scale prediction of atomic-level protein structure with a language model" — [Science](https://doi.org/10.1126/science.ade2574)
skills/proteomics-protein-engineering/hmdb-database/SKILL.md
- Network accessline 27
- **HMDB XML download**: Register at https://hmdb.ca/downloads — download `hmdb_metabolites.xml.zip` (~6 GB uncompressed) - Network accessline 27
- **HMDB XML download**: Register at https://hmdb.ca/downloads — download `hmdb_metabolites.xml.zip` (~6 GB uncompressed) - Network accessline 42
NS = {'hmdb': 'http://www.hmdb.ca'} - Network accessline 42
NS = {'hmdb': 'http://www.hmdb.ca'} - Network accessline 75
NS = {'hmdb': 'http://www.hmdb.ca'} - Network accessline 75
NS = {'hmdb': 'http://www.hmdb.ca'} - Network accessline 85
met_names = {} for event, elem in ET.iterparse('hmdb_metabolites.xml', events=('end',)): if elem.tag == '{http://www.hmdb.ca}metabolite': - Network accessline 85
met_names = {} for event, elem in ET.iterparse('hmdb_metabolites.xml', events=('end',)): if elem.tag == '{http://www.hmdb.ca}metabolite': - Network accessline 86
for event, elem in ET.iterparse('hmdb_metabolites.xml', events=('end',)): if elem.tag == '{http://www.hmdb.ca}metabolite': acc = elem.find('{http://www.hmdb.ca}accession') - Network accessline 86
for event, elem in ET.iterparse('hmdb_metabolites.xml', events=('end',)): if elem.tag == '{http://www.hmdb.ca}metabolite': acc = elem.find('{http://www.hmdb.ca}accession') - Network accessline 87
if elem.tag == '{http://www.hmdb.ca}metabolite': - Network accessline 87
if elem.tag == '{http://www.hmdb.ca}metabolite': - Network accessline 88
acc = elem.find('{http://www.hmdb.ca}accession') - Network accessline 88
acc = elem.find('{http://www.hmdb.ca}accession') - Network accessline 89
name = elem.find('{http://www.hmdb.ca}name') - Network accessline 89
name = elem.find('{http://www.hmdb.ca}name') - Network accessline 450
| Parameter | Function/Endpoint | Default | Description | |-----------|-------------------|---------|-------------| | `NS` (namespace dict) | All XPath queries | `{'hmdb': 'http://www.hmdb.ca'}` | Required for all `find`/`findall` calls | - Network accessline 450
| Parameter | Function/Endpoint | Default | Description | |-----------|-------------------|---------|-------------| | `NS` (namespace dict) | All XPath queries | `{'hmdb': 'http://www.hmdb.ca'}` | Required for all `find`/`findall` calls | - Network accessline 451
|-----------|-------------------|---------|-------------| | `NS` (namespace dict) | All XPath queries | `{'hmdb': 'http://www.hmdb.ca'}` | Required for all `find`/`findall` calls | | `tolerance_da` | MW matching | `0.01` | Mass tolerance in - Network accessline 451
|-----------|-------------------|---------|-------------| | `NS` (namespace dict) | All XPath queries | `{'hmdb': 'http://www.hmdb.ca'}` | Required for all `find`/`findall` calls | | `tolerance_da` | MW matching | `0.01` | Mass tolerance in - Network accessline 452
| `NS` (namespace dict) | All XPath queries | `{'hmdb': 'http://www.hmdb.ca'}` | Required for all `find`/`findall` calls | - Network accessline 452
| `NS` (namespace dict) | All XPath queries | `{'hmdb': 'http://www.hmdb.ca'}` | Required for all `find`/`findall` calls | - Network accessline 461
1. **Build an in-memory index on startup**: Parse once (60-120s), build dict by HMDB ID + lowercase name. Never re-parse inside a loop 2. **Always pass the namespace dict**: Every `find()`/`findall()` needs `NS = {'hmdb': 'http://www.hmdb.c - Network accessline 461
1. **Build an in-memory index on startup**: Parse once (60-120s), build dict by HMDB ID + lowercase name. Never re-parse inside a loop 2. **Always pass the namespace dict**: Every `find()`/`findall()` needs `NS = {'hmdb': 'http://www.hmdb.c - Network accessline 462
2. **Always pass the namespace dict**: Every `find()`/`findall()` needs `NS = {'hmdb': 'http://www.hmdb.ca'}`. Omitting it returns empty results - Network accessline 462
2. **Always pass the namespace dict**: Every `find()`/`findall()` needs `NS = {'hmdb': 'http://www.hmdb.ca'}`. Omitting it returns empty results - Network accessline 526
| Problem | Cause | Solution | |---------|-------|----------| | `find()` returns `None` for known elements | Missing XML namespace | Always pass `NS = {'hmdb': 'http://www.hmdb.ca'}` | - Network accessline 526
| Problem | Cause | Solution | |---------|-------|----------| | `find()` returns `None` for known elements | Missing XML namespace | Always pass `NS = {'hmdb': 'http://www.hmdb.ca'}` | - Network accessline 527
|---------|-------|----------| | `find()` returns `None` for known elements | Missing XML namespace | Always pass `NS = {'hmdb': 'http://www.hmdb.ca'}` | | `MemoryError` parsing full XML | ~8-12 GB needed in memory | Use `ET.iterparse()` wi - Network accessline 527
|---------|-------|----------| | `find()` returns `None` for known elements | Missing XML namespace | Always pass `NS = {'hmdb': 'http://www.hmdb.ca'}` | | `MemoryError` parsing full XML | ~8-12 GB needed in memory | Use `ET.iterparse()` wi - Network accessline 528
| `find()` returns `None` for known elements | Missing XML namespace | Always pass `NS = {'hmdb': 'http://www.hmdb.ca'}` | - Network accessline 528
| `find()` returns `None` for known elements | Missing XML namespace | Always pass `NS = {'hmdb': 'http://www.hmdb.ca'}` | - Network accessline 551
- HMDB website and downloads: https://hmdb.ca/ - Wishart DS et al. (2022). HMDB 5.0: the Human Metabolome Database for 2022. *Nucleic Acids Res.* 50(D1):D801-D816. https://doi.org/10.1093/nar/gkab1062 - HMDB data download page: https://hmdb - Network accessline 551
- HMDB website and downloads: https://hmdb.ca/ - Wishart DS et al. (2022). HMDB 5.0: the Human Metabolome Database for 2022. *Nucleic Acids Res.* 50(D1):D801-D816. https://doi.org/10.1093/nar/gkab1062 - HMDB data download page: https://hmdb - Network accessline 552
- Wishart DS et al. (2022). HMDB 5.0: the Human Metabolome Database for 2022. *Nucleic Acids Res.* 50(D1):D801-D816. https://doi.org/10.1093/nar/gkab1062 - HMDB data download page: https://hmdb.ca/downloads - MetaboAnalyst (complementary to - Network accessline 552
- Wishart DS et al. (2022). HMDB 5.0: the Human Metabolome Database for 2022. *Nucleic Acids Res.* 50(D1):D801-D816. https://doi.org/10.1093/nar/gkab1062 - HMDB data download page: https://hmdb.ca/downloads - MetaboAnalyst (complementary to - Network accessline 553
- HMDB data download page: https://hmdb.ca/downloads - Network accessline 553
- HMDB data download page: https://hmdb.ca/downloads - Network accessline 554
- MetaboAnalyst (complementary tool): https://www.metaboanalyst.ca/ - Network accessline 554
- MetaboAnalyst (complementary tool): https://www.metaboanalyst.ca/
skills/proteomics-protein-engineering/interpro-database/SKILL.md
- Network accessline 11
InterPro is the EBI's integrated protein family, domain, and functional site database. It consolidates signatures from 13 member databases (Pfam, PANTHER, PIRSF, PRINTS, PROSITE, SMART, CDD, NCBIfam, and others) into unified InterPro entrie - Network accessline 11
InterPro is the EBI's integrated protein family, domain, and functional site database. It consolidates signatures from 13 member databases (Pfam, PANTHER, PIRSF, PRINTS, PROSITE, SMART, CDD, NCBIfam, and others) into unified InterPro entrie - Network accessline 40
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 40
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 42
def interpro_get(path: str, params: dict = None) -> dict: """Send a GET request to the InterPro API and return parsed JSON.""" r = requests.get( - Network accessline 42
def interpro_get(path: str, params: dict = None) -> dict: """Send a GET request to the InterPro API and return parsed JSON.""" r = requests.get( - Network accessline 43
"""Send a GET request to the InterPro API and return parsed JSON.""" r = requests.get( f"{INTERPRO_BASE}/{path}", - Network accessline 43
"""Send a GET request to the InterPro API and return parsed JSON.""" r = requests.get( f"{INTERPRO_BASE}/{path}", - Network accessline 44
r = requests.get( - Network accessline 44
r = requests.get( - Network accessline 76
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 76
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 82
if entry_type: params["type"] = entry_type # family, domain, homologous_superfamily, repeat, site r = requests.get( - Network accessline 82
if entry_type: params["type"] = entry_type # family, domain, homologous_superfamily, repeat, site r = requests.get( - Network accessline 83
params["type"] = entry_type # family, domain, homologous_superfamily, repeat, site r = requests.get( f"{INTERPRO_BASE}/entry/interpro/", - Network accessline 83
params["type"] = entry_type # family, domain, homologous_superfamily, repeat, site r = requests.get( f"{INTERPRO_BASE}/entry/interpro/", - Network accessline 84
r = requests.get( - Network accessline 84
r = requests.get( - Network accessline 104
# Fetch a specific InterPro entry by accession r = requests.get( - Network accessline 104
# Fetch a specific InterPro entry by accession r = requests.get( - Network accessline 105
r = requests.get( - Network accessline 105
r = requests.get( - Network accessline 132
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 132
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 137
{count, next, previous, results}. Each result has metadata + a nested `proteins[0].entry_protein_locations` for the per-protein match.""" r = requests.get( - Network accessline 137
{count, next, previous, results}. Each result has metadata + a nested `proteins[0].entry_protein_locations` for the per-protein match.""" r = requests.get( - Network accessline 138
nested `proteins[0].entry_protein_locations` for the per-protein match.""" r = requests.get( f"{INTERPRO_BASE}/entry/interpro/protein/uniprot/{uniprot_acc}/", - Network accessline 138
nested `proteins[0].entry_protein_locations` for the per-protein match.""" r = requests.get( f"{INTERPRO_BASE}/entry/interpro/protein/uniprot/{uniprot_acc}/", - Network accessline 139
r = requests.get( - Network accessline 139
r = requests.get( - Network accessline 191
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 191
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 197
`entry/interpro/{IPR}/protein/{db}/` times out (408) on large families.""" db = "reviewed" if reviewed_only else "uniprot" r = requests.get( - Network accessline 197
`entry/interpro/{IPR}/protein/{db}/` times out (408) on large families.""" db = "reviewed" if reviewed_only else "uniprot" r = requests.get( - Network accessline 198
db = "reviewed" if reviewed_only else "uniprot" r = requests.get( f"{INTERPRO_BASE}/protein/{db}/entry/interpro/{interpro_acc}/", - Network accessline 198
db = "reviewed" if reviewed_only else "uniprot" r = requests.get( f"{INTERPRO_BASE}/protein/{db}/entry/interpro/{interpro_acc}/", - Network accessline 199
r = requests.get( - Network accessline 199
r = requests.get( - Network accessline 220
def get_all_entry_proteins(interpro_acc: str, reviewed_only: bool = True) -> list: INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 220
def get_all_entry_proteins(interpro_acc: str, reviewed_only: bool = True) -> list: INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 221
reviewed_only: bool = True) -> list: INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" db = "reviewed" if reviewed_only else "uniprot" - Network accessline 221
reviewed_only: bool = True) -> list: INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" db = "reviewed" if reviewed_only else "uniprot" - Network accessline 222
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 222
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 227
params = {"page_size": 200} while url: r = requests.get(url, params=params, - Network accessline 227
params = {"page_size": 200} while url: r = requests.get(url, params=params, - Network accessline 228
while url: r = requests.get(url, params=params, headers={"Accept": "application/json"}, timeout=60) - Network accessline 228
while url: r = requests.get(url, params=params, headers={"Accept": "application/json"}, timeout=60) - Network accessline 229
r = requests.get(url, params=params, - Network accessline 229
r = requests.get(url, params=params, - Network accessline 251
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 251
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 257
has `metadata` (taxon: accession=taxId, name, parent, children, rank) and `entries[]` (representative protein-match locations for that taxon).""" r = requests.get( - Network accessline 257
has `metadata` (taxon: accession=taxId, name, parent, children, rank) and `entries[]` (representative protein-match locations for that taxon).""" r = requests.get( - Network accessline 258
and `entries[]` (representative protein-match locations for that taxon).""" r = requests.get( f"{INTERPRO_BASE}/taxonomy/uniprot/entry/interpro/{interpro_acc}/", - Network accessline 258
and `entries[]` (representative protein-match locations for that taxon).""" r = requests.get( f"{INTERPRO_BASE}/taxonomy/uniprot/entry/interpro/{interpro_acc}/", - Network accessline 259
r = requests.get( - Network accessline 259
r = requests.get( - Network accessline 284
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 284
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 288
Path-inverted: `/structure/pdb/entry/interpro/{IPR}/`. The flat form with `?entry_interpro=...` is silently slow / 408s on this resource.""" r = requests.get( - Network accessline 288
Path-inverted: `/structure/pdb/entry/interpro/{IPR}/`. The flat form with `?entry_interpro=...` is silently slow / 408s on this resource.""" r = requests.get( - Network accessline 289
`?entry_interpro=...` is silently slow / 408s on this resource.""" r = requests.get( f"{INTERPRO_BASE}/structure/pdb/entry/interpro/{interpro_acc}/", - Network accessline 289
`?entry_interpro=...` is silently slow / 408s on this resource.""" r = requests.get( f"{INTERPRO_BASE}/structure/pdb/entry/interpro/{interpro_acc}/", - Network accessline 290
r = requests.get( - Network accessline 290
r = requests.get( - Network accessline 317
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 317
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 327
params = {"page_size": min(max_sequences, 200)} while url and len(proteins) < max_sequences: r = requests.get(url, params=params, - Network accessline 327
params = {"page_size": min(max_sequences, 200)} while url and len(proteins) < max_sequences: r = requests.get(url, params=params, - Network accessline 328
while url and len(proteins) < max_sequences: r = requests.get(url, params=params, headers={"Accept": "application/json"}, timeout=60) - Network accessline 328
while url and len(proteins) < max_sequences: r = requests.get(url, params=params, headers={"Accept": "application/json"}, timeout=60) - Network accessline 329
r = requests.get(url, params=params, - Network accessline 329
r = requests.get(url, params=params, - Network accessline 339
# Fetch FASTA from UniProt for each accession accessions = [p["metadata"]["accession"] for p in proteins[:max_sequences]] fasta_url = "https://rest.uniprot.org/uniprotkb/stream" - Network accessline 339
# Fetch FASTA from UniProt for each accession accessions = [p["metadata"]["accession"] for p in proteins[:max_sequences]] fasta_url = "https://rest.uniprot.org/uniprotkb/stream" - Network accessline 340
accessions = [p["metadata"]["accession"] for p in proteins[:max_sequences]] fasta_url = "https://rest.uniprot.org/uniprotkb/stream" query = " OR ".join(f"accession:{acc}" for acc in accessions) - Network accessline 340
accessions = [p["metadata"]["accession"] for p in proteins[:max_sequences]] fasta_url = "https://rest.uniprot.org/uniprotkb/stream" query = " OR ".join(f"accession:{acc}" for acc in accessions) - Network accessline 341
fasta_url = "https://rest.uniprot.org/uniprotkb/stream" query = " OR ".join(f"accession:{acc}" for acc in accessions) r = requests.get(fasta_url, - Network accessline 341
fasta_url = "https://rest.uniprot.org/uniprotkb/stream" query = " OR ".join(f"accession:{acc}" for acc in accessions) r = requests.get(fasta_url, - Network accessline 342
query = " OR ".join(f"accession:{acc}" for acc in accessions) r = requests.get(fasta_url, params={"query": query, "format": "fasta"}, - Network accessline 342
query = " OR ".join(f"accession:{acc}" for acc in accessions) r = requests.get(fasta_url, params={"query": query, "format": "fasta"}, - Network accessline 343
r = requests.get(fasta_url, - Network accessline 343
r = requests.get(fasta_url, - Network accessline 394
params = {"page_size": page_size} while url: r = requests.get(url, params=params, - Network accessline 394
params = {"page_size": page_size} while url: r = requests.get(url, params=params, - Network accessline 395
while url: r = requests.get(url, params=params, headers={"Accept": "application/json"}, timeout=60) - Network accessline 395
while url: r = requests.get(url, params=params, headers={"Accept": "application/json"}, timeout=60) - Network accessline 396
r = requests.get(url, params=params, - Network accessline 396
r = requests.get(url, params=params, - Network accessline 417
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 417
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 420
"""List InterPro entries for a protein. Uses the `entry/interpro/protein/uniprot/{acc}/` endpoint (keyed `results`).""" r = requests.get( - Network accessline 420
"""List InterPro entries for a protein. Uses the `entry/interpro/protein/uniprot/{acc}/` endpoint (keyed `results`).""" r = requests.get( - Network accessline 421
`entry/interpro/protein/uniprot/{acc}/` endpoint (keyed `results`).""" r = requests.get( f"{INTERPRO_BASE}/entry/interpro/protein/uniprot/{uniprot_acc}/", - Network accessline 421
`entry/interpro/protein/uniprot/{acc}/` endpoint (keyed `results`).""" r = requests.get( f"{INTERPRO_BASE}/entry/interpro/protein/uniprot/{uniprot_acc}/", - Network accessline 422
r = requests.get( - Network accessline 422
r = requests.get( - Network accessline 466
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 466
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 468
# Step 1: Get PDB structures linked to the protein kinase-like SF entry. # Use the path-inverted form; the flat `?entry_interpro=` filter 408s. r = requests.get( - Network accessline 468
# Step 1: Get PDB structures linked to the protein kinase-like SF entry. # Use the path-inverted form; the flat `?entry_interpro=` filter 408s. r = requests.get( - Network accessline 469
# Use the path-inverted form; the flat `?entry_interpro=` filter 408s. r = requests.get( f"{INTERPRO_BASE}/structure/pdb/entry/interpro/IPR011009/", - Network accessline 469
# Use the path-inverted form; the flat `?entry_interpro=` filter 408s. r = requests.get( f"{INTERPRO_BASE}/structure/pdb/entry/interpro/IPR011009/", - Network accessline 470
r = requests.get( - Network accessline 470
r = requests.get( - Network accessline 506
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 506
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 517
if not url: break r = requests.get(url, params=params, - Network accessline 517
if not url: break r = requests.get(url, params=params, - Network accessline 518
break r = requests.get(url, params=params, headers={"Accept": "application/json"}, timeout=90) - Network accessline 518
break r = requests.get(url, params=params, headers={"Accept": "application/json"}, timeout=90) - Network accessline 519
r = requests.get(url, params=params, - Network accessline 519
r = requests.get(url, params=params, - Network accessline 588
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 588
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 590
def list_protein_domains(uniprot_acc: str) -> list: """Return list of (accession, type, name) tuples for a protein.""" r = requests.get( - Network accessline 590
def list_protein_domains(uniprot_acc: str) -> list: """Return list of (accession, type, name) tuples for a protein.""" r = requests.get( - Network accessline 591
"""Return list of (accession, type, name) tuples for a protein.""" r = requests.get( f"{INTERPRO_BASE}/entry/interpro/protein/uniprot/{uniprot_acc}/", - Network accessline 591
"""Return list of (accession, type, name) tuples for a protein.""" r = requests.get( f"{INTERPRO_BASE}/entry/interpro/protein/uniprot/{uniprot_acc}/", - Network accessline 592
r = requests.get( - Network accessline 592
r = requests.get( - Network accessline 619
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 619
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 621
interpro_acc = "IPR000719" # Protein kinase domain r = requests.get( - Network accessline 621
interpro_acc = "IPR000719" # Protein kinase domain r = requests.get( - Network accessline 622
r = requests.get( - Network accessline 622
r = requests.get( - Network accessline 650
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 650
INTERPRO_BASE = "https://www.ebi.ac.uk/interpro/api" - Network accessline 652
def get_go_terms_for_entry(interpro_acc: str) -> list: """Return GO terms associated with an InterPro entry.""" r = requests.get( - Network accessline 652
def get_go_terms_for_entry(interpro_acc: str) -> list: """Return GO terms associated with an InterPro entry.""" r = requests.get( - Network accessline 653
"""Return GO terms associated with an InterPro entry.""" r = requests.get( f"{INTERPRO_BASE}/entry/interpro/{interpro_acc}/", - Network accessline 653
"""Return GO terms associated with an InterPro entry.""" r = requests.get( f"{INTERPRO_BASE}/entry/interpro/{interpro_acc}/", - Network accessline 654
r = requests.get( - Network accessline 654
r = requests.get( - Network accessline 700
- [InterPro REST API documentation](https://interpro-documentation.readthedocs.io/en/latest/api.html) — Endpoint reference, filters, and example queries - Network accessline 700
- [InterPro REST API documentation](https://interpro-documentation.readthedocs.io/en/latest/api.html) — Endpoint reference, filters, and example queries - Network accessline 701
- [Blum et al., Nucleic Acids Research 2021](https://doi.org/10.1093/nar/gkaa977) — InterPro flagship paper describing member database integration - Network accessline 701
- [Blum et al., Nucleic Acids Research 2021](https://doi.org/10.1093/nar/gkaa977) — InterPro flagship paper describing member database integration - Network accessline 702
- [InterPro web portal](https://www.ebi.ac.uk/interpro/) — Interactive protein domain browser - Network accessline 702
- [InterPro web portal](https://www.ebi.ac.uk/interpro/) — Interactive protein domain browser - Network accessline 703
- [Paysan-Lafosse et al., Nucleic Acids Research 2023](https://doi.org/10.1093/nar/gkac993) — InterPro 2023 update describing new entry types and member databases - Network accessline 703
- [Paysan-Lafosse et al., Nucleic Acids Research 2023](https://doi.org/10.1093/nar/gkac993) — InterPro 2023 update describing new entry types and member databases
skills/proteomics-protein-engineering/matchms-spectral-matching/SKILL.md
skills/proteomics-protein-engineering/maxquant-proteomics/SKILL.md
- Network accessline 27
- **MaxQuant**: Windows software; download from https://maxquant.org/ (v2.4+); requires .NET 6 runtime - Network accessline 27
- **MaxQuant**: Windows software; download from https://maxquant.org/ (v2.4+); requires .NET 6 runtime - Network accessline 609
fdr_threshold: float = 0.05) -> pd.DataFrame: """Query STRING /enrichment endpoint for GO/KEGG enrichment.""" url = "https://string-db.org/api/json/enrichment" - Network accessline 609
fdr_threshold: float = 0.05) -> pd.DataFrame: """Query STRING /enrichment endpoint for GO/KEGG enrichment.""" url = "https://string-db.org/api/json/enrichment" - Network accessline 610
"""Query STRING /enrichment endpoint for GO/KEGG enrichment.""" url = "https://string-db.org/api/json/enrichment" params = { - Network accessline 610
"""Query STRING /enrichment endpoint for GO/KEGG enrichment.""" url = "https://string-db.org/api/json/enrichment" params = { - Network accessline 611
url = "https://string-db.org/api/json/enrichment" - Network accessline 611
url = "https://string-db.org/api/json/enrichment" - Network accessline 615
"caller_identity": "maxquant_proteomics_skill", } response = requests.post(url, data=params) - Network accessline 615
"caller_identity": "maxquant_proteomics_skill", } response = requests.post(url, data=params) - Network accessline 616
} response = requests.post(url, data=params) response.raise_for_status() - Network accessline 616
} response = requests.post(url, data=params) response.raise_for_status() - Network accessline 617
response = requests.post(url, data=params) - Network accessline 617
response = requests.post(url, data=params) - Network accessline 688
- [MaxQuant software and documentation](https://maxquant.org/) — official MaxQuant download, manual, and parameter guide - Network accessline 688
- [MaxQuant software and documentation](https://maxquant.org/) — official MaxQuant download, manual, and parameter guide - Network accessline 689
- [Perseus computational platform](https://maxquant.net/perseus/) — MaxQuant companion statistical analysis tool; basis for the Python workflow above - Network accessline 689
- [Perseus computational platform](https://maxquant.net/perseus/) — MaxQuant companion statistical analysis tool; basis for the Python workflow above - Network accessline 690
- [Cox et al. (2008) *Nature Biotechnology* — MaxQuant paper](https://doi.org/10.1038/nbt.1511) — original MaxQuant publication describing the MaxLFQ algorithm - Network accessline 690
- [Cox et al. (2008) *Nature Biotechnology* — MaxQuant paper](https://doi.org/10.1038/nbt.1511) — original MaxQuant publication describing the MaxLFQ algorithm - Network accessline 691
- [Tyanova et al. (2016) *Nature Methods* — Perseus paper](https://doi.org/10.1038/nmeth.3901) — Perseus statistical framework reference; all workflow steps above mirror Perseus operations - Network accessline 691
- [Tyanova et al. (2016) *Nature Methods* — Perseus paper](https://doi.org/10.1038/nmeth.3901) — Perseus statistical framework reference; all workflow steps above mirror Perseus operations - Network accessline 692
- [pyMaxQuant GitHub](https://github.com/cshukai/pyMaxquant) — Python library for parsing MaxQuant output files - Network accessline 692
- [pyMaxQuant GitHub](https://github.com/cshukai/pyMaxquant) — Python library for parsing MaxQuant output files - Network accessline 693
- [gseapy documentation](https://gseapy.readthedocs.io/) — gene set enrichment analysis library used in Step 8 - Network accessline 693
- [gseapy documentation](https://gseapy.readthedocs.io/) — gene set enrichment analysis library used in Step 8 - Network accessline 694
- [STRING API enrichment](https://string-db.org/help/api/) — protein network enrichment endpoint used in Common Recipes - Network accessline 694
- [STRING API enrichment](https://string-db.org/help/api/) — protein network enrichment endpoint used in Common Recipes
skills/proteomics-protein-engineering/metabolomics-workbench-database/SKILL.md
- Network accessline 11
The Metabolomics Workbench (MW) REST API at `https://www.metabolomicsworkbench.org/rest/` exposes 4,200+ metabolomics studies hosted at UCSD under NIH Common Fund sponsorship. URL pattern is `/{context}/{input_item}/{input_value}/{output_it - Network accessline 11
The Metabolomics Workbench (MW) REST API at `https://www.metabolomicsworkbench.org/rest/` exposes 4,200+ metabolomics studies hosted at UCSD under NIH Common Fund sponsorship. URL pattern is `/{context}/{input_item}/{input_value}/{output_it - Network accessline 35
- **No API key required**: publicly accessible - **Rate limits**: MW does not enforce strict limits; add `time.sleep(0.3)` between bulk requests - **Base URL**: `https://www.metabolomicsworkbench.org/rest` - Network accessline 35
- **No API key required**: publicly accessible - **Rate limits**: MW does not enforce strict limits; add `time.sleep(0.3)` between bulk requests - **Base URL**: `https://www.metabolomicsworkbench.org/rest` - Network accessline 37
- **Base URL**: `https://www.metabolomicsworkbench.org/rest` - Network accessline 37
- **Base URL**: `https://www.metabolomicsworkbench.org/rest` - Network accessline 48
BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 48
BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 51
def lookup_by_name(name): # 1) Normalise to RefMet name r = requests.get(f"{BASE}/refmet/match/{name}", timeout=30) - Network accessline 51
def lookup_by_name(name): # 1) Normalise to RefMet name r = requests.get(f"{BASE}/refmet/match/{name}", timeout=30) - Network accessline 52
# 1) Normalise to RefMet name r = requests.get(f"{BASE}/refmet/match/{name}", timeout=30) r.raise_for_status() - Network accessline 52
# 1) Normalise to RefMet name r = requests.get(f"{BASE}/refmet/match/{name}", timeout=30) r.raise_for_status() - Network accessline 53
r = requests.get(f"{BASE}/refmet/match/{name}", timeout=30) - Network accessline 53
r = requests.get(f"{BASE}/refmet/match/{name}", timeout=30) - Network accessline 57
return None # 2) Pull full compound record by RefMet name (or by pubchem_cid) r2 = requests.get(f"{BASE}/refmet/name/{refmet['refmet_name']}/all", timeout=30) - Network accessline 57
return None # 2) Pull full compound record by RefMet name (or by pubchem_cid) r2 = requests.get(f"{BASE}/refmet/name/{refmet['refmet_name']}/all", timeout=30) - Network accessline 58
# 2) Pull full compound record by RefMet name (or by pubchem_cid) r2 = requests.get(f"{BASE}/refmet/name/{refmet['refmet_name']}/all", timeout=30) rec = r2.json() if r2.json() else {} - Network accessline 58
# 2) Pull full compound record by RefMet name (or by pubchem_cid) r2 = requests.get(f"{BASE}/refmet/name/{refmet['refmet_name']}/all", timeout=30) rec = r2.json() if r2.json() else {} - Network accessline 59
r2 = requests.get(f"{BASE}/refmet/name/{refmet['refmet_name']}/all", timeout=30) - Network accessline 59
r2 = requests.get(f"{BASE}/refmet/name/{refmet['refmet_name']}/all", timeout=30) - Network accessline 78
BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 78
BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 80
# By PubChem CID r = requests.get(f"{BASE}/compound/pubchem_cid/5793/all/json", timeout=30) - Network accessline 80
# By PubChem CID r = requests.get(f"{BASE}/compound/pubchem_cid/5793/all/json", timeout=30) - Network accessline 81
r = requests.get(f"{BASE}/compound/pubchem_cid/5793/all/json", timeout=30) - Network accessline 81
r = requests.get(f"{BASE}/compound/pubchem_cid/5793/all/json", timeout=30) - Network accessline 86
# By KEGG ID r = requests.get(f"{BASE}/compound/kegg_id/C00031/all/json", timeout=30) - Network accessline 86
# By KEGG ID r = requests.get(f"{BASE}/compound/kegg_id/C00031/all/json", timeout=30) - Network accessline 87
r = requests.get(f"{BASE}/compound/kegg_id/C00031/all/json", timeout=30) - Network accessline 87
r = requests.get(f"{BASE}/compound/kegg_id/C00031/all/json", timeout=30) - Network accessline 90
# By InChIKey r = requests.get(f"{BASE}/compound/inchi_key/WQZGKKKJIJFFOK-GASJEMHNSA-N/all/json", timeout=30) - Network accessline 90
# By InChIKey r = requests.get(f"{BASE}/compound/inchi_key/WQZGKKKJIJFFOK-GASJEMHNSA-N/all/json", timeout=30) - Network accessline 91
r = requests.get(f"{BASE}/compound/inchi_key/WQZGKKKJIJFFOK-GASJEMHNSA-N/all/json", timeout=30) - Network accessline 91
r = requests.get(f"{BASE}/compound/inchi_key/WQZGKKKJIJFFOK-GASJEMHNSA-N/all/json", timeout=30) - Network accessline 96
# Compound by formula returns a paged dict (multiple matches) import requests BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 96
# Compound by formula returns a paged dict (multiple matches) import requests BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 97
import requests BASE = "https://www.metabolomicsworkbench.org/rest" r = requests.get(f"{BASE}/compound/formula/C6H12O6/all/json", timeout=30) - Network accessline 97
import requests BASE = "https://www.metabolomicsworkbench.org/rest" r = requests.get(f"{BASE}/compound/formula/C6H12O6/all/json", timeout=30) - Network accessline 98
BASE = "https://www.metabolomicsworkbench.org/rest" r = requests.get(f"{BASE}/compound/formula/C6H12O6/all/json", timeout=30) matches = r.json() - Network accessline 98
BASE = "https://www.metabolomicsworkbench.org/rest" r = requests.get(f"{BASE}/compound/formula/C6H12O6/all/json", timeout=30) matches = r.json() - Network accessline 99
r = requests.get(f"{BASE}/compound/formula/C6H12O6/all/json", timeout=30) - Network accessline 99
r = requests.get(f"{BASE}/compound/formula/C6H12O6/all/json", timeout=30) - Network accessline 113
BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 113
BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 115
# Single-study summary — `study/study_id/{id}/summary` returns a flat dict # (keys: study_id, study_title, species, institute, analysis_type, ...) r = requests.get(f"{BASE}/study/study_id/ST000001/summary", timeout=30) - Network accessline 115
# Single-study summary — `study/study_id/{id}/summary` returns a flat dict # (keys: study_id, study_title, species, institute, analysis_type, ...) r = requests.get(f"{BASE}/study/study_id/ST000001/summary", timeout=30) - Network accessline 116
# (keys: study_id, study_title, species, institute, analysis_type, ...) r = requests.get(f"{BASE}/study/study_id/ST000001/summary", timeout=30) s = r.json() - Network accessline 116
# (keys: study_id, study_title, species, institute, analysis_type, ...) r = requests.get(f"{BASE}/study/study_id/ST000001/summary", timeout=30) s = r.json() - Network accessline 117
r = requests.get(f"{BASE}/study/study_id/ST000001/summary", timeout=30) - Network accessline 117
r = requests.get(f"{BASE}/study/study_id/ST000001/summary", timeout=30) - Network accessline 127
# to get title and species. import requests, pandas as pd BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 127
# to get title and species. import requests, pandas as pd BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 128
import requests, pandas as pd BASE = "https://www.metabolomicsworkbench.org/rest" r = requests.get(f"{BASE}/study/refmet_name/Glucose/summary", timeout=60) - Network accessline 128
import requests, pandas as pd BASE = "https://www.metabolomicsworkbench.org/rest" r = requests.get(f"{BASE}/study/refmet_name/Glucose/summary", timeout=60) - Network accessline 129
BASE = "https://www.metabolomicsworkbench.org/rest" r = requests.get(f"{BASE}/study/refmet_name/Glucose/summary", timeout=60) d = r.json() - Network accessline 129
BASE = "https://www.metabolomicsworkbench.org/rest" r = requests.get(f"{BASE}/study/refmet_name/Glucose/summary", timeout=60) d = r.json() - Network accessline 130
r = requests.get(f"{BASE}/study/refmet_name/Glucose/summary", timeout=60) - Network accessline 130
r = requests.get(f"{BASE}/study/refmet_name/Glucose/summary", timeout=60) - Network accessline 148
BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 148
BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 150
def normalise_to_refmet(user_text): r = requests.get(f"{BASE}/refmet/match/{user_text}", timeout=30) - Network accessline 150
def normalise_to_refmet(user_text): r = requests.get(f"{BASE}/refmet/match/{user_text}", timeout=30) - Network accessline 151
r = requests.get(f"{BASE}/refmet/match/{user_text}", timeout=30) - Network accessline 151
r = requests.get(f"{BASE}/refmet/match/{user_text}", timeout=30) - Network accessline 158
def refmet_full(refmet_name): r = requests.get(f"{BASE}/refmet/name/{refmet_name}/all", timeout=30) - Network accessline 158
def refmet_full(refmet_name): r = requests.get(f"{BASE}/refmet/name/{refmet_name}/all", timeout=30) - Network accessline 159
r = requests.get(f"{BASE}/refmet/name/{refmet_name}/all", timeout=30) - Network accessline 159
r = requests.get(f"{BASE}/refmet/name/{refmet_name}/all", timeout=30) - Network accessline 179
BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 179
BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 181
def study_ids_for_metabolite(refmet_name): """Return the study_id list that report a given RefMet name.""" r = requests.get(f"{BASE}/study/refmet_name/{refmet_name}/summary", timeout=60) - Network accessline 181
def study_ids_for_metabolite(refmet_name): """Return the study_id list that report a given RefMet name.""" r = requests.get(f"{BASE}/study/refmet_name/{refmet_name}/summary", timeout=60) - Network accessline 182
"""Return the study_id list that report a given RefMet name.""" r = requests.get(f"{BASE}/study/refmet_name/{refmet_name}/summary", timeout=60) r.raise_for_status() - Network accessline 182
"""Return the study_id list that report a given RefMet name.""" r = requests.get(f"{BASE}/study/refmet_name/{refmet_name}/summary", timeout=60) r.raise_for_status() - Network accessline 183
r = requests.get(f"{BASE}/study/refmet_name/{refmet_name}/summary", timeout=60) - Network accessline 183
r = requests.get(f"{BASE}/study/refmet_name/{refmet_name}/summary", timeout=60) - Network accessline 190
"""Pull full summary (title, species, institute, dates) for one study_id. Response is a flat dict with keys study_id/study_title/species/institute/...""" return requests.get(f"{BASE}/study/study_id/{study_id}/summary", timeout=30).j - Network accessline 190
"""Pull full summary (title, species, institute, dates) for one study_id. Response is a flat dict with keys study_id/study_title/species/institute/...""" return requests.get(f"{BASE}/study/study_id/{study_id}/summary", timeout=30).j - Network accessline 192
return requests.get(f"{BASE}/study/study_id/{study_id}/summary", timeout=30).json() - Network accessline 192
return requests.get(f"{BASE}/study/study_id/{study_id}/summary", timeout=30).json() - Network accessline 209
BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 209
BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 213
Response is TSV text — no JSON variant.""" assert db in {"REFMET", "LIPIDS", "MB"} r = requests.get(f"{BASE}/moverz/{db}/{mz}/{ion}/{tolerance}/txt", timeout=30) - Network accessline 213
Response is TSV text — no JSON variant.""" assert db in {"REFMET", "LIPIDS", "MB"} r = requests.get(f"{BASE}/moverz/{db}/{mz}/{ion}/{tolerance}/txt", timeout=30) - Network accessline 214
assert db in {"REFMET", "LIPIDS", "MB"} r = requests.get(f"{BASE}/moverz/{db}/{mz}/{ion}/{tolerance}/txt", timeout=30) r.raise_for_status() - Network accessline 214
assert db in {"REFMET", "LIPIDS", "MB"} r = requests.get(f"{BASE}/moverz/{db}/{mz}/{ion}/{tolerance}/txt", timeout=30) r.raise_for_status() - Network accessline 215
r = requests.get(f"{BASE}/moverz/{db}/{mz}/{ion}/{tolerance}/txt", timeout=30) - Network accessline 215
r = requests.get(f"{BASE}/moverz/{db}/{mz}/{ion}/{tolerance}/txt", timeout=30) - Network accessline 225
# Same query against the LIPIDS database import requests, io, pandas as pd BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 225
# Same query against the LIPIDS database import requests, io, pandas as pd BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 226
import requests, io, pandas as pd BASE = "https://www.metabolomicsworkbench.org/rest" r = requests.get(f"{BASE}/moverz/LIPIDS/760.585/M+H/0.01/txt", timeout=30) - Network accessline 226
import requests, io, pandas as pd BASE = "https://www.metabolomicsworkbench.org/rest" r = requests.get(f"{BASE}/moverz/LIPIDS/760.585/M+H/0.01/txt", timeout=30) - Network accessline 227
BASE = "https://www.metabolomicsworkbench.org/rest" r = requests.get(f"{BASE}/moverz/LIPIDS/760.585/M+H/0.01/txt", timeout=30) df_lipids = pd.read_csv(io.StringIO(r.text), sep="\t") - Network accessline 227
BASE = "https://www.metabolomicsworkbench.org/rest" r = requests.get(f"{BASE}/moverz/LIPIDS/760.585/M+H/0.01/txt", timeout=30) df_lipids = pd.read_csv(io.StringIO(r.text), sep="\t") - Network accessline 228
r = requests.get(f"{BASE}/moverz/LIPIDS/760.585/M+H/0.01/txt", timeout=30) - Network accessline 228
r = requests.get(f"{BASE}/moverz/LIPIDS/760.585/M+H/0.01/txt", timeout=30) - Network accessline 239
BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 239
BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 241
r = requests.get(f"{BASE}/gene/gene_symbol/HMGCR/all", timeout=30) - Network accessline 241
r = requests.get(f"{BASE}/gene/gene_symbol/HMGCR/all", timeout=30) - Network accessline 245
# Protein by UniProt accession r2 = requests.get(f"{BASE}/protein/uniprot_id/P04035/all", timeout=30) - Network accessline 245
# Protein by UniProt accession r2 = requests.get(f"{BASE}/protein/uniprot_id/P04035/all", timeout=30) - Network accessline 246
r2 = requests.get(f"{BASE}/protein/uniprot_id/P04035/all", timeout=30) - Network accessline 246
r2 = requests.get(f"{BASE}/protein/uniprot_id/P04035/all", timeout=30) - Network accessline 282
BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 282
BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 284
def resolve_to_compound(user_text): # 1) Normalise via refmet/match rm = requests.get(f"{BASE}/refmet/match/{user_text}", timeout=30).json() - Network accessline 284
def resolve_to_compound(user_text): # 1) Normalise via refmet/match rm = requests.get(f"{BASE}/refmet/match/{user_text}", timeout=30).json() - Network accessline 285
# 1) Normalise via refmet/match rm = requests.get(f"{BASE}/refmet/match/{user_text}", timeout=30).json() if not rm.get("refmet_name"): - Network accessline 285
# 1) Normalise via refmet/match rm = requests.get(f"{BASE}/refmet/match/{user_text}", timeout=30).json() if not rm.get("refmet_name"): - Dangerous shell operationline 286
rm = requests.get(f"{BASE}/refmet/match/{user_text}", timeout=30).json() - Dangerous shell operationline 286
rm = requests.get(f"{BASE}/refmet/match/{user_text}", timeout=30).json() - Network accessline 286
rm = requests.get(f"{BASE}/refmet/match/{user_text}", timeout=30).json() - Network accessline 286
rm = requests.get(f"{BASE}/refmet/match/{user_text}", timeout=30).json() - Network accessline 289
name = rm["refmet_name"] # 2) Fetch full RefMet record (includes pubchem_cid / inchi_key) full = requests.get(f"{BASE}/refmet/name/{name}/all", timeout=30).json() - Network accessline 289
name = rm["refmet_name"] # 2) Fetch full RefMet record (includes pubchem_cid / inchi_key) full = requests.get(f"{BASE}/refmet/name/{name}/all", timeout=30).json() - Network accessline 290
# 2) Fetch full RefMet record (includes pubchem_cid / inchi_key) full = requests.get(f"{BASE}/refmet/name/{name}/all", timeout=30).json() if not isinstance(full, dict) or not full: - Network accessline 290
# 2) Fetch full RefMet record (includes pubchem_cid / inchi_key) full = requests.get(f"{BASE}/refmet/name/{name}/all", timeout=30).json() if not isinstance(full, dict) or not full: - Network accessline 291
full = requests.get(f"{BASE}/refmet/name/{name}/all", timeout=30).json() - Network accessline 291
full = requests.get(f"{BASE}/refmet/name/{name}/all", timeout=30).json() - Network accessline 296
compound = None if cid: compound = requests.get(f"{BASE}/compound/pubchem_cid/{cid}/all/json", - Network accessline 296
compound = None if cid: compound = requests.get(f"{BASE}/compound/pubchem_cid/{cid}/all/json", - Network accessline 297
if cid: compound = requests.get(f"{BASE}/compound/pubchem_cid/{cid}/all/json", timeout=30).json() - Network accessline 297
if cid: compound = requests.get(f"{BASE}/compound/pubchem_cid/{cid}/all/json", timeout=30).json() - Network accessline 298
compound = requests.get(f"{BASE}/compound/pubchem_cid/{cid}/all/json", - Network accessline 298
compound = requests.get(f"{BASE}/compound/pubchem_cid/{cid}/all/json", - Network accessline 323
BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 323
BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 326
out = [] for mz in mz_values: r = requests.get(f"{BASE}/moverz/REFMET/{mz}/{ion}/{tolerance}/txt", timeout=30) - Network accessline 326
out = [] for mz in mz_values: r = requests.get(f"{BASE}/moverz/REFMET/{mz}/{ion}/{tolerance}/txt", timeout=30) - Network accessline 327
for mz in mz_values: r = requests.get(f"{BASE}/moverz/REFMET/{mz}/{ion}/{tolerance}/txt", timeout=30) if r.status_code != 200 or not r.text.strip(): - Network accessline 327
for mz in mz_values: r = requests.get(f"{BASE}/moverz/REFMET/{mz}/{ion}/{tolerance}/txt", timeout=30) if r.status_code != 200 or not r.text.strip(): - Network accessline 328
r = requests.get(f"{BASE}/moverz/REFMET/{mz}/{ion}/{tolerance}/txt", timeout=30) - Network accessline 328
r = requests.get(f"{BASE}/moverz/REFMET/{mz}/{ion}/{tolerance}/txt", timeout=30) - Network accessline 356
BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 356
BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 359
"""Return a DataFrame: study_id rows that report the metabolite, enriched with title + species for the first `enrich_n` IDs (via study/study_id/.../summary).""" r = requests.get(f"{BASE}/study/refmet_name/{refmet_name}/summary", tim - Network accessline 359
"""Return a DataFrame: study_id rows that report the metabolite, enriched with title + species for the first `enrich_n` IDs (via study/study_id/.../summary).""" r = requests.get(f"{BASE}/study/refmet_name/{refmet_name}/summary", tim - Network accessline 360
title + species for the first `enrich_n` IDs (via study/study_id/.../summary).""" r = requests.get(f"{BASE}/study/refmet_name/{refmet_name}/summary", timeout=60) r.raise_for_status() - Network accessline 360
title + species for the first `enrich_n` IDs (via study/study_id/.../summary).""" r = requests.get(f"{BASE}/study/refmet_name/{refmet_name}/summary", timeout=60) r.raise_for_status() - Network accessline 361
r = requests.get(f"{BASE}/study/refmet_name/{refmet_name}/summary", timeout=60) - Network accessline 361
r = requests.get(f"{BASE}/study/refmet_name/{refmet_name}/summary", timeout=60) - Network accessline 366
enriched = [] for sid in ids[:enrich_n]: enriched.append(requests.get( - Network accessline 366
enriched = [] for sid in ids[:enrich_n]: enriched.append(requests.get( - Network accessline 367
for sid in ids[:enrich_n]: enriched.append(requests.get( f"{BASE}/study/study_id/{sid}/summary", timeout=30).json()) - Network accessline 367
for sid in ids[:enrich_n]: enriched.append(requests.get( f"{BASE}/study/study_id/{sid}/summary", timeout=30).json()) - Network accessline 368
enriched.append(requests.get( - Network accessline 368
enriched.append(requests.get( - Network accessline 404
BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 404
BASE = "https://www.metabolomicsworkbench.org/rest" - Network accessline 406
def cross_refs(refmet_name): rm = requests.get(f"{BASE}/refmet/name/{refmet_name}/all", timeout=30).json() - Network accessline 406
def cross_refs(refmet_name): rm = requests.get(f"{BASE}/refmet/name/{refmet_name}/all", timeout=30).json() - Dangerous shell operationline 407
rm = requests.get(f"{BASE}/refmet/name/{refmet_name}/all", timeout=30).json() - Dangerous shell operationline 407
rm = requests.get(f"{BASE}/refmet/name/{refmet_name}/all", timeout=30).json() - Network accessline 407
rm = requests.get(f"{BASE}/refmet/name/{refmet_name}/all", timeout=30).json() - Network accessline 407
rm = requests.get(f"{BASE}/refmet/name/{refmet_name}/all", timeout=30).json() - Network accessline 411
if not cid: return {"refmet": refmet_name, "pubchem_cid": None} c = requests.get(f"{BASE}/compound/pubchem_cid/{cid}/all/json", timeout=30).json() - Network accessline 411
if not cid: return {"refmet": refmet_name, "pubchem_cid": None} c = requests.get(f"{BASE}/compound/pubchem_cid/{cid}/all/json", timeout=30).json() - Network accessline 412
return {"refmet": refmet_name, "pubchem_cid": None} c = requests.get(f"{BASE}/compound/pubchem_cid/{cid}/all/json", timeout=30).json() return {"refmet": refmet_name, - Network accessline 412
return {"refmet": refmet_name, "pubchem_cid": None} c = requests.get(f"{BASE}/compound/pubchem_cid/{cid}/all/json", timeout=30).json() return {"refmet": refmet_name, - Network accessline 413
c = requests.get(f"{BASE}/compound/pubchem_cid/{cid}/all/json", timeout=30).json() - Network accessline 413
c = requests.get(f"{BASE}/compound/pubchem_cid/{cid}/all/json", timeout=30).json() - Network accessline 429
BASE = "https://www.metabolomicsworkbench.org/rest" r = requests.get(f"{BASE}/study/study_id/ST000001/metabolites", timeout=60) - Network accessline 429
BASE = "https://www.metabolomicsworkbench.org/rest" r = requests.get(f"{BASE}/study/study_id/ST000001/metabolites", timeout=60) - Network accessline 430
r = requests.get(f"{BASE}/study/study_id/ST000001/metabolites", timeout=60) - Network accessline 430
r = requests.get(f"{BASE}/study/study_id/ST000001/metabolites", timeout=60) - Network accessline 443
BASE = "https://www.metabolomicsworkbench.org/rest" g = requests.get(f"{BASE}/gene/gene_symbol/HMGCR/all", timeout=30).json() - Network accessline 443
BASE = "https://www.metabolomicsworkbench.org/rest" g = requests.get(f"{BASE}/gene/gene_symbol/HMGCR/all", timeout=30).json() - Network accessline 444
g = requests.get(f"{BASE}/gene/gene_symbol/HMGCR/all", timeout=30).json() - Network accessline 444
g = requests.get(f"{BASE}/gene/gene_symbol/HMGCR/all", timeout=30).json() - Network accessline 470
- [Metabolomics Workbench home](https://www.metabolomicsworkbench.org/) - Network accessline 470
- [Metabolomics Workbench home](https://www.metabolomicsworkbench.org/) - Network accessline 471
- [REST API documentation (PDF)](https://www.metabolomicsworkbench.org/tools/MWRestAPIv1.0.pdf) - Network accessline 471
- [REST API documentation (PDF)](https://www.metabolomicsworkbench.org/tools/MWRestAPIv1.0.pdf) - Network accessline 472
- [RefMet nomenclature](https://www.metabolomicsworkbench.org/databases/refmet/index.php) - Network accessline 472
- [RefMet nomenclature](https://www.metabolomicsworkbench.org/databases/refmet/index.php) - Network accessline 473
- Sud M et al. "Metabolomics Workbench: An international repository for metabolomics data and metadata." *Nucleic Acids Research* 44(D1): D463–D470 (2016). https://doi.org/10.1093/nar/gkv1042 - Network accessline 473
- Sud M et al. "Metabolomics Workbench: An international repository for metabolomics data and metadata." *Nucleic Acids Research* 44(D1): D463–D470 (2016). https://doi.org/10.1093/nar/gkv1042
skills/proteomics-protein-engineering/pride-database/SKILL.md
- Network accessline 11
The PRIDE Archive (ProteomicsIDEntifications database) at EMBL-EBI is the world's largest public mass-spectrometry proteomics repository — 39,000+ projects and 3.4M+ deposited files as of 2026. Programmatic access is via a JSON REST API at - Network accessline 11
The PRIDE Archive (ProteomicsIDEntifications database) at EMBL-EBI is the world's largest public mass-spectrometry proteomics repository — 39,000+ projects and 3.4M+ deposited files as of 2026. Programmatic access is via a JSON REST API at - Network accessline 41
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 41
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 43
# 1) Free-text search for cancer proteomics projects projects = requests.get(f"{PRIDE}/search/projects", - Network accessline 43
# 1) Free-text search for cancer proteomics projects projects = requests.get(f"{PRIDE}/search/projects", - Network accessline 44
projects = requests.get(f"{PRIDE}/search/projects", - Network accessline 44
projects = requests.get(f"{PRIDE}/search/projects", - Network accessline 52
# 2) Drill into one project acc = projects[0]["accession"] proj = requests.get(f"{PRIDE}/projects/{acc}", timeout=30).json() - Network accessline 52
# 2) Drill into one project acc = projects[0]["accession"] proj = requests.get(f"{PRIDE}/projects/{acc}", timeout=30).json() - Network accessline 53
acc = projects[0]["accession"] proj = requests.get(f"{PRIDE}/projects/{acc}", timeout=30).json() print(f"\n{proj['accession']}: {proj['title'][:70]}") - Network accessline 53
acc = projects[0]["accession"] proj = requests.get(f"{PRIDE}/projects/{acc}", timeout=30).json() print(f"\n{proj['accession']}: {proj['title'][:70]}") - Network accessline 54
proj = requests.get(f"{PRIDE}/projects/{acc}", timeout=30).json() - Network accessline 54
proj = requests.get(f"{PRIDE}/projects/{acc}", timeout=30).json() - Network accessline 60
# 3) List files and total size files = requests.get(f"{PRIDE}/projects/{acc}/files/all", timeout=60).json() - Network accessline 60
# 3) List files and total size files = requests.get(f"{PRIDE}/projects/{acc}/files/all", timeout=60).json() - Network accessline 61
files = requests.get(f"{PRIDE}/projects/{acc}/files/all", timeout=60).json() - Network accessline 61
files = requests.get(f"{PRIDE}/projects/{acc}/files/all", timeout=60).json() - Network accessline 75
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 75
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 95
r = requests.get(f"{PRIDE}/search/projects", params=params, timeout=30) - Network accessline 95
r = requests.get(f"{PRIDE}/search/projects", params=params, timeout=30) - Network accessline 137
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 137
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 143
params = {"facetPageSize": facet_page_size} if keyword: params["keyword"] = keyword r = requests.get(f"{PRIDE}/facet/projects", params=params, timeout=30) - Network accessline 143
params = {"facetPageSize": facet_page_size} if keyword: params["keyword"] = keyword r = requests.get(f"{PRIDE}/facet/projects", params=params, timeout=30) - Network accessline 144
if keyword: params["keyword"] = keyword r = requests.get(f"{PRIDE}/facet/projects", params=params, timeout=30) r.raise_for_status() - Network accessline 144
if keyword: params["keyword"] = keyword r = requests.get(f"{PRIDE}/facet/projects", params=params, timeout=30) r.raise_for_status() - Network accessline 145
r = requests.get(f"{PRIDE}/facet/projects", params=params, timeout=30) - Network accessline 145
r = requests.get(f"{PRIDE}/facet/projects", params=params, timeout=30) - Network accessline 166
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 166
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 168
def get_project(accession): r = requests.get(f"{PRIDE}/projects/{accession}", timeout=30) - Network accessline 168
def get_project(accession): r = requests.get(f"{PRIDE}/projects/{accession}", timeout=30) - Network accessline 169
r = requests.get(f"{PRIDE}/projects/{accession}", timeout=30) - Network accessline 169
r = requests.get(f"{PRIDE}/projects/{accession}", timeout=30) - Network accessline 195
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 195
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 200
rows, page = [], 0 while True: r = requests.get(f"{PRIDE}/projects/{accession}/files", - Network accessline 200
rows, page = [], 0 while True: r = requests.get(f"{PRIDE}/projects/{accession}/files", - Network accessline 201
while True: r = requests.get(f"{PRIDE}/projects/{accession}/files", params={"pageSize": page_size, "page": page}, - Network accessline 201
while True: r = requests.get(f"{PRIDE}/projects/{accession}/files", params={"pageSize": page_size, "page": page}, - Network accessline 202
r = requests.get(f"{PRIDE}/projects/{accession}/files", - Network accessline 202
r = requests.get(f"{PRIDE}/projects/{accession}/files", - Network accessline 242
# /files/all returns every file in one response — convenient for small projects files = requests.get(f"{PRIDE}/projects/PXD000001/files/all", timeout=60).json() - Network accessline 242
# /files/all returns every file in one response — convenient for small projects files = requests.get(f"{PRIDE}/projects/PXD000001/files/all", timeout=60).json() - Network accessline 243
files = requests.get(f"{PRIDE}/projects/PXD000001/files/all", timeout=60).json() - Network accessline 243
files = requests.get(f"{PRIDE}/projects/PXD000001/files/all", timeout=60).json() - Network accessline 257
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 257
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 259
def get_sdrf(accession): """Fetch the SDRF sample-to-run mapping for a project (404 if not provided).""" r = requests.get(f"{PRIDE}/files/sdrf/{accession}", timeout=30) - Network accessline 259
def get_sdrf(accession): """Fetch the SDRF sample-to-run mapping for a project (404 if not provided).""" r = requests.get(f"{PRIDE}/files/sdrf/{accession}", timeout=30) - Network accessline 260
"""Fetch the SDRF sample-to-run mapping for a project (404 if not provided).""" r = requests.get(f"{PRIDE}/files/sdrf/{accession}", timeout=30) if r.status_code == 404: - Network accessline 260
"""Fetch the SDRF sample-to-run mapping for a project (404 if not provided).""" r = requests.get(f"{PRIDE}/files/sdrf/{accession}", timeout=30) if r.status_code == 404: - Network accessline 261
r = requests.get(f"{PRIDE}/files/sdrf/{accession}", timeout=30) - Network accessline 261
r = requests.get(f"{PRIDE}/files/sdrf/{accession}", timeout=30) - Network accessline 283
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 283
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 286
"""Return the list of PRIDE project accessions that mention this UniProt accession. No PSM/peptide/coverage counts are available at this endpoint.""" r = requests.get(f"{PRIDE}/proteins/{uniprot_acc}", timeout=30) - Network accessline 286
"""Return the list of PRIDE project accessions that mention this UniProt accession. No PSM/peptide/coverage counts are available at this endpoint.""" r = requests.get(f"{PRIDE}/proteins/{uniprot_acc}", timeout=30) - Network accessline 287
No PSM/peptide/coverage counts are available at this endpoint.""" r = requests.get(f"{PRIDE}/proteins/{uniprot_acc}", timeout=30) if r.status_code == 404: - Network accessline 287
No PSM/peptide/coverage counts are available at this endpoint.""" r = requests.get(f"{PRIDE}/proteins/{uniprot_acc}", timeout=30) if r.status_code == 404: - Network accessline 288
r = requests.get(f"{PRIDE}/proteins/{uniprot_acc}", timeout=30) - Network accessline 288
r = requests.get(f"{PRIDE}/proteins/{uniprot_acc}", timeout=30) - Network accessline 311
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 311
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 313
# Find similar projects to one of interest similar = requests.get(f"{PRIDE}/projects/PXD004131/similarProjects", - Network accessline 313
# Find similar projects to one of interest similar = requests.get(f"{PRIDE}/projects/PXD004131/similarProjects", - Network accessline 314
similar = requests.get(f"{PRIDE}/projects/PXD004131/similarProjects", - Network accessline 314
similar = requests.get(f"{PRIDE}/projects/PXD004131/similarProjects", - Network accessline 320
# Autocomplete suggestions for a project-title prefix suggestions = requests.get(f"{PRIDE}/search/autocomplete", - Network accessline 320
# Autocomplete suggestions for a project-title prefix suggestions = requests.get(f"{PRIDE}/search/autocomplete", - Network accessline 321
suggestions = requests.get(f"{PRIDE}/search/autocomplete", - Network accessline 321
suggestions = requests.get(f"{PRIDE}/search/autocomplete", - Network accessline 335
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 335
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 337
n_projects = int(requests.get(f"{PRIDE}/projects/count", timeout=30).text) - Network accessline 337
n_projects = int(requests.get(f"{PRIDE}/projects/count", timeout=30).text) - Network accessline 338
n_files = int(requests.get(f"{PRIDE}/files/count", timeout=30).text) - Network accessline 338
n_files = int(requests.get(f"{PRIDE}/files/count", timeout=30).text) - Network accessline 404
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 404
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 407
# 1) Inspect facet counts to learn which filter values dominate facets = requests.get(f"{PRIDE}/facet/projects", - Network accessline 407
# 1) Inspect facet counts to learn which filter values dominate facets = requests.get(f"{PRIDE}/facet/projects", - Network accessline 408
facets = requests.get(f"{PRIDE}/facet/projects", - Network accessline 408
facets = requests.get(f"{PRIDE}/facet/projects", - Network accessline 419
# 2) Build a filtered search using one top instrument target_instr = top_instr[0][0] projects = requests.get(f"{PRIDE}/search/projects", - Network accessline 419
# 2) Build a filtered search using one top instrument target_instr = top_instr[0][0] projects = requests.get(f"{PRIDE}/search/projects", - Network accessline 420
target_instr = top_instr[0][0] projects = requests.get(f"{PRIDE}/search/projects", params={"keyword": disease_kw, - Network accessline 420
target_instr = top_instr[0][0] projects = requests.get(f"{PRIDE}/search/projects", params={"keyword": disease_kw, - Network accessline 421
projects = requests.get(f"{PRIDE}/search/projects", - Network accessline 421
projects = requests.get(f"{PRIDE}/search/projects", - Network accessline 450
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 450
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 455
files = requests.get(f"{PRIDE}/projects/{accession}/files/all", timeout=120).json() - Network accessline 455
files = requests.get(f"{PRIDE}/projects/{accession}/files/all", timeout=120).json() - Network accessline 491
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 491
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 498
rows = [] for acc, sym in candidates.items(): r = requests.get(f"{PRIDE}/proteins/{acc}", timeout=30) - Network accessline 498
rows = [] for acc, sym in candidates.items(): r = requests.get(f"{PRIDE}/proteins/{acc}", timeout=30) - Network accessline 499
for acc, sym in candidates.items(): r = requests.get(f"{PRIDE}/proteins/{acc}", timeout=30) projs = r.json().get("projects", []) if r.status_code == 200 else [] - Network accessline 499
for acc, sym in candidates.items(): r = requests.get(f"{PRIDE}/proteins/{acc}", timeout=30) projs = r.json().get("projects", []) if r.status_code == 200 else [] - Network accessline 500
r = requests.get(f"{PRIDE}/proteins/{acc}", timeout=30) - Network accessline 500
r = requests.get(f"{PRIDE}/proteins/{acc}", timeout=30) - Network accessline 555
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 555
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 557
def project_file_summary(accession): files = requests.get(f"{PRIDE}/projects/{accession}/files/all", timeout=60).json() - Network accessline 557
def project_file_summary(accession): files = requests.get(f"{PRIDE}/projects/{accession}/files/all", timeout=60).json() - Network accessline 558
files = requests.get(f"{PRIDE}/projects/{accession}/files/all", timeout=60).json() - Network accessline 558
files = requests.get(f"{PRIDE}/projects/{accession}/files/all", timeout=60).json() - Network accessline 580
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 580
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 582
def pride_evidence(uniprot_acc): """Return (has_evidence, n_projects). PRIDE v3 only exposes project list, no PSM counts.""" r = requests.get(f"{PRIDE}/proteins/{uniprot_acc}", timeout=30) - Network accessline 582
def pride_evidence(uniprot_acc): """Return (has_evidence, n_projects). PRIDE v3 only exposes project list, no PSM counts.""" r = requests.get(f"{PRIDE}/proteins/{uniprot_acc}", timeout=30) - Network accessline 583
"""Return (has_evidence, n_projects). PRIDE v3 only exposes project list, no PSM counts.""" r = requests.get(f"{PRIDE}/proteins/{uniprot_acc}", timeout=30) if r.status_code != 200: - Network accessline 583
"""Return (has_evidence, n_projects). PRIDE v3 only exposes project list, no PSM counts.""" r = requests.get(f"{PRIDE}/proteins/{uniprot_acc}", timeout=30) if r.status_code != 200: - Network accessline 584
r = requests.get(f"{PRIDE}/proteins/{uniprot_acc}", timeout=30) - Network accessline 584
r = requests.get(f"{PRIDE}/proteins/{uniprot_acc}", timeout=30) - Network accessline 600
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 600
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 602
r = requests.get(f"{PRIDE}/search/projects", - Network accessline 602
r = requests.get(f"{PRIDE}/search/projects", - Network accessline 622
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 622
PRIDE = "https://www.ebi.ac.uk/pride/ws/archive/v3" - Network accessline 624
for prefix in ["alzheimer", "single cell", "brca"]: s = requests.get(f"{PRIDE}/search/autocomplete", - Network accessline 624
for prefix in ["alzheimer", "single cell", "brca"]: s = requests.get(f"{PRIDE}/search/autocomplete", - Network accessline 625
s = requests.get(f"{PRIDE}/search/autocomplete", - Network accessline 625
s = requests.get(f"{PRIDE}/search/autocomplete", - Network accessline 654
- [PRIDE Archive v3 REST API root](https://www.ebi.ac.uk/pride/ws/archive/v3/) — base URL - Network accessline 654
- [PRIDE Archive v3 REST API root](https://www.ebi.ac.uk/pride/ws/archive/v3/) — base URL - Network accessline 655
- [PRIDE v3 OpenAPI/Swagger spec](https://www.ebi.ac.uk/pride/ws/archive/v3/v3/api-docs) — authoritative endpoint inventory - Network accessline 655
- [PRIDE v3 OpenAPI/Swagger spec](https://www.ebi.ac.uk/pride/ws/archive/v3/v3/api-docs) — authoritative endpoint inventory - Network accessline 656
- [PRIDE Archive web portal](https://www.ebi.ac.uk/pride/) — interactive dataset browser - Network accessline 656
- [PRIDE Archive web portal](https://www.ebi.ac.uk/pride/) — interactive dataset browser - Network accessline 657
- [Perez-Riverol et al., *Nucleic Acids Research* 2022](https://doi.org/10.1093/nar/gkab1038) — PRIDE 2022 update describing repository and architecture - Network accessline 657
- [Perez-Riverol et al., *Nucleic Acids Research* 2022](https://doi.org/10.1093/nar/gkab1038) — PRIDE 2022 update describing repository and architecture - Network accessline 658
- [ProteomeXchange Consortium](http://www.proteomexchange.org/) — standard accession system shared across PRIDE, MassIVE, jPOST, iProX - Network accessline 658
- [ProteomeXchange Consortium](http://www.proteomexchange.org/) — standard accession system shared across PRIDE, MassIVE, jPOST, iProX - Network accessline 659
- [SDRF-Proteomics specification](https://github.com/bigbio/proteomics-sample-metadata) — format used by `/files/sdrf/{projectAccession}` - Network accessline 659
- [SDRF-Proteomics specification](https://github.com/bigbio/proteomics-sample-metadata) — format used by `/files/sdrf/{projectAccession}`
skills/proteomics-protein-engineering/pyopenms-mass-spectrometry/SKILL.md
- Network accessline 636
- PyOpenMS documentation: https://pyopenms.readthedocs.io - Network accessline 636
- PyOpenMS documentation: https://pyopenms.readthedocs.io - Network accessline 637
- OpenMS project: https://www.openms.org - Network accessline 637
- OpenMS project: https://www.openms.org - Network accessline 639
- GitHub: https://github.com/OpenMS/OpenMS - Network accessline 639
- GitHub: https://github.com/OpenMS/OpenMS
skills/proteomics-protein-engineering/uniprot-protein-database/SKILL.md
- Network accessline 37
# Search for human insulin proteins (reviewed/Swiss-Prot only) url = "https://rest.uniprot.org/uniprotkb/search" - Network accessline 37
# Search for human insulin proteins (reviewed/Swiss-Prot only) url = "https://rest.uniprot.org/uniprotkb/search" - Network accessline 38
url = "https://rest.uniprot.org/uniprotkb/search" - Network accessline 38
url = "https://rest.uniprot.org/uniprotkb/search" - Network accessline 39
params = {"query": "insulin AND organism_id:9606 AND reviewed:true", "format": "tsv", "fields": "accession,gene_names,protein_name,length"} response = requests.get(url, params=params) - Network accessline 39
params = {"query": "insulin AND organism_id:9606 AND reviewed:true", "format": "tsv", "fields": "accession,gene_names,protein_name,length"} response = requests.get(url, params=params) - Network accessline 40
"fields": "accession,gene_names,protein_name,length"} response = requests.get(url, params=params) print(response.text[:500]) - Network accessline 40
"fields": "accession,gene_names,protein_name,length"} response = requests.get(url, params=params) print(response.text[:500]) - Network accessline 41
response = requests.get(url, params=params) - Network accessline 41
response = requests.get(url, params=params) - Network accessline 57
BASE = "https://rest.uniprot.org/uniprotkb/search" - Network accessline 57
BASE = "https://rest.uniprot.org/uniprotkb/search" - Network accessline 62
if fields: params["fields"] = ",".join(fields) resp = requests.get(BASE, params=params) - Network accessline 62
if fields: params["fields"] = ",".join(fields) resp = requests.get(BASE, params=params) - Network accessline 63
params["fields"] = ",".join(fields) resp = requests.get(BASE, params=params) resp.raise_for_status() - Network accessline 63
params["fields"] = ",".join(fields) resp = requests.get(BASE, params=params) resp.raise_for_status() - Network accessline 64
resp = requests.get(BASE, params=params) - Network accessline 64
resp = requests.get(BASE, params=params) - Network accessline 103
def get_protein(accession, format="json"): """Retrieve a single protein entry.""" url = f"https://rest.uniprot.org/uniprotkb/{accession}" - Network accessline 103
def get_protein(accession, format="json"): """Retrieve a single protein entry.""" url = f"https://rest.uniprot.org/uniprotkb/{accession}" - Network accessline 104
"""Retrieve a single protein entry.""" url = f"https://rest.uniprot.org/uniprotkb/{accession}" resp = requests.get(url, headers={"Accept": f"application/{format}"}) - Network accessline 104
"""Retrieve a single protein entry.""" url = f"https://rest.uniprot.org/uniprotkb/{accession}" resp = requests.get(url, headers={"Accept": f"application/{format}"}) - Network accessline 105
url = f"https://rest.uniprot.org/uniprotkb/{accession}" resp = requests.get(url, headers={"Accept": f"application/{format}"}) resp.raise_for_status() - Network accessline 105
url = f"https://rest.uniprot.org/uniprotkb/{accession}" resp = requests.get(url, headers={"Accept": f"application/{format}"}) resp.raise_for_status() - Network accessline 106
resp = requests.get(url, headers={"Accept": f"application/{format}"}) - Network accessline 106
resp = requests.get(url, headers={"Accept": f"application/{format}"}) - Network accessline 117
# Get FASTA directly fasta = requests.get("https://rest.uniprot.org/uniprotkb/P01308.fasta").text - Network accessline 117
# Get FASTA directly fasta = requests.get("https://rest.uniprot.org/uniprotkb/P01308.fasta").text - Network accessline 118
fasta = requests.get("https://rest.uniprot.org/uniprotkb/P01308.fasta").text - Network accessline 118
fasta = requests.get("https://rest.uniprot.org/uniprotkb/P01308.fasta").text - Network accessline 131
"""Map identifiers between databases (async job).""" # Submit job resp = requests.post("https://rest.uniprot.org/idmapping/run", - Network accessline 131
"""Map identifiers between databases (async job).""" # Submit job resp = requests.post("https://rest.uniprot.org/idmapping/run", - Network accessline 132
# Submit job resp = requests.post("https://rest.uniprot.org/idmapping/run", data={"from": from_db, "to": to_db, "ids": ",".join(ids)}) - Network accessline 132
# Submit job resp = requests.post("https://rest.uniprot.org/idmapping/run", data={"from": from_db, "to": to_db, "ids": ",".join(ids)}) - Network accessline 133
resp = requests.post("https://rest.uniprot.org/idmapping/run", - Network accessline 133
resp = requests.post("https://rest.uniprot.org/idmapping/run", - Network accessline 138
# Poll for completion while True: status = requests.get(f"https://rest.uniprot.org/idmapping/status/{job_id}").json() - Network accessline 138
# Poll for completion while True: status = requests.get(f"https://rest.uniprot.org/idmapping/status/{job_id}").json() - Network accessline 139
while True: status = requests.get(f"https://rest.uniprot.org/idmapping/status/{job_id}").json() if "results" in status or "failedIds" in status: - Network accessline 139
while True: status = requests.get(f"https://rest.uniprot.org/idmapping/status/{job_id}").json() if "results" in status or "failedIds" in status: - Network accessline 140
status = requests.get(f"https://rest.uniprot.org/idmapping/status/{job_id}").json() - Network accessline 140
status = requests.get(f"https://rest.uniprot.org/idmapping/status/{job_id}").json() - Network accessline 145
# Get results results = requests.get(f"https://rest.uniprot.org/idmapping/results/{job_id}").json() - Network accessline 145
# Get results results = requests.get(f"https://rest.uniprot.org/idmapping/results/{job_id}").json() - Network accessline 146
results = requests.get(f"https://rest.uniprot.org/idmapping/results/{job_id}").json() - Network accessline 146
results = requests.get(f"https://rest.uniprot.org/idmapping/results/{job_id}").json() - Network accessline 174
if fields: params["fields"] = ",".join(fields) resp = requests.get("https://rest.uniprot.org/uniprotkb/search", params=params) - Network accessline 174
if fields: params["fields"] = ",".join(fields) resp = requests.get("https://rest.uniprot.org/uniprotkb/search", params=params) - Network accessline 175
params["fields"] = ",".join(fields) resp = requests.get("https://rest.uniprot.org/uniprotkb/search", params=params) resp.raise_for_status() - Network accessline 175
params["fields"] = ",".join(fields) resp = requests.get("https://rest.uniprot.org/uniprotkb/search", params=params) resp.raise_for_status() - Network accessline 176
resp = requests.get("https://rest.uniprot.org/uniprotkb/search", params=params) - Network accessline 176
resp = requests.get("https://rest.uniprot.org/uniprotkb/search", params=params) - Network accessline 186
def stream_query(query, format="fasta"): """Stream large result sets.""" url = f"https://rest.uniprot.org/uniprotkb/stream?query={query}&format={format}" - Network accessline 186
def stream_query(query, format="fasta"): """Stream large result sets.""" url = f"https://rest.uniprot.org/uniprotkb/stream?query={query}&format={format}" - Network accessline 187
"""Stream large result sets.""" url = f"https://rest.uniprot.org/uniprotkb/stream?query={query}&format={format}" resp = requests.get(url, stream=True) - Network accessline 187
"""Stream large result sets.""" url = f"https://rest.uniprot.org/uniprotkb/stream?query={query}&format={format}" resp = requests.get(url, stream=True) - Network accessline 188
url = f"https://rest.uniprot.org/uniprotkb/stream?query={query}&format={format}" resp = requests.get(url, stream=True) resp.raise_for_status() - Network accessline 188
url = f"https://rest.uniprot.org/uniprotkb/stream?query={query}&format={format}" resp = requests.get(url, stream=True) resp.raise_for_status() - Network accessline 189
resp = requests.get(url, stream=True) - Network accessline 189
resp = requests.get(url, stream=True) - Network accessline 209
if fields: params["fields"] = ",".join(fields) url = "https://rest.uniprot.org/uniprotkb/search" - Network accessline 209
if fields: params["fields"] = ",".join(fields) url = "https://rest.uniprot.org/uniprotkb/search" - Network accessline 210
params["fields"] = ",".join(fields) url = "https://rest.uniprot.org/uniprotkb/search" rows = [] - Network accessline 210
params["fields"] = ",".join(fields) url = "https://rest.uniprot.org/uniprotkb/search" rows = [] - Network accessline 211
url = "https://rest.uniprot.org/uniprotkb/search" - Network accessline 211
url = "https://rest.uniprot.org/uniprotkb/search" - Network accessline 213
header = None while url: resp = requests.get(url, params=params) - Network accessline 213
header = None while url: resp = requests.get(url, params=params) - Network accessline 214
while url: resp = requests.get(url, params=params) resp.raise_for_status() - Network accessline 214
while url: resp = requests.get(url, params=params) resp.raise_for_status() - Network accessline 215
resp = requests.get(url, params=params) - Network accessline 215
resp = requests.get(url, params=params) - Network accessline 249
"fields": "accession,gene_names,protein_name,go_p,go_f,go_c,cc_function,ft_domain", } resp = requests.get("https://rest.uniprot.org/uniprotkb/search", params=params) - Network accessline 249
"fields": "accession,gene_names,protein_name,go_p,go_f,go_c,cc_function,ft_domain", } resp = requests.get("https://rest.uniprot.org/uniprotkb/search", params=params) - Network accessline 250
} resp = requests.get("https://rest.uniprot.org/uniprotkb/search", params=params) df = pd.read_csv(StringIO(resp.text), sep="\t") - Network accessline 250
} resp = requests.get("https://rest.uniprot.org/uniprotkb/search", params=params) df = pd.read_csv(StringIO(resp.text), sep="\t") - Network accessline 251
resp = requests.get("https://rest.uniprot.org/uniprotkb/search", params=params) - Network accessline 251
resp = requests.get("https://rest.uniprot.org/uniprotkb/search", params=params) - Network accessline 299
url = "https://rest.uniprot.org/uniprotkb/stream" - Network accessline 299
url = "https://rest.uniprot.org/uniprotkb/stream" - Network accessline 303
"fields": "accession,gene_names,protein_name,length,go_f", } resp = requests.get(url, params=params) - Network accessline 303
"fields": "accession,gene_names,protein_name,length,go_f", } resp = requests.get(url, params=params) - Network accessline 304
} resp = requests.get(url, params=params) df = pd.read_csv(StringIO(resp.text), sep="\t") - Network accessline 304
} resp = requests.get(url, params=params) df = pd.read_csv(StringIO(resp.text), sep="\t") - Network accessline 305
resp = requests.get(url, params=params) - Network accessline 305
resp = requests.get(url, params=params) - Network accessline 325
"fields": "accession,gene_names,go_p,go_f,go_c", } resp = requests.get("https://rest.uniprot.org/uniprotkb/search", params=params) - Network accessline 325
"fields": "accession,gene_names,go_p,go_f,go_c", } resp = requests.get("https://rest.uniprot.org/uniprotkb/search", params=params) - Network accessline 326
} resp = requests.get("https://rest.uniprot.org/uniprotkb/search", params=params) df = pd.read_csv(StringIO(resp.text), sep="\t") - Network accessline 326
} resp = requests.get("https://rest.uniprot.org/uniprotkb/search", params=params) df = pd.read_csv(StringIO(resp.text), sep="\t") - Network accessline 327
resp = requests.get("https://rest.uniprot.org/uniprotkb/search", params=params) - Network accessline 327
resp = requests.get("https://rest.uniprot.org/uniprotkb/search", params=params) - Network accessline 338
accessions = ["P53_HUMAN", "P01308", "P00533"] # TP53, Insulin, EGFR resp = requests.post("https://rest.uniprot.org/idmapping/run", - Network accessline 338
accessions = ["P53_HUMAN", "P01308", "P00533"] # TP53, Insulin, EGFR resp = requests.post("https://rest.uniprot.org/idmapping/run", - Network accessline 339
resp = requests.post("https://rest.uniprot.org/idmapping/run", - Network accessline 339
resp = requests.post("https://rest.uniprot.org/idmapping/run", - Network accessline 341
job_id = resp.json()["jobId"] time.sleep(2) results = requests.get(f"https://rest.uniprot.org/idmapping/results/{job_id}").json() - Network accessline 341
job_id = resp.json()["jobId"] time.sleep(2) results = requests.get(f"https://rest.uniprot.org/idmapping/results/{job_id}").json() - Network accessline 342
time.sleep(2) results = requests.get(f"https://rest.uniprot.org/idmapping/results/{job_id}").json() for r in results.get("results", []): - Network accessline 342
time.sleep(2) results = requests.get(f"https://rest.uniprot.org/idmapping/results/{job_id}").json() for r in results.get("results", []): - Network accessline 343
results = requests.get(f"https://rest.uniprot.org/idmapping/results/{job_id}").json() - Network accessline 343
results = requests.get(f"https://rest.uniprot.org/idmapping/results/{job_id}").json() - Network accessline 368
- [UniProt REST API documentation](https://www.uniprot.org/help/api) — official API reference - Network accessline 368
- [UniProt REST API documentation](https://www.uniprot.org/help/api) — official API reference - Network accessline 369
- [UniProt query syntax](https://www.uniprot.org/help/query-fields) — field-specific search operators - Network accessline 369
- [UniProt query syntax](https://www.uniprot.org/help/query-fields) — field-specific search operators - Network accessline 370
- [UniProt Consortium (2023)](https://doi.org/10.1093/nar/gkac1052) — "UniProt: the Universal Protein Knowledgebase in 2023" — Nucleic Acids Research - Network accessline 370
- [UniProt Consortium (2023)](https://doi.org/10.1093/nar/gkac1052) — "UniProt: the Universal Protein Knowledgebase in 2023" — Nucleic Acids Research
skills/scientific-computing/aeon/SKILL.md
- Network accessline 364
- [aeon documentation](https://www.aeon-toolkit.org/) — API reference, algorithm catalog, and tutorials - Network accessline 364
- [aeon documentation](https://www.aeon-toolkit.org/) — API reference, algorithm catalog, and tutorials - Network accessline 365
- [aeon GitHub](https://github.com/aeon-toolkit/aeon) — source code, benchmarks, and contribution guide - Network accessline 365
- [aeon GitHub](https://github.com/aeon-toolkit/aeon) — source code, benchmarks, and contribution guide - Network accessline 366
- [ROCKET paper: Dempster et al. (2020), DMKD](https://doi.org/10.1007/s10618-020-00701-z) — ROCKET algorithm description and UCR benchmark results - Network accessline 366
- [ROCKET paper: Dempster et al. (2020), DMKD](https://doi.org/10.1007/s10618-020-00701-z) — ROCKET algorithm description and UCR benchmark results - Network accessline 367
- [MiniROCKET paper: Dempster et al. (2021), KDD](https://doi.org/10.1145/3447548.3467231) — faster variant with comparable accuracy - Network accessline 367
- [MiniROCKET paper: Dempster et al. (2021), KDD](https://doi.org/10.1145/3447548.3467231) — faster variant with comparable accuracy - Network accessline 368
- [UCR Time Series Archive](https://www.timeseriesclassification.com/) — 128 benchmark datasets for time series classification - Network accessline 368
- [UCR Time Series Archive](https://www.timeseriesclassification.com/) — 128 benchmark datasets for time series classification
skills/scientific-computing/astropy-astronomy/SKILL.md
- Network accessline 612
- Official Documentation: https://docs.astropy.org/en/stable/ - Network accessline 612
- Official Documentation: https://docs.astropy.org/en/stable/ - Network accessline 613
- Tutorials: https://learn.astropy.org/ - Network accessline 613
- Tutorials: https://learn.astropy.org/ - Network accessline 614
- GitHub: https://github.com/astropy/astropy - Network accessline 614
- GitHub: https://github.com/astropy/astropy - Network accessline 615
- Coordinate Frame Reference: https://docs.astropy.org/en/stable/coordinates/ - Network accessline 615
- Coordinate Frame Reference: https://docs.astropy.org/en/stable/coordinates/
skills/scientific-computing/astropy-astronomy/references/auxiliary_modules.md
- Network accessline 282
# Data downloads with caching from astropy.utils.data import download_file local_file = download_file('https://example.com/data.fits', cache=True) - Network accessline 282
# Data downloads with caching from astropy.utils.data import download_file local_file = download_file('https://example.com/data.fits', cache=True) - Network accessline 284
local_file = download_file('https://example.com/data.fits', cache=True) - Network accessline 284
local_file = download_file('https://example.com/data.fits', cache=True)
skills/scientific-computing/dask-parallel-computing/SKILL.md
- Network accessline 145
client = Client() # Local cluster with all cores print(client.dashboard_link) # http://localhost:8787 - Network accessline 145
client = Client() # Local cluster with all cores print(client.dashboard_link) # http://localhost:8787 - Network accessline 146
print(client.dashboard_link) # http://localhost:8787 - Network accessline 146
print(client.dashboard_link) # http://localhost:8787 - Network accessline 448
- Official Documentation: https://docs.dask.org/ - Network accessline 448
- Official Documentation: https://docs.dask.org/ - Network accessline 449
- Dask Best Practices: https://docs.dask.org/en/stable/best-practices.html - Network accessline 449
- Dask Best Practices: https://docs.dask.org/en/stable/best-practices.html - Network accessline 450
- Dask-ML: https://ml.dask.org/ - Network accessline 450
- Dask-ML: https://ml.dask.org/ - Network accessline 451
- Dask-Jobqueue (HPC): https://jobqueue.dask.org/ - Network accessline 451
- Dask-Jobqueue (HPC): https://jobqueue.dask.org/
skills/scientific-computing/dask-parallel-computing/references/distributed_computing.md
- Network accessline 267
client = Client() print(client.dashboard_link) # http://localhost:8787 - Network accessline 267
client = Client() print(client.dashboard_link) # http://localhost:8787 - Network accessline 268
print(client.dashboard_link) # http://localhost:8787 - Network accessline 268
print(client.dashboard_link) # http://localhost:8787
skills/scientific-computing/degenerate-input-filtering/SKILL.md
- Network accessline 256
- [scipy.stats documentation](https://docs.scipy.org/doc/scipy/reference/stats.html) -- Reference for statistical functions and their behavior on edge-case inputs including constant arrays and NaN values. - Network accessline 256
- [scipy.stats documentation](https://docs.scipy.org/doc/scipy/reference/stats.html) -- Reference for statistical functions and their behavior on edge-case inputs including constant arrays and NaN values. - Network accessline 257
- [pandas DataFrame.dropna](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dropna.html) -- Official documentation for NaN removal with axis and threshold control. - Network accessline 257
- [pandas DataFrame.dropna](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.dropna.html) -- Official documentation for NaN removal with axis and threshold control. - Network accessline 258
- [numpy NaN handling](https://numpy.org/doc/stable/user/misc.html) -- NumPy documentation covering NaN propagation semantics in arithmetic and aggregation functions. - Network accessline 258
- [numpy NaN handling](https://numpy.org/doc/stable/user/misc.html) -- NumPy documentation covering NaN propagation semantics in arithmetic and aggregation functions. - Network accessline 259
- [Lun, A. et al. (2016) "A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor"](https://doi.org/10.12688/f1000research.9501.2) -- Demonstrates gene-level quality control filtering including low-abund - Network accessline 259
- [Lun, A. et al. (2016) "A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor"](https://doi.org/10.12688/f1000research.9501.2) -- Demonstrates gene-level quality control filtering including low-abund - Network accessline 260
- [Law, C.W. et al. (2018) "RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR"](https://doi.org/10.12688/f1000research.9005.3) -- Covers filterByExpr-style gene filtering to remove lowly expressed and uninformative genes before - Network accessline 260
- [Law, C.W. et al. (2018) "RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR"](https://doi.org/10.12688/f1000research.9005.3) -- Covers filterByExpr-style gene filtering to remove lowly expressed and uninformative genes before
skills/scientific-computing/exploratory-data-analysis/SKILL.md
- Network accessline 177
- VanderPlas, J. (2016). *Python Data Science Handbook* — EDA with matplotlib and scikit-learn - Tukey, J. (1977). *Exploratory Data Analysis* — foundational EDA philosophy - Bio-Formats documentation: https://www.openmicroscopy.org/bio-for - Network accessline 177
- VanderPlas, J. (2016). *Python Data Science Handbook* — EDA with matplotlib and scikit-learn - Tukey, J. (1977). *Exploratory Data Analysis* — foundational EDA philosophy - Bio-Formats documentation: https://www.openmicroscopy.org/bio-for - Network accessline 178
- Tukey, J. (1977). *Exploratory Data Analysis* — foundational EDA philosophy - Bio-Formats documentation: https://www.openmicroscopy.org/bio-formats/ — microscopy format reference - PSI standard formats: https://www.psidev.info/ — proteomi - Network accessline 178
- Tukey, J. (1977). *Exploratory Data Analysis* — foundational EDA philosophy - Bio-Formats documentation: https://www.openmicroscopy.org/bio-formats/ — microscopy format reference - PSI standard formats: https://www.psidev.info/ — proteomi - Network accessline 179
- Bio-Formats documentation: https://www.openmicroscopy.org/bio-formats/ — microscopy format reference - Network accessline 179
- Bio-Formats documentation: https://www.openmicroscopy.org/bio-formats/ — microscopy format reference - Network accessline 180
- PSI standard formats: https://www.psidev.info/ — proteomics/MS data standards - Network accessline 180
- PSI standard formats: https://www.psidev.info/ — proteomics/MS data standards
skills/scientific-computing/geopandas-geospatial/SKILL.md
- Network accessline 407
- GeoPandas documentation: https://geopandas.org/ - Network accessline 407
- GeoPandas documentation: https://geopandas.org/ - Network accessline 408
- GeoPandas GitHub: https://github.com/geopandas/geopandas - Network accessline 408
- GeoPandas GitHub: https://github.com/geopandas/geopandas - Network accessline 409
- Shapely documentation: https://shapely.readthedocs.io/ - Network accessline 409
- Shapely documentation: https://shapely.readthedocs.io/ - Network accessline 410
- EPSG registry: https://epsg.io/ - Network accessline 410
- EPSG registry: https://epsg.io/
skills/scientific-computing/hypogenic-hypothesis-generation/SKILL.md
- Network accessline 33
# Optional: clone example datasets git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data - Network accessline 33
# Optional: clone example datasets git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data - Network accessline 34
git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data - Network accessline 34
git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data - Network accessline 35
git clone https://github.com/ChicagoHAI/Hypothesis-agent-datasets.git ./data_lit - Network accessline 35
git clone https://github.com/ChicagoHAI/Hypothesis-agent-datasets.git ./data_lit - Access to secretsline 293
[Credential-related source omitted] - Access to secretsline 293
[Credential-related source omitted] - Network accessline 427
- Zhou, Y., Liu, H., Srivastava, T., Mei, H., & Tan, C. (2024). "Hypothesis Generation with Large Language Models." EMNLP Workshop NLP for Science. https://aclanthology.org/2024.nlp4science-1.10/ - Network accessline 427
- Zhou, Y., Liu, H., Srivastava, T., Mei, H., & Tan, C. (2024). "Hypothesis Generation with Large Language Models." EMNLP Workshop NLP for Science. https://aclanthology.org/2024.nlp4science-1.10/ - Network accessline 428
- Liu, H., Zhou, Y., Li, M., Yuan, C., & Tan, C. (2024). "Literature Meets Data: A Synergistic Approach to Hypothesis Generation." arXiv:2410.17309. https://arxiv.org/abs/2410.17309 - Network accessline 428
- Liu, H., Zhou, Y., Li, M., Yuan, C., & Tan, C. (2024). "Literature Meets Data: A Synergistic Approach to Hypothesis Generation." arXiv:2410.17309. https://arxiv.org/abs/2410.17309 - Network accessline 429
- Liu, H., Huang, S., Hu, J., Zhou, Y., & Tan, C. (2025). "HypoBench: Towards Systematic and Principled Benchmarking for Hypothesis Generation." arXiv:2504.11524. https://arxiv.org/abs/2504.11524 - Network accessline 429
- Liu, H., Huang, S., Hu, J., Zhou, Y., & Tan, C. (2025). "HypoBench: Towards Systematic and Principled Benchmarking for Hypothesis Generation." arXiv:2504.11524. https://arxiv.org/abs/2504.11524 - Network accessline 430
- GitHub Repository: https://github.com/ChicagoHAI/hypothesis-generation - Network accessline 430
- GitHub Repository: https://github.com/ChicagoHAI/hypothesis-generation - Network accessline 431
- PyPI Package: https://pypi.org/project/hypogenic/ - Network accessline 431
- PyPI Package: https://pypi.org/project/hypogenic/
skills/scientific-computing/matlab-scientific-computing/SKILL.md
- Dangerous shell operationline 32
sudo apt install octave - Dangerous shell operationline 32
sudo apt install octave - Network accessline 495
- [GNU Octave documentation](https://docs.octave.org/latest/) — official manual and function reference - Network accessline 495
- [GNU Octave documentation](https://docs.octave.org/latest/) — official manual and function reference - Network accessline 496
- [MATLAB documentation](https://www.mathworks.com/help/matlab/) — MathWorks official reference - Network accessline 496
- [MATLAB documentation](https://www.mathworks.com/help/matlab/) — MathWorks official reference - Network accessline 497
- [MATLAB–Octave compatibility](https://wiki.octave.org/FAQ#Differences_between_Octave_and_Matlab) — key syntax and feature differences - Network accessline 497
- [MATLAB–Octave compatibility](https://wiki.octave.org/FAQ#Differences_between_Octave_and_Matlab) — key syntax and feature differences
skills/scientific-computing/nan-safe-correlation/SKILL.md
- Network accessline 241
- [scipy.stats.spearmanr documentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html) -- Official API reference - Network accessline 241
- [scipy.stats.spearmanr documentation](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html) -- Official API reference - Network accessline 242
- [pandas corrwith documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.corrwith.html) -- Known NaN handling behavior - Network accessline 242
- [pandas corrwith documentation](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.corrwith.html) -- Known NaN handling behavior - Network accessline 243
- [Pairwise vs Listwise Deletion](https://stefvanbuuren.name/fimd/sec-pairwise.html) -- Chapter from Flexible Imputation of Missing Data - Network accessline 243
- [Pairwise vs Listwise Deletion](https://stefvanbuuren.name/fimd/sec-pairwise.html) -- Chapter from Flexible Imputation of Missing Data
skills/scientific-computing/neb-irc-activation-energy/SKILL.md
- Network accessline 289
- [xtb documentation](https://xtb-docs.readthedocs.io/) — GFN2-xTB methods and CLI - Network accessline 289
- [xtb documentation](https://xtb-docs.readthedocs.io/) — GFN2-xTB methods and CLI - Network accessline 290
- [pysisyphus documentation](https://pysisyphus.readthedocs.io/) — COS/NEB, TS optimizers, IRC - Network accessline 290
- [pysisyphus documentation](https://pysisyphus.readthedocs.io/) — COS/NEB, TS optimizers, IRC - Network accessline 291
- Bannwarth, Ehlert, Grimme, *J. Chem. Theory Comput.* 2019, 15, 1652 — GFN2-xTB method paper - Steinmetzer, Kupfer, Gräfe, *Int. J. Quantum Chem.* 2021, 121, e26550 — pysisyphus paper - [ASE](https://wiki.fysik.dtu.dk/ase/) / [PySCF](https - Network accessline 291
- Bannwarth, Ehlert, Grimme, *J. Chem. Theory Comput.* 2019, 15, 1652 — GFN2-xTB method paper - Steinmetzer, Kupfer, Gräfe, *Int. J. Quantum Chem.* 2021, 121, e26550 — pysisyphus paper - [ASE](https://wiki.fysik.dtu.dk/ase/) / [PySCF](https - Network accessline 293
- [ASE](https://wiki.fysik.dtu.dk/ase/) / [PySCF](https://pyscf.org/) — geometry I/O and DFT single points - Network accessline 293
- [ASE](https://wiki.fysik.dtu.dk/ase/) / [PySCF](https://pyscf.org/) — geometry I/O and DFT single points
skills/scientific-computing/neb-irc-activation-energy/scripts/check_result.py
skills/scientific-computing/neb-irc-activation-energy/scripts/plot_irc.py
skills/scientific-computing/neb-irc-activation-energy/scripts/setup_env.sh
- Executable scriptline 1
#!/usr/bin/env bash - Executable scriptline 1
#!/usr/bin/env bash - Network accessline 18
if [ ! -d xtb-dist ]; then echo "fetching xtb ${XTB_VERSION} ..." curl -sfL -o xtb.tar.xz \ - Network accessline 18
if [ ! -d xtb-dist ]; then echo "fetching xtb ${XTB_VERSION} ..." curl -sfL -o xtb.tar.xz \ - Network accessline 19
echo "fetching xtb ${XTB_VERSION} ..." curl -sfL -o xtb.tar.xz \ "https://github.com/grimme-lab/xtb/releases/download/v${XTB_VERSION}/xtb-${XTB_VERSION}-linux-x86_64.tar.xz" - Network accessline 19
echo "fetching xtb ${XTB_VERSION} ..." curl -sfL -o xtb.tar.xz \ "https://github.com/grimme-lab/xtb/releases/download/v${XTB_VERSION}/xtb-${XTB_VERSION}-linux-x86_64.tar.xz" - Network accessline 20
curl -sfL -o xtb.tar.xz \ - Network accessline 20
curl -sfL -o xtb.tar.xz \ - Network accessline 21
"https://github.com/grimme-lab/xtb/releases/download/v${XTB_VERSION}/xtb-${XTB_VERSION}-linux-x86_64.tar.xz" - Network accessline 21
"https://github.com/grimme-lab/xtb/releases/download/v${XTB_VERSION}/xtb-${XTB_VERSION}-linux-x86_64.tar.xz" - Dangerous shell operationline 22
tar xf xtb.tar.xz && rm xtb.tar.xz - Dangerous shell operationline 22
tar xf xtb.tar.xz && rm xtb.tar.xz
skills/scientific-computing/networkx-graph-analysis/SKILL.md
- Network accessline 549
- [NetworkX documentation](https://networkx.org/documentation/latest/) -- official docs and API reference - Network accessline 549
- [NetworkX documentation](https://networkx.org/documentation/latest/) -- official docs and API reference - Network accessline 550
- [NetworkX tutorial](https://networkx.org/documentation/latest/tutorial.html) -- official getting started guide - Network accessline 550
- [NetworkX tutorial](https://networkx.org/documentation/latest/tutorial.html) -- official getting started guide - Network accessline 551
- [NetworkX GitHub](https://github.com/networkx/networkx) -- source code and issue tracker - Network accessline 551
- [NetworkX GitHub](https://github.com/networkx/networkx) -- source code and issue tracker - Network accessline 552
- [NetworkX gallery](https://networkx.org/documentation/latest/auto_examples/index.html) -- example gallery with visualizations - Network accessline 552
- [NetworkX gallery](https://networkx.org/documentation/latest/auto_examples/index.html) -- example gallery with visualizations
skills/scientific-computing/neurokit2/SKILL.md
- Network accessline 401
- [NeuroKit2 documentation](https://neuropsychology.github.io/NeuroKit/) — API reference, tutorials, and comparison of algorithms - Network accessline 401
- [NeuroKit2 documentation](https://neuropsychology.github.io/NeuroKit/) — API reference, tutorials, and comparison of algorithms - Network accessline 402
- [NeuroKit2 paper: Makowski et al. (2021), Behavior Research Methods](https://doi.org/10.3758/s13428-020-01516-y) — methodology and validation - Network accessline 402
- [NeuroKit2 paper: Makowski et al. (2021), Behavior Research Methods](https://doi.org/10.3758/s13428-020-01516-y) — methodology and validation - Network accessline 403
- [HRV standards: Task Force (1996), Circulation](https://doi.org/10.1161/01.CIR.93.5.1043) — authoritative definitions of HRV time and frequency domain measures - Network accessline 403
- [HRV standards: Task Force (1996), Circulation](https://doi.org/10.1161/01.CIR.93.5.1043) — authoritative definitions of HRV time and frequency domain measures - Network accessline 404
- [NeuroKit2 GitHub](https://github.com/neuropsychology/NeuroKit) — source, examples, and community - Network accessline 404
- [NeuroKit2 GitHub](https://github.com/neuropsychology/NeuroKit) — source, examples, and community - Network accessline 405
- [EDA review: Boucsein (2012), Electrodermal Activity](https://doi.org/10.1007/978-1-4614-1126-0) — reference text for EDA signal interpretation - Network accessline 405
- [EDA review: Boucsein (2012), Electrodermal Activity](https://doi.org/10.1007/978-1-4614-1126-0) — reference text for EDA signal interpretation
skills/scientific-computing/neuropixels-analysis/SKILL.md
- Network accessline 331
- [SpikeInterface documentation](https://spikeinterface.readthedocs.io/) — full API, tutorials, and sorter comparison guides - Network accessline 331
- [SpikeInterface documentation](https://spikeinterface.readthedocs.io/) — full API, tutorials, and sorter comparison guides - Network accessline 332
- [Kilosort4 paper: Pachitariu et al. (2024), Nature Methods](https://doi.org/10.1038/s41592-024-02232-7) — drift correction and template learning - Network accessline 332
- [Kilosort4 paper: Pachitariu et al. (2024), Nature Methods](https://doi.org/10.1038/s41592-024-02232-7) — drift correction and template learning - Network accessline 333
- [SpikeInterface paper: Buccino et al. (2020), eLife](https://doi.org/10.7554/eLife.61834) — unified framework for extracellular electrophysiology - Network accessline 333
- [SpikeInterface paper: Buccino et al. (2020), eLife](https://doi.org/10.7554/eLife.61834) — unified framework for extracellular electrophysiology - Network accessline 334
- [ProbeInterface documentation](https://probeinterface.readthedocs.io/) — probe geometry and channel map handling - Network accessline 334
- [ProbeInterface documentation](https://probeinterface.readthedocs.io/) — probe geometry and channel map handling - Network accessline 335
- [Neuropixels documentation](https://www.neuropixels.org/) — hardware specs, imec reference designs - Network accessline 335
- [Neuropixels documentation](https://www.neuropixels.org/) — hardware specs, imec reference designs - Network accessline 336
- [NWB documentation](https://www.nwb.org/) — neurophysiology data standard for archiving and sharing - Network accessline 336
- [NWB documentation](https://www.nwb.org/) — neurophysiology data standard for archiving and sharing
skills/scientific-computing/nextflow-workflow-engine/SKILL.md
- Dangerous shell operationline 32
# Install Nextflow (self-contained JAR — no sudo required) - Dangerous shell operationline 32
# Install Nextflow (self-contained JAR — no sudo required) - Network accessline 32
# Install Nextflow (self-contained JAR — no sudo required) curl -s https://get.nextflow.io | bash - Network accessline 32
# Install Nextflow (self-contained JAR — no sudo required) curl -s https://get.nextflow.io | bash - Dangerous shell operationline 33
curl -s https://get.nextflow.io | bash - Dangerous shell operationline 33
curl -s https://get.nextflow.io | bash - Network accessline 33
curl -s https://get.nextflow.io | bash - Network accessline 33
curl -s https://get.nextflow.io | bash - Network accessline 442
- [Nextflow documentation](https://www.nextflow.io/docs/latest/) — official language reference and executor guides - Network accessline 442
- [Nextflow documentation](https://www.nextflow.io/docs/latest/) — official language reference and executor guides - Network accessline 443
- [Nextflow GitHub: nextflow-io/nextflow](https://github.com/nextflow-io/nextflow) — source code and releases - Network accessline 443
- [Nextflow GitHub: nextflow-io/nextflow](https://github.com/nextflow-io/nextflow) — source code and releases - Network accessline 444
- Di Tommaso P et al. (2017) "Nextflow enables reproducible computational workflows" — *Nature Biotechnology* 35:316-319. [DOI:10.1038/nbt.3820](https://doi.org/10.1038/nbt.3820) - Network accessline 444
- Di Tommaso P et al. (2017) "Nextflow enables reproducible computational workflows" — *Nature Biotechnology* 35:316-319. [DOI:10.1038/nbt.3820](https://doi.org/10.1038/nbt.3820) - Network accessline 445
- [nf-core](https://nf-co.re/) — community curated Nextflow pipelines with automated testing and containers - Network accessline 445
- [nf-core](https://nf-co.re/) — community curated Nextflow pipelines with automated testing and containers
skills/scientific-computing/polars-dataframes/SKILL.md
- Network accessline 679
- Polars User Guide: https://docs.pola.rs/ - Network accessline 679
- Polars User Guide: https://docs.pola.rs/ - Network accessline 680
- Polars API Reference: https://docs.pola.rs/api/python/stable/reference/ - Network accessline 680
- Polars API Reference: https://docs.pola.rs/api/python/stable/reference/ - Network accessline 681
- GitHub: https://github.com/pola-rs/polars - Network accessline 681
- GitHub: https://github.com/pola-rs/polars - Network accessline 682
- Polars Cookbook: https://docs.pola.rs/user-guide/ - Network accessline 682
- Polars Cookbook: https://docs.pola.rs/user-guide/
skills/scientific-computing/polars-dataframes/references/io_best_practices.md
- Access to secretsline 225
[Credential-related source omitted] - Access to secretsline 225
[Credential-related source omitted] - Access to secretsline 226
[Credential-related source omitted] - Access to secretsline 226
[Credential-related source omitted] - Access to secretsline 227
[Credential-related source omitted] - Access to secretsline 227
[Credential-related source omitted] - Access to secretsline 237
[Credential-related source omitted] - Access to secretsline 237
[Credential-related source omitted] - Access to secretsline 238
[Credential-related source omitted] - Access to secretsline 238
[Credential-related source omitted] - Access to secretsline 247
[Credential-related source omitted] - Access to secretsline 247
[Credential-related source omitted]
skills/scientific-computing/pyhealth/SKILL.md
- Network accessline 25
- **Python packages**: `pyhealth`, `torch`, `pandas`, `scikit-learn` - **Data requirements**: MIMIC-III/IV CSV files (requires PhysioNet credentialing), eICU, or OMOP-CDM database - **MIMIC access**: request at [physionet.org](https://physi - Network accessline 25
- **Python packages**: `pyhealth`, `torch`, `pandas`, `scikit-learn` - **Data requirements**: MIMIC-III/IV CSV files (requires PhysioNet credentialing), eICU, or OMOP-CDM database - **MIMIC access**: request at [physionet.org](https://physi - Network accessline 27
- **MIMIC access**: request at [physionet.org](https://physionet.org/) (free; requires CITI training, ~1 week) - Network accessline 27
- **MIMIC access**: request at [physionet.org](https://physionet.org/) (free; requires CITI training, ~1 week) - Network accessline 30
pip install pyhealth torch pandas scikit-learn # Download MIMIC-III: https://physionet.org/content/mimiciii/ - Network accessline 30
pip install pyhealth torch pandas scikit-learn # Download MIMIC-III: https://physionet.org/content/mimiciii/ - Network accessline 31
# Download MIMIC-III: https://physionet.org/content/mimiciii/ - Network accessline 31
# Download MIMIC-III: https://physionet.org/content/mimiciii/ - Network accessline 32
# Download MIMIC-IV: https://physionet.org/content/mimiciv/ - Network accessline 32
# Download MIMIC-IV: https://physionet.org/content/mimiciv/ - Network accessline 484
- [PyHealth documentation](https://pyhealth.readthedocs.io/) — API reference, tutorials, and task descriptions - Network accessline 484
- [PyHealth documentation](https://pyhealth.readthedocs.io/) — API reference, tutorials, and task descriptions - Network accessline 485
- [PyHealth paper: Zhao et al. (2021), arXiv](https://arxiv.org/abs/2101.04209) — library design and benchmark tasks - Network accessline 485
- [PyHealth paper: Zhao et al. (2021), arXiv](https://arxiv.org/abs/2101.04209) — library design and benchmark tasks - Network accessline 486
- [MIMIC-III paper: Johnson et al. (2016), Nature Scientific Data](https://doi.org/10.1038/sdata.2016.35) — dataset description - Network accessline 486
- [MIMIC-III paper: Johnson et al. (2016), Nature Scientific Data](https://doi.org/10.1038/sdata.2016.35) — dataset description - Network accessline 487
- [MIMIC-IV paper: Johnson et al. (2023), Nature Scientific Data](https://doi.org/10.1038/s41597-022-01899-x) — updated dataset - Network accessline 487
- [MIMIC-IV paper: Johnson et al. (2023), Nature Scientific Data](https://doi.org/10.1038/s41597-022-01899-x) — updated dataset - Network accessline 488
- [RETAIN paper: Choi et al. (2016), NeurIPS](https://arxiv.org/abs/1608.05745) — interpretable clinical prediction model - Network accessline 488
- [RETAIN paper: Choi et al. (2016), NeurIPS](https://arxiv.org/abs/1608.05745) — interpretable clinical prediction model - Network accessline 489
- [PyHealth GitHub](https://github.com/sunlabuiuc/PyHealth) — source code and examples - Network accessline 489
- [PyHealth GitHub](https://github.com/sunlabuiuc/PyHealth) — source code and examples
skills/scientific-computing/pymatgen/SKILL.md
- Network accessline 25
- **Python packages**: `pymatgen`, `mp-api` (Materials Project client) - **Data requirements**: structure files (CIF, POSCAR) or Materials Project API key - **API key**: free at [materialsproject.org](https://materialsproject.org/) — set `P - Network accessline 25
- **Python packages**: `pymatgen`, `mp-api` (Materials Project client) - **Data requirements**: structure files (CIF, POSCAR) or Materials Project API key - **API key**: free at [materialsproject.org](https://materialsproject.org/) — set `P - Network accessline 27
- **API key**: free at [materialsproject.org](https://materialsproject.org/) — set `PMG_MAPI_KEY` env var - Network accessline 27
- **API key**: free at [materialsproject.org](https://materialsproject.org/) — set `PMG_MAPI_KEY` env var - Access to secretsline 32
[Credential-related source omitted] - Access to secretsline 32
[Credential-related source omitted] - Access to secretsline 110
[Credential-related source omitted] - Access to secretsline 110
[Credential-related source omitted] - Access to secretsline 112
[Credential-related source omitted] - Access to secretsline 112
[Credential-related source omitted] - Access to secretsline 127
[Credential-related source omitted] - Access to secretsline 127
[Credential-related source omitted] - Access to secretsline 173
[Credential-related source omitted] - Access to secretsline 173
[Credential-related source omitted] - Access to secretsline 175
[Credential-related source omitted] - Access to secretsline 175
[Credential-related source omitted] - Access to secretsline 180
[Credential-related source omitted] - Access to secretsline 180
[Credential-related source omitted] - Access to secretsline 272
[Credential-related source omitted] - Access to secretsline 272
[Credential-related source omitted] - Access to secretsline 278
[Credential-related source omitted] - Access to secretsline 278
[Credential-related source omitted] - Access to secretsline 373
[Credential-related source omitted] - Access to secretsline 373
[Credential-related source omitted] - Access to secretsline 374
[Credential-related source omitted] - Access to secretsline 374
[Credential-related source omitted] - Access to secretsline 392
[Credential-related source omitted] - Access to secretsline 392
[Credential-related source omitted] - Network accessline 408
- [pymatgen documentation](https://pymatgen.org/) — full API reference, tutorials, and compatibility matrix - Network accessline 408
- [pymatgen documentation](https://pymatgen.org/) — full API reference, tutorials, and compatibility matrix - Network accessline 409
- [Materials Project API docs](https://api.materialsproject.org/) — REST API and mp-api client reference - Network accessline 409
- [Materials Project API docs](https://api.materialsproject.org/) — REST API and mp-api client reference - Network accessline 410
- [pymatgen paper: Ong et al. (2013), CMS](https://doi.org/10.1016/j.commatsci.2012.10.028) — original publication and design philosophy - Network accessline 410
- [pymatgen paper: Ong et al. (2013), CMS](https://doi.org/10.1016/j.commatsci.2012.10.028) — original publication and design philosophy - Network accessline 411
- [Materials Project paper: Jain et al. (2013), APL Materials](https://doi.org/10.1063/1.4812323) — database description and computed properties - Network accessline 411
- [Materials Project paper: Jain et al. (2013), APL Materials](https://doi.org/10.1063/1.4812323) — database description and computed properties - Network accessline 412
- [pymatgen GitHub](https://github.com/materialsproject/pymatgen) — source code and examples - Network accessline 412
- [pymatgen GitHub](https://github.com/materialsproject/pymatgen) — source code and examples
skills/scientific-computing/pymoo/SKILL.md
- Network accessline 481
- [pymoo documentation](https://pymoo.org/) — algorithm catalog, API reference, and tutorials - Network accessline 481
- [pymoo documentation](https://pymoo.org/) — algorithm catalog, API reference, and tutorials - Network accessline 482
- [pymoo paper: Blank & Deb (2020), IEEE Access](https://doi.org/10.1109/ACCESS.2020.2990567) — algorithm descriptions and benchmarks - Network accessline 482
- [pymoo paper: Blank & Deb (2020), IEEE Access](https://doi.org/10.1109/ACCESS.2020.2990567) — algorithm descriptions and benchmarks - Network accessline 483
- [NSGA-II paper: Deb et al. (2002), IEEE TEC](https://doi.org/10.1109/4235.996017) — foundational multi-objective EA - Network accessline 483
- [NSGA-II paper: Deb et al. (2002), IEEE TEC](https://doi.org/10.1109/4235.996017) — foundational multi-objective EA - Network accessline 484
- [NSGA-III paper: Deb & Jain (2014), IEEE TEC](https://doi.org/10.1109/TEVC.2013.2281535) — many-objective extension - Network accessline 484
- [NSGA-III paper: Deb & Jain (2014), IEEE TEC](https://doi.org/10.1109/TEVC.2013.2281535) — many-objective extension - Network accessline 485
- [pymoo GitHub](https://github.com/anyoptimization/pymoo) — source, examples, and issue tracker - Network accessline 485
- [pymoo GitHub](https://github.com/anyoptimization/pymoo) — source, examples, and issue tracker
skills/scientific-computing/scikit-learn-machine-learning/SKILL.md
- Network accessline 446
- [scikit-learn User Guide](https://scikit-learn.org/stable/user_guide.html) — official documentation - Network accessline 446
- [scikit-learn User Guide](https://scikit-learn.org/stable/user_guide.html) — official documentation - Network accessline 447
- [scikit-learn API Reference](https://scikit-learn.org/stable/api/index.html) — complete API - Network accessline 447
- [scikit-learn API Reference](https://scikit-learn.org/stable/api/index.html) — complete API - Network accessline 448
- [scikit-learn Examples Gallery](https://scikit-learn.org/stable/auto_examples/) — tutorials - Network accessline 448
- [scikit-learn Examples Gallery](https://scikit-learn.org/stable/auto_examples/) — tutorials
skills/scientific-computing/shap-model-explainability/SKILL.md
- Network accessline 345
- Lundberg & Lee (2017). "A Unified Approach to Interpreting Model Predictions" (NeurIPS) - Lundberg et al. (2020). "From local explanations to global understanding with explainable AI for trees" (Nature Machine Intelligence) - Official doc - Network accessline 345
- Lundberg & Lee (2017). "A Unified Approach to Interpreting Model Predictions" (NeurIPS) - Lundberg et al. (2020). "From local explanations to global understanding with explainable AI for trees" (Nature Machine Intelligence) - Official doc - Network accessline 346
- Lundberg et al. (2020). "From local explanations to global understanding with explainable AI for trees" (Nature Machine Intelligence) - Official documentation: https://shap.readthedocs.io/ - GitHub: https://github.com/shap/shap - Network accessline 346
- Lundberg et al. (2020). "From local explanations to global understanding with explainable AI for trees" (Nature Machine Intelligence) - Official documentation: https://shap.readthedocs.io/ - GitHub: https://github.com/shap/shap - Network accessline 347
- Official documentation: https://shap.readthedocs.io/ - Network accessline 347
- Official documentation: https://shap.readthedocs.io/ - Network accessline 348
- GitHub: https://github.com/shap/shap - Network accessline 348
- GitHub: https://github.com/shap/shap
skills/scientific-computing/simpy-discrete-event-simulation/SKILL.md
skills/scientific-computing/snakemake-workflow-engine/SKILL.md
- Network accessline 357
ensure("refs/genome.fa", min_size=1_000_000) shell: "wget -O {output} https://example.com/genome.fa" - Network accessline 357
ensure("refs/genome.fa", min_size=1_000_000) shell: "wget -O {output} https://example.com/genome.fa" - Network accessline 359
"wget -O {output} https://example.com/genome.fa" - Network accessline 359
"wget -O {output} https://example.com/genome.fa" - Network accessline 560
- [Snakemake documentation](https://snakemake.readthedocs.io/) — rules, wildcards, profiles, API reference - Network accessline 560
- [Snakemake documentation](https://snakemake.readthedocs.io/) — rules, wildcards, profiles, API reference - Network accessline 561
- [Snakemake GitHub](https://github.com/snakemake/snakemake) — source, releases, issue tracker - Network accessline 561
- [Snakemake GitHub](https://github.com/snakemake/snakemake) — source, releases, issue tracker - Network accessline 562
- Mölder et al. (2021) "Sustainable data analysis with Snakemake" — [F1000Research 10:33](https://doi.org/10.12688/f1000research.29032.2) - Network accessline 562
- Mölder et al. (2021) "Sustainable data analysis with Snakemake" — [F1000Research 10:33](https://doi.org/10.12688/f1000research.29032.2) - Network accessline 563
- [Snakemake workflow catalog](https://snakemake.github.io/snakemake-workflow-catalog/) — community-maintained reference pipelines - Network accessline 563
- [Snakemake workflow catalog](https://snakemake.github.io/snakemake-workflow-catalog/) — community-maintained reference pipelines
skills/scientific-computing/spikeinterface-electrophysiology/SKILL.md
- Network accessline 687
- [SpikeInterface documentation](https://spikeinterface.readthedocs.io/) — full API reference, tutorials, and sorter-specific guides - Network accessline 687
- [SpikeInterface documentation](https://spikeinterface.readthedocs.io/) — full API reference, tutorials, and sorter-specific guides - Network accessline 688
- [SpikeInterface GitHub](https://github.com/SpikeInterface/spikeinterface) — source code, changelogs, and issue tracker - Network accessline 688
- [SpikeInterface GitHub](https://github.com/SpikeInterface/spikeinterface) — source code, changelogs, and issue tracker - Network accessline 689
- [Buccino et al. (2020), eLife — SpikeInterface paper](https://doi.org/10.7554/eLife.61834) — unified framework design and benchmarks across sorters - Network accessline 689
- [Buccino et al. (2020), eLife — SpikeInterface paper](https://doi.org/10.7554/eLife.61834) — unified framework design and benchmarks across sorters - Network accessline 690
- [ProbeInterface documentation](https://probeinterface.readthedocs.io/) — probe geometry handling and channel map formats - Network accessline 690
- [ProbeInterface documentation](https://probeinterface.readthedocs.io/) — probe geometry handling and channel map formats - Network accessline 691
- [SpikeInterface sorter list](https://spikeinterface.readthedocs.io/en/latest/modules/sorters.html) — supported sorters, requirements, and container images - Network accessline 691
- [SpikeInterface sorter list](https://spikeinterface.readthedocs.io/en/latest/modules/sorters.html) — supported sorters, requirements, and container images - Network accessline 692
- [NWB documentation](https://www.nwb.org/) — Neurodata Without Borders format for neurophysiology data sharing - Network accessline 692
- [NWB documentation](https://www.nwb.org/) — Neurodata Without Borders format for neurophysiology data sharing
skills/scientific-computing/sympy-symbolic-math/SKILL.md
- Network accessline 462
- [SymPy documentation](https://docs.sympy.org/) — official API reference and tutorials - Network accessline 462
- [SymPy documentation](https://docs.sympy.org/) — official API reference and tutorials - Network accessline 463
- [SymPy tutorial](https://docs.sympy.org/latest/tutorials/intro-tutorial/index.html) — introduction for new users - Network accessline 463
- [SymPy tutorial](https://docs.sympy.org/latest/tutorials/intro-tutorial/index.html) — introduction for new users - Network accessline 464
- [SymPy GitHub](https://github.com/sympy/sympy) — source code, examples, and issues - Network accessline 464
- [SymPy GitHub](https://github.com/sympy/sympy) — source code, examples, and issues - Network accessline 465
- Meurer et al. (2017) "SymPy: symbolic computing in Python" — [PeerJ Computer Science](https://doi.org/10.7717/peerj-cs.103) - Network accessline 465
- Meurer et al. (2017) "SymPy: symbolic computing in Python" — [PeerJ Computer Science](https://doi.org/10.7717/peerj-cs.103)
skills/scientific-computing/torch-geometric-graph-neural-networks/SKILL.md
- Network accessline 532
- PyTorch Geometric Documentation: https://pytorch-geometric.readthedocs.io/ - Network accessline 532
- PyTorch Geometric Documentation: https://pytorch-geometric.readthedocs.io/ - Network accessline 533
- PyG GitHub: https://github.com/pyg-team/pytorch_geometric - Network accessline 533
- PyG GitHub: https://github.com/pyg-team/pytorch_geometric
skills/scientific-computing/transformers-bio-nlp/SKILL.md
- Network accessline 30
pip install transformers torch datasets accelerate sentencepiece # For GPU (CUDA 11.8) pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118 - Network accessline 30
pip install transformers torch datasets accelerate sentencepiece # For GPU (CUDA 11.8) pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118 - Network accessline 32
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118 - Network accessline 32
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118 - Network accessline 471
- [HuggingFace Transformers docs](https://huggingface.co/docs/transformers/) — pipeline, tokenizer, and training API - Network accessline 471
- [HuggingFace Transformers docs](https://huggingface.co/docs/transformers/) — pipeline, tokenizer, and training API - Network accessline 472
- [BioBERT paper: Lee et al. (2020), Bioinformatics](https://doi.org/10.1093/bioinformatics/btz682) — pre-training on PubMed and PMC - Network accessline 472
- [BioBERT paper: Lee et al. (2020), Bioinformatics](https://doi.org/10.1093/bioinformatics/btz682) — pre-training on PubMed and PMC - Network accessline 473
- [PubMedBERT paper: Gu et al. (2021), ACL](https://doi.org/10.1145/3458754) — from-scratch pre-training on PubMed - Network accessline 473
- [PubMedBERT paper: Gu et al. (2021), ACL](https://doi.org/10.1145/3458754) — from-scratch pre-training on PubMed - Network accessline 474
- [BioGPT paper: Luo et al. (2022), Briefings in Bioinformatics](https://doi.org/10.1093/bib/bbac409) — generative biomedical language model - Network accessline 474
- [BioGPT paper: Luo et al. (2022), Briefings in Bioinformatics](https://doi.org/10.1093/bib/bbac409) — generative biomedical language model - Network accessline 475
- [BioCreative benchmarks](https://biocreative.bioinformatics.udel.edu/) — standard NER and relation extraction datasets - Network accessline 475
- [BioCreative benchmarks](https://biocreative.bioinformatics.udel.edu/) — standard NER and relation extraction datasets
skills/scientific-computing/umap-learn/SKILL.md
- Network accessline 497
- Sainburg T, McInnes L, Gentner TQ. Parametric UMAP Embeddings for Representation and Semisupervised Learning. Neural Computation (2021) - Narayan A, Berger B, Cho H. Assessing single-cell transcriptomic variability through density-preserv - Network accessline 497
- Sainburg T, McInnes L, Gentner TQ. Parametric UMAP Embeddings for Representation and Semisupervised Learning. Neural Computation (2021) - Narayan A, Berger B, Cho H. Assessing single-cell transcriptomic variability through density-preserv - Network accessline 498
- Narayan A, Berger B, Cho H. Assessing single-cell transcriptomic variability through density-preserving data visualization. Nature Biotechnology (2021) — DensMAP - Official docs: https://umap-learn.readthedocs.io/ - GitHub: https://github - Network accessline 498
- Narayan A, Berger B, Cho H. Assessing single-cell transcriptomic variability through density-preserving data visualization. Nature Biotechnology (2021) — DensMAP - Official docs: https://umap-learn.readthedocs.io/ - GitHub: https://github - Network accessline 499
- Official docs: https://umap-learn.readthedocs.io/ - Network accessline 499
- Official docs: https://umap-learn.readthedocs.io/ - Network accessline 500
- GitHub: https://github.com/lmcinnes/umap - Network accessline 500
- GitHub: https://github.com/lmcinnes/umap
skills/scientific-computing/uspto-database/SKILL.md
- Network accessline 42
# Search PatentsView API: patents assigned to "Genentech" in CPC class C12N url = "https://api.patentsview.org/patents/query" - Network accessline 42
# Search PatentsView API: patents assigned to "Genentech" in CPC class C12N url = "https://api.patentsview.org/patents/query" - Network accessline 43
url = "https://api.patentsview.org/patents/query" - Network accessline 43
url = "https://api.patentsview.org/patents/query" - Network accessline 50
"o": {"per_page": 25}, } resp = requests.post(url, json=payload) - Network accessline 50
"o": {"per_page": 25}, } resp = requests.post(url, json=payload) - Network accessline 51
} resp = requests.post(url, json=payload) data = resp.json() - Network accessline 51
} resp = requests.post(url, json=payload) data = resp.json() - Network accessline 52
resp = requests.post(url, json=payload) - Network accessline 52
resp = requests.post(url, json=payload) - Network accessline 69
def search_by_assignee(assignee_name: str, per_page: int = 100) -> pd.DataFrame: url = "https://api.patentsview.org/patents/query" - Network accessline 69
def search_by_assignee(assignee_name: str, per_page: int = 100) -> pd.DataFrame: url = "https://api.patentsview.org/patents/query" - Network accessline 70
url = "https://api.patentsview.org/patents/query" - Network accessline 70
url = "https://api.patentsview.org/patents/query" - Network accessline 77
"o": {"per_page": per_page, "sort": [{"patent_date": "desc"}]}, } resp = requests.post(url, json=payload) - Network accessline 77
"o": {"per_page": per_page, "sort": [{"patent_date": "desc"}]}, } resp = requests.post(url, json=payload) - Network accessline 78
} resp = requests.post(url, json=payload) resp.raise_for_status() - Network accessline 78
} resp = requests.post(url, json=payload) resp.raise_for_status() - Network accessline 79
resp = requests.post(url, json=payload) - Network accessline 79
resp = requests.post(url, json=payload) - Network accessline 92
# Paginate through all results for large portfolios def search_assignee_all_pages(assignee_name: str, page_size: int = 100) -> pd.DataFrame: url = "https://api.patentsview.org/patents/query" - Network accessline 92
# Paginate through all results for large portfolios def search_assignee_all_pages(assignee_name: str, page_size: int = 100) -> pd.DataFrame: url = "https://api.patentsview.org/patents/query" - Network accessline 93
def search_assignee_all_pages(assignee_name: str, page_size: int = 100) -> pd.DataFrame: url = "https://api.patentsview.org/patents/query" all_patents = [] - Network accessline 93
def search_assignee_all_pages(assignee_name: str, page_size: int = 100) -> pd.DataFrame: url = "https://api.patentsview.org/patents/query" all_patents = [] - Network accessline 94
url = "https://api.patentsview.org/patents/query" - Network accessline 94
url = "https://api.patentsview.org/patents/query" - Network accessline 101
"o": {"per_page": page_size, "page": page}, } resp = requests.post(url, json=payload) - Network accessline 101
"o": {"per_page": page_size, "page": page}, } resp = requests.post(url, json=payload) - Network accessline 102
} resp = requests.post(url, json=payload) data = resp.json() - Network accessline 102
} resp = requests.post(url, json=payload) data = resp.json() - Network accessline 103
resp = requests.post(url, json=payload) - Network accessline 103
resp = requests.post(url, json=payload) - Network accessline 127
# Search by CPC subgroup: C12N15 (mutation/genetic engineering) url = "https://api.patentsview.org/patents/query" - Network accessline 127
# Search by CPC subgroup: C12N15 (mutation/genetic engineering) url = "https://api.patentsview.org/patents/query" - Network accessline 128
url = "https://api.patentsview.org/patents/query" - Network accessline 128
url = "https://api.patentsview.org/patents/query" - Network accessline 135
"o": {"per_page": 50, "sort": [{"patent_date": "desc"}]}, } resp = requests.post(url, json=payload) - Network accessline 135
"o": {"per_page": 50, "sort": [{"patent_date": "desc"}]}, } resp = requests.post(url, json=payload) - Network accessline 136
} resp = requests.post(url, json=payload) data = resp.json() - Network accessline 136
} resp = requests.post(url, json=payload) data = resp.json() - Network accessline 137
resp = requests.post(url, json=payload) - Network accessline 137
resp = requests.post(url, json=payload) - Network accessline 168
def keyword_search(keyword: str, per_page: int = 50) -> pd.DataFrame: url = "https://api.patentsview.org/patents/query" - Network accessline 168
def keyword_search(keyword: str, per_page: int = 50) -> pd.DataFrame: url = "https://api.patentsview.org/patents/query" - Network accessline 169
url = "https://api.patentsview.org/patents/query" - Network accessline 169
url = "https://api.patentsview.org/patents/query" - Network accessline 179
"o": {"per_page": per_page, "sort": [{"patent_date": "desc"}]}, } resp = requests.post(url, json=payload) - Network accessline 179
"o": {"per_page": per_page, "sort": [{"patent_date": "desc"}]}, } resp = requests.post(url, json=payload) - Network accessline 180
} resp = requests.post(url, json=payload) resp.raise_for_status() - Network accessline 180
} resp = requests.post(url, json=payload) resp.raise_for_status() - Network accessline 181
resp = requests.post(url, json=payload) - Network accessline 181
resp = requests.post(url, json=payload) - Network accessline 201
# Search by inventor name url = "https://api.patentsview.org/inventors/query" - Network accessline 201
# Search by inventor name url = "https://api.patentsview.org/inventors/query" - Network accessline 202
url = "https://api.patentsview.org/inventors/query" - Network accessline 202
url = "https://api.patentsview.org/inventors/query" - Network accessline 210
"o": {"per_page": 10}, } resp = requests.post(url, json=payload) - Network accessline 210
"o": {"per_page": 10}, } resp = requests.post(url, json=payload) - Network accessline 211
} resp = requests.post(url, json=payload) data = resp.json() - Network accessline 211
} resp = requests.post(url, json=payload) data = resp.json() - Network accessline 212
resp = requests.post(url, json=payload) - Network accessline 212
resp = requests.post(url, json=payload) - Network accessline 223
url = "https://api.patentsview.org/patents/query" - Network accessline 223
url = "https://api.patentsview.org/patents/query" - Network accessline 227
"o": {"per_page": 100, "sort": [{"patent_date": "desc"}]}, } resp = requests.post(url, json=payload) - Network accessline 227
"o": {"per_page": 100, "sort": [{"patent_date": "desc"}]}, } resp = requests.post(url, json=payload) - Network accessline 228
} resp = requests.post(url, json=payload) data = resp.json() - Network accessline 228
} resp = requests.post(url, json=payload) data = resp.json() - Network accessline 229
resp = requests.post(url, json=payload) - Network accessline 229
resp = requests.post(url, json=payload) - Network accessline 244
# Patents in gene therapy (CPC A61K48) filed 2020-2024 by a US assignee url = "https://api.patentsview.org/patents/query" - Network accessline 244
# Patents in gene therapy (CPC A61K48) filed 2020-2024 by a US assignee url = "https://api.patentsview.org/patents/query" - Network accessline 245
url = "https://api.patentsview.org/patents/query" - Network accessline 245
url = "https://api.patentsview.org/patents/query" - Network accessline 257
"o": {"per_page": 100, "sort": [{"patent_date": "desc"}]}, } resp = requests.post(url, json=payload) - Network accessline 257
"o": {"per_page": 100, "sort": [{"patent_date": "desc"}]}, } resp = requests.post(url, json=payload) - Network accessline 258
} resp = requests.post(url, json=payload) data = resp.json() - Network accessline 258
} resp = requests.post(url, json=payload) data = resp.json() - Network accessline 259
resp = requests.post(url, json=payload) - Network accessline 259
resp = requests.post(url, json=payload) - Network accessline 319
cache_file = cache / "genentech_patents.json" if not cache_file.exists(): resp = requests.post(url, json=payload) - Network accessline 319
cache_file = cache / "genentech_patents.json" if not cache_file.exists(): resp = requests.post(url, json=payload) - Network accessline 320
if not cache_file.exists(): resp = requests.post(url, json=payload) cache_file.write_text(resp.text) - Network accessline 320
if not cache_file.exists(): resp = requests.post(url, json=payload) cache_file.write_text(resp.text) - Network accessline 321
resp = requests.post(url, json=payload) - Network accessline 321
resp = requests.post(url, json=payload) - Network accessline 342
def count_patents_by_year(cpc_prefix: str, start_year: int = 2010) -> pd.DataFrame: url = "https://api.patentsview.org/patents/query" - Network accessline 342
def count_patents_by_year(cpc_prefix: str, start_year: int = 2010) -> pd.DataFrame: url = "https://api.patentsview.org/patents/query" - Network accessline 343
url = "https://api.patentsview.org/patents/query" - Network accessline 343
url = "https://api.patentsview.org/patents/query" - Network accessline 353
"o": {"per_page": 10000, "page": page}, } resp = requests.post(url, json=payload) - Network accessline 353
"o": {"per_page": 10000, "page": page}, } resp = requests.post(url, json=payload) - Network accessline 354
} resp = requests.post(url, json=payload) patents = resp.json().get("patents", []) - Network accessline 354
} resp = requests.post(url, json=payload) patents = resp.json().get("patents", []) - Network accessline 355
resp = requests.post(url, json=payload) - Network accessline 355
resp = requests.post(url, json=payload) - Network accessline 392
def count_patents_by_assignee(assignees: list, cpc_prefix: str) -> pd.DataFrame: url = "https://api.patentsview.org/patents/query" - Network accessline 392
def count_patents_by_assignee(assignees: list, cpc_prefix: str) -> pd.DataFrame: url = "https://api.patentsview.org/patents/query" - Network accessline 393
url = "https://api.patentsview.org/patents/query" - Network accessline 393
url = "https://api.patentsview.org/patents/query" - Network accessline 402
"o": {"per_page": 1}, # only need total count } resp = requests.post(url, json=payload) - Network accessline 402
"o": {"per_page": 1}, # only need total count } resp = requests.post(url, json=payload) - Network accessline 403
} resp = requests.post(url, json=payload) total = resp.json().get("total_patent_count", 0) - Network accessline 403
} resp = requests.post(url, json=payload) total = resp.json().get("total_patent_count", 0) - Network accessline 404
resp = requests.post(url, json=payload) - Network accessline 404
resp = requests.post(url, json=payload) - Network accessline 434
|---------|-------|----------| | `HTTPError 429 Too Many Requests` | Exceeded 45 req/min rate limit | Add `time.sleep(1.5)` between requests; request a free API key | | Empty `patents` list in response | Query too narrow or field name incor - Network accessline 434
|---------|-------|----------| | `HTTPError 429 Too Many Requests` | Exceeded 45 req/min rate limit | Add `time.sleep(1.5)` between requests; request a free API key | | Empty `patents` list in response | Query too narrow or field name incor - Network accessline 435
| `HTTPError 429 Too Many Requests` | Exceeded 45 req/min rate limit | Add `time.sleep(1.5)` between requests; request a free API key | | Empty `patents` list in response | Query too narrow or field name incorrect | Check field names in [Pa - Network accessline 435
| `HTTPError 429 Too Many Requests` | Exceeded 45 req/min rate limit | Add `time.sleep(1.5)` between requests; request a free API key | | Empty `patents` list in response | Query too narrow or field name incorrect | Check field names in [Pa - Network accessline 436
| Empty `patents` list in response | Query too narrow or field name incorrect | Check field names in [PatentsView API docs](https://patentsview.org/apis/api-endpoints/patents); test query in the web UI first | - Network accessline 436
| Empty `patents` list in response | Query too narrow or field name incorrect | Check field names in [PatentsView API docs](https://patentsview.org/apis/api-endpoints/patents); test query in the web UI first | - Network accessline 438
| `KeyError: patent_date` | Field not requested in `"f"` list | Add `"patent_date"` to the `"f"` array | | BigQuery auth error | GCP credentials not configured | Run `gcloud auth application-default login` or set `GOOGLE_APPLICATION_CREDENT - Network accessline 438
| `KeyError: patent_date` | Field not requested in `"f"` list | Add `"patent_date"` to the `"f"` array | | BigQuery auth error | GCP credentials not configured | Run `gcloud auth application-default login` or set `GOOGLE_APPLICATION_CREDENT - Network accessline 440
| CPC prefix returns no results | Invalid CPC code or typo | Verify code at [CPC classification browser](https://www.cooperativepatentclassification.org/cpcSchemeAndDefinitions/table.html) | - Network accessline 440
| CPC prefix returns no results | Invalid CPC code or typo | Verify code at [CPC classification browser](https://www.cooperativepatentclassification.org/cpcSchemeAndDefinitions/table.html) | - Network accessline 444
- [PatentsView API Documentation](https://patentsview.org/apis/api-endpoints/patents) — full field list and query operator reference - Network accessline 444
- [PatentsView API Documentation](https://patentsview.org/apis/api-endpoints/patents) — full field list and query operator reference - Network accessline 445
- [PatentsView API Explorer](https://patentsview.org/query) — browser-based query builder for testing - Network accessline 445
- [PatentsView API Explorer](https://patentsview.org/query) — browser-based query builder for testing - Network accessline 446
- [Google Patents Public Data (BigQuery)](https://cloud.google.com/blog/topics/public-datasets/google-patents-public-data-connecting-public-paid-and-private-patent-data) — schema documentation - Network accessline 446
- [Google Patents Public Data (BigQuery)](https://cloud.google.com/blog/topics/public-datasets/google-patents-public-data-connecting-public-paid-and-private-patent-data) — schema documentation - Network accessline 447
- [CPC Classification Browser](https://www.cooperativepatentclassification.org/cpcSchemeAndDefinitions/table.html) — browse life sciences CPC codes - Network accessline 447
- [CPC Classification Browser](https://www.cooperativepatentclassification.org/cpcSchemeAndDefinitions/table.html) — browse life sciences CPC codes - Network accessline 448
- [USPTO Bulk Data Storage System](https://bulkdata.uspto.gov/) — full-text patent XML downloads - Network accessline 448
- [USPTO Bulk Data Storage System](https://bulkdata.uspto.gov/) — full-text patent XML downloads
skills/scientific-computing/vaex-dataframes/SKILL.md
- Network accessline 599
- Vaex documentation: https://vaex.io/docs/latest/ - Network accessline 599
- Vaex documentation: https://vaex.io/docs/latest/ - Network accessline 600
- Vaex GitHub repository: https://github.com/vaexio/vaex - Network accessline 600
- Vaex GitHub repository: https://github.com/vaexio/vaex - Network accessline 601
- Vaex PyPI: https://pypi.org/project/vaex/ - Network accessline 601
- Vaex PyPI: https://pypi.org/project/vaex/ - Network accessline 602
- Breddels & Veljanoski (2018), "Vaex: Big Data exploration in the era of Gaia", Astronomy & Astrophysics, https://doi.org/10.1051/0004-6361/201732493 - Network accessline 602
- Breddels & Veljanoski (2018), "Vaex: Big Data exploration in the era of Gaia", Astronomy & Astrophysics, https://doi.org/10.1051/0004-6361/201732493
skills/scientific-computing/vaex-dataframes/references/io_performance.md
- Access to secretsline 103
[Credential-related source omitted] - Access to secretsline 103
[Credential-related source omitted] - Access to secretsline 300
[Credential-related source omitted] - Access to secretsline 300
[Credential-related source omitted] - Access to secretsline 312
[Credential-related source omitted] - Access to secretsline 312
[Credential-related source omitted]
skills/scientific-computing/zarr-python/SKILL.md
- Network accessline 534
- [Zarr Python documentation](https://zarr.readthedocs.io/) — official user guide and API reference - Network accessline 534
- [Zarr Python documentation](https://zarr.readthedocs.io/) — official user guide and API reference - Network accessline 535
- [Zarr specifications](https://zarr-specs.readthedocs.io/) — file format specification (V2, V3) - Network accessline 535
- [Zarr specifications](https://zarr-specs.readthedocs.io/) — file format specification (V2, V3) - Network accessline 536
- [Zarr GitHub](https://github.com/zarr-developers/zarr-python) — source code, issues, changelog - Network accessline 536
- [Zarr GitHub](https://github.com/zarr-developers/zarr-python) — source code, issues, changelog - Network accessline 537
- [NumCodecs](https://numcodecs.readthedocs.io/) — compression codec library used by Zarr - Network accessline 537
- [NumCodecs](https://numcodecs.readthedocs.io/) — compression codec library used by Zarr
skills/scientific-writing/biorxiv-database/SKILL.md
- Network accessline 38
BASE = "https://api.biorxiv.org" - Network accessline 38
BASE = "https://api.biorxiv.org" - Network accessline 40
# Retrieve recent bioinformatics preprints r = requests.get(f"{BASE}/details/biorxiv/2024-01-01/2024-01-07/0", - Network accessline 40
# Retrieve recent bioinformatics preprints r = requests.get(f"{BASE}/details/biorxiv/2024-01-01/2024-01-07/0", - Network accessline 41
r = requests.get(f"{BASE}/details/biorxiv/2024-01-01/2024-01-07/0", - Network accessline 41
r = requests.get(f"{BASE}/details/biorxiv/2024-01-01/2024-01-07/0", - Network accessline 62
BASE = "https://api.biorxiv.org" - Network accessline 62
BASE = "https://api.biorxiv.org" - Network accessline 69
""" url = f"{BASE}/details/{server}/{date_from}/{date_to}/{cursor}" r = requests.get(url) - Network accessline 69
""" url = f"{BASE}/details/{server}/{date_from}/{date_to}/{cursor}" r = requests.get(url) - Network accessline 70
url = f"{BASE}/details/{server}/{date_from}/{date_to}/{cursor}" r = requests.get(url) r.raise_for_status() - Network accessline 70
url = f"{BASE}/details/{server}/{date_from}/{date_to}/{cursor}" r = requests.get(url) r.raise_for_status() - Network accessline 71
r = requests.get(url) - Network accessline 71
r = requests.get(url) - Network accessline 121
BASE = "https://api.biorxiv.org" - Network accessline 121
BASE = "https://api.biorxiv.org" - Network accessline 126
def get_by_doi(server, doi): r = requests.get(f"{BASE}/details/{server}/{doi}") - Network accessline 126
def get_by_doi(server, doi): r = requests.get(f"{BASE}/details/{server}/{doi}") - Network accessline 127
r = requests.get(f"{BASE}/details/{server}/{doi}") - Network accessline 127
r = requests.get(f"{BASE}/details/{server}/{doi}") - Network accessline 131
# Generic example using bioRxiv DOI pattern r = requests.get(f"{BASE}/details/biorxiv/10.1101/2024.05.28.596311") - Network accessline 131
# Generic example using bioRxiv DOI pattern r = requests.get(f"{BASE}/details/biorxiv/10.1101/2024.05.28.596311") - Network accessline 132
r = requests.get(f"{BASE}/details/biorxiv/10.1101/2024.05.28.596311") - Network accessline 132
r = requests.get(f"{BASE}/details/biorxiv/10.1101/2024.05.28.596311") - Network accessline 154
BASE = "https://api.biorxiv.org" - Network accessline 154
BASE = "https://api.biorxiv.org" - Network accessline 156
def check_published(server, doi): """Check if a preprint DOI has a corresponding published article.""" r = requests.get(f"{BASE}/publisher/{server}/{doi}") - Network accessline 156
def check_published(server, doi): """Check if a preprint DOI has a corresponding published article.""" r = requests.get(f"{BASE}/publisher/{server}/{doi}") - Network accessline 157
"""Check if a preprint DOI has a corresponding published article.""" r = requests.get(f"{BASE}/publisher/{server}/{doi}") r.raise_for_status() - Network accessline 157
"""Check if a preprint DOI has a corresponding published article.""" r = requests.get(f"{BASE}/publisher/{server}/{doi}") r.raise_for_status() - Network accessline 158
r = requests.get(f"{BASE}/publisher/{server}/{doi}") - Network accessline 158
r = requests.get(f"{BASE}/publisher/{server}/{doi}") - Network accessline 182
BASE = "https://api.biorxiv.org" - Network accessline 182
BASE = "https://api.biorxiv.org" - Network accessline 194
cursor = 0 while True: r = requests.get(f"{BASE}/details/biorxiv/{date_from}/{date_to}/{cursor}") - Network accessline 194
cursor = 0 while True: r = requests.get(f"{BASE}/details/biorxiv/{date_from}/{date_to}/{cursor}") - Network accessline 195
while True: r = requests.get(f"{BASE}/details/biorxiv/{date_from}/{date_to}/{cursor}") data = r.json() - Network accessline 195
while True: r = requests.get(f"{BASE}/details/biorxiv/{date_from}/{date_to}/{cursor}") data = r.json() - Network accessline 196
r = requests.get(f"{BASE}/details/biorxiv/{date_from}/{date_to}/{cursor}") - Network accessline 196
r = requests.get(f"{BASE}/details/biorxiv/{date_from}/{date_to}/{cursor}") - Network accessline 218
BASE = "https://api.biorxiv.org" - Network accessline 218
BASE = "https://api.biorxiv.org" - Network accessline 223
r = requests.get(f"{BASE}/details/medrxiv/2024-01-01/2024-01-07/0") - Network accessline 223
r = requests.get(f"{BASE}/details/medrxiv/2024-01-01/2024-01-07/0") - Network accessline 244
BASE = "https://api.biorxiv.org" - Network accessline 244
BASE = "https://api.biorxiv.org" - Network accessline 252
rows = [] for doi in dois: r = requests.get(f"{BASE}/details/biorxiv/{doi}") - Network accessline 252
rows = [] for doi in dois: r = requests.get(f"{BASE}/details/biorxiv/{doi}") - Network accessline 253
for doi in dois: r = requests.get(f"{BASE}/details/biorxiv/{doi}") if r.ok: - Network accessline 253
for doi in dois: r = requests.get(f"{BASE}/details/biorxiv/{doi}") if r.ok: - Network accessline 254
r = requests.get(f"{BASE}/details/biorxiv/{doi}") - Network accessline 254
r = requests.get(f"{BASE}/details/biorxiv/{doi}") - Network accessline 280
The bioRxiv API follows the pattern: `https://api.biorxiv.org/details/{server}/{interval}/{cursor}` - Network accessline 280
The bioRxiv API follows the pattern: `https://api.biorxiv.org/details/{server}/{interval}/{cursor}` - Network accessline 299
BASE = "https://api.biorxiv.org" - Network accessline 299
BASE = "https://api.biorxiv.org" - Network accessline 311
cursor = 0 while True: r = requests.get(f"{BASE}/details/biorxiv/{date_from}/{date_to}/{cursor}") - Network accessline 311
cursor = 0 while True: r = requests.get(f"{BASE}/details/biorxiv/{date_from}/{date_to}/{cursor}") - Network accessline 312
while True: r = requests.get(f"{BASE}/details/biorxiv/{date_from}/{date_to}/{cursor}") r.raise_for_status() - Network accessline 312
while True: r = requests.get(f"{BASE}/details/biorxiv/{date_from}/{date_to}/{cursor}") r.raise_for_status() - Network accessline 313
r = requests.get(f"{BASE}/details/biorxiv/{date_from}/{date_to}/{cursor}") - Network accessline 313
r = requests.get(f"{BASE}/details/biorxiv/{date_from}/{date_to}/{cursor}") - Network accessline 346
BASE = "https://api.biorxiv.org" - Network accessline 346
BASE = "https://api.biorxiv.org" - Network accessline 354
for doi in preprint_dois: # Get preprint metadata r_meta = requests.get(f"{BASE}/details/biorxiv/{doi}") - Network accessline 354
for doi in preprint_dois: # Get preprint metadata r_meta = requests.get(f"{BASE}/details/biorxiv/{doi}") - Network accessline 355
# Get preprint metadata r_meta = requests.get(f"{BASE}/details/biorxiv/{doi}") meta = {} - Network accessline 355
# Get preprint metadata r_meta = requests.get(f"{BASE}/details/biorxiv/{doi}") meta = {} - Network accessline 356
r_meta = requests.get(f"{BASE}/details/biorxiv/{doi}") - Network accessline 356
r_meta = requests.get(f"{BASE}/details/biorxiv/{doi}") - Network accessline 363
# Check publication status r_pub = requests.get(f"{BASE}/publisher/biorxiv/{doi}") - Network accessline 363
# Check publication status r_pub = requests.get(f"{BASE}/publisher/biorxiv/{doi}") - Network accessline 364
r_pub = requests.get(f"{BASE}/publisher/biorxiv/{doi}") - Network accessline 364
r_pub = requests.get(f"{BASE}/publisher/biorxiv/{doi}") - Network accessline 406
When to use: Retrieve full-text PDF for a bioRxiv preprint. **Caveat**: as of 2026, `www.biorxiv.org` is fronted by Cloudflare's anti-bot challenge — direct `requests.get(..., headers={"User-Agent": "Mozilla/5.0"})` consistently returns HTT - Network accessline 406
When to use: Retrieve full-text PDF for a bioRxiv preprint. **Caveat**: as of 2026, `www.biorxiv.org` is fronted by Cloudflare's anti-bot challenge — direct `requests.get(..., headers={"User-Agent": "Mozilla/5.0"})` consistently returns HTT - Network accessline 412
"""Best-effort PDF download. If Cloudflare blocks, return False so the caller can fall back to EuropePMC metadata or open the landing page in a browser.""" pdf_url = f"https://www.biorxiv.org/content/{doi}.full.pdf" - Network accessline 412
"""Best-effort PDF download. If Cloudflare blocks, return False so the caller can fall back to EuropePMC metadata or open the landing page in a browser.""" pdf_url = f"https://www.biorxiv.org/content/{doi}.full.pdf" - Network accessline 413
can fall back to EuropePMC metadata or open the landing page in a browser.""" pdf_url = f"https://www.biorxiv.org/content/{doi}.full.pdf" s = requests.Session() - Network accessline 413
can fall back to EuropePMC metadata or open the landing page in a browser.""" pdf_url = f"https://www.biorxiv.org/content/{doi}.full.pdf" s = requests.Session() - Network accessline 414
pdf_url = f"https://www.biorxiv.org/content/{doi}.full.pdf" s = requests.Session() s.headers.update({ - Network accessline 414
pdf_url = f"https://www.biorxiv.org/content/{doi}.full.pdf" s = requests.Session() s.headers.update({ - Network accessline 415
s = requests.Session() - Network accessline 415
s = requests.Session() - Network accessline 421
}) # Warm up the landing page first (sometimes lets Cloudflare's "trust" cookie set) s.get(f"https://www.biorxiv.org/content/{doi}v1", timeout=30) - Network accessline 421
}) # Warm up the landing page first (sometimes lets Cloudflare's "trust" cookie set) s.get(f"https://www.biorxiv.org/content/{doi}v1", timeout=30) - Network accessline 422
# Warm up the landing page first (sometimes lets Cloudflare's "trust" cookie set) s.get(f"https://www.biorxiv.org/content/{doi}v1", timeout=30) r = s.get(pdf_url, timeout=60) - Network accessline 422
# Warm up the landing page first (sometimes lets Cloudflare's "trust" cookie set) s.get(f"https://www.biorxiv.org/content/{doi}v1", timeout=30) r = s.get(pdf_url, timeout=60) - Network accessline 423
s.get(f"https://www.biorxiv.org/content/{doi}v1", timeout=30) - Network accessline 423
s.get(f"https://www.biorxiv.org/content/{doi}v1", timeout=30) - Network accessline 435
"""Fetch preprint metadata via EuropePMC when bioRxiv PDF is blocked. EuropePMC indexes bioRxiv as source 'PPR' and exposes a stable landing URL.""" r = requests.get("https://www.ebi.ac.uk/europepmc/webservices/rest/search", - Network accessline 435
"""Fetch preprint metadata via EuropePMC when bioRxiv PDF is blocked. EuropePMC indexes bioRxiv as source 'PPR' and exposes a stable landing URL.""" r = requests.get("https://www.ebi.ac.uk/europepmc/webservices/rest/search", - Network accessline 436
EuropePMC indexes bioRxiv as source 'PPR' and exposes a stable landing URL.""" r = requests.get("https://www.ebi.ac.uk/europepmc/webservices/rest/search", params={"query": f"DOI:{doi}", "format": "json"}, timeout=30 - Network accessline 436
EuropePMC indexes bioRxiv as source 'PPR' and exposes a stable landing URL.""" r = requests.get("https://www.ebi.ac.uk/europepmc/webservices/rest/search", params={"query": f"DOI:{doi}", "format": "json"}, timeout=30 - Network accessline 437
r = requests.get("https://www.ebi.ac.uk/europepmc/webservices/rest/search", - Network accessline 437
r = requests.get("https://www.ebi.ac.uk/europepmc/webservices/rest/search", - Network accessline 446
"epmc_id": h.get("id"), # e.g. 'PPR860608' "title": h.get("title"), "landing_url": f"https://europepmc.org/article/{h.get('source')}/{h.get('id')}", - Network accessline 446
"epmc_id": h.get("id"), # e.g. 'PPR860608' "title": h.get("title"), "landing_url": f"https://europepmc.org/article/{h.get('source')}/{h.get('id')}", - Network accessline 447
"title": h.get("title"), "landing_url": f"https://europepmc.org/article/{h.get('source')}/{h.get('id')}", } - Network accessline 447
"title": h.get("title"), "landing_url": f"https://europepmc.org/article/{h.get('source')}/{h.get('id')}", } - Network accessline 448
"landing_url": f"https://europepmc.org/article/{h.get('source')}/{h.get('id')}", - Network accessline 448
"landing_url": f"https://europepmc.org/article/{h.get('source')}/{h.get('id')}", - Network accessline 466
r = requests.get("https://api.biorxiv.org/details/biorxiv/2024-01-01/2024-01-07/0") - Network accessline 466
r = requests.get("https://api.biorxiv.org/details/biorxiv/2024-01-01/2024-01-07/0") - Network accessline 471
all_articles = data["collection"] for cursor in range(100, min(total, 1000), 100): r2 = requests.get(f"https://api.biorxiv.org/details/biorxiv/2024-01-01/2024-01-07/{cursor}") - Network accessline 471
all_articles = data["collection"] for cursor in range(100, min(total, 1000), 100): r2 = requests.get(f"https://api.biorxiv.org/details/biorxiv/2024-01-01/2024-01-07/{cursor}") - Network accessline 472
for cursor in range(100, min(total, 1000), 100): r2 = requests.get(f"https://api.biorxiv.org/details/biorxiv/2024-01-01/2024-01-07/{cursor}") all_articles.extend(r2.json()["collection"]) - Network accessline 472
for cursor in range(100, min(total, 1000), 100): r2 = requests.get(f"https://api.biorxiv.org/details/biorxiv/2024-01-01/2024-01-07/{cursor}") all_articles.extend(r2.json()["collection"]) - Network accessline 473
r2 = requests.get(f"https://api.biorxiv.org/details/biorxiv/2024-01-01/2024-01-07/{cursor}") - Network accessline 473
r2 = requests.get(f"https://api.biorxiv.org/details/biorxiv/2024-01-01/2024-01-07/{cursor}") - Network accessline 488
doi = "10.1101/2024.05.28.596311" r = requests.get(f"https://api.biorxiv.org/publisher/biorxiv/{doi}") - Network accessline 488
doi = "10.1101/2024.05.28.596311" r = requests.get(f"https://api.biorxiv.org/publisher/biorxiv/{doi}") - Network accessline 489
r = requests.get(f"https://api.biorxiv.org/publisher/biorxiv/{doi}") - Network accessline 489
r = requests.get(f"https://api.biorxiv.org/publisher/biorxiv/{doi}") - Network accessline 505
| PDF download blocked (HTTP 403 "Just a moment...") | Cloudflare anti-bot on `www.biorxiv.org/.../*.full.pdf` (cannot be bypassed by a `Mozilla/5.0` UA alone, nor by a Session + landing-page warmup) | Try the Session + warmup recipe; if st - Network accessline 505
| PDF download blocked (HTTP 403 "Just a moment...") | Cloudflare anti-bot on `www.biorxiv.org/.../*.full.pdf` (cannot be bypassed by a `Mozilla/5.0` UA alone, nor by a Session + landing-page warmup) | Try the Session + warmup recipe; if st - Network accessline 506
| `cursor >= total` never triggers; loop runs forever | `data['messages'][0]['total']` is returned as a **string** (e.g. `'1119'`); `int_cursor >= str_total` raises `TypeError` or compares lexically | Cast explicitly: `int(data["messages"][ - Network accessline 506
| `cursor >= total` never triggers; loop runs forever | `data['messages'][0]['total']` is returned as a **string** (e.g. `'1119'`); `int_cursor >= str_total` raises `TypeError` or compares lexically | Cast explicitly: `int(data["messages"][ - Network accessline 507
| `collection` empty for a specific DOI | The DOI never resolved to a real preprint (e.g. fake placeholder like `2023.01.01.000001`, or a stale/withdrawn DOI) | Verify the DOI on `https://www.biorxiv.org/content/{doi}v1` first; recent DOIs - Network accessline 507
| `collection` empty for a specific DOI | The DOI never resolved to a real preprint (e.g. fake placeholder like `2023.01.01.000001`, or a stale/withdrawn DOI) | Verify the DOI on `https://www.biorxiv.org/content/{doi}v1` first; recent DOIs - Network accessline 519
- [bioRxiv API documentation](https://api.biorxiv.org/) — Official API endpoint and response format - Network accessline 519
- [bioRxiv API documentation](https://api.biorxiv.org/) — Official API endpoint and response format - Network accessline 520
- [bioRxiv about page](https://www.biorxiv.org/about/home) — Server overview, submission guidelines - Network accessline 520
- [bioRxiv about page](https://www.biorxiv.org/about/home) — Server overview, submission guidelines - Network accessline 521
- [medRxiv about page](https://www.medrxiv.org/about/home) — Health science preprint server details - Network accessline 521
- [medRxiv about page](https://www.medrxiv.org/about/home) — Health science preprint server details - Network accessline 522
- [Preprint guidelines review (Fraser et al. 2021)](https://doi.org/10.1371/journal.pbio.3001285) — Best practices for citing and using preprints in research - Network accessline 522
- [Preprint guidelines review (Fraser et al. 2021)](https://doi.org/10.1371/journal.pbio.3001285) — Best practices for citing and using preprints in research
skills/scientific-writing/cancer-research-figure-guide/SKILL.md
- Network accessline 17
**Official reference**: https://aacrjournals.org/pages/article-style-and-format - Network accessline 17
**Official reference**: https://aacrjournals.org/pages/article-style-and-format - Network accessline 302
- AACR Article Style and Format: https://aacrjournals.org/pages/article-style-and-format - Network accessline 302
- AACR Article Style and Format: https://aacrjournals.org/pages/article-style-and-format - Network accessline 303
- AACR Editorial Policies: https://aacrjournals.org/pages/editorial-policies - Network accessline 303
- AACR Editorial Policies: https://aacrjournals.org/pages/editorial-policies - Network accessline 304
- Cancer Research Author Guidelines: https://aacrjournals.org/cancerresearch/pages/prep - Network accessline 304
- Cancer Research Author Guidelines: https://aacrjournals.org/cancerresearch/pages/prep
skills/scientific-writing/cell-figure-guide/SKILL.md
- Network accessline 17
**Official reference**: https://www.cell.com/information-for-authors/figure-guidelines - Network accessline 17
**Official reference**: https://www.cell.com/information-for-authors/figure-guidelines - Network accessline 394
- Cell Press Figure Guidelines: https://www.cell.com/information-for-authors/figure-guidelines - Network accessline 394
- Cell Press Figure Guidelines: https://www.cell.com/information-for-authors/figure-guidelines - Network accessline 395
- Cell Press Digital Image Guidelines: https://www.cell.com/matter/figureguidelines - Network accessline 395
- Cell Press Digital Image Guidelines: https://www.cell.com/matter/figureguidelines - Network accessline 396
- Cell Press Image Integrity Policy: https://www.cell.com/cell/image-integrity - Network accessline 396
- Cell Press Image Integrity Policy: https://www.cell.com/cell/image-integrity
skills/scientific-writing/citation-management/SKILL.md
- Network accessline 56
**DOI format and resolution**: - All DOIs begin with `10.` followed by a registrant code (e.g., `10.1038/`) and a suffix - Always resolve DOIs through `https://doi.org/10.XXXX/suffix` — a bare DOI is not a clickable URL - Network accessline 56
**DOI format and resolution**: - All DOIs begin with `10.` followed by a registrant code (e.g., `10.1038/`) and a suffix - Always resolve DOIs through `https://doi.org/10.XXXX/suffix` — a bare DOI is not a clickable URL - Network accessline 57
- All DOIs begin with `10.` followed by a registrant code (e.g., `10.1038/`) and a suffix - Always resolve DOIs through `https://doi.org/10.XXXX/suffix` — a bare DOI is not a clickable URL - Do not use `http://dx.doi.org/` (old URL scheme); - Network accessline 57
- All DOIs begin with `10.` followed by a registrant code (e.g., `10.1038/`) and a suffix - Always resolve DOIs through `https://doi.org/10.XXXX/suffix` — a bare DOI is not a clickable URL - Do not use `http://dx.doi.org/` (old URL scheme); - Network accessline 58
- Always resolve DOIs through `https://doi.org/10.XXXX/suffix` — a bare DOI is not a clickable URL - Network accessline 58
- Always resolve DOIs through `https://doi.org/10.XXXX/suffix` — a bare DOI is not a clickable URL - Network accessline 59
- Do not use `http://dx.doi.org/` (old URL scheme); use `https://doi.org/` (current) - Network accessline 59
- Do not use `http://dx.doi.org/` (old URL scheme); use `https://doi.org/` (current) - Network accessline 153
2. **Using the wrong citation style for the target journal**: Reformatting 80 references from APA to Vancouver by hand introduces errors in author lists, punctuation, and journal name abbreviation. Journals reject or return manuscripts with - Network accessline 153
2. **Using the wrong citation style for the target journal**: Reformatting 80 references from APA to Vancouver by hand introduces errors in author lists, punctuation, and journal name abbreviation. Journals reject or return manuscripts with - Network accessline 154
- *How to avoid*: Check the journal's "Instructions for Authors" before writing, not before submission. Set the correct style in your reference manager plugin at the start of the project. Most CSL files are maintained in the [Zotero style r - Network accessline 154
- *How to avoid*: Check the journal's "Instructions for Authors" before writing, not before submission. Set the correct style in your reference manager plugin at the start of the project. Most CSL files are maintained in the [Zotero style r - Network accessline 208
- [Zotero documentation](https://www.zotero.org/support/) — complete reference for collections, tags, groups, and plugins - Network accessline 208
- [Zotero documentation](https://www.zotero.org/support/) — complete reference for collections, tags, groups, and plugins - Network accessline 209
- [Better BibTeX for Zotero](https://retorque.re/zotero-better-bibtex/) — LaTeX integration, citation key management, auto-export - Network accessline 209
- [Better BibTeX for Zotero](https://retorque.re/zotero-better-bibtex/) — LaTeX integration, citation key management, auto-export - Network accessline 210
- [Citation Style Language (CSL) style repository](https://www.zotero.org/styles) — 10,000+ journal-specific styles for all major managers - Network accessline 210
- [Citation Style Language (CSL) style repository](https://www.zotero.org/styles) — 10,000+ journal-specific styles for all major managers - Network accessline 211
- [ICMJE citation recommendations](https://www.icmje.org/recommendations/browse/manuscript-preparation/preparing-for-submission.html) — medical journal citation standards and authorship guidelines - Network accessline 211
- [ICMJE citation recommendations](https://www.icmje.org/recommendations/browse/manuscript-preparation/preparing-for-submission.html) — medical journal citation standards and authorship guidelines - Network accessline 212
- [CrossRef DOI registration and resolution](https://www.crossref.org/) — authoritative DOI resolver and metadata registry - Network accessline 212
- [CrossRef DOI registration and resolution](https://www.crossref.org/) — authoritative DOI resolver and metadata registry
skills/scientific-writing/clinical-decision-support-documents/SKILL.md
- Network accessline 161
- [GRADE Handbook](https://gdt.gradepro.org/app/handbook/handbook.html) — official GRADE methodology documentation - Network accessline 161
- [GRADE Handbook](https://gdt.gradepro.org/app/handbook/handbook.html) — official GRADE methodology documentation - Network accessline 162
- [RECIST 1.1 Guidelines](https://doi.org/10.1016/j.ejca.2008.10.026) — response evaluation criteria in solid tumors - Network accessline 162
- [RECIST 1.1 Guidelines](https://doi.org/10.1016/j.ejca.2008.10.026) — response evaluation criteria in solid tumors - Network accessline 163
- [CONSORT Statement](https://www.consort-statement.org/) — reporting standards for randomized trials - Network accessline 163
- [CONSORT Statement](https://www.consort-statement.org/) — reporting standards for randomized trials - Network accessline 164
- [STROBE Statement](https://www.strobe-statement.org/) — reporting standards for observational studies - Network accessline 164
- [STROBE Statement](https://www.strobe-statement.org/) — reporting standards for observational studies - Network accessline 165
- [ICH E9 Guidelines](https://www.ich.org/page/efficacy-guidelines) — statistical principles for clinical trials - Network accessline 165
- [ICH E9 Guidelines](https://www.ich.org/page/efficacy-guidelines) — statistical principles for clinical trials
skills/scientific-writing/elife-figure-guide/SKILL.md
- Network accessline 17
**Official reference**: https://elife-rp.msubmit.net/html/elife-rp_author_instructions.html - Network accessline 17
**Official reference**: https://elife-rp.msubmit.net/html/elife-rp_author_instructions.html - Network accessline 343
- eLife Author Guide: https://elife-rp.msubmit.net/html/elife-rp_author_instructions.html - Network accessline 343
- eLife Author Guide: https://elife-rp.msubmit.net/html/elife-rp_author_instructions.html - Network accessline 344
- eLife Image Screening Policy: https://elifesciences.org/inside-elife/77dd2808/taking-a-closer-look-screening-of-images - Network accessline 344
- eLife Image Screening Policy: https://elifesciences.org/inside-elife/77dd2808/taking-a-closer-look-screening-of-images - Network accessline 345
- eLife Submission Guidelines: https://reviewer.elifesciences.org/author-guide/full - Network accessline 345
- eLife Submission Guidelines: https://reviewer.elifesciences.org/author-guide/full
skills/scientific-writing/general-figure-guide/SKILL.md
- Network accessline 92
- [Matplotlib Figure Layout Guide](https://matplotlib.org/stable/users/explain/axes/tight_layout_guide.html) -- tight_layout and constrained_layout usage - Network accessline 92
- [Matplotlib Figure Layout Guide](https://matplotlib.org/stable/users/explain/axes/tight_layout_guide.html) -- tight_layout and constrained_layout usage - Network accessline 93
- [Ten Simple Rules for Better Figures (PLOS)](https://doi.org/10.1371/journal.pcbi.1003833) -- general principles for scientific figure design - Network accessline 93
- [Ten Simple Rules for Better Figures (PLOS)](https://doi.org/10.1371/journal.pcbi.1003833) -- general principles for scientific figure design - Network accessline 94
- [Points of View: Nature Methods Column](https://www.nature.com/collections/qghhqm/pointsofview) -- visual design principles for scientific data - Network accessline 94
- [Points of View: Nature Methods Column](https://www.nature.com/collections/qghhqm/pointsofview) -- visual design principles for scientific data
skills/scientific-writing/hypothesis-generation/SKILL.md
- Network accessline 117
- Popper, KR (1959) "The Logic of Scientific Discovery" — foundational framework for hypothesis falsification - Chamberlin, TC (1890) "The Method of Multiple Working Hypotheses" — *Science* 15:92-96. Original argument for generating competi - Network accessline 117
- Popper, KR (1959) "The Logic of Scientific Discovery" — foundational framework for hypothesis falsification - Chamberlin, TC (1890) "The Method of Multiple Working Hypotheses" — *Science* 15:92-96. Original argument for generating competi - Network accessline 118
- Chamberlin, TC (1890) "The Method of Multiple Working Hypotheses" — *Science* 15:92-96. Original argument for generating competing explanations - [NIH Guide to Hypothesis Development](https://www.niaid.nih.gov/grants-contracts/write-resea - Network accessline 118
- Chamberlin, TC (1890) "The Method of Multiple Working Hypotheses" — *Science* 15:92-96. Original argument for generating competing explanations - [NIH Guide to Hypothesis Development](https://www.niaid.nih.gov/grants-contracts/write-resea - Network accessline 119
- [NIH Guide to Hypothesis Development](https://www.niaid.nih.gov/grants-contracts/write-research-plan) — practical guidance for grant-writing hypothesis sections - Network accessline 119
- [NIH Guide to Hypothesis Development](https://www.niaid.nih.gov/grants-contracts/write-research-plan) — practical guidance for grant-writing hypothesis sections
skills/scientific-writing/lancet-figure-guide/SKILL.md
- Network accessline 17
**Official reference**: https://www.thelancet.com/submission-guidelines - Network accessline 17
**Official reference**: https://www.thelancet.com/submission-guidelines - Network accessline 300
- Lancet Submission Guidelines: https://www.thelancet.com/submission-guidelines - Network accessline 300
- Lancet Submission Guidelines: https://www.thelancet.com/submission-guidelines - Network accessline 301
- Lancet Artwork Guidelines: https://www.thelancet.com/pb/assets/raw/Lancet/test/artwork-guidelines-aug2015.pdf - Network accessline 301
- Lancet Artwork Guidelines: https://www.thelancet.com/pb/assets/raw/Lancet/test/artwork-guidelines-aug2015.pdf - Network accessline 302
- Lancet Information for Authors: https://www.thelancet.com/information-for-authors - Network accessline 302
- Lancet Information for Authors: https://www.thelancet.com/information-for-authors
skills/scientific-writing/latex-research-posters/SKILL.md
- Network accessline 235
- [beamerposter CTAN page](https://ctan.org/pkg/beamerposter) — official documentation and examples - Network accessline 235
- [beamerposter CTAN page](https://ctan.org/pkg/beamerposter) — official documentation and examples - Network accessline 236
- [tikzposter CTAN page](https://ctan.org/pkg/tikzposter) — themes, color styles, block customization - Network accessline 236
- [tikzposter CTAN page](https://ctan.org/pkg/tikzposter) — themes, color styles, block customization - Network accessline 237
- [baposter project page](http://www.brian-amberg.de/uni/poster/) — box-based layout documentation - Network accessline 237
- [baposter project page](http://www.brian-amberg.de/uni/poster/) — box-based layout documentation - Network accessline 238
- Colin Purrington, "Designing conference posters" — evidence-based design guidelines (https://colinpurrington.com/tips/poster-design) - Network accessline 238
- Colin Purrington, "Designing conference posters" — evidence-based design guidelines (https://colinpurrington.com/tips/poster-design) - Network accessline 239
- [Web AIM Contrast Checker](https://webaim.org/resources/contrastchecker/) — WCAG compliance verification - Network accessline 239
- [Web AIM Contrast Checker](https://webaim.org/resources/contrastchecker/) — WCAG compliance verification
skills/scientific-writing/latex-research-posters/assets/baposter_template.tex
- Network accessline 145
\headerbox{Scan for More}{name=qr,column=2,above=bottom}{ \begin{center} \qrcode[height=4cm]{https://doi.org/10.1234/your-paper}\\ - Network accessline 145
\headerbox{Scan for More}{name=qr,column=2,above=bottom}{ \begin{center} \qrcode[height=4cm]{https://doi.org/10.1234/your-paper}\\ - Network accessline 146
\begin{center} \qrcode[height=4cm]{https://doi.org/10.1234/your-paper}\\ \vspace{0.3cm} - Network accessline 146
\begin{center} \qrcode[height=4cm]{https://doi.org/10.1234/your-paper}\\ \vspace{0.3cm} - Network accessline 147
\qrcode[height=4cm]{https://doi.org/10.1234/your-paper}\\ - Network accessline 147
\qrcode[height=4cm]{https://doi.org/10.1234/your-paper}\\
skills/scientific-writing/latex-research-posters/assets/beamerposter_template.tex
- Network accessline 165
\begin{column}{.2\linewidth} \centering \qrcode[height=3.5cm]{https://doi.org/10.1234/your-paper}\\ - Network accessline 165
\begin{column}{.2\linewidth} \centering \qrcode[height=3.5cm]{https://doi.org/10.1234/your-paper}\\ - Network accessline 166
\centering \qrcode[height=3.5cm]{https://doi.org/10.1234/your-paper}\\ \tiny Scan for full paper - Network accessline 166
\centering \qrcode[height=3.5cm]{https://doi.org/10.1234/your-paper}\\ \tiny Scan for full paper - Network accessline 167
\qrcode[height=3.5cm]{https://doi.org/10.1234/your-paper}\\ - Network accessline 167
\qrcode[height=3.5cm]{https://doi.org/10.1234/your-paper}\\
skills/scientific-writing/latex-research-posters/assets/tikzposter_template.tex
- Network accessline 144
\block{Scan for More}{ \begin{center} \qrcode[height=5cm]{https://doi.org/10.1234/your-paper}\\ - Network accessline 144
\block{Scan for More}{ \begin{center} \qrcode[height=5cm]{https://doi.org/10.1234/your-paper}\\ - Network accessline 145
\begin{center} \qrcode[height=5cm]{https://doi.org/10.1234/your-paper}\\ \vspace{0.5cm} - Network accessline 145
\begin{center} \qrcode[height=5cm]{https://doi.org/10.1234/your-paper}\\ \vspace{0.5cm} - Network accessline 146
\qrcode[height=5cm]{https://doi.org/10.1234/your-paper}\\ - Network accessline 146
\qrcode[height=5cm]{https://doi.org/10.1234/your-paper}\\
skills/scientific-writing/literature-review/SKILL.md
- Network accessline 192
- [PRISMA 2020 statement and checklist](https://www.prisma-statement.org/) — the standard reporting guideline for systematic reviews and meta-analyses - Network accessline 192
- [PRISMA 2020 statement and checklist](https://www.prisma-statement.org/) — the standard reporting guideline for systematic reviews and meta-analyses - Network accessline 193
- [Cochrane Handbook for Systematic Reviews of Interventions](https://training.cochrane.org/handbook) — comprehensive methodological reference; freely available - Network accessline 193
- [Cochrane Handbook for Systematic Reviews of Interventions](https://training.cochrane.org/handbook) — comprehensive methodological reference; freely available - Network accessline 194
- [PROSPERO registration](https://www.crd.york.ac.uk/prospero/) — international register for systematic review protocols - Network accessline 194
- [PROSPERO registration](https://www.crd.york.ac.uk/prospero/) — international register for systematic review protocols - Network accessline 195
- [Rayyan systematic review screening tool](https://www.rayyan.ai/) — free web-based title/abstract screening platform - Network accessline 195
- [Rayyan systematic review screening tool](https://www.rayyan.ai/) — free web-based title/abstract screening platform - Network accessline 196
- [GRADE Working Group](https://www.gradeworkinggroup.org/) — evidence quality assessment framework used in clinical systematic reviews - Network accessline 196
- [GRADE Working Group](https://www.gradeworkinggroup.org/) — evidence quality assessment framework used in clinical systematic reviews
skills/scientific-writing/nature-figure-guide/SKILL.md
- Network accessline 17
**Official reference**: https://www.nature.com/nature/for-authors/formatting-guide - Network accessline 17
**Official reference**: https://www.nature.com/nature/for-authors/formatting-guide - Network accessline 388
- Nature Formatting Guide: https://www.nature.com/nature/for-authors/formatting-guide - Network accessline 388
- Nature Formatting Guide: https://www.nature.com/nature/for-authors/formatting-guide - Network accessline 389
- Nature Final Submission: https://www.nature.com/nature/for-authors/final-submission - Network accessline 389
- Nature Final Submission: https://www.nature.com/nature/for-authors/final-submission - Network accessline 390
- Nature Research Figure Guide: https://research-figure-guide.nature.com/figures/preparing-figures-our-specifications/ - Network accessline 390
- Nature Research Figure Guide: https://research-figure-guide.nature.com/figures/preparing-figures-our-specifications/
skills/scientific-writing/nejm-figure-guide/SKILL.md
- Network accessline 17
**Official reference**: https://www.nejm.org/author-center/new-manuscripts - Network accessline 17
**Official reference**: https://www.nejm.org/author-center/new-manuscripts - Network accessline 305
- NEJM Author Center: https://www.nejm.org/author-center/new-manuscripts - Network accessline 305
- NEJM Author Center: https://www.nejm.org/author-center/new-manuscripts - Network accessline 306
- NEJM Technical Guidelines for Figures: https://www.nejm.org/pb-assets/pdfs/Technical-Guidelines-for-Figures.pdf - Network accessline 306
- NEJM Technical Guidelines for Figures: https://www.nejm.org/pb-assets/pdfs/Technical-Guidelines-for-Figures.pdf - Network accessline 307
- NEJM Editorial Policies: https://www.nejm.org/about-nejm/editorial-policies - Network accessline 307
- NEJM Editorial Policies: https://www.nejm.org/about-nejm/editorial-policies
skills/scientific-writing/openalex-database/SKILL.md
- Network accessline 39
BASE = "https://api.openalex.org" - Network accessline 39
BASE = "https://api.openalex.org" - Network accessline 41
# Search for works on CRISPR r = requests.get(f"{BASE}/works", - Network accessline 41
# Search for works on CRISPR r = requests.get(f"{BASE}/works", - Network accessline 42
r = requests.get(f"{BASE}/works", - Network accessline 42
r = requests.get(f"{BASE}/works", - Network accessline 63
BASE = "https://api.openalex.org" - Network accessline 63
BASE = "https://api.openalex.org" - Network accessline 67
if filters: params["filter"] = ",".join(f"{k}:{v}" for k, v in filters.items()) r = requests.get(f"{BASE}/works", params=params) - Network accessline 67
if filters: params["filter"] = ",".join(f"{k}:{v}" for k, v in filters.items()) r = requests.get(f"{BASE}/works", params=params) - Network accessline 68
params["filter"] = ",".join(f"{k}:{v}" for k, v in filters.items()) r = requests.get(f"{BASE}/works", params=params) r.raise_for_status() - Network accessline 68
params["filter"] = ",".join(f"{k}:{v}" for k, v in filters.items()) r = requests.get(f"{BASE}/works", params=params) r.raise_for_status() - Network accessline 69
r = requests.get(f"{BASE}/works", params=params) - Network accessline 69
r = requests.get(f"{BASE}/works", params=params) - Network accessline 102
if filters: params["filter"] = ",".join(f"{k}:{v}" for k, v in filters.items()) r = requests.get(f"{BASE}/works", params=params) - Network accessline 102
if filters: params["filter"] = ",".join(f"{k}:{v}" for k, v in filters.items()) r = requests.get(f"{BASE}/works", params=params) - Network accessline 103
params["filter"] = ",".join(f"{k}:{v}" for k, v in filters.items()) r = requests.get(f"{BASE}/works", params=params) data = r.json() - Network accessline 103
params["filter"] = ",".join(f"{k}:{v}" for k, v in filters.items()) r = requests.get(f"{BASE}/works", params=params) data = r.json() - Network accessline 104
r = requests.get(f"{BASE}/works", params=params) - Network accessline 104
r = requests.get(f"{BASE}/works", params=params) - Network accessline 123
BASE = "https://api.openalex.org" - Network accessline 123
BASE = "https://api.openalex.org" - Network accessline 125
# By DOI doi = "10.1038/s41592-019-0458-z" # Scanpy paper r = requests.get(f"{BASE}/works/https://doi.org/{doi}", - Network accessline 125
# By DOI doi = "10.1038/s41592-019-0458-z" # Scanpy paper r = requests.get(f"{BASE}/works/https://doi.org/{doi}", - Network accessline 126
doi = "10.1038/s41592-019-0458-z" # Scanpy paper r = requests.get(f"{BASE}/works/https://doi.org/{doi}", params={"mailto": "your@email.com"}) - Network accessline 126
doi = "10.1038/s41592-019-0458-z" # Scanpy paper r = requests.get(f"{BASE}/works/https://doi.org/{doi}", params={"mailto": "your@email.com"}) - Network accessline 127
r = requests.get(f"{BASE}/works/https://doi.org/{doi}", - Network accessline 127
r = requests.get(f"{BASE}/works/https://doi.org/{doi}", - Network accessline 151
BASE = "https://api.openalex.org" - Network accessline 151
BASE = "https://api.openalex.org" - Network accessline 153
# Search for an author r = requests.get(f"{BASE}/authors", - Network accessline 153
# Search for an author r = requests.get(f"{BASE}/authors", - Network accessline 154
r = requests.get(f"{BASE}/authors", - Network accessline 154
r = requests.get(f"{BASE}/authors", - Network accessline 173
# Get all papers by an author (by ORCID) orcid = "0000-0001-9161-999X" # Jennifer A. Doudna (correct ORCID; the 8742-3594 variant 404s) r = requests.get(f"{BASE}/works", - Network accessline 173
# Get all papers by an author (by ORCID) orcid = "0000-0001-9161-999X" # Jennifer A. Doudna (correct ORCID; the 8742-3594 variant 404s) r = requests.get(f"{BASE}/works", - Network accessline 174
orcid = "0000-0001-9161-999X" # Jennifer A. Doudna (correct ORCID; the 8742-3594 variant 404s) r = requests.get(f"{BASE}/works", params={"filter": f"author.orcid:{orcid}", - Network accessline 174
orcid = "0000-0001-9161-999X" # Jennifer A. Doudna (correct ORCID; the 8742-3594 variant 404s) r = requests.get(f"{BASE}/works", params={"filter": f"author.orcid:{orcid}", - Network accessline 175
r = requests.get(f"{BASE}/works", - Network accessline 175
r = requests.get(f"{BASE}/works", - Network accessline 192
BASE = "https://api.openalex.org" - Network accessline 192
BASE = "https://api.openalex.org" - Network accessline 196
# Get what this paper references r = requests.get(f"{BASE}/works/{work_id}", - Network accessline 196
# Get what this paper references r = requests.get(f"{BASE}/works/{work_id}", - Network accessline 197
r = requests.get(f"{BASE}/works/{work_id}", - Network accessline 197
r = requests.get(f"{BASE}/works/{work_id}", - Network accessline 206
if ref_ids: ids_str = "|".join(id.split("/")[-1] for id in ref_ids[:10]) r2 = requests.get(f"{BASE}/works", - Network accessline 206
if ref_ids: ids_str = "|".join(id.split("/")[-1] for id in ref_ids[:10]) r2 = requests.get(f"{BASE}/works", - Network accessline 207
ids_str = "|".join(id.split("/")[-1] for id in ref_ids[:10]) r2 = requests.get(f"{BASE}/works", params={"filter": f"openalex_id:{ids_str}", - Network accessline 207
ids_str = "|".join(id.split("/")[-1] for id in ref_ids[:10]) r2 = requests.get(f"{BASE}/works", params={"filter": f"openalex_id:{ids_str}", - Network accessline 208
r2 = requests.get(f"{BASE}/works", - Network accessline 208
r2 = requests.get(f"{BASE}/works", - Network accessline 224
BASE = "https://api.openalex.org" - Network accessline 224
BASE = "https://api.openalex.org" - Network accessline 226
# Get concept ID for "Machine Learning". OpenAlex concept search is brittle for # multi-word phrases ("machine learning biology" returns 0); use the single core term. r = requests.get(f"{BASE}/concepts", - Network accessline 226
# Get concept ID for "Machine Learning". OpenAlex concept search is brittle for # multi-word phrases ("machine learning biology" returns 0); use the single core term. r = requests.get(f"{BASE}/concepts", - Network accessline 227
# multi-word phrases ("machine learning biology" returns 0); use the single core term. r = requests.get(f"{BASE}/concepts", params={"search": "machine learning", - Network accessline 227
# multi-word phrases ("machine learning biology" returns 0); use the single core term. r = requests.get(f"{BASE}/concepts", params={"search": "machine learning", - Network accessline 228
r = requests.get(f"{BASE}/concepts", - Network accessline 228
r = requests.get(f"{BASE}/concepts", - Network accessline 236
# Count papers per year for a concept concept_id = "C154945302" # Machine learning (OpenAlex ID) r2 = requests.get(f"{BASE}/works", - Network accessline 236
# Count papers per year for a concept concept_id = "C154945302" # Machine learning (OpenAlex ID) r2 = requests.get(f"{BASE}/works", - Network accessline 237
concept_id = "C154945302" # Machine learning (OpenAlex ID) r2 = requests.get(f"{BASE}/works", params={"filter": f"concepts.id:{concept_id},publication_year:2015-2024", - Network accessline 237
concept_id = "C154945302" # Machine learning (OpenAlex ID) r2 = requests.get(f"{BASE}/works", params={"filter": f"concepts.id:{concept_id},publication_year:2015-2024", - Network accessline 238
r2 = requests.get(f"{BASE}/works", - Network accessline 238
r2 = requests.get(f"{BASE}/works", - Network accessline 256
BASE = "https://api.openalex.org" - Network accessline 256
BASE = "https://api.openalex.org" - Network accessline 258
# Papers from a specific journal in the last year r = requests.get(f"{BASE}/works", - Network accessline 258
# Papers from a specific journal in the last year r = requests.get(f"{BASE}/works", - Network accessline 259
r = requests.get(f"{BASE}/works", - Network accessline 259
r = requests.get(f"{BASE}/works", - Network accessline 291
BASE = "https://api.openalex.org" - Network accessline 291
BASE = "https://api.openalex.org" - Network accessline 300
while len(all_results) < max_results: r = requests.get(f"{BASE}/works", - Network accessline 300
while len(all_results) < max_results: r = requests.get(f"{BASE}/works", - Network accessline 301
r = requests.get(f"{BASE}/works", - Network accessline 301
r = requests.get(f"{BASE}/works", - Network accessline 342
BASE = "https://api.openalex.org" - Network accessline 342
BASE = "https://api.openalex.org" - Network accessline 345
def get_author_works(orcid, max_papers=50): r = requests.get(f"{BASE}/works", - Network accessline 345
def get_author_works(orcid, max_papers=50): r = requests.get(f"{BASE}/works", - Network accessline 346
r = requests.get(f"{BASE}/works", - Network accessline 346
r = requests.get(f"{BASE}/works", - Network accessline 411
BASE = "https://api.openalex.org" - Network accessline 411
BASE = "https://api.openalex.org" - Network accessline 419
rows = [] for doi in dois: r = requests.get(f"{BASE}/works/https://doi.org/{doi}", - Network accessline 419
rows = [] for doi in dois: r = requests.get(f"{BASE}/works/https://doi.org/{doi}", - Network accessline 420
for doi in dois: r = requests.get(f"{BASE}/works/https://doi.org/{doi}", params={"select": "title,publication_year,cited_by_count,open_access", - Network accessline 420
for doi in dois: r = requests.get(f"{BASE}/works/https://doi.org/{doi}", params={"select": "title,publication_year,cited_by_count,open_access", - Network accessline 421
r = requests.get(f"{BASE}/works/https://doi.org/{doi}", - Network accessline 421
r = requests.get(f"{BASE}/works/https://doi.org/{doi}", - Network accessline 445
r = requests.get( - Network accessline 445
r = requests.get( - Network accessline 446
"https://api.openalex.org/works", - Network accessline 446
"https://api.openalex.org/works", - Network accessline 464
r = requests.get( - Network accessline 464
r = requests.get( - Network accessline 465
"https://api.openalex.org/works", - Network accessline 465
"https://api.openalex.org/works", - Network accessline 495
- [OpenAlex documentation](https://docs.openalex.org/) — Full API reference and data model - Network accessline 495
- [OpenAlex documentation](https://docs.openalex.org/) — Full API reference and data model - Network accessline 496
- [OpenAlex API endpoint](https://api.openalex.org/) — Interactive API explorer - Network accessline 496
- [OpenAlex API endpoint](https://api.openalex.org/) — Interactive API explorer - Network accessline 497
- [OpenAlex paper (Priem et al. 2022)](https://arxiv.org/abs/2205.01833) — Description of the OpenAlex data system - Network accessline 497
- [OpenAlex paper (Priem et al. 2022)](https://arxiv.org/abs/2205.01833) — Description of the OpenAlex data system - Network accessline 498
- [OpenAlex entity types](https://docs.openalex.org/api-entities/works) — Works, Authors, Sources, Institutions, Concepts documentation - Network accessline 498
- [OpenAlex entity types](https://docs.openalex.org/api-entities/works) — Works, Authors, Sources, Institutions, Concepts documentation
skills/scientific-writing/peer-review-methodology/SKILL.md
- Network accessline 137
- [COPE Ethical Guidelines for Peer Reviewers](https://publicationethics.org/resources/guidelines-new/cope-ethical-guidelines-peer-reviewers) — Committee on Publication Ethics - Network accessline 137
- [COPE Ethical Guidelines for Peer Reviewers](https://publicationethics.org/resources/guidelines-new/cope-ethical-guidelines-peer-reviewers) — Committee on Publication Ethics - Network accessline 138
- [EQUATOR Network](https://www.equator-network.org/) — reporting guidelines for all study types - Network accessline 138
- [EQUATOR Network](https://www.equator-network.org/) — reporting guidelines for all study types - Network accessline 139
- Stahel & Moore (2014) "Peer review for biomedical publications: we can improve the system" — *BMC Medicine* 12:179 - Lovejoy et al. (2011) "An introduction to systematic reviewing" — *BMJ Open* - [How to peer review](https://authorservice - Network accessline 139
- Stahel & Moore (2014) "Peer review for biomedical publications: we can improve the system" — *BMC Medicine* 12:179 - Lovejoy et al. (2011) "An introduction to systematic reviewing" — *BMJ Open* - [How to peer review](https://authorservice - Network accessline 141
- [How to peer review](https://authorservices.wiley.com/Reviewers/journal-reviewers/how-to-perform-a-peer-review/index.html) — Wiley reviewer guide - Network accessline 141
- [How to peer review](https://authorservices.wiley.com/Reviewers/journal-reviewers/how-to-perform-a-peer-review/index.html) — Wiley reviewer guide
skills/scientific-writing/pnas-figure-guide/SKILL.md
- Network accessline 17
**Official reference**: https://www.pnas.org/author-center/submitting-your-manuscript - Network accessline 17
**Official reference**: https://www.pnas.org/author-center/submitting-your-manuscript - Network accessline 378
- PNAS Submission Guidelines: https://www.pnas.org/author-center/submitting-your-manuscript - Network accessline 378
- PNAS Submission Guidelines: https://www.pnas.org/author-center/submitting-your-manuscript - Network accessline 379
- PNAS Digital Art Guidelines: https://www.pnas.org/pb-assets/authors/digitalart.pdf - Network accessline 379
- PNAS Digital Art Guidelines: https://www.pnas.org/pb-assets/authors/digitalart.pdf - Network accessline 380
- PNAS Editorial Policies: https://www.pnas.org/author-center/editorial-and-journal-policies - Network accessline 380
- PNAS Editorial Policies: https://www.pnas.org/author-center/editorial-and-journal-policies
skills/scientific-writing/pubmed-database/SKILL.md
- Network accessline 36
**API Rate Limits**: - Without API key: **3 requests/second** - With API key: **10 requests/second** (register at https://www.ncbi.nlm.nih.gov/account/) - Network accessline 36
**API Rate Limits**: - Without API key: **3 requests/second** - With API key: **10 requests/second** (register at https://www.ncbi.nlm.nih.gov/account/) - Network accessline 37
- Without API key: **3 requests/second** - With API key: **10 requests/second** (register at https://www.ncbi.nlm.nih.gov/account/) - Always include `User-Agent` header with contact email - Network accessline 37
- Without API key: **3 requests/second** - With API key: **10 requests/second** (register at https://www.ncbi.nlm.nih.gov/account/) - Always include `User-Agent` header with contact email - Network accessline 38
- With API key: **10 requests/second** (register at https://www.ncbi.nlm.nih.gov/account/) - Network accessline 38
- With API key: **10 requests/second** (register at https://www.ncbi.nlm.nih.gov/account/) - Network accessline 47
BASE_URL = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/" - Network accessline 47
BASE_URL = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/" - Access to secretsline 48
[Credential-related source omitted] - Access to secretsline 48
[Credential-related source omitted] - Network accessline 51
"""Reusable helper for E-utilities API calls with rate limiting.""" params.setdefault("api_key", API_KEY) response = requests.get(f"{BASE_URL}{endpoint}", params=params) - Network accessline 51
"""Reusable helper for E-utilities API calls with rate limiting.""" params.setdefault("api_key", API_KEY) response = requests.get(f"{BASE_URL}{endpoint}", params=params) - Network accessline 52
[Credential-related source omitted] - Network accessline 52
[Credential-related source omitted] - Access to secretsline 52
[Credential-related source omitted] - Access to secretsline 52
[Credential-related source omitted] - Network accessline 53
response = requests.get(f"{BASE_URL}{endpoint}", params=params) - Network accessline 53
response = requests.get(f"{BASE_URL}{endpoint}", params=params) - Access to secretsline 55
[Credential-related source omitted] - Access to secretsline 55
[Credential-related source omitted] - Access to secretsline 418
[Credential-related source omitted] - Access to secretsline 418
[Credential-related source omitted] - Network accessline 453
def fetch_abstracts(gene_list, max_per_gene=5): """Retrieve PubMed abstracts for each gene in a list.""" base = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 453
def fetch_abstracts(gene_list, max_per_gene=5): """Retrieve PubMed abstracts for each gene in a list.""" base = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 454
"""Retrieve PubMed abstracts for each gene in a list.""" base = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" records = [] - Network accessline 454
"""Retrieve PubMed abstracts for each gene in a list.""" base = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" records = [] - Network accessline 455
base = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 455
base = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 456
records = [] for gene in gene_list: r = requests.get(f"{base}/esearch.fcgi", - Network accessline 456
records = [] for gene in gene_list: r = requests.get(f"{base}/esearch.fcgi", - Network accessline 457
for gene in gene_list: r = requests.get(f"{base}/esearch.fcgi", params={"db": "pubmed", "term": f"{gene}[gene] AND Homo sapiens[orgn]", - Network accessline 457
for gene in gene_list: r = requests.get(f"{base}/esearch.fcgi", params={"db": "pubmed", "term": f"{gene}[gene] AND Homo sapiens[orgn]", - Network accessline 458
r = requests.get(f"{base}/esearch.fcgi", - Network accessline 458
r = requests.get(f"{base}/esearch.fcgi", - Network accessline 461
ids = r.json()["esearchresult"]["idlist"] if ids: fetch = requests.get(f"{base}/efetch.fcgi", - Network accessline 461
ids = r.json()["esearchresult"]["idlist"] if ids: fetch = requests.get(f"{base}/efetch.fcgi", - Network accessline 462
if ids: fetch = requests.get(f"{base}/efetch.fcgi", params={"db": "pubmed", "id": ",".join(ids), "rettype": "abstract"}) - Network accessline 462
if ids: fetch = requests.get(f"{base}/efetch.fcgi", params={"db": "pubmed", "id": ",".join(ids), "rettype": "abstract"}) - Network accessline 463
fetch = requests.get(f"{base}/efetch.fcgi", - Network accessline 463
fetch = requests.get(f"{base}/efetch.fcgi", - Network accessline 484
resp = requests.get("https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi", - Network accessline 484
resp = requests.get("https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi", - Network accessline 502
- PubMed Help: https://pubmed.ncbi.nlm.nih.gov/help/ - Network accessline 502
- PubMed Help: https://pubmed.ncbi.nlm.nih.gov/help/ - Network accessline 503
- E-utilities Documentation: https://www.ncbi.nlm.nih.gov/books/NBK25501/ - Network accessline 503
- E-utilities Documentation: https://www.ncbi.nlm.nih.gov/books/NBK25501/ - Network accessline 504
- NCBI API Key Registration: https://www.ncbi.nlm.nih.gov/account/ - Network accessline 504
- NCBI API Key Registration: https://www.ncbi.nlm.nih.gov/account/
skills/scientific-writing/science-figure-guide/SKILL.md
- Network accessline 17
**Official reference**: https://www.science.org/content/page/instructions-preparing-initial-manuscript - Network accessline 17
**Official reference**: https://www.science.org/content/page/instructions-preparing-initial-manuscript - Network accessline 337
- Science Initial Manuscript Instructions: https://www.science.org/content/page/instructions-preparing-initial-manuscript - Network accessline 337
- Science Initial Manuscript Instructions: https://www.science.org/content/page/instructions-preparing-initial-manuscript - Network accessline 338
- Science Revised Article Instructions: https://www.science.org/content/page/instructions-authors-revised-research-articles - Network accessline 338
- Science Revised Article Instructions: https://www.science.org/content/page/instructions-authors-revised-research-articles - Network accessline 339
- Science Editorial Policies: https://www.science.org/content/page/science-journals-editorial-policies - Network accessline 339
- Science Editorial Policies: https://www.science.org/content/page/science-journals-editorial-policies
skills/scientific-writing/scientific-brainstorming/SKILL.md
- Network accessline 297
- [Lateral Thinking by Edward de Bono](https://en.wikipedia.org/wiki/Lateral_thinking) -- foundational work on deliberate creativity techniques including the Provocation (Po) method and Six Thinking Hats - Network accessline 297
- [Lateral Thinking by Edward de Bono](https://en.wikipedia.org/wiki/Lateral_thinking) -- foundational work on deliberate creativity techniques including the Provocation (Po) method and Six Thinking Hats - Network accessline 298
- [TRIZ: Theory of Inventive Problem Solving](https://www.triz-journal.com/) -- comprehensive resource for contradiction matrices, inventive principles, and ideal final result methodology - Network accessline 298
- [TRIZ: Theory of Inventive Problem Solving](https://www.triz-journal.com/) -- comprehensive resource for contradiction matrices, inventive principles, and ideal final result methodology - Network accessline 299
- [Biomimicry Institute](https://biomimicry.org/) -- resources for nature-inspired design including the AskNature database of biological strategies - Network accessline 299
- [Biomimicry Institute](https://biomimicry.org/) -- resources for nature-inspired design including the AskNature database of biological strategies - Network accessline 300
- [Morphological Analysis (Fritz Zwicky)](https://en.wikipedia.org/wiki/Morphological_analysis_(problem-solving)) -- systematic exploration of multi-dimensional solution spaces, originally developed for astrophysics and engineering - Network accessline 300
- [Morphological Analysis (Fritz Zwicky)](https://en.wikipedia.org/wiki/Morphological_analysis_(problem-solving)) -- systematic exploration of multi-dimensional solution spaces, originally developed for astrophysics and engineering - Network accessline 301
- [SCAMPER technique (Bob Eberle)](https://en.wikipedia.org/wiki/S.C.A.M.P.E.R) -- structured checklist for creative modification of existing products, processes, or methods - Network accessline 301
- [SCAMPER technique (Bob Eberle)](https://en.wikipedia.org/wiki/S.C.A.M.P.E.R) -- structured checklist for creative modification of existing products, processes, or methods
skills/scientific-writing/scientific-critical-thinking/SKILL.md
- Network accessline 213
- [EQUATOR Network reporting guidelines](https://www.equator-network.org/) — all major reporting checklists (CONSORT, STROBE, PRISMA, STARD, TRIPOD) - Network accessline 213
- [EQUATOR Network reporting guidelines](https://www.equator-network.org/) — all major reporting checklists (CONSORT, STROBE, PRISMA, STARD, TRIPOD) - Network accessline 214
- [Cochrane Handbook — bias assessment tools](https://training.cochrane.org/handbook/current) — RoB 2, ROBINS-I, and GRADE methods - Network accessline 214
- [Cochrane Handbook — bias assessment tools](https://training.cochrane.org/handbook/current) — RoB 2, ROBINS-I, and GRADE methods - Network accessline 215
- [GRADE Working Group](https://www.gradeworkinggroup.org/) — evidence certainty framework and training materials - Network accessline 215
- [GRADE Working Group](https://www.gradeworkinggroup.org/) — evidence certainty framework and training materials - Network accessline 216
- [Ioannidis JPA (2005) Why most published research findings are false. *PLoS Med* 2:e124](https://doi.org/10.1371/journal.pmed.0020124) — foundational paper on false discovery rates in research - Network accessline 216
- [Ioannidis JPA (2005) Why most published research findings are false. *PLoS Med* 2:e124](https://doi.org/10.1371/journal.pmed.0020124) — foundational paper on false discovery rates in research - Network accessline 217
- [Greenland S et al. (2016) Statistical tests, P values, confidence intervals, and power. *Eur J Epidemiol* 31:337-350](https://doi.org/10.1007/s10654-016-0149-3) — authoritative guide to p-value interpretation - Network accessline 217
- [Greenland S et al. (2016) Statistical tests, P values, confidence intervals, and power. *Eur J Epidemiol* 31:337-350](https://doi.org/10.1007/s10654-016-0149-3) — authoritative guide to p-value interpretation
skills/scientific-writing/scientific-literature-search/SKILL.md
- Network accessline 292
# Extract article content from URL (clean main text, drops nav/ads) downloaded = trafilatura.fetch_url("https://www.nature.com/articles/nature12373") - Network accessline 292
# Extract article content from URL (clean main text, drops nav/ads) downloaded = trafilatura.fetch_url("https://www.nature.com/articles/nature12373") - Network accessline 293
downloaded = trafilatura.fetch_url("https://www.nature.com/articles/nature12373") - Network accessline 293
downloaded = trafilatura.fetch_url("https://www.nature.com/articles/nature12373") - Network accessline 296
# Extract text from a PDF pdf_bytes = requests.get("https://arxiv.org/pdf/1706.03762.pdf", timeout=30).content - Network accessline 296
# Extract text from a PDF pdf_bytes = requests.get("https://arxiv.org/pdf/1706.03762.pdf", timeout=30).content - Network accessline 297
pdf_bytes = requests.get("https://arxiv.org/pdf/1706.03762.pdf", timeout=30).content - Network accessline 297
pdf_bytes = requests.get("https://arxiv.org/pdf/1706.03762.pdf", timeout=30).content - Network accessline 301
# Download supplementary files via Crossref DOI metadata doi = "10.1038/nature12373" meta = requests.get(f"https://api.crossref.org/works/{doi}", timeout=30).json() - Network accessline 301
# Download supplementary files via Crossref DOI metadata doi = "10.1038/nature12373" meta = requests.get(f"https://api.crossref.org/works/{doi}", timeout=30).json() - Network accessline 302
doi = "10.1038/nature12373" meta = requests.get(f"https://api.crossref.org/works/{doi}", timeout=30).json() out_dir = Path("./supplementary_materials") - Network accessline 302
doi = "10.1038/nature12373" meta = requests.get(f"https://api.crossref.org/works/{doi}", timeout=30).json() out_dir = Path("./supplementary_materials") - Network accessline 303
meta = requests.get(f"https://api.crossref.org/works/{doi}", timeout=30).json() - Network accessline 303
meta = requests.get(f"https://api.crossref.org/works/{doi}", timeout=30).json() - Network accessline 309
continue fname = os.path.basename(urlparse(url).path) or "supplement.bin" (out_dir / fname).write_bytes(requests.get(url, timeout=60).content) - Network accessline 309
continue fname = os.path.basename(urlparse(url).path) or "supplement.bin" (out_dir / fname).write_bytes(requests.get(url, timeout=60).content) - Network accessline 311
(out_dir / fname).write_bytes(requests.get(url, timeout=60).content) - Network accessline 311
(out_dir / fname).write_bytes(requests.get(url, timeout=60).content) - Network accessline 476
- [PubMed Help](https://pubmed.ncbi.nlm.nih.gov/help/) -- Official guide to PubMed search syntax, field tags, filters, and advanced features - Network accessline 476
- [PubMed Help](https://pubmed.ncbi.nlm.nih.gov/help/) -- Official guide to PubMed search syntax, field tags, filters, and advanced features - Network accessline 477
- [arXiv Help Pages](https://info.arxiv.org/help/index.html) -- Documentation on arXiv search, subject categories, and submission process - Network accessline 477
- [arXiv Help Pages](https://info.arxiv.org/help/index.html) -- Documentation on arXiv search, subject categories, and submission process - Network accessline 478
- [MeSH Browser](https://meshb.nlm.nih.gov/) -- NLM tool for browsing and searching the Medical Subject Headings controlled vocabulary - Network accessline 478
- [MeSH Browser](https://meshb.nlm.nih.gov/) -- NLM tool for browsing and searching the Medical Subject Headings controlled vocabulary - Network accessline 479
- [PRISMA Statement](http://www.prisma-statement.org/) -- Guidelines for transparent reporting of systematic reviews and meta-analyses - Network accessline 479
- [PRISMA Statement](http://www.prisma-statement.org/) -- Guidelines for transparent reporting of systematic reviews and meta-analyses - Network accessline 480
- [Cochrane Handbook for Systematic Reviews](https://training.cochrane.org/handbook) -- Gold-standard methodology for systematic literature reviews - Network accessline 480
- [Cochrane Handbook for Systematic Reviews](https://training.cochrane.org/handbook) -- Gold-standard methodology for systematic literature reviews
skills/scientific-writing/scientific-manuscript-writing/SKILL.md
- Network accessline 229
- [EQUATOR Network](https://www.equator-network.org/) -- Comprehensive library of reporting guidelines for health research - Network accessline 229
- [EQUATOR Network](https://www.equator-network.org/) -- Comprehensive library of reporting guidelines for health research - Network accessline 230
- [ICMJE Recommendations](http://www.icmje.org/) -- Uniform requirements for manuscripts submitted to biomedical journals - Network accessline 230
- [ICMJE Recommendations](http://www.icmje.org/) -- Uniform requirements for manuscripts submitted to biomedical journals - Network accessline 231
- [APA Style Guide](https://apastyle.apa.org/) -- Official APA Publication Manual resources - Network accessline 231
- [APA Style Guide](https://apastyle.apa.org/) -- Official APA Publication Manual resources - Network accessline 232
- [Nature Masterclasses](https://masterclasses.nature.com/) -- Scientific writing courses from Nature - Network accessline 232
- [Nature Masterclasses](https://masterclasses.nature.com/) -- Scientific writing courses from Nature - Network accessline 233
- [Academic Phrasebank](https://www.phrasebank.manchester.ac.uk/) -- Common academic phrases organized by function (University of Manchester) - Network accessline 233
- [Academic Phrasebank](https://www.phrasebank.manchester.ac.uk/) -- Common academic phrases organized by function (University of Manchester)
skills/scientific-writing/scientific-schematics/SKILL.md
- Network accessline 186
- [BioRender scientific communication guide](https://www.biorender.com/learn) — tutorials for biological icon usage and layout - Network accessline 186
- [BioRender scientific communication guide](https://www.biorender.com/learn) — tutorials for biological icon usage and layout - Network accessline 187
- [Fundamentals of Data Visualization — Wilke (open access)](https://clauswilke.com/dataviz/figure-titles-captions.html) — caption writing and figure composition principles - Network accessline 187
- [Fundamentals of Data Visualization — Wilke (open access)](https://clauswilke.com/dataviz/figure-titles-captions.html) — caption writing and figure composition principles - Network accessline 188
- [Bioicons](https://bioicons.com/) — free, CC-licensed biological icons for use in any tool - Network accessline 188
- [Bioicons](https://bioicons.com/) — free, CC-licensed biological icons for use in any tool - Network accessline 189
- [Reactome pathway diagrams](https://reactome.org/) — freely reusable (CC-BY) curated pathway schematics - Network accessline 189
- [Reactome pathway diagrams](https://reactome.org/) — freely reusable (CC-BY) curated pathway schematics - Network accessline 190
- [NIH Figure Guidelines](https://www.ncbi.nlm.nih.gov/pmc/about/guidelines/) — formatting requirements for PMC figure submissions - Network accessline 190
- [NIH Figure Guidelines](https://www.ncbi.nlm.nih.gov/pmc/about/guidelines/) — formatting requirements for PMC figure submissions - Network accessline 191
- [Ten Simple Rules for Better Figures (Rougier et al., PLOS Comp Biol, 2014)](https://doi.org/10.1371/journal.pcbi.1003833) — peer-reviewed design guidelines - Network accessline 191
- [Ten Simple Rules for Better Figures (Rougier et al., PLOS Comp Biol, 2014)](https://doi.org/10.1371/journal.pcbi.1003833) — peer-reviewed design guidelines
skills/scientific-writing/scientific-slides/SKILL.md
- Network accessline 232
- Edward Tufte, *The Visual Display of Quantitative Information* — foundational principles for data visualization - [Web AIM Contrast Checker](https://webaim.org/resources/contrastchecker/) — WCAG compliance for slide readability - Network accessline 232
- Edward Tufte, *The Visual Display of Quantitative Information* — foundational principles for data visualization - [Web AIM Contrast Checker](https://webaim.org/resources/contrastchecker/) — WCAG compliance for slide readability - Network accessline 233
- [Web AIM Contrast Checker](https://webaim.org/resources/contrastchecker/) — WCAG compliance for slide readability - Network accessline 233
- [Web AIM Contrast Checker](https://webaim.org/resources/contrastchecker/) — WCAG compliance for slide readability - Network accessline 235
- [Coblis Color Blindness Simulator](https://www.color-blindness.com/coblis-color-blindness-simulator/) — test slide accessibility - Network accessline 235
- [Coblis Color Blindness Simulator](https://www.color-blindness.com/coblis-color-blindness-simulator/) — test slide accessibility
skills/structural-biology-drug-discovery/aizynthfinder-retrosynthesis/SKILL.md
- Network accessline 388
- [AiZynthFinder documentation](https://molecularai.github.io/aizynthfinder/) — configuration, CLI, Python interface, stocks, scoring, how-to - Network accessline 388
- [AiZynthFinder documentation](https://molecularai.github.io/aizynthfinder/) — configuration, CLI, Python interface, stocks, scoring, how-to - Network accessline 389
- [MolecularAI/aizynthfinder on GitHub](https://github.com/MolecularAI/aizynthfinder) — source, releases, `plugins/` (Chemformer, disconnection-aware expansion) - Network accessline 389
- [MolecularAI/aizynthfinder on GitHub](https://github.com/MolecularAI/aizynthfinder) — source, releases, `plugins/` (Chemformer, disconnection-aware expansion) - Network accessline 390
- [Genheden et al. (2020), J Cheminform 12:70](https://doi.org/10.1186/s13321-020-00472-1) — original paper, algorithm and benchmark numbers - Network accessline 390
- [Genheden et al. (2020), J Cheminform 12:70](https://doi.org/10.1186/s13321-020-00472-1) — original paper, algorithm and benchmark numbers - Network accessline 391
- [aizynthfinder on PyPI](https://pypi.org/project/aizynthfinder/) — versions, Python compatibility, extras - Network accessline 391
- [aizynthfinder on PyPI](https://pypi.org/project/aizynthfinder/) — versions, Python compatibility, extras
skills/structural-biology-drug-discovery/alphafold-database-access/SKILL.md
- Network accessline 52
# 3. Check confidence conf = requests.get(f"https://alphafold.ebi.ac.uk/files/{af_id}-confidence_v4.json").json() - Network accessline 52
# 3. Check confidence conf = requests.get(f"https://alphafold.ebi.ac.uk/files/{af_id}-confidence_v4.json").json() - Network accessline 53
conf = requests.get(f"https://alphafold.ebi.ac.uk/files/{af_id}-confidence_v4.json").json() - Network accessline 53
conf = requests.get(f"https://alphafold.ebi.ac.uk/files/{af_id}-confidence_v4.json").json() - Network accessline 82
uniprot_id = "P00520" url = f"https://alphafold.ebi.ac.uk/api/prediction/{uniprot_id}" response = requests.get(url) - Network accessline 82
uniprot_id = "P00520" url = f"https://alphafold.ebi.ac.uk/api/prediction/{uniprot_id}" response = requests.get(url) - Network accessline 83
url = f"https://alphafold.ebi.ac.uk/api/prediction/{uniprot_id}" response = requests.get(url) data = response.json() - Network accessline 83
url = f"https://alphafold.ebi.ac.uk/api/prediction/{uniprot_id}" response = requests.get(url) data = response.json() - Network accessline 84
response = requests.get(url) - Network accessline 84
response = requests.get(url) - Network accessline 97
url = f"https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/api/uniprot/summary/{uniprot_id}.json" data = requests.get(url).json() - Network accessline 97
url = f"https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/api/uniprot/summary/{uniprot_id}.json" data = requests.get(url).json() - Network accessline 98
data = requests.get(url).json() - Network accessline 98
data = requests.get(url).json() - Network accessline 107
af_id = "AF-P00520-F1" version = "v4" base = "https://alphafold.ebi.ac.uk/files" - Network accessline 107
af_id = "AF-P00520-F1" version = "v4" base = "https://alphafold.ebi.ac.uk/files" - Network accessline 109
base = "https://alphafold.ebi.ac.uk/files" - Network accessline 109
base = "https://alphafold.ebi.ac.uk/files" - Network accessline 111
# mmCIF (recommended — full metadata, supports large structures) cif = requests.get(f"{base}/{af_id}-model_{version}.cif") - Network accessline 111
# mmCIF (recommended — full metadata, supports large structures) cif = requests.get(f"{base}/{af_id}-model_{version}.cif") - Network accessline 112
cif = requests.get(f"{base}/{af_id}-model_{version}.cif") - Network accessline 112
cif = requests.get(f"{base}/{af_id}-model_{version}.cif") - Network accessline 116
# PDB format (legacy — limited to 99,999 atoms) pdb = requests.get(f"{base}/{af_id}-model_{version}.pdb") - Network accessline 116
# PDB format (legacy — limited to 99,999 atoms) pdb = requests.get(f"{base}/{af_id}-model_{version}.pdb") - Network accessline 117
pdb = requests.get(f"{base}/{af_id}-model_{version}.pdb") - Network accessline 117
pdb = requests.get(f"{base}/{af_id}-model_{version}.pdb") - Network accessline 121
# Confidence JSON (per-residue pLDDT scores) conf = requests.get(f"{base}/{af_id}-confidence_{version}.json").json() - Network accessline 121
# Confidence JSON (per-residue pLDDT scores) conf = requests.get(f"{base}/{af_id}-confidence_{version}.json").json() - Network accessline 122
conf = requests.get(f"{base}/{af_id}-confidence_{version}.json").json() - Network accessline 122
conf = requests.get(f"{base}/{af_id}-confidence_{version}.json").json() - Network accessline 124
# PAE matrix JSON (inter-residue confidence) pae = requests.get(f"{base}/{af_id}-predicted_aligned_error_{version}.json").json() - Network accessline 124
# PAE matrix JSON (inter-residue confidence) pae = requests.get(f"{base}/{af_id}-predicted_aligned_error_{version}.json").json() - Network accessline 125
pae = requests.get(f"{base}/{af_id}-predicted_aligned_error_{version}.json").json() - Network accessline 125
pae = requests.get(f"{base}/{af_id}-predicted_aligned_error_{version}.json").json() - Network accessline 135
conf_url = f"https://alphafold.ebi.ac.uk/files/{af_id}-confidence_v4.json" conf = requests.get(conf_url).json() - Network accessline 135
conf_url = f"https://alphafold.ebi.ac.uk/files/{af_id}-confidence_v4.json" conf = requests.get(conf_url).json() - Network accessline 136
conf = requests.get(conf_url).json() - Network accessline 136
conf = requests.get(conf_url).json() - Network accessline 152
pae_url = f"https://alphafold.ebi.ac.uk/files/{af_id}-predicted_aligned_error_v4.json" pae = requests.get(pae_url).json() - Network accessline 152
pae_url = f"https://alphafold.ebi.ac.uk/files/{af_id}-predicted_aligned_error_v4.json" pae = requests.get(pae_url).json() - Network accessline 153
pae = requests.get(pae_url).json() - Network accessline 153
pae = requests.get(pae_url).json() - Network accessline 296
continue af_id = preds[0]['entryId'] conf = requests.get(f"https://alphafold.ebi.ac.uk/files/{af_id}-confidence_v4.json").json() - Network accessline 296
continue af_id = preds[0]['entryId'] conf = requests.get(f"https://alphafold.ebi.ac.uk/files/{af_id}-confidence_v4.json").json() - Network accessline 297
af_id = preds[0]['entryId'] conf = requests.get(f"https://alphafold.ebi.ac.uk/files/{af_id}-confidence_v4.json").json() scores = conf['confidenceScore'] - Network accessline 297
af_id = preds[0]['entryId'] conf = requests.get(f"https://alphafold.ebi.ac.uk/files/{af_id}-confidence_v4.json").json() scores = conf['confidenceScore'] - Network accessline 298
conf = requests.get(f"https://alphafold.ebi.ac.uk/files/{af_id}-confidence_v4.json").json() - Network accessline 298
conf = requests.get(f"https://alphafold.ebi.ac.uk/files/{af_id}-confidence_v4.json").json() - Network accessline 420
- AlphaFold DB: https://alphafold.ebi.ac.uk/ - Network accessline 420
- AlphaFold DB: https://alphafold.ebi.ac.uk/ - Network accessline 421
- API Documentation: https://alphafold.ebi.ac.uk/api-docs - Network accessline 421
- API Documentation: https://alphafold.ebi.ac.uk/api-docs - Network accessline 422
- Jumper et al. (2021) Nature 596, 583–589: https://doi.org/10.1038/s41586-021-03819-2 - Network accessline 422
- Jumper et al. (2021) Nature 596, 583–589: https://doi.org/10.1038/s41586-021-03819-2 - Network accessline 423
- Varadi et al. (2024) Nucleic Acids Res. 52, D368–D375: https://doi.org/10.1093/nar/gkad1011 - Network accessline 423
- Varadi et al. (2024) Nucleic Acids Res. 52, D368–D375: https://doi.org/10.1093/nar/gkad1011 - Network accessline 424
- BioPython AlphaFold module: https://biopython.org/docs/dev/api/Bio.PDB.alphafold_db.html - Network accessline 424
- BioPython AlphaFold module: https://biopython.org/docs/dev/api/Bio.PDB.alphafold_db.html - Network accessline 425
- Google Cloud AlphaFold: https://console.cloud.google.com/marketplace/product/bigquery-public-data/deepmind-alphafold - Network accessline 425
- Google Cloud AlphaFold: https://console.cloud.google.com/marketplace/product/bigquery-public-data/deepmind-alphafold
skills/structural-biology-drug-discovery/alphafold-database-access/references/api_schemas_reference.md
- Network accessline 9
`GET https://alphafold.ebi.ac.uk/api/prediction/{uniprot_id}` returns a JSON array: - Network accessline 9
`GET https://alphafold.ebi.ac.uk/api/prediction/{uniprot_id}` returns a JSON array: - Network accessline 34
`GET https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/api/uniprot/summary/{uniprot_id}.json` - Network accessline 34
`GET https://www.ebi.ac.uk/pdbe/pdbe-kb/3dbeacons/api/uniprot/summary/{uniprot_id}.json` - Network accessline 178
Condensed from original: api_reference.md (423 lines). Retained: all REST API response fields, mmCIF schema with atom_site fields, confidence JSON schema, PAE JSON schema with asymmetry note, BigQuery metadata table with example queries, GC - Network accessline 178
Condensed from original: api_reference.md (423 lines). Retained: all REST API response fields, mmCIF schema with atom_site fields, confidence JSON schema, PAE JSON schema with asymmetry note, BigQuery metadata table with example queries, GC
skills/structural-biology-drug-discovery/autodock-vina-docking/SKILL.md
- Network accessline 26
- **Python packages**: `vina`, `meeko`, `rdkit`, `prody` (for PDB fetch), `py3Dmol` (for visualization) - **External tools**: `ADFR Suite` (provides `prepare_receptor` for PDBQT conversion) — download from https://ccsb.scripps.edu/adfr/down - Network accessline 26
- **Python packages**: `vina`, `meeko`, `rdkit`, `prody` (for PDB fetch), `py3Dmol` (for visualization) - **External tools**: `ADFR Suite` (provides `prepare_receptor` for PDBQT conversion) — download from https://ccsb.scripps.edu/adfr/down - Network accessline 27
- **External tools**: `ADFR Suite` (provides `prepare_receptor` for PDBQT conversion) — download from https://ccsb.scripps.edu/adfr/downloads/ - Network accessline 27
- **External tools**: `ADFR Suite` (provides `prepare_receptor` for PDBQT conversion) — download from https://ccsb.scripps.edu/adfr/downloads/ - Network accessline 403
- [AutoDock Vina documentation](https://autodock-vina.readthedocs.io/) — Official docs and Python API (Apache-2.0) - Network accessline 403
- [AutoDock Vina documentation](https://autodock-vina.readthedocs.io/) — Official docs and Python API (Apache-2.0) - Network accessline 404
- [Eberhardt et al. (2021)](https://doi.org/10.1021/acs.jcim.1c00203) — "AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings", *J Chem Inf Model* - Network accessline 404
- [Eberhardt et al. (2021)](https://doi.org/10.1021/acs.jcim.1c00203) — "AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings", *J Chem Inf Model* - Network accessline 405
- [Meeko: Preparation of small molecules for AutoDock](https://github.com/forlilab/Meeko) — Ligand PDBQT preparation from RDKit (LGPL) - Network accessline 405
- [Meeko: Preparation of small molecules for AutoDock](https://github.com/forlilab/Meeko) — Ligand PDBQT preparation from RDKit (LGPL) - Network accessline 406
- [ADFR Suite](https://ccsb.scripps.edu/adfr/downloads/) — Receptor preparation tools from Scripps (free academic license) - Network accessline 406
- [ADFR Suite](https://ccsb.scripps.edu/adfr/downloads/) — Receptor preparation tools from Scripps (free academic license) - Network accessline 407
- [Forli et al. (2016)](https://doi.org/10.1038/nprot.2016.051) — "Computational protein-ligand docking and virtual drug screening with the AutoDock suite", *Nat Protoc* - Network accessline 407
- [Forli et al. (2016)](https://doi.org/10.1038/nprot.2016.051) — "Computational protein-ligand docking and virtual drug screening with the AutoDock suite", *Nat Protoc* - Network accessline 408
- [RDKit documentation](https://www.rdkit.org/docs/) — Cheminformatics toolkit for ligand handling (BSD) - Network accessline 408
- [RDKit documentation](https://www.rdkit.org/docs/) — Cheminformatics toolkit for ligand handling (BSD)
skills/structural-biology-drug-discovery/chembl-database-bioactivity/SKILL.md
- Network accessline 9
> **Why no SDK?** The `chembl_webresource_client` package is convenient sugar over a public, no-auth REST/JSON API at `https://www.ebi.ac.uk/chembl/api/data/`. When the SDK is unavailable, every operation can be reproduced with plain `reque - Network accessline 9
> **Why no SDK?** The `chembl_webresource_client` package is convenient sugar over a public, no-auth REST/JSON API at `https://www.ebi.ac.uk/chembl/api/data/`. When the SDK is unavailable, every operation can be reproduced with plain `reque - Network accessline 13
ChEMBL is EMBL-EBI's bioactive molecule database: 2M+ compounds, 19M+ bioactivity measurements (IC50, Ki, EC50, Kd, …), 13K+ targets. The REST API at `https://www.ebi.ac.uk/chembl/api/data/` returns JSON (append `.json`) or XML/YAML, requir - Network accessline 13
ChEMBL is EMBL-EBI's bioactive molecule database: 2M+ compounds, 19M+ bioactivity measurements (IC50, Ki, EC50, Kd, …), 13K+ targets. The REST API at `https://www.ebi.ac.uk/chembl/api/data/` returns JSON (append `.json`) or XML/YAML, requir - Network accessline 44
BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 44
BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 46
# Retrieve a molecule by ChEMBL ID r = requests.get(f"{BASE}/molecule/CHEMBL25.json", timeout=15) - Network accessline 46
# Retrieve a molecule by ChEMBL ID r = requests.get(f"{BASE}/molecule/CHEMBL25.json", timeout=15) - Network accessline 47
r = requests.get(f"{BASE}/molecule/CHEMBL25.json", timeout=15) - Network accessline 47
r = requests.get(f"{BASE}/molecule/CHEMBL25.json", timeout=15) - Network accessline 53
# Search targets by full name (acronyms like 'EGFR' don't match pref_name — use full term) r = requests.get( - Network accessline 53
# Search targets by full name (acronyms like 'EGFR' don't match pref_name — use full term) r = requests.get( - Network accessline 54
r = requests.get( - Network accessline 54
r = requests.get( - Network accessline 63
# Potent bioactivities: EGFR (CHEMBL203) IC50 <= 100 nM r = requests.get( - Network accessline 63
# Potent bioactivities: EGFR (CHEMBL203) IC50 <= 100 nM r = requests.get( - Network accessline 64
r = requests.get( - Network accessline 64
r = requests.get( - Network accessline 96
When passed via `requests.get(..., params={...})`, the library handles URL encoding automatically (including the commas in `__range` and `__in`). - Network accessline 96
When passed via `requests.get(..., params={...})`, the library handles URL encoding automatically (including the commas in `__range` and `__in`). - Network accessline 135
Walk `page_meta.next` (a relative URL — prefix with `https://www.ebi.ac.uk`) until it becomes `null`. - Network accessline 135
Walk `page_meta.next` (a relative URL — prefix with `https://www.ebi.ac.uk`) until it becomes `null`. - Network accessline 182
import requests BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 182
import requests BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 183
BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 183
BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 185
# By ChEMBL ID r = requests.get(f"{BASE}/molecule/CHEMBL25.json", timeout=15) - Network accessline 185
# By ChEMBL ID r = requests.get(f"{BASE}/molecule/CHEMBL25.json", timeout=15) - Network accessline 186
r = requests.get(f"{BASE}/molecule/CHEMBL25.json", timeout=15) - Network accessline 186
r = requests.get(f"{BASE}/molecule/CHEMBL25.json", timeout=15) - Network accessline 190
# By name (case-insensitive substring) r = requests.get(f"{BASE}/molecule.json", - Network accessline 190
# By name (case-insensitive substring) r = requests.get(f"{BASE}/molecule.json", - Network accessline 191
r = requests.get(f"{BASE}/molecule.json", - Network accessline 191
r = requests.get(f"{BASE}/molecule.json", - Network accessline 197
# By Lipinski-compliant property ranges r = requests.get(f"{BASE}/molecule.json", - Network accessline 197
# By Lipinski-compliant property ranges r = requests.get(f"{BASE}/molecule.json", - Network accessline 198
r = requests.get(f"{BASE}/molecule.json", - Network accessline 198
r = requests.get(f"{BASE}/molecule.json", - Network accessline 211
import requests BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 211
import requests BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 212
BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 212
BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 214
# By ChEMBL ID r = requests.get(f"{BASE}/target/CHEMBL203.json", timeout=15) - Network accessline 214
# By ChEMBL ID r = requests.get(f"{BASE}/target/CHEMBL203.json", timeout=15) - Network accessline 215
r = requests.get(f"{BASE}/target/CHEMBL203.json", timeout=15) - Network accessline 215
r = requests.get(f"{BASE}/target/CHEMBL203.json", timeout=15) - Network accessline 219
# Search by full name (NOT acronym) + type r = requests.get(f"{BASE}/target.json", - Network accessline 219
# Search by full name (NOT acronym) + type r = requests.get(f"{BASE}/target.json", - Network accessline 220
r = requests.get(f"{BASE}/target.json", - Network accessline 220
r = requests.get(f"{BASE}/target.json", - Network accessline 229
# By organism r = requests.get(f"{BASE}/target.json", - Network accessline 229
# By organism r = requests.get(f"{BASE}/target.json", - Network accessline 230
r = requests.get(f"{BASE}/target.json", - Network accessline 230
r = requests.get(f"{BASE}/target.json", - Network accessline 239
import requests BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 239
import requests BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 240
BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 240
BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 242
# Potent inhibitors for a target (EGFR = CHEMBL203) r = requests.get(f"{BASE}/activity.json", - Network accessline 242
# Potent inhibitors for a target (EGFR = CHEMBL203) r = requests.get(f"{BASE}/activity.json", - Network accessline 243
r = requests.get(f"{BASE}/activity.json", - Network accessline 243
r = requests.get(f"{BASE}/activity.json", - Network accessline 256
# All pChEMBL-tagged activities for a compound r = requests.get(f"{BASE}/activity.json", - Network accessline 256
# All pChEMBL-tagged activities for a compound r = requests.get(f"{BASE}/activity.json", - Network accessline 257
r = requests.get(f"{BASE}/activity.json", - Network accessline 257
r = requests.get(f"{BASE}/activity.json", - Network accessline 264
# Multiple activity types (CHEMBL240 = D2 dopamine receptor) r = requests.get(f"{BASE}/activity.json", - Network accessline 264
# Multiple activity types (CHEMBL240 = D2 dopamine receptor) r = requests.get(f"{BASE}/activity.json", - Network accessline 265
r = requests.get(f"{BASE}/activity.json", - Network accessline 265
r = requests.get(f"{BASE}/activity.json", - Network accessline 276
import requests from urllib.parse import quote BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 276
import requests from urllib.parse import quote BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 278
BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 278
BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 282
# The SMILES MUST be URL-encoded (it contains '/', '(', ')' etc.) aspirin_smiles = quote("CC(=O)Oc1ccccc1C(=O)O", safe="") r = requests.get(f"{BASE}/similarity/{aspirin_smiles}/85.json", - Network accessline 282
# The SMILES MUST be URL-encoded (it contains '/', '(', ')' etc.) aspirin_smiles = quote("CC(=O)Oc1ccccc1C(=O)O", safe="") r = requests.get(f"{BASE}/similarity/{aspirin_smiles}/85.json", - Network accessline 283
aspirin_smiles = quote("CC(=O)Oc1ccccc1C(=O)O", safe="") r = requests.get(f"{BASE}/similarity/{aspirin_smiles}/85.json", params={"limit": 5}, timeout=30) - Network accessline 283
aspirin_smiles = quote("CC(=O)Oc1ccccc1C(=O)O", safe="") r = requests.get(f"{BASE}/similarity/{aspirin_smiles}/85.json", params={"limit": 5}, timeout=30) - Network accessline 284
r = requests.get(f"{BASE}/similarity/{aspirin_smiles}/85.json", - Network accessline 284
r = requests.get(f"{BASE}/similarity/{aspirin_smiles}/85.json", - Network accessline 293
# Substructure search benzimidazole = quote("c1ccc2[nH]cnc2c1", safe="") r = requests.get(f"{BASE}/substructure/{benzimidazole}.json", - Network accessline 293
# Substructure search benzimidazole = quote("c1ccc2[nH]cnc2c1", safe="") r = requests.get(f"{BASE}/substructure/{benzimidazole}.json", - Network accessline 294
benzimidazole = quote("c1ccc2[nH]cnc2c1", safe="") r = requests.get(f"{BASE}/substructure/{benzimidazole}.json", params={"limit": 3}, timeout=30) - Network accessline 294
benzimidazole = quote("c1ccc2[nH]cnc2c1", safe="") r = requests.get(f"{BASE}/substructure/{benzimidazole}.json", params={"limit": 3}, timeout=30) - Network accessline 295
r = requests.get(f"{BASE}/substructure/{benzimidazole}.json", - Network accessline 295
r = requests.get(f"{BASE}/substructure/{benzimidazole}.json", - Network accessline 303
import requests BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 303
import requests BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 304
BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 304
BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 306
# Drug record (max clinical phase, ATC class, etc.) r = requests.get(f"{BASE}/drug/CHEMBL941.json", timeout=15) # imatinib - Network accessline 306
# Drug record (max clinical phase, ATC class, etc.) r = requests.get(f"{BASE}/drug/CHEMBL941.json", timeout=15) # imatinib - Network accessline 307
r = requests.get(f"{BASE}/drug/CHEMBL941.json", timeout=15) # imatinib - Network accessline 307
r = requests.get(f"{BASE}/drug/CHEMBL941.json", timeout=15) # imatinib - Network accessline 311
# Mechanisms of action — note: not every drug has mechanism records. # Imatinib (CHEMBL941) returns 0 mechanism rows; sunitinib (CHEMBL535) has many. r = requests.get(f"{BASE}/mechanism.json", - Network accessline 311
# Mechanisms of action — note: not every drug has mechanism records. # Imatinib (CHEMBL941) returns 0 mechanism rows; sunitinib (CHEMBL535) has many. r = requests.get(f"{BASE}/mechanism.json", - Network accessline 312
# Imatinib (CHEMBL941) returns 0 mechanism rows; sunitinib (CHEMBL535) has many. r = requests.get(f"{BASE}/mechanism.json", params={"molecule_chembl_id": "CHEMBL535"}, timeout=15) - Network accessline 312
# Imatinib (CHEMBL941) returns 0 mechanism rows; sunitinib (CHEMBL535) has many. r = requests.get(f"{BASE}/mechanism.json", params={"molecule_chembl_id": "CHEMBL535"}, timeout=15) - Network accessline 313
r = requests.get(f"{BASE}/mechanism.json", - Network accessline 313
r = requests.get(f"{BASE}/mechanism.json", - Network accessline 318
# Therapeutic indications r = requests.get(f"{BASE}/drug_indication.json", - Network accessline 318
# Therapeutic indications r = requests.get(f"{BASE}/drug_indication.json", - Network accessline 319
r = requests.get(f"{BASE}/drug_indication.json", - Network accessline 319
r = requests.get(f"{BASE}/drug_indication.json", - Network accessline 327
# SVG molecular structure image — direct binary response, NOT JSON # Do NOT call /image/{cid}.json — that endpoint raises JSONDecodeError. r = requests.get(f"{BASE}/image/CHEMBL25.svg", timeout=15) - Network accessline 327
# SVG molecular structure image — direct binary response, NOT JSON # Do NOT call /image/{cid}.json — that endpoint raises JSONDecodeError. r = requests.get(f"{BASE}/image/CHEMBL25.svg", timeout=15) - Network accessline 328
# Do NOT call /image/{cid}.json — that endpoint raises JSONDecodeError. r = requests.get(f"{BASE}/image/CHEMBL25.svg", timeout=15) r.raise_for_status() - Network accessline 328
# Do NOT call /image/{cid}.json — that endpoint raises JSONDecodeError. r = requests.get(f"{BASE}/image/CHEMBL25.svg", timeout=15) r.raise_for_status() - Network accessline 329
r = requests.get(f"{BASE}/image/CHEMBL25.svg", timeout=15) - Network accessline 329
r = requests.get(f"{BASE}/image/CHEMBL25.svg", timeout=15) - Network accessline 343
import requests, pandas as pd, time BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 343
import requests, pandas as pd, time BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 344
BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 344
BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 346
# Step 1: Resolve the target by full name r = requests.get(f"{BASE}/target.json", - Network accessline 346
# Step 1: Resolve the target by full name r = requests.get(f"{BASE}/target.json", - Network accessline 347
r = requests.get(f"{BASE}/target.json", - Network accessline 347
r = requests.get(f"{BASE}/target.json", - Network accessline 364
records = [] while url and len(records) < 500: r = requests.get(url, timeout=30) - Network accessline 364
records = [] while url and len(records) < 500: r = requests.get(url, timeout=30) - Network accessline 365
while url and len(records) < 500: r = requests.get(url, timeout=30) r.raise_for_status() - Network accessline 365
while url and len(records) < 500: r = requests.get(url, timeout=30) r.raise_for_status() - Network accessline 366
r = requests.get(url, timeout=30) - Network accessline 366
r = requests.get(url, timeout=30) - Network accessline 369
records.extend(data["activities"]) nxt = data["page_meta"].get("next") url = f"https://www.ebi.ac.uk{nxt}" if nxt else None - Network accessline 369
records.extend(data["activities"]) nxt = data["page_meta"].get("next") url = f"https://www.ebi.ac.uk{nxt}" if nxt else None - Network accessline 370
nxt = data["page_meta"].get("next") url = f"https://www.ebi.ac.uk{nxt}" if nxt else None time.sleep(0.2) - Network accessline 370
nxt = data["page_meta"].get("next") url = f"https://www.ebi.ac.uk{nxt}" if nxt else None time.sleep(0.2) - Network accessline 371
url = f"https://www.ebi.ac.uk{nxt}" if nxt else None - Network accessline 371
url = f"https://www.ebi.ac.uk{nxt}" if nxt else None - Network accessline 383
import requests BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 383
import requests BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 384
BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 384
BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 390
# Molecule record m = requests.get(f"{BASE}/molecule/{chembl_id}.json", timeout=15).json() - Network accessline 390
# Molecule record m = requests.get(f"{BASE}/molecule/{chembl_id}.json", timeout=15).json() - Network accessline 391
m = requests.get(f"{BASE}/molecule/{chembl_id}.json", timeout=15).json() - Network accessline 391
m = requests.get(f"{BASE}/molecule/{chembl_id}.json", timeout=15).json() - Network accessline 395
# Mechanisms mechs = requests.get(f"{BASE}/mechanism.json", - Network accessline 395
# Mechanisms mechs = requests.get(f"{BASE}/mechanism.json", - Network accessline 396
mechs = requests.get(f"{BASE}/mechanism.json", - Network accessline 396
mechs = requests.get(f"{BASE}/mechanism.json", - Network accessline 402
# Indications inds = requests.get(f"{BASE}/drug_indication.json", - Network accessline 402
# Indications inds = requests.get(f"{BASE}/drug_indication.json", - Network accessline 403
inds = requests.get(f"{BASE}/drug_indication.json", - Network accessline 403
inds = requests.get(f"{BASE}/drug_indication.json", - Network accessline 410
# Bioactivity record count total = requests.get(f"{BASE}/activity.json", - Network accessline 410
# Bioactivity record count total = requests.get(f"{BASE}/activity.json", - Network accessline 411
total = requests.get(f"{BASE}/activity.json", - Network accessline 411
total = requests.get(f"{BASE}/activity.json", - Network accessline 422
import requests, pandas as pd, time from urllib.parse import quote BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 422
import requests, pandas as pd, time from urllib.parse import quote BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 424
BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 424
BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 426
# Step 1: Similar compounds to a lead (e.g., quinoline scaffold) lead_smiles = "c1ccc2c(c1)cc(nc2N)c3ccc(cc3)NC(=O)c4ccccc4" r = requests.get(f"{BASE}/similarity/{quote(lead_smiles, safe='')}/80.json", - Network accessline 426
# Step 1: Similar compounds to a lead (e.g., quinoline scaffold) lead_smiles = "c1ccc2c(c1)cc(nc2N)c3ccc(cc3)NC(=O)c4ccccc4" r = requests.get(f"{BASE}/similarity/{quote(lead_smiles, safe='')}/80.json", - Network accessline 427
lead_smiles = "c1ccc2c(c1)cc(nc2N)c3ccc(cc3)NC(=O)c4ccccc4" r = requests.get(f"{BASE}/similarity/{quote(lead_smiles, safe='')}/80.json", params={"limit": 20}, timeout=30) - Network accessline 427
lead_smiles = "c1ccc2c(c1)cc(nc2N)c3ccc(cc3)NC(=O)c4ccccc4" r = requests.get(f"{BASE}/similarity/{quote(lead_smiles, safe='')}/80.json", params={"limit": 20}, timeout=30) - Network accessline 428
r = requests.get(f"{BASE}/similarity/{quote(lead_smiles, safe='')}/80.json", - Network accessline 428
r = requests.get(f"{BASE}/similarity/{quote(lead_smiles, safe='')}/80.json", - Network accessline 435
for compound in analogs[:20]: cid = compound["molecule_chembl_id"] acts = requests.get(f"{BASE}/activity.json", - Network accessline 435
for compound in analogs[:20]: cid = compound["molecule_chembl_id"] acts = requests.get(f"{BASE}/activity.json", - Network accessline 436
cid = compound["molecule_chembl_id"] acts = requests.get(f"{BASE}/activity.json", params={"molecule_chembl_id": cid, - Network accessline 436
cid = compound["molecule_chembl_id"] acts = requests.get(f"{BASE}/activity.json", params={"molecule_chembl_id": cid, - Network accessline 437
acts = requests.get(f"{BASE}/activity.json", - Network accessline 437
acts = requests.get(f"{BASE}/activity.json", - Network accessline 465
import requests BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 465
import requests BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 466
BASE = "https://www.ebi.ac.uk/chembl/api/data" r = requests.get(f"{BASE}/molecule.json", params={"molecule_properties__mw_freebase__range": "300,500", - Network accessline 466
BASE = "https://www.ebi.ac.uk/chembl/api/data" r = requests.get(f"{BASE}/molecule.json", params={"molecule_properties__mw_freebase__range": "300,500", - Network accessline 467
r = requests.get(f"{BASE}/molecule.json", - Network accessline 467
r = requests.get(f"{BASE}/molecule.json", - Network accessline 481
import requests, pandas as pd, time BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 481
import requests, pandas as pd, time BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 482
BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 482
BASE = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 489
all_acts = [] while url: r = requests.get(url, timeout=60) - Network accessline 489
all_acts = [] while url: r = requests.get(url, timeout=60) - Network accessline 490
while url: r = requests.get(url, timeout=60) r.raise_for_status() - Network accessline 490
while url: r = requests.get(url, timeout=60) r.raise_for_status() - Network accessline 491
r = requests.get(url, timeout=60) - Network accessline 491
r = requests.get(url, timeout=60) - Network accessline 494
all_acts.extend(data["activities"]) nxt = data["page_meta"].get("next") url = f"https://www.ebi.ac.uk{nxt}" if nxt else None - Network accessline 494
all_acts.extend(data["activities"]) nxt = data["page_meta"].get("next") url = f"https://www.ebi.ac.uk{nxt}" if nxt else None - Network accessline 495
nxt = data["page_meta"].get("next") url = f"https://www.ebi.ac.uk{nxt}" if nxt else None time.sleep(0.3) - Network accessline 495
nxt = data["page_meta"].get("next") url = f"https://www.ebi.ac.uk{nxt}" if nxt else None time.sleep(0.3) - Network accessline 496
url = f"https://www.ebi.ac.uk{nxt}" if nxt else None - Network accessline 496
url = f"https://www.ebi.ac.uk{nxt}" if nxt else None - Network accessline 511
def chembl_session(retries=3, backoff=1.0): s = requests.Session() - Network accessline 511
def chembl_session(retries=3, backoff=1.0): s = requests.Session() - Network accessline 512
s = requests.Session() - Network accessline 512
s = requests.Session() - Network accessline 520
session = chembl_session() r = session.get("https://www.ebi.ac.uk/chembl/api/data/molecule/CHEMBL25.json", timeout=15) - Network accessline 520
session = chembl_session() r = session.get("https://www.ebi.ac.uk/chembl/api/data/molecule/CHEMBL25.json", timeout=15) - Network accessline 521
r = session.get("https://www.ebi.ac.uk/chembl/api/data/molecule/CHEMBL25.json", timeout=15) - Network accessline 521
r = session.get("https://www.ebi.ac.uk/chembl/api/data/molecule/CHEMBL25.json", timeout=15) - Network accessline 528
import requests r = requests.get("https://www.ebi.ac.uk/chembl/api/data/image/CHEMBL25.svg", timeout=15) - Network accessline 528
import requests r = requests.get("https://www.ebi.ac.uk/chembl/api/data/image/CHEMBL25.svg", timeout=15) - Network accessline 529
r = requests.get("https://www.ebi.ac.uk/chembl/api/data/image/CHEMBL25.svg", timeout=15) - Network accessline 529
r = requests.get("https://www.ebi.ac.uk/chembl/api/data/image/CHEMBL25.svg", timeout=15) - Network accessline 573
- **URL-encode SMILES** in path-style endpoints (`/similarity/{smiles}/...`, `/substructure/{smiles}`) with `urllib.parse.quote(smi, safe="")`. - **Use a `Session` with retry adapter** for batch work (see Recipe) — ChEMBL handles a fair amo - Network accessline 573
- **URL-encode SMILES** in path-style endpoints (`/similarity/{smiles}/...`, `/substructure/{smiles}`) with `urllib.parse.quote(smi, safe="")`. - **Use a `Session` with retry adapter** for batch work (see Recipe) — ChEMBL handles a fair amo - Network accessline 574
- **Use a `Session` with retry adapter** for batch work (see Recipe) — ChEMBL handles a fair amount of traffic and occasionally returns 502/503. - **For >100K records** prefer the [ChEMBL FTP downloads](https://chembl.gitbook.io/chembl-inte - Network accessline 574
- **Use a `Session` with retry adapter** for batch work (see Recipe) — ChEMBL handles a fair amount of traffic and occasionally returns 502/503. - **For >100K records** prefer the [ChEMBL FTP downloads](https://chembl.gitbook.io/chembl-inte - Network accessline 575
- **For >100K records** prefer the [ChEMBL FTP downloads](https://chembl.gitbook.io/chembl-interface-documentation/downloads) over paginated API calls. - Network accessline 575
- **For >100K records** prefer the [ChEMBL FTP downloads](https://chembl.gitbook.io/chembl-interface-documentation/downloads) over paginated API calls. - Network accessline 588
- ChEMBL website: https://www.ebi.ac.uk/chembl/ - Network accessline 588
- ChEMBL website: https://www.ebi.ac.uk/chembl/ - Network accessline 589
- REST API root: https://www.ebi.ac.uk/chembl/api/data/ - Network accessline 589
- REST API root: https://www.ebi.ac.uk/chembl/api/data/ - Network accessline 590
- API docs: https://www.ebi.ac.uk/chembl/api/data/docs - Network accessline 590
- API docs: https://www.ebi.ac.uk/chembl/api/data/docs - Network accessline 591
- Interface docs (Django filter syntax): https://chembl.gitbook.io/chembl-interface-documentation/web-services - Network accessline 591
- Interface docs (Django filter syntax): https://chembl.gitbook.io/chembl-interface-documentation/web-services - Network accessline 592
- Bulk downloads (for >100K records): https://chembl.gitbook.io/chembl-interface-documentation/downloads - Network accessline 592
- Bulk downloads (for >100K records): https://chembl.gitbook.io/chembl-interface-documentation/downloads
skills/structural-biology-drug-discovery/clinicaltrials-database-search/SKILL.md
- Network accessline 31
**API details**: - Base URL: `https://clinicaltrials.gov/api/v2` - Network accessline 31
**API details**: - Base URL: `https://clinicaltrials.gov/api/v2` - Network accessline 32
- Base URL: `https://clinicaltrials.gov/api/v2` - Authentication: None required (public API) - Rate limit: ~50 requests/minute per IP - Network accessline 32
- Base URL: `https://clinicaltrials.gov/api/v2` - Authentication: None required (public API) - Rate limit: ~50 requests/minute per IP - Network accessline 45
CT_API = "https://clinicaltrials.gov/api/v2" - Network accessline 45
CT_API = "https://clinicaltrials.gov/api/v2" - Network accessline 47
def ct_search(params): """Reusable helper for ClinicalTrials.gov searches.""" response = requests.get(f"{CT_API}/studies", params=params, timeout=30) - Network accessline 47
def ct_search(params): """Reusable helper for ClinicalTrials.gov searches.""" response = requests.get(f"{CT_API}/studies", params=params, timeout=30) - Network accessline 48
"""Reusable helper for ClinicalTrials.gov searches.""" response = requests.get(f"{CT_API}/studies", params=params, timeout=30) response.raise_for_status() - Network accessline 48
"""Reusable helper for ClinicalTrials.gov searches.""" response = requests.get(f"{CT_API}/studies", params=params, timeout=30) response.raise_for_status() - Network accessline 49
response = requests.get(f"{CT_API}/studies", params=params, timeout=30) - Network accessline 49
response = requests.get(f"{CT_API}/studies", params=params, timeout=30) - Network accessline 196
nct_id = "NCT04852770" response = requests.get(f"{CT_API}/studies/{nct_id}", timeout=30) - Network accessline 196
nct_id = "NCT04852770" response = requests.get(f"{CT_API}/studies/{nct_id}", timeout=30) - Network accessline 197
response = requests.get(f"{CT_API}/studies/{nct_id}", timeout=30) - Network accessline 197
response = requests.get(f"{CT_API}/studies/{nct_id}", timeout=30) - Network accessline 243
response = requests.get(f"{CT_API}/studies", params={ - Network accessline 243
response = requests.get(f"{CT_API}/studies", params={ - Network accessline 262
CT_API = "https://clinicaltrials.gov/api/v2" - Network accessline 262
CT_API = "https://clinicaltrials.gov/api/v2" - Network accessline 264
def ct_search(params): response = requests.get(f"{CT_API}/studies", params=params, timeout=30) - Network accessline 264
def ct_search(params): response = requests.get(f"{CT_API}/studies", params=params, timeout=30) - Network accessline 265
response = requests.get(f"{CT_API}/studies", params=params, timeout=30) - Network accessline 265
response = requests.get(f"{CT_API}/studies", params=params, timeout=30) - Network accessline 312
if with_results: nct = with_results[0]['protocolSection']['identificationModule']['nctId'] detail = requests.get(f"{CT_API}/studies/{nct}", timeout=30).json() - Network accessline 312
if with_results: nct = with_results[0]['protocolSection']['identificationModule']['nctId'] detail = requests.get(f"{CT_API}/studies/{nct}", timeout=30).json() - Network accessline 314
detail = requests.get(f"{CT_API}/studies/{nct}", timeout=30).json() - Network accessline 314
detail = requests.get(f"{CT_API}/studies/{nct}", timeout=30).json() - Network accessline 344
for attempt in range(max_retries): try: response = requests.get(f"{CT_API}/studies", params=params, timeout=30) - Network accessline 344
for attempt in range(max_retries): try: response = requests.get(f"{CT_API}/studies", params=params, timeout=30) - Network accessline 345
try: response = requests.get(f"{CT_API}/studies", params=params, timeout=30) response.raise_for_status() - Network accessline 345
try: response = requests.get(f"{CT_API}/studies", params=params, timeout=30) response.raise_for_status() - Network accessline 346
response = requests.get(f"{CT_API}/studies", params=params, timeout=30) - Network accessline 346
response = requests.get(f"{CT_API}/studies", params=params, timeout=30) - Network accessline 453
- ClinicalTrials.gov API documentation: https://clinicaltrials.gov/data-api/api - Network accessline 453
- ClinicalTrials.gov API documentation: https://clinicaltrials.gov/data-api/api - Network accessline 454
- API migration guide (v1→v2): https://clinicaltrials.gov/data-api/about-api/api-migration - Network accessline 454
- API migration guide (v1→v2): https://clinicaltrials.gov/data-api/about-api/api-migration - Network accessline 455
- ClinicalTrials.gov homepage: https://clinicaltrials.gov/ - Network accessline 455
- ClinicalTrials.gov homepage: https://clinicaltrials.gov/ - Network accessline 456
- OpenAPI specification: https://clinicaltrials.gov/data-api/about-api/api-spec - Network accessline 456
- OpenAPI specification: https://clinicaltrials.gov/data-api/about-api/api-spec - Network accessline 463
- `SKILL.md` (507 lines) → Core API modules 1-7 (condition, intervention, location, sponsor, details, pagination, CSV export). "Core Capabilities" sections 1-10 consolidated: Search by Condition → Module 1, Search by Intervention → Module 2 - Network accessline 463
- `SKILL.md` (507 lines) → Core API modules 1-7 (condition, intervention, location, sponsor, details, pagination, CSV export). "Core Capabilities" sections 1-10 consolidated: Search by Condition → Module 1, Search by Intervention → Module 2 - Network accessline 465
- `scripts/query_clinicaltrials.py` (216 lines) → Helper function pattern: `search_studies()` → Quick Start `ct_search()` helper; `get_study_details()` → Module 5 inline; `search_with_all_results()` → Module 6 pagination pattern; `extract_s - Network accessline 465
- `scripts/query_clinicaltrials.py` (216 lines) → Helper function pattern: `search_studies()` → Quick Start `ct_search()` helper; `get_study_details()` → Module 5 inline; `search_with_all_results()` → Module 6 pagination pattern; `extract_s
skills/structural-biology-drug-discovery/dailymed-database/SKILL.md
- Network accessline 40
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 40
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 42
# Search drug labels by name r = requests.get(f"{BASE}/spls.json", params={"drug_name": "metformin", "pagesize": 5}) - Network accessline 42
# Search drug labels by name r = requests.get(f"{BASE}/spls.json", params={"drug_name": "metformin", "pagesize": 5}) - Network accessline 43
r = requests.get(f"{BASE}/spls.json", params={"drug_name": "metformin", "pagesize": 5}) - Network accessline 43
r = requests.get(f"{BASE}/spls.json", params={"drug_name": "metformin", "pagesize": 5}) - Network accessline 61
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 61
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 63
def search_spls(drug_name, pagesize=20, page=1): """Search DailyMed SPLs by drug name. Returns list of label summaries.""" r = requests.get(f"{BASE}/spls.json", - Network accessline 63
def search_spls(drug_name, pagesize=20, page=1): """Search DailyMed SPLs by drug name. Returns list of label summaries.""" r = requests.get(f"{BASE}/spls.json", - Network accessline 64
"""Search DailyMed SPLs by drug name. Returns list of label summaries.""" r = requests.get(f"{BASE}/spls.json", params={"drug_name": drug_name, "pagesize": pagesize, "page": page}, - Network accessline 64
"""Search DailyMed SPLs by drug name. Returns list of label summaries.""" r = requests.get(f"{BASE}/spls.json", params={"drug_name": drug_name, "pagesize": pagesize, "page": page}, - Network accessline 65
r = requests.get(f"{BASE}/spls.json", - Network accessline 65
r = requests.get(f"{BASE}/spls.json", - Network accessline 89
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 89
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 91
def get_spl(setid): """Retrieve full SPL document by set ID. Returns label metadata and XML/JSON.""" r = requests.get(f"{BASE}/spls/{setid}.json", timeout=20) - Network accessline 91
def get_spl(setid): """Retrieve full SPL document by set ID. Returns label metadata and XML/JSON.""" r = requests.get(f"{BASE}/spls/{setid}.json", timeout=20) - Network accessline 92
"""Retrieve full SPL document by set ID. Returns label metadata and XML/JSON.""" r = requests.get(f"{BASE}/spls/{setid}.json", timeout=20) r.raise_for_status() - Network accessline 92
"""Retrieve full SPL document by set ID. Returns label metadata and XML/JSON.""" r = requests.get(f"{BASE}/spls/{setid}.json", timeout=20) r.raise_for_status() - Network accessline 93
r = requests.get(f"{BASE}/spls/{setid}.json", timeout=20) - Network accessline 93
r = requests.get(f"{BASE}/spls/{setid}.json", timeout=20) - Network accessline 122
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 122
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 124
def search_by_ndc(ndc_code): """Find SPL by NDC code (formatted as XXXXX-XXXX-XX or without dashes).""" r = requests.get(f"{BASE}/spls.json", - Network accessline 124
def search_by_ndc(ndc_code): """Find SPL by NDC code (formatted as XXXXX-XXXX-XX or without dashes).""" r = requests.get(f"{BASE}/spls.json", - Network accessline 125
"""Find SPL by NDC code (formatted as XXXXX-XXXX-XX or without dashes).""" r = requests.get(f"{BASE}/spls.json", params={"ndc": ndc_code}, - Network accessline 125
"""Find SPL by NDC code (formatted as XXXXX-XXXX-XX or without dashes).""" r = requests.get(f"{BASE}/spls.json", params={"ndc": ndc_code}, - Network accessline 126
r = requests.get(f"{BASE}/spls.json", - Network accessline 126
r = requests.get(f"{BASE}/spls.json", - Network accessline 151
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 151
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 153
def get_packaging(setid): """Retrieve packaging information for a label (NDC codes, dosage forms, quantities).""" r = requests.get(f"{BASE}/spls/{setid}/packaging.json", timeout=15) - Network accessline 153
def get_packaging(setid): """Retrieve packaging information for a label (NDC codes, dosage forms, quantities).""" r = requests.get(f"{BASE}/spls/{setid}/packaging.json", timeout=15) - Network accessline 154
"""Retrieve packaging information for a label (NDC codes, dosage forms, quantities).""" r = requests.get(f"{BASE}/spls/{setid}/packaging.json", timeout=15) r.raise_for_status() - Network accessline 154
"""Retrieve packaging information for a label (NDC codes, dosage forms, quantities).""" r = requests.get(f"{BASE}/spls/{setid}/packaging.json", timeout=15) r.raise_for_status() - Network accessline 155
r = requests.get(f"{BASE}/spls/{setid}/packaging.json", timeout=15) - Network accessline 155
r = requests.get(f"{BASE}/spls/{setid}/packaging.json", timeout=15) - Network accessline 181
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 181
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 183
def get_spls_by_rxcui(rxcui): """Get all SPLs associated with an RxCUI. Returns list of set IDs and titles.""" r = requests.get(f"{BASE}/rxcuis/{rxcui}/spls.json", timeout=15) - Network accessline 183
def get_spls_by_rxcui(rxcui): """Get all SPLs associated with an RxCUI. Returns list of set IDs and titles.""" r = requests.get(f"{BASE}/rxcuis/{rxcui}/spls.json", timeout=15) - Network accessline 184
"""Get all SPLs associated with an RxCUI. Returns list of set IDs and titles.""" r = requests.get(f"{BASE}/rxcuis/{rxcui}/spls.json", timeout=15) r.raise_for_status() - Network accessline 184
"""Get all SPLs associated with an RxCUI. Returns list of set IDs and titles.""" r = requests.get(f"{BASE}/rxcuis/{rxcui}/spls.json", timeout=15) r.raise_for_status() - Network accessline 185
r = requests.get(f"{BASE}/rxcuis/{rxcui}/spls.json", timeout=15) - Network accessline 185
r = requests.get(f"{BASE}/rxcuis/{rxcui}/spls.json", timeout=15) - Network accessline 205
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 205
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 207
def search_drug_names(query, pagesize=20): """Search the DailyMed drug name index for name normalization.""" r = requests.get(f"{BASE}/drugnames.json", - Network accessline 207
def search_drug_names(query, pagesize=20): """Search the DailyMed drug name index for name normalization.""" r = requests.get(f"{BASE}/drugnames.json", - Network accessline 208
"""Search the DailyMed drug name index for name normalization.""" r = requests.get(f"{BASE}/drugnames.json", params={"drug_name": query, "pagesize": pagesize}, - Network accessline 208
"""Search the DailyMed drug name index for name normalization.""" r = requests.get(f"{BASE}/drugnames.json", params={"drug_name": query, "pagesize": pagesize}, - Network accessline 209
r = requests.get(f"{BASE}/drugnames.json", - Network accessline 209
r = requests.get(f"{BASE}/drugnames.json", - Network accessline 233
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 233
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 245
def get_label_sections(setid): """Extract structured sections from an SPL by set ID.""" r = requests.get(f"{BASE}/spls/{setid}.json", timeout=20) - Network accessline 245
def get_label_sections(setid): """Extract structured sections from an SPL by set ID.""" r = requests.get(f"{BASE}/spls/{setid}.json", timeout=20) - Network accessline 246
"""Extract structured sections from an SPL by set ID.""" r = requests.get(f"{BASE}/spls/{setid}.json", timeout=20) r.raise_for_status() - Network accessline 246
"""Extract structured sections from an SPL by set ID.""" r = requests.get(f"{BASE}/spls/{setid}.json", timeout=20) r.raise_for_status() - Network accessline 247
r = requests.get(f"{BASE}/spls/{setid}.json", timeout=20) - Network accessline 247
r = requests.get(f"{BASE}/spls/{setid}.json", timeout=20) - Network accessline 313
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 313
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 315
def search_spls(drug_name, pagesize=50): r = requests.get(f"{BASE}/spls.json", - Network accessline 315
def search_spls(drug_name, pagesize=50): r = requests.get(f"{BASE}/spls.json", - Network accessline 316
r = requests.get(f"{BASE}/spls.json", - Network accessline 316
r = requests.get(f"{BASE}/spls.json", - Network accessline 322
def get_packaging(setid): r = requests.get(f"{BASE}/spls/{setid}/packaging.json", timeout=15) - Network accessline 322
def get_packaging(setid): r = requests.get(f"{BASE}/spls/{setid}/packaging.json", timeout=15) - Network accessline 323
r = requests.get(f"{BASE}/spls/{setid}/packaging.json", timeout=15) - Network accessline 323
r = requests.get(f"{BASE}/spls/{setid}/packaging.json", timeout=15) - Network accessline 367
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 367
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 376
def search_and_get_sections(drug_name, max_labels=5): """Search for labels and extract safety-relevant sections.""" search_r = requests.get(f"{BASE}/spls.json", - Network accessline 376
def search_and_get_sections(drug_name, max_labels=5): """Search for labels and extract safety-relevant sections.""" search_r = requests.get(f"{BASE}/spls.json", - Network accessline 377
"""Search for labels and extract safety-relevant sections.""" search_r = requests.get(f"{BASE}/spls.json", params={"drug_name": drug_name, "pagesize": max_labels}, - Network accessline 377
"""Search for labels and extract safety-relevant sections.""" search_r = requests.get(f"{BASE}/spls.json", params={"drug_name": drug_name, "pagesize": max_labels}, - Network accessline 378
search_r = requests.get(f"{BASE}/spls.json", - Network accessline 378
search_r = requests.get(f"{BASE}/spls.json", - Network accessline 386
setid = label["setid"] try: label_r = requests.get(f"{BASE}/spls/{setid}.json", timeout=20) - Network accessline 386
setid = label["setid"] try: label_r = requests.get(f"{BASE}/spls/{setid}.json", timeout=20) - Network accessline 387
try: label_r = requests.get(f"{BASE}/spls/{setid}.json", timeout=20) label_r.raise_for_status() - Network accessline 387
try: label_r = requests.get(f"{BASE}/spls/{setid}.json", timeout=20) label_r.raise_for_status() - Network accessline 388
label_r = requests.get(f"{BASE}/spls/{setid}.json", timeout=20) - Network accessline 388
label_r = requests.get(f"{BASE}/spls/{setid}.json", timeout=20) - Network accessline 432
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 432
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 434
def count_labels(drug_name): """Return total SPL count for a drug name.""" r = requests.get(f"{BASE}/spls.json", - Network accessline 434
def count_labels(drug_name): """Return total SPL count for a drug name.""" r = requests.get(f"{BASE}/spls.json", - Network accessline 435
"""Return total SPL count for a drug name.""" r = requests.get(f"{BASE}/spls.json", params={"drug_name": drug_name, "pagesize": 1}, - Network accessline 435
"""Return total SPL count for a drug name.""" r = requests.get(f"{BASE}/spls.json", params={"drug_name": drug_name, "pagesize": 1}, - Network accessline 436
r = requests.get(f"{BASE}/spls.json", - Network accessline 436
r = requests.get(f"{BASE}/spls.json", - Network accessline 523
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 523
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 526
def has_black_box_warning(setid): """Return True and warning text if drug label has a black box (boxed) warning.""" r = requests.get(f"{BASE}/spls/{setid}.json", timeout=20) - Network accessline 526
def has_black_box_warning(setid): """Return True and warning text if drug label has a black box (boxed) warning.""" r = requests.get(f"{BASE}/spls/{setid}.json", timeout=20) - Network accessline 527
"""Return True and warning text if drug label has a black box (boxed) warning.""" r = requests.get(f"{BASE}/spls/{setid}.json", timeout=20) r.raise_for_status() - Network accessline 527
"""Return True and warning text if drug label has a black box (boxed) warning.""" r = requests.get(f"{BASE}/spls/{setid}.json", timeout=20) r.raise_for_status() - Network accessline 528
r = requests.get(f"{BASE}/spls/{setid}.json", timeout=20) - Network accessline 528
r = requests.get(f"{BASE}/spls/{setid}.json", timeout=20) - Network accessline 552
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 552
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 557
page = 1 while True: r = requests.get(f"{BASE}/spls.json", - Network accessline 557
page = 1 while True: r = requests.get(f"{BASE}/spls.json", - Network accessline 558
while True: r = requests.get(f"{BASE}/spls.json", params={"drug_name": drug_name, "pagesize": pagesize, "page": page}, - Network accessline 558
while True: r = requests.get(f"{BASE}/spls.json", params={"drug_name": drug_name, "pagesize": pagesize, "page": page}, - Network accessline 559
r = requests.get(f"{BASE}/spls.json", - Network accessline 559
r = requests.get(f"{BASE}/spls.json", - Network accessline 583
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 583
BASE = "https://dailymed.nlm.nih.gov/dailymed/services/v2" - Network accessline 585
def export_drug_labels(drug_name, pagesize=50): """Export label summaries for a drug name to DataFrame.""" r = requests.get(f"{BASE}/spls.json", - Network accessline 585
def export_drug_labels(drug_name, pagesize=50): """Export label summaries for a drug name to DataFrame.""" r = requests.get(f"{BASE}/spls.json", - Network accessline 586
"""Export label summaries for a drug name to DataFrame.""" r = requests.get(f"{BASE}/spls.json", params={"drug_name": drug_name, "pagesize": pagesize}, - Network accessline 586
"""Export label summaries for a drug name to DataFrame.""" r = requests.get(f"{BASE}/spls.json", params={"drug_name": drug_name, "pagesize": pagesize}, - Network accessline 587
r = requests.get(f"{BASE}/spls.json", - Network accessline 587
r = requests.get(f"{BASE}/spls.json", - Network accessline 623
- [DailyMed](https://dailymed.nlm.nih.gov/dailymed/) — official drug label repository (NLM/NIH) - Network accessline 623
- [DailyMed](https://dailymed.nlm.nih.gov/dailymed/) — official drug label repository (NLM/NIH) - Network accessline 624
- [DailyMed Web Services API v2](https://dailymed.nlm.nih.gov/dailymed/webservices.cfm) — complete REST API documentation - Network accessline 624
- [DailyMed Web Services API v2](https://dailymed.nlm.nih.gov/dailymed/webservices.cfm) — complete REST API documentation - Network accessline 625
- [SPL Standard (HL7)](https://www.hl7.org/implement/standards/product_brief.cfm?product_id=4) — Structured Product Labeling standard documentation - Network accessline 625
- [SPL Standard (HL7)](https://www.hl7.org/implement/standards/product_brief.cfm?product_id=4) — Structured Product Labeling standard documentation - Network accessline 626
- [LOINC Document Ontology](https://loinc.org/document-ontology/) — LOINC codes for SPL section identification - Network accessline 626
- [LOINC Document Ontology](https://loinc.org/document-ontology/) — LOINC codes for SPL section identification - Network accessline 627
- [NLM DailyMed Overview](https://dailymed.nlm.nih.gov/dailymed/about-dailymed.cfm) — data coverage and update frequency - Network accessline 627
- [NLM DailyMed Overview](https://dailymed.nlm.nih.gov/dailymed/about-dailymed.cfm) — data coverage and update frequency
skills/structural-biology-drug-discovery/datamol-cheminformatics/SKILL.md
- Network accessline 461
- Datamol documentation: https://docs.datamol.io/ - Network accessline 461
- Datamol documentation: https://docs.datamol.io/ - Network accessline 462
- RDKit documentation: https://www.rdkit.org/docs/ - Network accessline 462
- RDKit documentation: https://www.rdkit.org/docs/ - Network accessline 463
- GitHub repository: https://github.com/datamol-io/datamol - Network accessline 463
- GitHub repository: https://github.com/datamol-io/datamol
skills/structural-biology-drug-discovery/ddinter-database/SKILL.md
- Network accessline 11
DDInter is an open, curated database of drug-drug interactions (DDIs) covering 2,400+ drugs and 1.7M+ pairwise interactions with structured severity levels (major, moderate, minor), mechanistic annotations, and clinical management recommend - Network accessline 11
DDInter is an open, curated database of drug-drug interactions (DDIs) covering 2,400+ drugs and 1.7M+ pairwise interactions with structured severity levels (major, moderate, minor), mechanistic annotations, and clinical management recommend - Network accessline 41
BASE = "https://ddinter.scbdd.com/api" - Network accessline 41
BASE = "https://ddinter.scbdd.com/api" - Network accessline 43
# Search for a drug by name r = requests.get(f"{BASE}/drug/", params={"drug_name": "warfarin", "format": "json"}, timeout=15) - Network accessline 43
# Search for a drug by name r = requests.get(f"{BASE}/drug/", params={"drug_name": "warfarin", "format": "json"}, timeout=15) - Network accessline 44
r = requests.get(f"{BASE}/drug/", params={"drug_name": "warfarin", "format": "json"}, timeout=15) - Network accessline 44
r = requests.get(f"{BASE}/drug/", params={"drug_name": "warfarin", "format": "json"}, timeout=15) - Network accessline 64
BASE = "https://ddinter.scbdd.com/api" - Network accessline 64
BASE = "https://ddinter.scbdd.com/api" - Network accessline 66
def search_drug(drug_name): """Search DDInter for a drug by name. Returns list of matching drug records.""" r = requests.get(f"{BASE}/drug/", - Network accessline 66
def search_drug(drug_name): """Search DDInter for a drug by name. Returns list of matching drug records.""" r = requests.get(f"{BASE}/drug/", - Network accessline 67
"""Search DDInter for a drug by name. Returns list of matching drug records.""" r = requests.get(f"{BASE}/drug/", params={"drug_name": drug_name, "format": "json"}, - Network accessline 67
"""Search DDInter for a drug by name. Returns list of matching drug records.""" r = requests.get(f"{BASE}/drug/", params={"drug_name": drug_name, "format": "json"}, - Network accessline 68
r = requests.get(f"{BASE}/drug/", - Network accessline 68
r = requests.get(f"{BASE}/drug/", - Network accessline 103
BASE = "https://ddinter.scbdd.com/api" - Network accessline 103
BASE = "https://ddinter.scbdd.com/api" - Network accessline 111
while url: r = requests.get(url, params=params, timeout=20) - Network accessline 111
while url: r = requests.get(url, params=params, timeout=20) - Network accessline 112
r = requests.get(url, params=params, timeout=20) - Network accessline 112
r = requests.get(url, params=params, timeout=20) - Network accessline 144
BASE = "https://ddinter.scbdd.com/api" - Network accessline 144
BASE = "https://ddinter.scbdd.com/api" - Network accessline 146
def get_interaction_detail(interaction_id): """Get full details for a specific interaction by its DDInter interaction ID.""" r = requests.get(f"{BASE}/interaction/{interaction_id}/", - Network accessline 146
def get_interaction_detail(interaction_id): """Get full details for a specific interaction by its DDInter interaction ID.""" r = requests.get(f"{BASE}/interaction/{interaction_id}/", - Network accessline 147
"""Get full details for a specific interaction by its DDInter interaction ID.""" r = requests.get(f"{BASE}/interaction/{interaction_id}/", params={"format": "json"}, - Network accessline 147
"""Get full details for a specific interaction by its DDInter interaction ID.""" r = requests.get(f"{BASE}/interaction/{interaction_id}/", params={"format": "json"}, - Network accessline 148
r = requests.get(f"{BASE}/interaction/{interaction_id}/", - Network accessline 148
r = requests.get(f"{BASE}/interaction/{interaction_id}/", - Network accessline 177
BASE = "https://ddinter.scbdd.com/api" - Network accessline 177
BASE = "https://ddinter.scbdd.com/api" - Network accessline 179
def check_drug_pair(drug_id_1, drug_id_2): """Check interactions between two specific drugs by their DDInter IDs.""" r = requests.get(f"{BASE}/between/", - Network accessline 179
def check_drug_pair(drug_id_1, drug_id_2): """Check interactions between two specific drugs by their DDInter IDs.""" r = requests.get(f"{BASE}/between/", - Network accessline 180
"""Check interactions between two specific drugs by their DDInter IDs.""" r = requests.get(f"{BASE}/between/", params={"drug1": drug_id_1, "drug2": drug_id_2, "format": "json"}, - Network accessline 180
"""Check interactions between two specific drugs by their DDInter IDs.""" r = requests.get(f"{BASE}/between/", params={"drug1": drug_id_1, "drug2": drug_id_2, "format": "json"}, - Network accessline 181
r = requests.get(f"{BASE}/between/", - Network accessline 181
r = requests.get(f"{BASE}/between/", - Network accessline 187
def find_drug_id(drug_name): """Helper: resolve drug name to DDInter ID.""" r = requests.get(f"{BASE}/drug/", - Network accessline 187
def find_drug_id(drug_name): """Helper: resolve drug name to DDInter ID.""" r = requests.get(f"{BASE}/drug/", - Network accessline 188
"""Helper: resolve drug name to DDInter ID.""" r = requests.get(f"{BASE}/drug/", params={"drug_name": drug_name, "format": "json"}, - Network accessline 188
"""Helper: resolve drug name to DDInter ID.""" r = requests.get(f"{BASE}/drug/", params={"drug_name": drug_name, "format": "json"}, - Network accessline 189
r = requests.get(f"{BASE}/drug/", - Network accessline 189
r = requests.get(f"{BASE}/drug/", - Network accessline 220
BASE = "https://ddinter.scbdd.com/api" - Network accessline 220
BASE = "https://ddinter.scbdd.com/api" - Network accessline 223
"""Get only major-severity interactions for a drug.""" all_interactions = [] r = requests.get(f"{BASE}/interaction/", - Network accessline 223
"""Get only major-severity interactions for a drug.""" all_interactions = [] r = requests.get(f"{BASE}/interaction/", - Network accessline 224
all_interactions = [] r = requests.get(f"{BASE}/interaction/", params={"drug_id": drug_id, "format": "json", "page_size": 200}, - Network accessline 224
all_interactions = [] r = requests.get(f"{BASE}/interaction/", params={"drug_id": drug_id, "format": "json", "page_size": 200}, - Network accessline 225
r = requests.get(f"{BASE}/interaction/", - Network accessline 225
r = requests.get(f"{BASE}/interaction/", - Network accessline 262
BASE = "https://ddinter.scbdd.com/api" - Network accessline 262
BASE = "https://ddinter.scbdd.com/api" - Network accessline 264
def find_drug_id(drug_name): r = requests.get(f"{BASE}/drug/", - Network accessline 264
def find_drug_id(drug_name): r = requests.get(f"{BASE}/drug/", - Network accessline 265
r = requests.get(f"{BASE}/drug/", - Network accessline 265
r = requests.get(f"{BASE}/drug/", - Network accessline 272
def check_pair(id1, id2): r = requests.get(f"{BASE}/between/", - Network accessline 272
def check_pair(id1, id2): r = requests.get(f"{BASE}/between/", - Network accessline 273
r = requests.get(f"{BASE}/between/", - Network accessline 273
r = requests.get(f"{BASE}/between/", - Network accessline 326
BASE = "https://ddinter.scbdd.com/api" - Network accessline 326
BASE = "https://ddinter.scbdd.com/api" - Network accessline 328
def find_drug_id(drug_name): r = requests.get(f"{BASE}/drug/", - Network accessline 328
def find_drug_id(drug_name): r = requests.get(f"{BASE}/drug/", - Network accessline 329
r = requests.get(f"{BASE}/drug/", - Network accessline 329
r = requests.get(f"{BASE}/drug/", - Network accessline 336
def check_pair(id1, id2): r = requests.get(f"{BASE}/between/", - Network accessline 336
def check_pair(id1, id2): r = requests.get(f"{BASE}/between/", - Network accessline 337
r = requests.get(f"{BASE}/between/", - Network accessline 337
r = requests.get(f"{BASE}/between/", - Network accessline 426
BASE = "https://ddinter.scbdd.com/api" - Network accessline 426
BASE = "https://ddinter.scbdd.com/api" - Network accessline 428
def find_drug_id(name): r = requests.get(f"{BASE}/drug/", - Network accessline 428
def find_drug_id(name): r = requests.get(f"{BASE}/drug/", - Network accessline 429
r = requests.get(f"{BASE}/drug/", - Network accessline 429
r = requests.get(f"{BASE}/drug/", - Network accessline 439
params = {"drug_id": drug_id, "format": "json", "page_size": page_size} while url: r = requests.get(url, params=params, timeout=30) - Network accessline 439
params = {"drug_id": drug_id, "format": "json", "page_size": page_size} while url: r = requests.get(url, params=params, timeout=30) - Network accessline 440
while url: r = requests.get(url, params=params, timeout=30) r.raise_for_status() - Network accessline 440
while url: r = requests.get(url, params=params, timeout=30) r.raise_for_status() - Network accessline 441
r = requests.get(url, params=params, timeout=30) - Network accessline 441
r = requests.get(url, params=params, timeout=30) - Network accessline 488
BASE = "https://ddinter.scbdd.com/api" - Network accessline 488
BASE = "https://ddinter.scbdd.com/api" - Network accessline 492
def find_drug_id(name): r = requests.get(f"{BASE}/drug/", - Network accessline 492
def find_drug_id(name): r = requests.get(f"{BASE}/drug/", - Network accessline 493
r = requests.get(f"{BASE}/drug/", - Network accessline 493
r = requests.get(f"{BASE}/drug/", - Network accessline 500
def check_pair(id1, id2): r = requests.get(f"{BASE}/between/", - Network accessline 500
def check_pair(id1, id2): r = requests.get(f"{BASE}/between/", - Network accessline 501
r = requests.get(f"{BASE}/between/", - Network accessline 501
r = requests.get(f"{BASE}/between/", - Network accessline 568
while url: data = requests.get(url, params=params).json() - Network accessline 568
while url: data = requests.get(url, params=params).json() - Network accessline 569
data = requests.get(url, params=params).json() - Network accessline 569
data = requests.get(url, params=params).json() - Network accessline 596
BASE = "https://ddinter.scbdd.com/api" - Network accessline 596
BASE = "https://ddinter.scbdd.com/api" - Network accessline 599
"""Check interaction between two drugs by name. Returns severity or 'No interaction found'.""" def get_id(name): r = requests.get(f"{BASE}/drug/", - Network accessline 599
"""Check interaction between two drugs by name. Returns severity or 'No interaction found'.""" def get_id(name): r = requests.get(f"{BASE}/drug/", - Network accessline 600
def get_id(name): r = requests.get(f"{BASE}/drug/", params={"drug_name": name, "format": "json"}, - Network accessline 600
def get_id(name): r = requests.get(f"{BASE}/drug/", params={"drug_name": name, "format": "json"}, - Network accessline 601
r = requests.get(f"{BASE}/drug/", - Network accessline 601
r = requests.get(f"{BASE}/drug/", - Network accessline 614
r = requests.get(f"{BASE}/between/", - Network accessline 614
r = requests.get(f"{BASE}/between/", - Network accessline 640
BASE = "https://ddinter.scbdd.com/api" - Network accessline 640
BASE = "https://ddinter.scbdd.com/api" - Network accessline 642
def get_severity_summary(drug_name): """Return severity counts (major/moderate/minor) for a drug.""" r = requests.get(f"{BASE}/drug/", - Network accessline 642
def get_severity_summary(drug_name): """Return severity counts (major/moderate/minor) for a drug.""" r = requests.get(f"{BASE}/drug/", - Network accessline 643
"""Return severity counts (major/moderate/minor) for a drug.""" r = requests.get(f"{BASE}/drug/", params={"drug_name": drug_name, "format": "json"}, - Network accessline 643
"""Return severity counts (major/moderate/minor) for a drug.""" r = requests.get(f"{BASE}/drug/", params={"drug_name": drug_name, "format": "json"}, - Network accessline 644
r = requests.get(f"{BASE}/drug/", - Network accessline 644
r = requests.get(f"{BASE}/drug/", - Network accessline 656
params = {"drug_id": drug_id, "format": "json", "page_size": 200} while url: r2 = requests.get(url, params=params, timeout=20) - Network accessline 656
params = {"drug_id": drug_id, "format": "json", "page_size": 200} while url: r2 = requests.get(url, params=params, timeout=20) - Network accessline 657
while url: r2 = requests.get(url, params=params, timeout=20) r2.raise_for_status() - Network accessline 657
while url: r2 = requests.get(url, params=params, timeout=20) r2.raise_for_status() - Network accessline 658
r2 = requests.get(url, params=params, timeout=20) - Network accessline 658
r2 = requests.get(url, params=params, timeout=20) - Network accessline 699
BASE = "https://ddinter.scbdd.com/api" - Network accessline 699
BASE = "https://ddinter.scbdd.com/api" - Network accessline 704
for drug_name in drug_list: r = requests.get(f"{BASE}/drug/", - Network accessline 704
for drug_name in drug_list: r = requests.get(f"{BASE}/drug/", - Network accessline 705
r = requests.get(f"{BASE}/drug/", - Network accessline 705
r = requests.get(f"{BASE}/drug/", - Network accessline 713
r2 = requests.get(f"{BASE}/interaction/", - Network accessline 713
r2 = requests.get(f"{BASE}/interaction/", - Network accessline 755
- [DDInter Database](https://ddinter.scbdd.com/) — official web interface and documentation - Network accessline 755
- [DDInter Database](https://ddinter.scbdd.com/) — official web interface and documentation - Network accessline 756
- [DDInter REST API](https://ddinter.scbdd.com/api/) — browsable API with endpoint documentation - Network accessline 756
- [DDInter REST API](https://ddinter.scbdd.com/api/) — browsable API with endpoint documentation - Network accessline 757
- [Xiong et al., Nucleic Acids Research 2022](https://doi.org/10.1093/nar/gkab880) — DDInter database paper describing curation methodology, data sources, and coverage - Network accessline 757
- [Xiong et al., Nucleic Acids Research 2022](https://doi.org/10.1093/nar/gkab880) — DDInter database paper describing curation methodology, data sources, and coverage - Network accessline 758
- [WHO INN Drug Names](https://www.who.int/teams/health-product-and-policy-standards/inn) — International Nonproprietary Names for resolving drug name variants - Network accessline 758
- [WHO INN Drug Names](https://www.who.int/teams/health-product-and-policy-standards/inn) — International Nonproprietary Names for resolving drug name variants
skills/structural-biology-drug-discovery/deepchem/SKILL.md
- Network accessline 555
- [DeepChem documentation](https://deepchem.readthedocs.io/) -- official API docs and tutorials - Network accessline 555
- [DeepChem documentation](https://deepchem.readthedocs.io/) -- official API docs and tutorials - Network accessline 556
- [DeepChem GitHub](https://github.com/deepchem/deepchem) -- source code, examples, issues - Network accessline 556
- [DeepChem GitHub](https://github.com/deepchem/deepchem) -- source code, examples, issues - Network accessline 557
- [MoleculeNet benchmark paper](https://doi.org/10.1039/C7SC02664A) -- Wu et al. 2018, benchmark dataset descriptions - Network accessline 557
- [MoleculeNet benchmark paper](https://doi.org/10.1039/C7SC02664A) -- Wu et al. 2018, benchmark dataset descriptions - Network accessline 558
- [DeepChem tutorials](https://github.com/deepchem/deepchem/tree/master/examples/tutorials) -- Jupyter notebook tutorials - Network accessline 558
- [DeepChem tutorials](https://github.com/deepchem/deepchem/tree/master/examples/tutorials) -- Jupyter notebook tutorials
skills/structural-biology-drug-discovery/diffdock/SKILL.md
- Network accessline 32
# Recommended: clone and install from source git clone https://github.com/gcorso/DiffDock.git - Network accessline 32
# Recommended: clone and install from source git clone https://github.com/gcorso/DiffDock.git - Network accessline 33
git clone https://github.com/gcorso/DiffDock.git - Network accessline 33
git clone https://github.com/gcorso/DiffDock.git - Network accessline 35
conda create -n diffdock python=3.9 conda activate diffdock pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu118 - Network accessline 35
conda create -n diffdock python=3.9 conda activate diffdock pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu118 - Network accessline 36
conda activate diffdock pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu118 pip install -r requirements.txt - Network accessline 36
conda activate diffdock pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu118 pip install -r requirements.txt - Network accessline 37
pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu118 - Network accessline 37
pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu118 - Network accessline 314
- [DiffDock GitHub (gcorso/DiffDock)](https://github.com/gcorso/DiffDock) — source code, installation, and pretrained weights - Network accessline 314
- [DiffDock GitHub (gcorso/DiffDock)](https://github.com/gcorso/DiffDock) — source code, installation, and pretrained weights - Network accessline 315
- [Corso et al. (2023), ICLR — DiffDock paper](https://arxiv.org/abs/2210.01776) — original diffusion docking publication - Network accessline 315
- [Corso et al. (2023), ICLR — DiffDock paper](https://arxiv.org/abs/2210.01776) — original diffusion docking publication - Network accessline 316
- [DiffDock web server](https://huggingface.co/spaces/reginabarzilaygroup/DiffDock-Web) — browser-based inference without local installation - Network accessline 316
- [DiffDock web server](https://huggingface.co/spaces/reginabarzilaygroup/DiffDock-Web) — browser-based inference without local installation - Network accessline 317
- [PatentsView API Documentation](https://patentsview.org/apis/api-endpoints/patents) — for IP analysis context - Network accessline 317
- [PatentsView API Documentation](https://patentsview.org/apis/api-endpoints/patents) — for IP analysis context
skills/structural-biology-drug-discovery/drugbank-database-access/SKILL.md
- Network accessline 26
- **DrugBank account**: Register at https://go.drugbank.com/ (free academic license) - **XML download**: Download `drugbank_all_full_database.xml.zip` after registration (~1.5 GB uncompressed) - Network accessline 26
- **DrugBank account**: Register at https://go.drugbank.com/ (free academic license) - **XML download**: Download `drugbank_all_full_database.xml.zip` after registration (~1.5 GB uncompressed) - Network accessline 42
NS = {'db': 'http://www.drugbank.ca'} # Required for ALL XPath queries - Network accessline 42
NS = {'db': 'http://www.drugbank.ca'} # Required for ALL XPath queries - Network accessline 79
NS = {'db': 'http://www.drugbank.ca'} - Network accessline 79
NS = {'db': 'http://www.drugbank.ca'} - Network accessline 88
drug_names = {} for event, elem in ET.iterparse('drugbank_all_full_database.xml', events=('end',)): if elem.tag == '{http://www.drugbank.ca}drug': - Network accessline 88
drug_names = {} for event, elem in ET.iterparse('drugbank_all_full_database.xml', events=('end',)): if elem.tag == '{http://www.drugbank.ca}drug': - Network accessline 89
for event, elem in ET.iterparse('drugbank_all_full_database.xml', events=('end',)): if elem.tag == '{http://www.drugbank.ca}drug': db_id = elem.find('{http://www.drugbank.ca}drugbank-id[@primary="true"]') - Network accessline 89
for event, elem in ET.iterparse('drugbank_all_full_database.xml', events=('end',)): if elem.tag == '{http://www.drugbank.ca}drug': db_id = elem.find('{http://www.drugbank.ca}drugbank-id[@primary="true"]') - Network accessline 90
if elem.tag == '{http://www.drugbank.ca}drug': - Network accessline 90
if elem.tag == '{http://www.drugbank.ca}drug': - Network accessline 91
db_id = elem.find('{http://www.drugbank.ca}drugbank-id[@primary="true"]') - Network accessline 91
db_id = elem.find('{http://www.drugbank.ca}drugbank-id[@primary="true"]') - Network accessline 92
name = elem.find('{http://www.drugbank.ca}name') - Network accessline 92
name = elem.find('{http://www.drugbank.ca}name') - Network accessline 320
NS = {'db': 'http://www.drugbank.ca'} - Network accessline 320
NS = {'db': 'http://www.drugbank.ca'} - Network accessline 321
name = drug.find('db:name', NS).text # CORRECT name = drug.find('name') # WRONG — returns None! # For iterparse, use full URI: '{http://www.drugbank.ca}drug' - Network accessline 321
name = drug.find('db:name', NS).text # CORRECT name = drug.find('name') # WRONG — returns None! # For iterparse, use full URI: '{http://www.drugbank.ca}drug' - Network accessline 323
# For iterparse, use full URI: '{http://www.drugbank.ca}drug' - Network accessline 323
# For iterparse, use full URI: '{http://www.drugbank.ca}drug' - Network accessline 364
NS = {'db': 'http://www.drugbank.ca'} - Network accessline 364
NS = {'db': 'http://www.drugbank.ca'} - Network accessline 398
NS = {'db': 'http://www.drugbank.ca'} - Network accessline 398
NS = {'db': 'http://www.drugbank.ca'} - Network accessline 440
| Parameter | Function/Endpoint | Default | Description | |-----------|-------------------|---------|-------------| | `NS` (namespace dict) | All XPath queries | `{'db': 'http://www.drugbank.ca'}` | Required for all `find`/`findall` calls | - Network accessline 440
| Parameter | Function/Endpoint | Default | Description | |-----------|-------------------|---------|-------------| | `NS` (namespace dict) | All XPath queries | `{'db': 'http://www.drugbank.ca'}` | Required for all `find`/`findall` calls | - Network accessline 441
|-----------|-------------------|---------|-------------| | `NS` (namespace dict) | All XPath queries | `{'db': 'http://www.drugbank.ca'}` | Required for all `find`/`findall` calls | | `@primary="true"` | `db:drugbank-id` | — | Selects the - Network accessline 441
|-----------|-------------------|---------|-------------| | `NS` (namespace dict) | All XPath queries | `{'db': 'http://www.drugbank.ca'}` | Required for all `find`/`findall` calls | | `@primary="true"` | `db:drugbank-id` | — | Selects the - Network accessline 442
| `NS` (namespace dict) | All XPath queries | `{'db': 'http://www.drugbank.ca'}` | Required for all `find`/`findall` calls | - Network accessline 442
| `NS` (namespace dict) | All XPath queries | `{'db': 'http://www.drugbank.ca'}` | Required for all `find`/`findall` calls | - Network accessline 511
| Problem | Cause | Solution | |---------|-------|----------| | `find()` returns `None` for known elements | Missing XML namespace | Always pass `NS = {'db': 'http://www.drugbank.ca'}` to `find()`/`findall()` | - Network accessline 511
| Problem | Cause | Solution | |---------|-------|----------| | `find()` returns `None` for known elements | Missing XML namespace | Always pass `NS = {'db': 'http://www.drugbank.ca'}` to `find()`/`findall()` | - Network accessline 512
|---------|-------|----------| | `find()` returns `None` for known elements | Missing XML namespace | Always pass `NS = {'db': 'http://www.drugbank.ca'}` to `find()`/`findall()` | | `MemoryError` parsing full XML | ~2-3 GB in memory | Use ` - Network accessline 512
|---------|-------|----------| | `find()` returns `None` for known elements | Missing XML namespace | Always pass `NS = {'db': 'http://www.drugbank.ca'}` to `find()`/`findall()` | | `MemoryError` parsing full XML | ~2-3 GB in memory | Use ` - Network accessline 513
| `find()` returns `None` for known elements | Missing XML namespace | Always pass `NS = {'db': 'http://www.drugbank.ca'}` to `find()`/`findall()` | - Network accessline 513
| `find()` returns `None` for known elements | Missing XML namespace | Always pass `NS = {'db': 'http://www.drugbank.ca'}` to `find()`/`findall()` | - Network accessline 518
| `AttributeError: 'NoneType'` | Optional XML element absent | Guard with `el is not None` before `.text` | | Asymmetric interaction counts | Interactions not symmetric in XML | Check both directions or build symmetric index | | `drugbank-d - Network accessline 518
| `AttributeError: 'NoneType'` | Optional XML element absent | Guard with `el is not None` before `.text` | | Asymmetric interaction counts | Interactions not symmetric in XML | Check both directions or build symmetric index | | `drugbank-d - Network accessline 519
| Asymmetric interaction counts | Interactions not symmetric in XML | Check both directions or build symmetric index | | `drugbank-downloader` auth failure | Invalid credentials | Verify account at https://go.drugbank.com/ | | REST API `429 - Network accessline 519
| Asymmetric interaction counts | Interactions not symmetric in XML | Check both directions or build symmetric index | | `drugbank-downloader` auth failure | Invalid credentials | Verify account at https://go.drugbank.com/ | | REST API `429 - Network accessline 520
| `drugbank-downloader` auth failure | Invalid credentials | Verify account at https://go.drugbank.com/ | - Network accessline 520
| `drugbank-downloader` auth failure | Invalid credentials | Verify account at https://go.drugbank.com/ | - Network accessline 547
- DrugBank website: https://go.drugbank.com/ - Network accessline 547
- DrugBank website: https://go.drugbank.com/ - Network accessline 548
- DrugBank XML schema: https://docs.drugbank.com/xml/ - Network accessline 548
- DrugBank XML schema: https://docs.drugbank.com/xml/ - Network accessline 549
- drugbank-downloader: https://pypi.org/project/drugbank-downloader/ - Network accessline 549
- drugbank-downloader: https://pypi.org/project/drugbank-downloader/ - Network accessline 550
- Wishart DS et al. (2018). DrugBank 5.0. *Nucleic Acids Res.* 46(D1):D1074-D1082. https://doi.org/10.1093/nar/gkx1037 - Network accessline 550
- Wishart DS et al. (2018). DrugBank 5.0. *Nucleic Acids Res.* 46(D1):D1074-D1082. https://doi.org/10.1093/nar/gkx1037
skills/structural-biology-drug-discovery/drugbank-database-access/references/chemical_analysis.md
skills/structural-biology-drug-discovery/drugbank-database-access/references/interactions_targets.md
- Network accessline 8
import xml.etree.ElementTree as ET from collections import defaultdict, Counter NS = {'db': 'http://www.drugbank.ca'} - Network accessline 8
import xml.etree.ElementTree as ET from collections import defaultdict, Counter NS = {'db': 'http://www.drugbank.ca'} - Network accessline 9
from collections import defaultdict, Counter NS = {'db': 'http://www.drugbank.ca'} tree = ET.parse('drugbank_all_full_database.xml') - Network accessline 9
from collections import defaultdict, Counter NS = {'db': 'http://www.drugbank.ca'} tree = ET.parse('drugbank_all_full_database.xml') - Network accessline 10
NS = {'db': 'http://www.drugbank.ca'} - Network accessline 10
NS = {'db': 'http://www.drugbank.ca'}
skills/structural-biology-drug-discovery/emdb-database/SKILL.md
- Network accessline 13
- **EMDB Entry API** (`https://www.ebi.ac.uk/emdb/api/entry/{EMD-XXXXX}`) — the canonical per-entry JSON containing metadata, map header, fitted PDB list, and citation. Sub-endpoints like `/map`, `/fitted`, `/publications` do **not** exist - Network accessline 13
- **EMDB Entry API** (`https://www.ebi.ac.uk/emdb/api/entry/{EMD-XXXXX}`) — the canonical per-entry JSON containing metadata, map header, fitted PDB list, and citation. Sub-endpoints like `/map`, `/fitted`, `/publications` do **not** exist - Network accessline 14
- **EBI Search WS** (`https://www.ebi.ac.uk/ebisearch/ws/rest/emdb`) — the real keyword search backend (the bare `https://www.ebi.ac.uk/emdb/api/search/` endpoint ignores the query and just returns recent entries). - Network accessline 14
- **EBI Search WS** (`https://www.ebi.ac.uk/ebisearch/ws/rest/emdb`) — the real keyword search backend (the bare `https://www.ebi.ac.uk/emdb/api/search/` endpoint ignores the query and just returns recent entries). - Network accessline 45
EMDB_API = "https://www.ebi.ac.uk/emdb/api" - Network accessline 45
EMDB_API = "https://www.ebi.ac.uk/emdb/api" - Network accessline 46
EBI_SEARCH = "https://www.ebi.ac.uk/ebisearch/ws/rest/emdb" - Network accessline 46
EBI_SEARCH = "https://www.ebi.ac.uk/ebisearch/ws/rest/emdb" - Network accessline 48
# Keyword search via EBI Search WS (NOT /emdb/api/search/, which ignores q=) r = requests.get(EBI_SEARCH, - Network accessline 48
# Keyword search via EBI Search WS (NOT /emdb/api/search/, which ignores q=) r = requests.get(EBI_SEARCH, - Network accessline 49
r = requests.get(EBI_SEARCH, - Network accessline 49
r = requests.get(EBI_SEARCH, - Network accessline 72
EBI_SEARCH = "https://www.ebi.ac.uk/ebisearch/ws/rest/emdb" - Network accessline 72
EBI_SEARCH = "https://www.ebi.ac.uk/ebisearch/ws/rest/emdb" - Network accessline 74
def emdb_search(query, size=20, start=0, fields="id,name,resolution,em_method,organism"): r = requests.get(EBI_SEARCH, - Network accessline 74
def emdb_search(query, size=20, start=0, fields="id,name,resolution,em_method,organism"): r = requests.get(EBI_SEARCH, - Network accessline 75
fields="id,name,resolution,em_method,organism"): r = requests.get(EBI_SEARCH, params={"query": query, "size": size, "start": start, - Network accessline 75
fields="id,name,resolution,em_method,organism"): r = requests.get(EBI_SEARCH, params={"query": query, "size": size, "start": start, - Network accessline 76
r = requests.get(EBI_SEARCH, - Network accessline 76
r = requests.get(EBI_SEARCH, - Network accessline 124
EMDB_API = "https://www.ebi.ac.uk/emdb/api" - Network accessline 124
EMDB_API = "https://www.ebi.ac.uk/emdb/api" - Network accessline 126
def emdb_entry(emdb_id): r = requests.get(f"{EMDB_API}/entry/{emdb_id}", timeout=30) - Network accessline 126
def emdb_entry(emdb_id): r = requests.get(f"{EMDB_API}/entry/{emdb_id}", timeout=30) - Network accessline 127
r = requests.get(f"{EMDB_API}/entry/{emdb_id}", timeout=30) - Network accessline 127
r = requests.get(f"{EMDB_API}/entry/{emdb_id}", timeout=30) - Network accessline 150
EMDB_API = "https://www.ebi.ac.uk/emdb/api" e = requests.get(f"{EMDB_API}/entry/EMD-30210", timeout=30).json() - Network accessline 150
EMDB_API = "https://www.ebi.ac.uk/emdb/api" e = requests.get(f"{EMDB_API}/entry/EMD-30210", timeout=30).json() - Network accessline 151
e = requests.get(f"{EMDB_API}/entry/EMD-30210", timeout=30).json() - Network accessline 151
e = requests.get(f"{EMDB_API}/entry/EMD-30210", timeout=30).json() - Network accessline 160
# Conventional FTP/HTTPS download URL pattern (mirror at EBI): EMDB_FTP = "https://ftp.ebi.ac.uk/pub/databases/emdb/structures" - Network accessline 160
# Conventional FTP/HTTPS download URL pattern (mirror at EBI): EMDB_FTP = "https://ftp.ebi.ac.uk/pub/databases/emdb/structures" - Network accessline 161
EMDB_FTP = "https://ftp.ebi.ac.uk/pub/databases/emdb/structures" emdb_num = "EMD-30210" print(f"Download URL : {EMDB_FTP}/{emdb_num}/map/{m['file']}") - Network accessline 161
EMDB_FTP = "https://ftp.ebi.ac.uk/pub/databases/emdb/structures" emdb_num = "EMD-30210" print(f"Download URL : {EMDB_FTP}/{emdb_num}/map/{m['file']}") - Network accessline 173
EMDB_API = "https://www.ebi.ac.uk/emdb/api" e = requests.get(f"{EMDB_API}/entry/EMD-30210", timeout=30).json() - Network accessline 173
EMDB_API = "https://www.ebi.ac.uk/emdb/api" e = requests.get(f"{EMDB_API}/entry/EMD-30210", timeout=30).json() - Network accessline 174
e = requests.get(f"{EMDB_API}/entry/EMD-30210", timeout=30).json() - Network accessline 174
e = requests.get(f"{EMDB_API}/entry/EMD-30210", timeout=30).json() - Network accessline 192
EMDB_API = "https://www.ebi.ac.uk/emdb/api" e = requests.get(f"{EMDB_API}/entry/EMD-30210", timeout=30).json() - Network accessline 192
EMDB_API = "https://www.ebi.ac.uk/emdb/api" e = requests.get(f"{EMDB_API}/entry/EMD-30210", timeout=30).json() - Network accessline 193
e = requests.get(f"{EMDB_API}/entry/EMD-30210", timeout=30).json() - Network accessline 193
e = requests.get(f"{EMDB_API}/entry/EMD-30210", timeout=30).json() - Network accessline 222
EMDB_API = "https://www.ebi.ac.uk/emdb/api" e = requests.get(f"{EMDB_API}/entry/EMD-30210", timeout=30).json() - Network accessline 222
EMDB_API = "https://www.ebi.ac.uk/emdb/api" e = requests.get(f"{EMDB_API}/entry/EMD-30210", timeout=30).json() - Network accessline 223
e = requests.get(f"{EMDB_API}/entry/EMD-30210", timeout=30).json() - Network accessline 223
e = requests.get(f"{EMDB_API}/entry/EMD-30210", timeout=30).json() - Network accessline 270
EBI_SEARCH = "https://www.ebi.ac.uk/ebisearch/ws/rest/emdb" - Network accessline 270
EBI_SEARCH = "https://www.ebi.ac.uk/ebisearch/ws/rest/emdb" - Network accessline 271
EMDB_API = "https://www.ebi.ac.uk/emdb/api" - Network accessline 271
EMDB_API = "https://www.ebi.ac.uk/emdb/api" - Network accessline 274
hits = [] for start in (0, 50): r = requests.get(EBI_SEARCH, - Network accessline 274
hits = [] for start in (0, 50): r = requests.get(EBI_SEARCH, - Network accessline 275
for start in (0, 50): r = requests.get(EBI_SEARCH, params={"query": "sars-cov-2 spike", "size": 50, "start": start, - Network accessline 275
for start in (0, 50): r = requests.get(EBI_SEARCH, params={"query": "sars-cov-2 spike", "size": 50, "start": start, - Network accessline 276
r = requests.get(EBI_SEARCH, - Network accessline 276
r = requests.get(EBI_SEARCH, - Network accessline 300
# Step 3: pull fitted PDB ids for the top 5 for emdb_id in df["emdb_id"].head(5): e = requests.get(f"{EMDB_API}/entry/{emdb_id}", timeout=30).json() - Network accessline 300
# Step 3: pull fitted PDB ids for the top 5 for emdb_id in df["emdb_id"].head(5): e = requests.get(f"{EMDB_API}/entry/{emdb_id}", timeout=30).json() - Network accessline 301
for emdb_id in df["emdb_id"].head(5): e = requests.get(f"{EMDB_API}/entry/{emdb_id}", timeout=30).json() pdblist = e.get("crossreferences", {}).get("pdb_list", {}) or {} - Network accessline 301
for emdb_id in df["emdb_id"].head(5): e = requests.get(f"{EMDB_API}/entry/{emdb_id}", timeout=30).json() pdblist = e.get("crossreferences", {}).get("pdb_list", {}) or {} - Network accessline 302
e = requests.get(f"{EMDB_API}/entry/{emdb_id}", timeout=30).json() - Network accessline 302
e = requests.get(f"{EMDB_API}/entry/{emdb_id}", timeout=30).json() - Network accessline 316
EBI_SEARCH = "https://www.ebi.ac.uk/ebisearch/ws/rest/emdb" - Network accessline 316
EBI_SEARCH = "https://www.ebi.ac.uk/ebisearch/ws/rest/emdb" - Network accessline 317
EMDB_API = "https://www.ebi.ac.uk/emdb/api" - Network accessline 317
EMDB_API = "https://www.ebi.ac.uk/emdb/api" - Network accessline 318
EMDB_FTP = "https://ftp.ebi.ac.uk/pub/databases/emdb/structures" - Network accessline 318
EMDB_FTP = "https://ftp.ebi.ac.uk/pub/databases/emdb/structures" - Network accessline 320
def cohort_table(query, size=10): r = requests.get(EBI_SEARCH, - Network accessline 320
def cohort_table(query, size=10): r = requests.get(EBI_SEARCH, - Network accessline 321
r = requests.get(EBI_SEARCH, - Network accessline 321
r = requests.get(EBI_SEARCH, - Network accessline 326
for h in r.json()["entries"]: emdb_id = h["id"] e = requests.get(f"{EMDB_API}/entry/{emdb_id}", timeout=30).json() - Network accessline 326
for h in r.json()["entries"]: emdb_id = h["id"] e = requests.get(f"{EMDB_API}/entry/{emdb_id}", timeout=30).json() - Network accessline 327
emdb_id = h["id"] e = requests.get(f"{EMDB_API}/entry/{emdb_id}", timeout=30).json() sd = e["structure_determination_list"]["structure_determination"][0] - Network accessline 327
emdb_id = h["id"] e = requests.get(f"{EMDB_API}/entry/{emdb_id}", timeout=30).json() sd = e["structure_determination_list"]["structure_determination"][0] - Network accessline 328
e = requests.get(f"{EMDB_API}/entry/{emdb_id}", timeout=30).json() - Network accessline 328
e = requests.get(f"{EMDB_API}/entry/{emdb_id}", timeout=30).json() - Network accessline 364
1. **Always use EBI Search WS for keyword search.** `https://www.ebi.ac.uk/emdb/api/search/` ignores `q=` and just returns the latest releases — relying on it produces silently wrong cohorts. - Network accessline 364
1. **Always use EBI Search WS for keyword search.** `https://www.ebi.ac.uk/emdb/api/search/` ignores `q=` and just returns the latest releases — relying on it produces silently wrong cohorts. - Network accessline 367
4. **EBI Search field values arrive as lists.** Even single-valued fields like `name` come as `{"name": ["..."]}` — always index `[0]` or join. 5. **Add `time.sleep(0.2)` in entry-by-entry loops.** No rate limit is published, but the API is - Network accessline 367
4. **EBI Search field values arrive as lists.** Even single-valued fields like `name` come as `{"name": ["..."]}` — always index `[0]` or join. 5. **Add `time.sleep(0.2)` in entry-by-entry loops.** No rate limit is published, but the API is - Network accessline 369
6. **Map download URLs follow `https://ftp.ebi.ac.uk/pub/databases/emdb/structures/{EMDB_ID}/map/{file}`** — derive them from `entry["map"]["file"]`, don't hardcode. - Network accessline 369
6. **Map download URLs follow `https://ftp.ebi.ac.uk/pub/databases/emdb/structures/{EMDB_ID}/map/{file}`** — derive them from `entry["map"]["file"]`, don't hardcode. - Network accessline 378
# Given an EMDB ID, get its fitted PDB; given a PDB ID, you'd query # RCSB PDB /rest/v2/entry/{pdb_id} and read `rcsb_external_references.emdb_id`. EMDB_API = "https://www.ebi.ac.uk/emdb/api" - Network accessline 378
# Given an EMDB ID, get its fitted PDB; given a PDB ID, you'd query # RCSB PDB /rest/v2/entry/{pdb_id} and read `rcsb_external_references.emdb_id`. EMDB_API = "https://www.ebi.ac.uk/emdb/api" - Network accessline 379
# RCSB PDB /rest/v2/entry/{pdb_id} and read `rcsb_external_references.emdb_id`. EMDB_API = "https://www.ebi.ac.uk/emdb/api" e = requests.get(f"{EMDB_API}/entry/EMD-30210", timeout=30).json() - Network accessline 379
# RCSB PDB /rest/v2/entry/{pdb_id} and read `rcsb_external_references.emdb_id`. EMDB_API = "https://www.ebi.ac.uk/emdb/api" e = requests.get(f"{EMDB_API}/entry/EMD-30210", timeout=30).json() - Network accessline 380
EMDB_API = "https://www.ebi.ac.uk/emdb/api" e = requests.get(f"{EMDB_API}/entry/EMD-30210", timeout=30).json() pdblist = e.get("crossreferences", {}).get("pdb_list") or {} - Network accessline 380
EMDB_API = "https://www.ebi.ac.uk/emdb/api" e = requests.get(f"{EMDB_API}/entry/EMD-30210", timeout=30).json() pdblist = e.get("crossreferences", {}).get("pdb_list") or {} - Network accessline 381
e = requests.get(f"{EMDB_API}/entry/EMD-30210", timeout=30).json() - Network accessline 381
e = requests.get(f"{EMDB_API}/entry/EMD-30210", timeout=30).json() - Network accessline 392
EBI_SEARCH = "https://www.ebi.ac.uk/ebisearch/ws/rest/emdb" - Network accessline 392
EBI_SEARCH = "https://www.ebi.ac.uk/ebisearch/ws/rest/emdb" - Network accessline 394
def best_by_organism(organism, size=50): r = requests.get(EBI_SEARCH, - Network accessline 394
def best_by_organism(organism, size=50): r = requests.get(EBI_SEARCH, - Network accessline 395
r = requests.get(EBI_SEARCH, - Network accessline 395
r = requests.get(EBI_SEARCH, - Network accessline 420
EMDB_API = "https://www.ebi.ac.uk/emdb/api" e = requests.get(f"{EMDB_API}/entry/EMD-30210", timeout=30).json() - Network accessline 420
EMDB_API = "https://www.ebi.ac.uk/emdb/api" e = requests.get(f"{EMDB_API}/entry/EMD-30210", timeout=30).json() - Network accessline 421
e = requests.get(f"{EMDB_API}/entry/EMD-30210", timeout=30).json() - Network accessline 421
e = requests.get(f"{EMDB_API}/entry/EMD-30210", timeout=30).json() - Network accessline 443
| Resolution comes back as a string | EMDB stores numeric fields as strings | `float(entry[...]['resolution']['valueOf_'])` — wrap in try/except | | EBI Search fields look like single-element lists | EBI Search returns multi-valued fields a - Network accessline 443
| Resolution comes back as a string | EMDB stores numeric fields as strings | `float(entry[...]['resolution']['valueOf_'])` — wrap in try/except | | EBI Search fields look like single-element lists | EBI Search returns multi-valued fields a - Network accessline 444
| EBI Search fields look like single-element lists | EBI Search returns multi-valued fields as lists | Read `f["name"][0]` (or fall back to `(... or [""])[0]`) | | Empty `entries` from EBI Search | Wrong field qualifier or typo | Drop the f - Network accessline 444
| EBI Search fields look like single-element lists | EBI Search returns multi-valued fields as lists | Read `f["name"][0]` (or fall back to `(... or [""])[0]`) | | Empty `entries` from EBI Search | Wrong field qualifier or typo | Drop the f - Network accessline 445
| Empty `entries` from EBI Search | Wrong field qualifier or typo | Drop the field qualifier, search plain text; check at https://www.ebi.ac.uk/ebisearch/ | - Network accessline 445
| Empty `entries` from EBI Search | Wrong field qualifier or typo | Drop the field qualifier, search plain text; check at https://www.ebi.ac.uk/ebisearch/ | - Network accessline 458
- [EMDB at EBI](https://www.ebi.ac.uk/emdb/) — Browse, download, statistics, deposition - Network accessline 458
- [EMDB at EBI](https://www.ebi.ac.uk/emdb/) — Browse, download, statistics, deposition - Network accessline 459
- [EMDB Entry API](https://www.ebi.ac.uk/emdb/api/entry/EMD-30210) — Example entry JSON - Network accessline 459
- [EMDB Entry API](https://www.ebi.ac.uk/emdb/api/entry/EMD-30210) — Example entry JSON - Network accessline 460
- [EBI Search WS — EMDB](https://www.ebi.ac.uk/ebisearch/swagger.ebi#/emdb) — Keyword search docs - Network accessline 460
- [EBI Search WS — EMDB](https://www.ebi.ac.uk/ebisearch/swagger.ebi#/emdb) — Keyword search docs - Network accessline 461
- [EMDB FTP mirror](https://ftp.ebi.ac.uk/pub/databases/emdb/structures/) — Density map and metadata downloads - Network accessline 461
- [EMDB FTP mirror](https://ftp.ebi.ac.uk/pub/databases/emdb/structures/) — Density map and metadata downloads - Network accessline 462
- Lawson CL et al. "EMDB — the Electron Microscopy Data Bank." *Nucleic Acids Research* 52(D1): D456–D465 (2024). https://doi.org/10.1093/nar/gkad1019 - Network accessline 462
- Lawson CL et al. "EMDB — the Electron Microscopy Data Bank." *Nucleic Acids Research* 52(D1): D456–D465 (2024). https://doi.org/10.1093/nar/gkad1019
skills/structural-biology-drug-discovery/fda-database/SKILL.md
- Network accessline 26
- **Data requirements**: drug names, active ingredients, MedDRA terms, NDC codes - **Environment**: internet connection; no authentication required for basic use - **Rate limits**: 1000 req/day without API key; 120,000 req/day with free API - Network accessline 26
- **Data requirements**: drug names, active ingredients, MedDRA terms, NDC codes - **Environment**: internet connection; no authentication required for basic use - **Rate limits**: 1000 req/day without API key; 120,000 req/day with free API - Network accessline 28
- **Rate limits**: 1000 req/day without API key; 120,000 req/day with free API key from https://open.fda.gov/apis/authentication/ - Network accessline 28
- **Rate limits**: 1000 req/day without API key; 120,000 req/day with free API key from https://open.fda.gov/apis/authentication/ - Network accessline 39
BASE = "https://api.fda.gov/drug" - Network accessline 39
BASE = "https://api.fda.gov/drug" - Access to secretsline 40
[Credential-related source omitted] - Access to secretsline 40
[Credential-related source omitted] - Network accessline 42
# Find adverse events for aspirin r = requests.get( - Network accessline 42
# Find adverse events for aspirin r = requests.get( - Network accessline 43
r = requests.get( - Network accessline 43
r = requests.get( - Network accessline 67
BASE = "https://api.fda.gov/drug" - Network accessline 67
BASE = "https://api.fda.gov/drug" - Network accessline 69
def faers_search(drug_name, limit=100): """Search FAERS for adverse event reports mentioning a drug.""" r = requests.get(f"{BASE}/event.json", - Network accessline 69
def faers_search(drug_name, limit=100): """Search FAERS for adverse event reports mentioning a drug.""" r = requests.get(f"{BASE}/event.json", - Network accessline 70
"""Search FAERS for adverse event reports mentioning a drug.""" r = requests.get(f"{BASE}/event.json", params={"search": f'patient.drug.medicinalproduct:"{drug_name}"', - Network accessline 70
"""Search FAERS for adverse event reports mentioning a drug.""" r = requests.get(f"{BASE}/event.json", params={"search": f'patient.drug.medicinalproduct:"{drug_name}"', - Network accessline 71
r = requests.get(f"{BASE}/event.json", - Network accessline 71
r = requests.get(f"{BASE}/event.json", - Network accessline 99
BASE = "https://api.fda.gov/drug" - Network accessline 99
BASE = "https://api.fda.gov/drug" - Network accessline 101
def top_adverse_events(drug_name, limit=20): """Get the most frequently reported adverse events for a drug.""" r = requests.get(f"{BASE}/event.json", - Network accessline 101
def top_adverse_events(drug_name, limit=20): """Get the most frequently reported adverse events for a drug.""" r = requests.get(f"{BASE}/event.json", - Network accessline 102
"""Get the most frequently reported adverse events for a drug.""" r = requests.get(f"{BASE}/event.json", params={ - Network accessline 102
"""Get the most frequently reported adverse events for a drug.""" r = requests.get(f"{BASE}/event.json", params={ - Network accessline 103
r = requests.get(f"{BASE}/event.json", - Network accessline 103
r = requests.get(f"{BASE}/event.json", - Network accessline 136
BASE = "https://api.fda.gov/drug" - Network accessline 136
BASE = "https://api.fda.gov/drug" - Network accessline 138
def get_label(drug_name): """Retrieve FDA drug label by brand or generic name.""" r = requests.get(f"{BASE}/label.json", - Network accessline 138
def get_label(drug_name): """Retrieve FDA drug label by brand or generic name.""" r = requests.get(f"{BASE}/label.json", - Network accessline 139
"""Retrieve FDA drug label by brand or generic name.""" r = requests.get(f"{BASE}/label.json", params={"search": f'openfda.brand_name:"{drug_name}"', - Network accessline 139
"""Retrieve FDA drug label by brand or generic name.""" r = requests.get(f"{BASE}/label.json", params={"search": f'openfda.brand_name:"{drug_name}"', - Network accessline 140
r = requests.get(f"{BASE}/label.json", - Network accessline 140
r = requests.get(f"{BASE}/label.json", - Network accessline 142
"limit": 1}) if r.status_code == 404: r = requests.get(f"{BASE}/label.json", - Network accessline 142
"limit": 1}) if r.status_code == 404: r = requests.get(f"{BASE}/label.json", - Network accessline 143
if r.status_code == 404: r = requests.get(f"{BASE}/label.json", params={"search": f'openfda.generic_name:"{drug_name}"', - Network accessline 143
if r.status_code == 404: r = requests.get(f"{BASE}/label.json", params={"search": f'openfda.generic_name:"{drug_name}"', - Network accessline 144
r = requests.get(f"{BASE}/label.json", - Network accessline 144
r = requests.get(f"{BASE}/label.json", - Network accessline 167
BASE = "https://api.fda.gov/drug" - Network accessline 167
BASE = "https://api.fda.gov/drug" - Network accessline 170
"""Search NDC directory for drug product information.""" # Search by product name or NDC r = requests.get(f"{BASE}/ndc.json", - Network accessline 170
"""Search NDC directory for drug product information.""" # Search by product name or NDC r = requests.get(f"{BASE}/ndc.json", - Network accessline 171
# Search by product name or NDC r = requests.get(f"{BASE}/ndc.json", params={"search": f'generic_name:"{ndc_or_name}"', - Network accessline 171
# Search by product name or NDC r = requests.get(f"{BASE}/ndc.json", params={"search": f'generic_name:"{ndc_or_name}"', - Network accessline 172
r = requests.get(f"{BASE}/ndc.json", - Network accessline 172
r = requests.get(f"{BASE}/ndc.json", - Network accessline 203
BASE = "https://api.fda.gov/drug" - Network accessline 203
BASE = "https://api.fda.gov/drug" - Network accessline 205
def drug_recalls(drug_name, limit=20): """Find FDA drug recalls for a given drug name.""" r = requests.get(f"{BASE}/enforcement.json", - Network accessline 205
def drug_recalls(drug_name, limit=20): """Find FDA drug recalls for a given drug name.""" r = requests.get(f"{BASE}/enforcement.json", - Network accessline 206
"""Find FDA drug recalls for a given drug name.""" r = requests.get(f"{BASE}/enforcement.json", params={ - Network accessline 206
"""Find FDA drug recalls for a given drug name.""" r = requests.get(f"{BASE}/enforcement.json", params={ - Network accessline 207
r = requests.get(f"{BASE}/enforcement.json", - Network accessline 207
r = requests.get(f"{BASE}/enforcement.json", - Network accessline 238
BASE = "https://api.fda.gov/drug" - Network accessline 238
BASE = "https://api.fda.gov/drug" - Network accessline 240
def products_by_ingredient(ingredient, limit=50): """Find all FDA-listed products with a given active ingredient.""" r = requests.get(f"{BASE}/ndc.json", - Network accessline 240
def products_by_ingredient(ingredient, limit=50): """Find all FDA-listed products with a given active ingredient.""" r = requests.get(f"{BASE}/ndc.json", - Network accessline 241
"""Find all FDA-listed products with a given active ingredient.""" r = requests.get(f"{BASE}/ndc.json", params={ - Network accessline 241
"""Find all FDA-listed products with a given active ingredient.""" r = requests.get(f"{BASE}/ndc.json", params={ - Network accessline 242
r = requests.get(f"{BASE}/ndc.json", - Network accessline 242
r = requests.get(f"{BASE}/ndc.json", - Network accessline 293
BASE = "https://api.fda.gov/drug" - Network accessline 293
BASE = "https://api.fda.gov/drug" - Network accessline 297
def count_reactions(drug, limit=20): r = requests.get(f"{BASE}/event.json", - Network accessline 297
def count_reactions(drug, limit=20): r = requests.get(f"{BASE}/event.json", - Network accessline 298
r = requests.get(f"{BASE}/event.json", - Network accessline 298
r = requests.get(f"{BASE}/event.json", - Network accessline 328
BASE = "https://api.fda.gov/drug" - Network accessline 328
BASE = "https://api.fda.gov/drug" - Network accessline 330
def get_label_sections(drug_name): r = requests.get(f"{BASE}/label.json", - Network accessline 330
def get_label_sections(drug_name): r = requests.get(f"{BASE}/label.json", - Network accessline 331
r = requests.get(f"{BASE}/label.json", - Network accessline 331
r = requests.get(f"{BASE}/label.json", - Access to secretsline 369
[Credential-related source omitted] - Access to secretsline 369
[Credential-related source omitted] - Network accessline 374
1. **Get a free API key**: Registration at https://open.fda.gov/apis/authentication/ is instant and raises your limit from 1,000 to 120,000 requests/day — essential for production use. - Network accessline 374
1. **Get a free API key**: Registration at https://open.fda.gov/apis/authentication/ is instant and raises your limit from 1,000 to 120,000 requests/day — essential for production use. - Network accessline 393
drug = "ibuprofen" r = requests.get("https://api.fda.gov/drug/event.json", - Network accessline 393
drug = "ibuprofen" r = requests.get("https://api.fda.gov/drug/event.json", - Network accessline 394
r = requests.get("https://api.fda.gov/drug/event.json", - Network accessline 394
r = requests.get("https://api.fda.gov/drug/event.json", - Network accessline 408
r = requests.get("https://api.fda.gov/drug/event.json", - Network accessline 408
r = requests.get("https://api.fda.gov/drug/event.json", - Network accessline 426
r = requests.get("https://api.fda.gov/drug/label.json", - Network accessline 426
r = requests.get("https://api.fda.gov/drug/label.json", - Network accessline 455
- [openFDA API documentation](https://open.fda.gov/apis/) — Full API reference, endpoints, and query syntax - Network accessline 455
- [openFDA API documentation](https://open.fda.gov/apis/) — Full API reference, endpoints, and query syntax - Network accessline 456
- [openFDA API key registration](https://open.fda.gov/apis/authentication/) — Free registration for increased rate limits - [FAERS overview](https://www.fda.gov/drugs/surveillance/questions-and-answers-fdas-adverse-event-reporting-system-fa - Network accessline 456
- [openFDA API key registration](https://open.fda.gov/apis/authentication/) — Free registration for increased rate limits - [FAERS overview](https://www.fda.gov/drugs/surveillance/questions-and-answers-fdas-adverse-event-reporting-system-fa - Network accessline 457
- [FAERS overview](https://www.fda.gov/drugs/surveillance/questions-and-answers-fdas-adverse-event-reporting-system-faers) — Understanding FAERS data and limitations - Network accessline 457
- [FAERS overview](https://www.fda.gov/drugs/surveillance/questions-and-answers-fdas-adverse-event-reporting-system-faers) — Understanding FAERS data and limitations - Network accessline 458
- [openFDA GitHub](https://github.com/FDA/openfda) — Source code and data download references - Network accessline 458
- [openFDA GitHub](https://github.com/FDA/openfda) — Source code and data download references
skills/structural-biology-drug-discovery/gtopdb-database/SKILL.md
- Network accessline 11
The IUPHAR/BPS Guide to Pharmacology (GtoPdb) catalogues drug targets, ligands, and quantitative interactions across receptor pharmacology. The web services REST API at `https://www.guidetopharmacology.org/services/` returns JSON for target - Network accessline 11
The IUPHAR/BPS Guide to Pharmacology (GtoPdb) catalogues drug targets, ligands, and quantitative interactions across receptor pharmacology. The web services REST API at `https://www.guidetopharmacology.org/services/` returns JSON for target - Network accessline 40
BASE = "https://www.guidetopharmacology.org/services" - Network accessline 40
BASE = "https://www.guidetopharmacology.org/services" - Network accessline 43
# exact match. (name= matches across all fields and silently returns the # wrong target — never use it for canonical lookups.) r = requests.get(f"{BASE}/targets", params={"geneSymbol": "OPRM1"}, timeout=30) - Network accessline 43
# exact match. (name= matches across all fields and silently returns the # wrong target — never use it for canonical lookups.) r = requests.get(f"{BASE}/targets", params={"geneSymbol": "OPRM1"}, timeout=30) - Network accessline 44
# wrong target — never use it for canonical lookups.) r = requests.get(f"{BASE}/targets", params={"geneSymbol": "OPRM1"}, timeout=30) targets = r.json() - Network accessline 44
# wrong target — never use it for canonical lookups.) r = requests.get(f"{BASE}/targets", params={"geneSymbol": "OPRM1"}, timeout=30) targets = r.json() - Network accessline 45
r = requests.get(f"{BASE}/targets", params={"geneSymbol": "OPRM1"}, timeout=30) - Network accessline 45
r = requests.get(f"{BASE}/targets", params={"geneSymbol": "OPRM1"}, timeout=30) - Network accessline 60
BASE = "https://www.guidetopharmacology.org/services" - Network accessline 60
BASE = "https://www.guidetopharmacology.org/services" - Network accessline 68
else: raise ValueError("provide geneSymbol or accession") r = requests.get(f"{BASE}/targets", params=params, timeout=30) - Network accessline 68
else: raise ValueError("provide geneSymbol or accession") r = requests.get(f"{BASE}/targets", params=params, timeout=30) - Network accessline 69
raise ValueError("provide geneSymbol or accession") r = requests.get(f"{BASE}/targets", params=params, timeout=30) r.raise_for_status() - Network accessline 69
raise ValueError("provide geneSymbol or accession") r = requests.get(f"{BASE}/targets", params=params, timeout=30) r.raise_for_status() - Network accessline 70
r = requests.get(f"{BASE}/targets", params=params, timeout=30) - Network accessline 70
r = requests.get(f"{BASE}/targets", params=params, timeout=30) - Network accessline 83
# synonym text. Read sub-resources to get those. import requests BASE = "https://www.guidetopharmacology.org/services" - Network accessline 83
# synonym text. Read sub-resources to get those. import requests BASE = "https://www.guidetopharmacology.org/services" - Network accessline 84
import requests BASE = "https://www.guidetopharmacology.org/services" print(requests.get(f"{BASE}/targets/319", timeout=30).json().keys()) - Network accessline 84
import requests BASE = "https://www.guidetopharmacology.org/services" print(requests.get(f"{BASE}/targets/319", timeout=30).json().keys()) - Network accessline 85
BASE = "https://www.guidetopharmacology.org/services" print(requests.get(f"{BASE}/targets/319", timeout=30).json().keys()) # dict_keys(['targetId','name','type','familyIds','subunitIds','complexIds']) - Network accessline 85
BASE = "https://www.guidetopharmacology.org/services" print(requests.get(f"{BASE}/targets/319", timeout=30).json().keys()) # dict_keys(['targetId','name','type','familyIds','subunitIds','complexIds']) - Network accessline 86
print(requests.get(f"{BASE}/targets/319", timeout=30).json().keys()) - Network accessline 86
print(requests.get(f"{BASE}/targets/319", timeout=30).json().keys()) - Network accessline 95
BASE = "https://www.guidetopharmacology.org/services" - Network accessline 95
BASE = "https://www.guidetopharmacology.org/services" - Network accessline 97
def target_xrefs(target_id): """Cross-database accessions: UniProt, HGNC, ChEMBL Target, Ensembl, etc.""" r = requests.get(f"{BASE}/targets/{target_id}/databaseLinks", timeout=30) - Network accessline 97
def target_xrefs(target_id): """Cross-database accessions: UniProt, HGNC, ChEMBL Target, Ensembl, etc.""" r = requests.get(f"{BASE}/targets/{target_id}/databaseLinks", timeout=30) - Network accessline 98
"""Cross-database accessions: UniProt, HGNC, ChEMBL Target, Ensembl, etc.""" r = requests.get(f"{BASE}/targets/{target_id}/databaseLinks", timeout=30) r.raise_for_status() - Network accessline 98
"""Cross-database accessions: UniProt, HGNC, ChEMBL Target, Ensembl, etc.""" r = requests.get(f"{BASE}/targets/{target_id}/databaseLinks", timeout=30) r.raise_for_status() - Network accessline 99
r = requests.get(f"{BASE}/targets/{target_id}/databaseLinks", timeout=30) - Network accessline 99
r = requests.get(f"{BASE}/targets/{target_id}/databaseLinks", timeout=30) - Network accessline 103
def target_synonyms(target_id): r = requests.get(f"{BASE}/targets/{target_id}/synonyms", timeout=30) - Network accessline 103
def target_synonyms(target_id): r = requests.get(f"{BASE}/targets/{target_id}/synonyms", timeout=30) - Network accessline 104
r = requests.get(f"{BASE}/targets/{target_id}/synonyms", timeout=30) - Network accessline 104
r = requests.get(f"{BASE}/targets/{target_id}/synonyms", timeout=30) - Network accessline 123
BASE = "https://www.guidetopharmacology.org/services" - Network accessline 123
BASE = "https://www.guidetopharmacology.org/services" - Network accessline 127
Each row carries ligandId, ligandName, type (Agonist/Antagonist/etc.), action, affinity (string), affinityParameter (pKi/pIC50/...), refs.""" r = requests.get(f"{BASE}/targets/{target_id}/interactions", timeout=60) - Network accessline 127
Each row carries ligandId, ligandName, type (Agonist/Antagonist/etc.), action, affinity (string), affinityParameter (pKi/pIC50/...), refs.""" r = requests.get(f"{BASE}/targets/{target_id}/interactions", timeout=60) - Network accessline 128
action, affinity (string), affinityParameter (pKi/pIC50/...), refs.""" r = requests.get(f"{BASE}/targets/{target_id}/interactions", timeout=60) r.raise_for_status() - Network accessline 128
action, affinity (string), affinityParameter (pKi/pIC50/...), refs.""" r = requests.get(f"{BASE}/targets/{target_id}/interactions", timeout=60) r.raise_for_status() - Network accessline 129
r = requests.get(f"{BASE}/targets/{target_id}/interactions", timeout=60) - Network accessline 129
r = requests.get(f"{BASE}/targets/{target_id}/interactions", timeout=60) - Network accessline 160
BASE = "https://www.guidetopharmacology.org/services" - Network accessline 160
BASE = "https://www.guidetopharmacology.org/services" - Network accessline 164
abbreviation, inn, whoEssential, withdrawn, antibacterial, immuno, malaria, labelled, radioactive, activeDrugIds, prodrugIds, subunitIds, complexIds.""" r = requests.get(f"{BASE}/ligands/{ligand_id}", timeout=30) - Network accessline 164
abbreviation, inn, whoEssential, withdrawn, antibacterial, immuno, malaria, labelled, radioactive, activeDrugIds, prodrugIds, subunitIds, complexIds.""" r = requests.get(f"{BASE}/ligands/{ligand_id}", timeout=30) - Network accessline 165
labelled, radioactive, activeDrugIds, prodrugIds, subunitIds, complexIds.""" r = requests.get(f"{BASE}/ligands/{ligand_id}", timeout=30) r.raise_for_status() - Network accessline 165
labelled, radioactive, activeDrugIds, prodrugIds, subunitIds, complexIds.""" r = requests.get(f"{BASE}/ligands/{ligand_id}", timeout=30) r.raise_for_status() - Network accessline 166
r = requests.get(f"{BASE}/ligands/{ligand_id}", timeout=30) - Network accessline 166
r = requests.get(f"{BASE}/ligands/{ligand_id}", timeout=30) - Network accessline 170
def ligand_structure(ligand_id): """Structure descriptors: smiles, inchi, inchiKey, iupacName.""" r = requests.get(f"{BASE}/ligands/{ligand_id}/structure", timeout=30) - Network accessline 170
def ligand_structure(ligand_id): """Structure descriptors: smiles, inchi, inchiKey, iupacName.""" r = requests.get(f"{BASE}/ligands/{ligand_id}/structure", timeout=30) - Network accessline 171
"""Structure descriptors: smiles, inchi, inchiKey, iupacName.""" r = requests.get(f"{BASE}/ligands/{ligand_id}/structure", timeout=30) r.raise_for_status() - Network accessline 171
"""Structure descriptors: smiles, inchi, inchiKey, iupacName.""" r = requests.get(f"{BASE}/ligands/{ligand_id}/structure", timeout=30) r.raise_for_status() - Network accessline 172
r = requests.get(f"{BASE}/ligands/{ligand_id}/structure", timeout=30) - Network accessline 172
r = requests.get(f"{BASE}/ligands/{ligand_id}/structure", timeout=30) - Network accessline 184
# Ligand cross-references: PubChem CID, ChEMBL, DrugBank, CAS, ChEBI, BindingDB import requests BASE = "https://www.guidetopharmacology.org/services" - Network accessline 184
# Ligand cross-references: PubChem CID, ChEMBL, DrugBank, CAS, ChEBI, BindingDB import requests BASE = "https://www.guidetopharmacology.org/services" - Network accessline 186
BASE = "https://www.guidetopharmacology.org/services" - Network accessline 186
BASE = "https://www.guidetopharmacology.org/services" - Network accessline 188
def ligand_xrefs(ligand_id): r = requests.get(f"{BASE}/ligands/{ligand_id}/databaseLinks", timeout=30) - Network accessline 188
def ligand_xrefs(ligand_id): r = requests.get(f"{BASE}/ligands/{ligand_id}/databaseLinks", timeout=30) - Network accessline 189
r = requests.get(f"{BASE}/ligands/{ligand_id}/databaseLinks", timeout=30) - Network accessline 189
r = requests.get(f"{BASE}/ligands/{ligand_id}/databaseLinks", timeout=30) - Network accessline 203
BASE = "https://www.guidetopharmacology.org/services" - Network accessline 203
BASE = "https://www.guidetopharmacology.org/services" - Network accessline 205
# Correct endpoint is /targets/families (NOT /families, which is 404) def list_families(): r = requests.get(f"{BASE}/targets/families", timeout=30) - Network accessline 205
# Correct endpoint is /targets/families (NOT /families, which is 404) def list_families(): r = requests.get(f"{BASE}/targets/families", timeout=30) - Network accessline 206
def list_families(): r = requests.get(f"{BASE}/targets/families", timeout=30) r.raise_for_status() - Network accessline 206
def list_families(): r = requests.get(f"{BASE}/targets/families", timeout=30) r.raise_for_status() - Network accessline 207
r = requests.get(f"{BASE}/targets/families", timeout=30) - Network accessline 207
r = requests.get(f"{BASE}/targets/families", timeout=30) - Network accessline 222
BASE = "https://www.guidetopharmacology.org/services" - Network accessline 222
BASE = "https://www.guidetopharmacology.org/services" - Network accessline 225
# ligands. The `approved=true` query param is silently ignored — use type=. def approved_ligands(): r = requests.get(f"{BASE}/ligands", params={"type": "Approved"}, timeout=60) - Network accessline 225
# ligands. The `approved=true` query param is silently ignored — use type=. def approved_ligands(): r = requests.get(f"{BASE}/ligands", params={"type": "Approved"}, timeout=60) - Network accessline 226
def approved_ligands(): r = requests.get(f"{BASE}/ligands", params={"type": "Approved"}, timeout=60) r.raise_for_status() - Network accessline 226
def approved_ligands(): r = requests.get(f"{BASE}/ligands", params={"type": "Approved"}, timeout=60) r.raise_for_status() - Network accessline 227
r = requests.get(f"{BASE}/ligands", params={"type": "Approved"}, timeout=60) - Network accessline 227
r = requests.get(f"{BASE}/ligands", params={"type": "Approved"}, timeout=60) - Network accessline 275
BASE = "https://www.guidetopharmacology.org/services" - Network accessline 275
BASE = "https://www.guidetopharmacology.org/services" - Network accessline 277
def target_profile(gene_symbol, min_pki=7.0): t = requests.get(f"{BASE}/targets", - Network accessline 277
def target_profile(gene_symbol, min_pki=7.0): t = requests.get(f"{BASE}/targets", - Network accessline 278
t = requests.get(f"{BASE}/targets", - Network accessline 278
t = requests.get(f"{BASE}/targets", - Network accessline 281
return None tid = t[0]["targetId"] ints = requests.get(f"{BASE}/targets/{tid}/interactions", timeout=60).json() - Network accessline 281
return None tid = t[0]["targetId"] ints = requests.get(f"{BASE}/targets/{tid}/interactions", timeout=60).json() - Network accessline 282
tid = t[0]["targetId"] ints = requests.get(f"{BASE}/targets/{tid}/interactions", timeout=60).json() rows = [] - Network accessline 282
tid = t[0]["targetId"] ints = requests.get(f"{BASE}/targets/{tid}/interactions", timeout=60).json() rows = [] - Network accessline 283
ints = requests.get(f"{BASE}/targets/{tid}/interactions", timeout=60).json() - Network accessline 283
ints = requests.get(f"{BASE}/targets/{tid}/interactions", timeout=60).json() - Network accessline 294
continue # Check approval via the ligand base record lig = requests.get(f"{BASE}/ligands/{i['ligandId']}", timeout=30).json() - Network accessline 294
continue # Check approval via the ligand base record lig = requests.get(f"{BASE}/ligands/{i['ligandId']}", timeout=30).json() - Network accessline 295
# Check approval via the ligand base record lig = requests.get(f"{BASE}/ligands/{i['ligandId']}", timeout=30).json() rows.append({ - Network accessline 295
# Check approval via the ligand base record lig = requests.get(f"{BASE}/ligands/{i['ligandId']}", timeout=30).json() rows.append({ - Network accessline 296
lig = requests.get(f"{BASE}/ligands/{i['ligandId']}", timeout=30).json() - Network accessline 296
lig = requests.get(f"{BASE}/ligands/{i['ligandId']}", timeout=30).json() - Network accessline 319
BASE = "https://www.guidetopharmacology.org/services" - Network accessline 319
BASE = "https://www.guidetopharmacology.org/services" - Network accessline 321
def ligand_targets(ligand_id): r = requests.get(f"{BASE}/ligands/{ligand_id}/interactions", timeout=60) - Network accessline 321
def ligand_targets(ligand_id): r = requests.get(f"{BASE}/ligands/{ligand_id}/interactions", timeout=60) - Network accessline 322
r = requests.get(f"{BASE}/ligands/{ligand_id}/interactions", timeout=60) - Network accessline 322
r = requests.get(f"{BASE}/ligands/{ligand_id}/interactions", timeout=60) - Network accessline 368
import requests BASE = "https://www.guidetopharmacology.org/services" - Network accessline 368
import requests BASE = "https://www.guidetopharmacology.org/services" - Network accessline 369
BASE = "https://www.guidetopharmacology.org/services" - Network accessline 369
BASE = "https://www.guidetopharmacology.org/services" - Network accessline 371
def gene_to_uniprot(symbol): t = requests.get(f"{BASE}/targets", params={"geneSymbol": symbol}, timeout=30).json() - Network accessline 371
def gene_to_uniprot(symbol): t = requests.get(f"{BASE}/targets", params={"geneSymbol": symbol}, timeout=30).json() - Network accessline 372
t = requests.get(f"{BASE}/targets", params={"geneSymbol": symbol}, timeout=30).json() - Network accessline 372
t = requests.get(f"{BASE}/targets", params={"geneSymbol": symbol}, timeout=30).json() - Network accessline 373
if not t: return None links = requests.get(f"{BASE}/targets/{t[0]['targetId']}/databaseLinks", timeout=30).json() - Network accessline 373
if not t: return None links = requests.get(f"{BASE}/targets/{t[0]['targetId']}/databaseLinks", timeout=30).json() - Network accessline 374
return None links = requests.get(f"{BASE}/targets/{t[0]['targetId']}/databaseLinks", timeout=30).json() for x in links: - Network accessline 374
return None links = requests.get(f"{BASE}/targets/{t[0]['targetId']}/databaseLinks", timeout=30).json() for x in links: - Network accessline 375
links = requests.get(f"{BASE}/targets/{t[0]['targetId']}/databaseLinks", timeout=30).json() - Network accessline 375
links = requests.get(f"{BASE}/targets/{t[0]['targetId']}/databaseLinks", timeout=30).json() - Network accessline 388
import requests, pandas as pd BASE = "https://www.guidetopharmacology.org/services" - Network accessline 388
import requests, pandas as pd BASE = "https://www.guidetopharmacology.org/services" - Network accessline 389
BASE = "https://www.guidetopharmacology.org/services" - Network accessline 389
BASE = "https://www.guidetopharmacology.org/services" - Network accessline 391
def family_targets(family_id): fams = requests.get(f"{BASE}/targets/families", timeout=30).json() - Network accessline 391
def family_targets(family_id): fams = requests.get(f"{BASE}/targets/families", timeout=30).json() - Network accessline 392
fams = requests.get(f"{BASE}/targets/families", timeout=30).json() - Network accessline 392
fams = requests.get(f"{BASE}/targets/families", timeout=30).json() - Network accessline 396
rows = [] for tid in family_targets(family_id): ints = requests.get(f"{BASE}/targets/{tid}/interactions", timeout=60).json() - Network accessline 396
rows = [] for tid in family_targets(family_id): ints = requests.get(f"{BASE}/targets/{tid}/interactions", timeout=60).json() - Network accessline 397
for tid in family_targets(family_id): ints = requests.get(f"{BASE}/targets/{tid}/interactions", timeout=60).json() for i in ints: - Network accessline 397
for tid in family_targets(family_id): ints = requests.get(f"{BASE}/targets/{tid}/interactions", timeout=60).json() for i in ints: - Network accessline 398
ints = requests.get(f"{BASE}/targets/{tid}/interactions", timeout=60).json() - Network accessline 398
ints = requests.get(f"{BASE}/targets/{tid}/interactions", timeout=60).json() - Network accessline 399
for i in ints: lig = requests.get(f"{BASE}/ligands/{i['ligandId']}", timeout=30).json() if not lig.get("approved"): - Network accessline 399
for i in ints: lig = requests.get(f"{BASE}/ligands/{i['ligandId']}", timeout=30).json() if not lig.get("approved"): - Network accessline 400
lig = requests.get(f"{BASE}/ligands/{i['ligandId']}", timeout=30).json() - Network accessline 400
lig = requests.get(f"{BASE}/ligands/{i['ligandId']}", timeout=30).json() - Network accessline 417
import requests, pandas as pd, time BASE = "https://www.guidetopharmacology.org/services" - Network accessline 417
import requests, pandas as pd, time BASE = "https://www.guidetopharmacology.org/services" - Network accessline 418
BASE = "https://www.guidetopharmacology.org/services" - Network accessline 418
BASE = "https://www.guidetopharmacology.org/services" - Network accessline 421
rows = [] for lid in ligand_ids: s = requests.get(f"{BASE}/ligands/{lid}/structure", timeout=30).json() - Network accessline 421
rows = [] for lid in ligand_ids: s = requests.get(f"{BASE}/ligands/{lid}/structure", timeout=30).json() - Network accessline 422
for lid in ligand_ids: s = requests.get(f"{BASE}/ligands/{lid}/structure", timeout=30).json() rows.append({"ligandId": lid, "smiles": s.get("smiles"), - Network accessline 422
for lid in ligand_ids: s = requests.get(f"{BASE}/ligands/{lid}/structure", timeout=30).json() rows.append({"ligandId": lid, "smiles": s.get("smiles"), - Network accessline 423
s = requests.get(f"{BASE}/ligands/{lid}/structure", timeout=30).json() - Network accessline 423
s = requests.get(f"{BASE}/ligands/{lid}/structure", timeout=30).json() - Network accessline 454
- [Guide to Pharmacology home](https://www.guidetopharmacology.org/) - Network accessline 454
- [Guide to Pharmacology home](https://www.guidetopharmacology.org/) - Network accessline 455
- [Web services API reference](https://www.guidetopharmacology.org/webServices.jsp) - Network accessline 455
- [Web services API reference](https://www.guidetopharmacology.org/webServices.jsp) - Network accessline 456
- [Nomenclature & Curation](https://www.guidetopharmacology.org/helpPage.jsp) - Network accessline 456
- [Nomenclature & Curation](https://www.guidetopharmacology.org/helpPage.jsp) - Network accessline 457
- Harding SD et al. "The IUPHAR/BPS Guide to PHARMACOLOGY in 2024." *Nucleic Acids Research* 52(D1): D1438–D1449 (2024). https://doi.org/10.1093/nar/gkad944 - Network accessline 457
- Harding SD et al. "The IUPHAR/BPS Guide to PHARMACOLOGY in 2024." *Nucleic Acids Research* 52(D1): D1438–D1449 (2024). https://doi.org/10.1093/nar/gkad944
skills/structural-biology-drug-discovery/mdanalysis-trajectory/SKILL.md
- Network accessline 383
- [MDAnalysis documentation](https://docs.mdanalysis.org/) — official API reference and user guide - Network accessline 383
- [MDAnalysis documentation](https://docs.mdanalysis.org/) — official API reference and user guide - Network accessline 384
- [MDAnalysis GitHub: MDAnalysis/mdanalysis](https://github.com/MDAnalysis/mdanalysis) — source code, tutorials, and issue tracker - Network accessline 384
- [MDAnalysis GitHub: MDAnalysis/mdanalysis](https://github.com/MDAnalysis/mdanalysis) — source code, tutorials, and issue tracker - Network accessline 385
- Michaud-Agrawal N et al. (2011) "MDAnalysis: A toolkit for the analysis of molecular dynamics simulations" — *J Comput Chem* 32:2319-2327. [DOI:10.1002/jcc.21787](https://doi.org/10.1002/jcc.21787) - Network accessline 385
- Michaud-Agrawal N et al. (2011) "MDAnalysis: A toolkit for the analysis of molecular dynamics simulations" — *J Comput Chem* 32:2319-2327. [DOI:10.1002/jcc.21787](https://doi.org/10.1002/jcc.21787) - Network accessline 386
- Gowers RJ et al. (2016) "MDAnalysis: A Python Package for the Rapid Analysis of Molecular Dynamics Simulations" — *Proc SciPy 2016*. [DOI:10.25080/majora-629e541a-00e](https://doi.org/10.25080/majora-629e541a-00e) - Network accessline 386
- Gowers RJ et al. (2016) "MDAnalysis: A Python Package for the Rapid Analysis of Molecular Dynamics Simulations" — *Proc SciPy 2016*. [DOI:10.25080/majora-629e541a-00e](https://doi.org/10.25080/majora-629e541a-00e)
skills/structural-biology-drug-discovery/mdtraj-trajectory-analysis/SKILL.md
- Network accessline 444
- [mdtraj documentation](https://mdtraj.org/) — official docs, API reference, tutorials - Network accessline 444
- [mdtraj documentation](https://mdtraj.org/) — official docs, API reference, tutorials - Network accessline 445
- [McGibbon et al. (2015)](https://doi.org/10.1016/j.bpj.2015.08.015) — "MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories", *Biophys J* - Network accessline 445
- [McGibbon et al. (2015)](https://doi.org/10.1016/j.bpj.2015.08.015) — "MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories", *Biophys J* - Network accessline 446
- [DSSP algorithm](https://doi.org/10.1002/bip.360221211) — Kabsch & Sander (1983) - Network accessline 446
- [DSSP algorithm](https://doi.org/10.1002/bip.360221211) — Kabsch & Sander (1983) - Network accessline 447
- [Baker–Hubbard H-bond criterion](https://doi.org/10.1016/S0006-3495(99)77244-7) — Baker & Hubbard (1984) - Network accessline 447
- [Baker–Hubbard H-bond criterion](https://doi.org/10.1016/S0006-3495(99)77244-7) — Baker & Hubbard (1984) - Network accessline 448
- [Shrake–Rupley SASA](https://doi.org/10.1016/0022-2836(73)90011-9) — Shrake & Rupley (1973) - Network accessline 448
- [Shrake–Rupley SASA](https://doi.org/10.1016/0022-2836(73)90011-9) — Shrake & Rupley (1973) - Network accessline 449
- [mdtraj GitHub](https://github.com/mdtraj/mdtraj) — source, issues, examples - Network accessline 449
- [mdtraj GitHub](https://github.com/mdtraj/mdtraj) — source, issues, examples
skills/structural-biology-drug-discovery/medchem/SKILL.md
- Network accessline 463
- Johnson TW et al. J Med Chem (2009) 52:5487-5500 — Golden Triangle - Walters WP & Murcko MA. Adv Drug Deliv Rev (2002) 54:255-271 — REOS - Official docs: https://medchem-docs.datamol.io/ - Network accessline 463
- Johnson TW et al. J Med Chem (2009) 52:5487-5500 — Golden Triangle - Walters WP & Murcko MA. Adv Drug Deliv Rev (2002) 54:255-271 — REOS - Official docs: https://medchem-docs.datamol.io/ - Network accessline 464
- Walters WP & Murcko MA. Adv Drug Deliv Rev (2002) 54:255-271 — REOS - Official docs: https://medchem-docs.datamol.io/ - GitHub: https://github.com/datamol-io/medchem - Network accessline 464
- Walters WP & Murcko MA. Adv Drug Deliv Rev (2002) 54:255-271 — REOS - Official docs: https://medchem-docs.datamol.io/ - GitHub: https://github.com/datamol-io/medchem - Network accessline 465
- Official docs: https://medchem-docs.datamol.io/ - Network accessline 465
- Official docs: https://medchem-docs.datamol.io/ - Network accessline 466
- GitHub: https://github.com/datamol-io/medchem - Network accessline 466
- GitHub: https://github.com/datamol-io/medchem
skills/structural-biology-drug-discovery/molfeat-molecular-featurization/SKILL.md
- Network accessline 390
- Official documentation: https://molfeat-docs.datamol.io/ - Network accessline 390
- Official documentation: https://molfeat-docs.datamol.io/ - Network accessline 391
- GitHub repository: https://github.com/datamol-io/molfeat - Network accessline 391
- GitHub repository: https://github.com/datamol-io/molfeat - Network accessline 392
- PyPI package: https://pypi.org/project/molfeat/ - Network accessline 392
- PyPI package: https://pypi.org/project/molfeat/ - Network accessline 393
- Tutorial: https://portal.valencelabs.com/datamol/post/types-of-featurizers-b1e8HHrbFMkbun6 - Network accessline 393
- Tutorial: https://portal.valencelabs.com/datamol/post/types-of-featurizers-b1e8HHrbFMkbun6
skills/structural-biology-drug-discovery/opentargets-database/SKILL.md
- Network accessline 39
OT_URL = "https://api.platform.opentargets.org/api/v4/graphql" - Network accessline 39
OT_URL = "https://api.platform.opentargets.org/api/v4/graphql" - Network accessline 41
def ot_query(gql, variables=None): r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 41
def ot_query(gql, variables=None): r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 42
r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 42
r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 77
OT_URL = "https://api.platform.opentargets.org/api/v4/graphql" - Network accessline 77
OT_URL = "https://api.platform.opentargets.org/api/v4/graphql" - Network accessline 79
def ot_query(gql, variables=None): r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 79
def ot_query(gql, variables=None): r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 80
r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 80
r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 138
OT_URL = "https://api.platform.opentargets.org/api/v4/graphql" - Network accessline 138
OT_URL = "https://api.platform.opentargets.org/api/v4/graphql" - Network accessline 140
def ot_query(gql, variables=None): r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 140
def ot_query(gql, variables=None): r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 141
r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 141
r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 188
OT_URL = "https://api.platform.opentargets.org/api/v4/graphql" - Network accessline 188
OT_URL = "https://api.platform.opentargets.org/api/v4/graphql" - Network accessline 190
def ot_query(gql, variables=None): r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 190
def ot_query(gql, variables=None): r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 191
r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 191
r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 228
OT_URL = "https://api.platform.opentargets.org/api/v4/graphql" - Network accessline 228
OT_URL = "https://api.platform.opentargets.org/api/v4/graphql" - Network accessline 230
def ot_query(gql, variables=None): r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 230
def ot_query(gql, variables=None): r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 231
r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 231
r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 281
OT_URL = "https://api.platform.opentargets.org/api/v4/graphql" - Network accessline 281
OT_URL = "https://api.platform.opentargets.org/api/v4/graphql" - Network accessline 283
def ot_query(gql, variables=None): r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 283
def ot_query(gql, variables=None): r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 284
r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 284
r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 324
OT_URL = "https://api.platform.opentargets.org/api/v4/graphql" - Network accessline 324
OT_URL = "https://api.platform.opentargets.org/api/v4/graphql" - Network accessline 326
def ot_query(gql, variables=None): r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 326
def ot_query(gql, variables=None): r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 327
r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 327
r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 375
OT_URL = "https://api.platform.opentargets.org/api/v4/graphql" - Network accessline 375
OT_URL = "https://api.platform.opentargets.org/api/v4/graphql" - Network accessline 377
def ot_query(gql, variables=None): r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 377
def ot_query(gql, variables=None): r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 378
r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 378
r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 436
OT_URL = "https://api.platform.opentargets.org/api/v4/graphql" - Network accessline 436
OT_URL = "https://api.platform.opentargets.org/api/v4/graphql" - Network accessline 438
def ot_query(gql, variables=None): r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 438
def ot_query(gql, variables=None): r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 439
r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 439
r = requests.post(OT_URL, json={"query": gql, "variables": variables or {}}) - Network accessline 518
OT_URL = "https://api.platform.opentargets.org/api/v4/graphql" - Network accessline 518
OT_URL = "https://api.platform.opentargets.org/api/v4/graphql" - Network accessline 524
} }""" r = requests.post(OT_URL, json={"query": query, "variables": {"q": "breast cancer"}}) - Network accessline 524
} }""" r = requests.post(OT_URL, json={"query": query, "variables": {"q": "breast cancer"}}) - Network accessline 525
}""" r = requests.post(OT_URL, json={"query": query, "variables": {"q": "breast cancer"}}) for hit in r.json()["data"]["search"]["hits"][:5]: - Network accessline 525
}""" r = requests.post(OT_URL, json={"query": query, "variables": {"q": "breast cancer"}}) for hit in r.json()["data"]["search"]["hits"][:5]: - Network accessline 526
r = requests.post(OT_URL, json={"query": query, "variables": {"q": "breast cancer"}}) - Network accessline 526
r = requests.post(OT_URL, json={"query": query, "variables": {"q": "breast cancer"}}) - Network accessline 538
OT_URL = "https://api.platform.opentargets.org/api/v4/graphql" - Network accessline 538
OT_URL = "https://api.platform.opentargets.org/api/v4/graphql" - Network accessline 545
} }""" r = requests.post(OT_URL, json={"query": query, "variables": {"ensgId": "ENSG00000141510"}}) - Network accessline 545
} }""" r = requests.post(OT_URL, json={"query": query, "variables": {"ensgId": "ENSG00000141510"}}) - Network accessline 546
}""" r = requests.post(OT_URL, json={"query": query, "variables": {"ensgId": "ENSG00000141510"}}) t = r.json()["data"]["target"] - Network accessline 546
}""" r = requests.post(OT_URL, json={"query": query, "variables": {"ensgId": "ENSG00000141510"}}) t = r.json()["data"]["target"] - Network accessline 547
r = requests.post(OT_URL, json={"query": query, "variables": {"ensgId": "ENSG00000141510"}}) - Network accessline 547
r = requests.post(OT_URL, json={"query": query, "variables": {"ensgId": "ENSG00000141510"}}) - Network accessline 562
OT_URL = "https://api.platform.opentargets.org/api/v4/graphql" - Network accessline 562
OT_URL = "https://api.platform.opentargets.org/api/v4/graphql" - Network accessline 572
} }""" r = requests.post(OT_URL, json={"query": query, "variables": {"efoId": "EFO_0000305"}}) - Network accessline 572
} }""" r = requests.post(OT_URL, json={"query": query, "variables": {"efoId": "EFO_0000305"}}) - Network accessline 573
}""" r = requests.post(OT_URL, json={"query": query, "variables": {"efoId": "EFO_0000305"}}) data = r.json()["data"]["disease"] - Network accessline 573
}""" r = requests.post(OT_URL, json={"query": query, "variables": {"efoId": "EFO_0000305"}}) data = r.json()["data"]["disease"] - Network accessline 574
r = requests.post(OT_URL, json={"query": query, "variables": {"efoId": "EFO_0000305"}}) - Network accessline 574
r = requests.post(OT_URL, json={"query": query, "variables": {"efoId": "EFO_0000305"}}) - Network accessline 584
| Problem | Cause | Solution | |---------|-------|----------| | `HTTP 400` with GraphQL error | Malformed query or invalid field name | Check query against GraphQL schema at https://api.platform.opentargets.org/api/v4/graphql | - Network accessline 584
| Problem | Cause | Solution | |---------|-------|----------| | `HTTP 400` with GraphQL error | Malformed query or invalid field name | Check query against GraphQL schema at https://api.platform.opentargets.org/api/v4/graphql | - Network accessline 585
|---------|-------|----------| | `HTTP 400` with GraphQL error | Malformed query or invalid field name | Check query against GraphQL schema at https://api.platform.opentargets.org/api/v4/graphql | | Empty `rows` in associations | EFO ID not - Network accessline 585
|---------|-------|----------| | `HTTP 400` with GraphQL error | Malformed query or invalid field name | Check query against GraphQL schema at https://api.platform.opentargets.org/api/v4/graphql | | Empty `rows` in associations | EFO ID not - Network accessline 586
| `HTTP 400` with GraphQL error | Malformed query or invalid field name | Check query against GraphQL schema at https://api.platform.opentargets.org/api/v4/graphql | - Network accessline 586
| `HTTP 400` with GraphQL error | Malformed query or invalid field name | Check query against GraphQL schema at https://api.platform.opentargets.org/api/v4/graphql | - Network accessline 602
- [Open Targets Platform](https://platform.opentargets.org/) — Interactive target-disease browser - Network accessline 602
- [Open Targets Platform](https://platform.opentargets.org/) — Interactive target-disease browser - Network accessline 603
- [Open Targets GraphQL API documentation](https://platform-docs.opentargets.org/data-access/graphql-api) — Query reference and schema - Network accessline 603
- [Open Targets GraphQL API documentation](https://platform-docs.opentargets.org/data-access/graphql-api) — Query reference and schema - Network accessline 604
- [GraphQL playground](https://api.platform.opentargets.org/api/v4/graphql) — Interactive query builder - Network accessline 604
- [GraphQL playground](https://api.platform.opentargets.org/api/v4/graphql) — Interactive query builder - Network accessline 605
- [Open Targets data sources](https://platform-docs.opentargets.org/evidence) — Evidence types and scoring methodology - Network accessline 605
- [Open Targets data sources](https://platform-docs.opentargets.org/evidence) — Evidence types and scoring methodology
skills/structural-biology-drug-discovery/pdb-database/SKILL.md
- Network accessline 15
| API | Base URL | Method | Purpose | |---|---|---|---| | **Search** | `https://search.rcsb.org/rcsbsearch/v2/query` | `POST` JSON | Find PDB IDs by text, attribute filters, sequence, or 3D similarity | - Network accessline 15
| API | Base URL | Method | Purpose | |---|---|---|---| | **Search** | `https://search.rcsb.org/rcsbsearch/v2/query` | `POST` JSON | Find PDB IDs by text, attribute filters, sequence, or 3D similarity | - Network accessline 16
|---|---|---|---| | **Search** | `https://search.rcsb.org/rcsbsearch/v2/query` | `POST` JSON | Find PDB IDs by text, attribute filters, sequence, or 3D similarity | | **Data** | `https://data.rcsb.org/graphql` | `POST` GraphQL | Retrieve st - Network accessline 16
|---|---|---|---| | **Search** | `https://search.rcsb.org/rcsbsearch/v2/query` | `POST` JSON | Find PDB IDs by text, attribute filters, sequence, or 3D similarity | | **Data** | `https://data.rcsb.org/graphql` | `POST` GraphQL | Retrieve st - Network accessline 17
| **Search** | `https://search.rcsb.org/rcsbsearch/v2/query` | `POST` JSON | Find PDB IDs by text, attribute filters, sequence, or 3D similarity | | **Data** | `https://data.rcsb.org/graphql` | `POST` GraphQL | Retrieve structured metadata - Network accessline 17
| **Search** | `https://search.rcsb.org/rcsbsearch/v2/query` | `POST` JSON | Find PDB IDs by text, attribute filters, sequence, or 3D similarity | | **Data** | `https://data.rcsb.org/graphql` | `POST` GraphQL | Retrieve structured metadata - Network accessline 18
| **Data** | `https://data.rcsb.org/graphql` | `POST` GraphQL | Retrieve structured metadata (entries, polymer entities, assemblies, ligands) | - Network accessline 18
| **Data** | `https://data.rcsb.org/graphql` | `POST` GraphQL | Retrieve structured metadata (entries, polymer entities, assemblies, ligands) | - Network accessline 19
| **Files** | `https://files.rcsb.org/download/{id}.{format}` | `GET` | Download coordinate files (mmCIF, PDB, FASTA) | - Network accessline 19
| **Files** | `https://files.rcsb.org/download/{id}.{format}` | `GET` | Download coordinate files (mmCIF, PDB, FASTA) | - Network accessline 53
SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 53
SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 54
DATA = "https://data.rcsb.org/graphql" - Network accessline 54
DATA = "https://data.rcsb.org/graphql" - Network accessline 72
"request_options": {"paginate": {"rows": 10}}, } r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 72
"request_options": {"paginate": {"rows": 10}}, } r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 73
} r = requests.post(SEARCH, json=payload, timeout=30) r.raise_for_status() - Network accessline 73
} r = requests.post(SEARCH, json=payload, timeout=30) r.raise_for_status() - Network accessline 74
r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 74
r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 84
rcsb_entry_info { resolution_combined deposited_atom_count polymer_entity_count } } }""" % pdb_ids[0] r2 = requests.post(DATA, json={"query": gql}, timeout=30) - Network accessline 84
rcsb_entry_info { resolution_combined deposited_atom_count polymer_entity_count } } }""" % pdb_ids[0] r2 = requests.post(DATA, json={"query": gql}, timeout=30) - Network accessline 85
} }""" % pdb_ids[0] r2 = requests.post(DATA, json={"query": gql}, timeout=30) entry = r2.json()["data"]["entry"] - Network accessline 85
} }""" % pdb_ids[0] r2 = requests.post(DATA, json={"query": gql}, timeout=30) entry = r2.json()["data"]["entry"] - Network accessline 86
r2 = requests.post(DATA, json={"query": gql}, timeout=30) - Network accessline 86
r2 = requests.post(DATA, json={"query": gql}, timeout=30) - Network accessline 99
import requests SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 99
import requests SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 100
SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 100
SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 107
"request_options": {"paginate": {"rows": rows}}, } r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 107
"request_options": {"paginate": {"rows": rows}}, } r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 108
} r = requests.post(SEARCH, json=payload, timeout=30) r.raise_for_status() - Network accessline 108
} r = requests.post(SEARCH, json=payload, timeout=30) r.raise_for_status() - Network accessline 109
r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 109
r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 121
import requests SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 121
import requests SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 122
SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 122
SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 131
"request_options": {"paginate": {"rows": rows}}, } r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 131
"request_options": {"paginate": {"rows": rows}}, } r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 132
} r = requests.post(SEARCH, json=payload, timeout=30) r.raise_for_status() - Network accessline 132
} r = requests.post(SEARCH, json=payload, timeout=30) r.raise_for_status() - Network accessline 133
r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 133
r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 160
import requests SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 160
import requests SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 161
SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 161
SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 178
"request_options": {"paginate": {"rows": 10}}, } r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 178
"request_options": {"paginate": {"rows": 10}}, } r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 179
} r = requests.post(SEARCH, json=payload, timeout=30) r.raise_for_status() - Network accessline 179
} r = requests.post(SEARCH, json=payload, timeout=30) r.raise_for_status() - Network accessline 180
r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 180
r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 193
import requests SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 193
import requests SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 194
SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 194
SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 205
"request_options": {"paginate": {"rows": 10}}, } r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 205
"request_options": {"paginate": {"rows": 10}}, } r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 206
} r = requests.post(SEARCH, json=payload, timeout=30) r.raise_for_status() - Network accessline 206
} r = requests.post(SEARCH, json=payload, timeout=30) r.raise_for_status() - Network accessline 207
r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 207
r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 220
import requests DATA = "https://data.rcsb.org/graphql" - Network accessline 220
import requests DATA = "https://data.rcsb.org/graphql" - Network accessline 221
DATA = "https://data.rcsb.org/graphql" - Network accessline 221
DATA = "https://data.rcsb.org/graphql" - Network accessline 228
rcsb_accession_info { deposit_date initial_release_date } } }""" r = requests.post(DATA, json={"query": gql}, timeout=30) - Network accessline 228
rcsb_accession_info { deposit_date initial_release_date } } }""" r = requests.post(DATA, json={"query": gql}, timeout=30) - Network accessline 229
} }""" r = requests.post(DATA, json={"query": gql}, timeout=30) entry = r.json()["data"]["entry"] - Network accessline 229
} }""" r = requests.post(DATA, json={"query": gql}, timeout=30) entry = r.json()["data"]["entry"] - Network accessline 230
r = requests.post(DATA, json={"query": gql}, timeout=30) - Network accessline 230
r = requests.post(DATA, json={"query": gql}, timeout=30) - Network accessline 243
rcsb_entity_source_organism { scientific_name ncbi_taxonomy_id } } }""" r = requests.post(DATA, json={"query": gql}, timeout=30) - Network accessline 243
rcsb_entity_source_organism { scientific_name ncbi_taxonomy_id } } }""" r = requests.post(DATA, json={"query": gql}, timeout=30) - Network accessline 244
} }""" r = requests.post(DATA, json={"query": gql}, timeout=30) pe = r.json()["data"]["polymer_entity"] - Network accessline 244
} }""" r = requests.post(DATA, json={"query": gql}, timeout=30) pe = r.json()["data"]["polymer_entity"] - Network accessline 245
r = requests.post(DATA, json={"query": gql}, timeout=30) - Network accessline 245
r = requests.post(DATA, json={"query": gql}, timeout=30) - Network accessline 258
rcsb_entry_info { resolution_combined } } }""" r = requests.post(DATA, json={"query": gql}, timeout=30) - Network accessline 258
rcsb_entry_info { resolution_combined } } }""" r = requests.post(DATA, json={"query": gql}, timeout=30) - Network accessline 259
} }""" r = requests.post(DATA, json={"query": gql}, timeout=30) for e in r.json()["data"]["entries"]: - Network accessline 259
} }""" r = requests.post(DATA, json={"query": gql}, timeout=30) for e in r.json()["data"]["entries"]: - Network accessline 260
r = requests.post(DATA, json={"query": gql}, timeout=30) - Network accessline 260
r = requests.post(DATA, json={"query": gql}, timeout=30) - Network accessline 273
def download_structure(pdb_id, fmt="cif", output_dir="."): """Download mmCIF / PDB / FASTA. URLs: .pdb, .cif, /fasta/entry/{ID}, .pdb1 (assembly).""" url = f"https://files.rcsb.org/download/{pdb_id}.{fmt}" - Network accessline 273
def download_structure(pdb_id, fmt="cif", output_dir="."): """Download mmCIF / PDB / FASTA. URLs: .pdb, .cif, /fasta/entry/{ID}, .pdb1 (assembly).""" url = f"https://files.rcsb.org/download/{pdb_id}.{fmt}" - Network accessline 274
"""Download mmCIF / PDB / FASTA. URLs: .pdb, .cif, /fasta/entry/{ID}, .pdb1 (assembly).""" url = f"https://files.rcsb.org/download/{pdb_id}.{fmt}" r = requests.get(url, timeout=60) - Network accessline 274
"""Download mmCIF / PDB / FASTA. URLs: .pdb, .cif, /fasta/entry/{ID}, .pdb1 (assembly).""" url = f"https://files.rcsb.org/download/{pdb_id}.{fmt}" r = requests.get(url, timeout=60) - Network accessline 275
url = f"https://files.rcsb.org/download/{pdb_id}.{fmt}" - Network accessline 275
url = f"https://files.rcsb.org/download/{pdb_id}.{fmt}" - Network accessline 276
r = requests.get(url, timeout=60) - Network accessline 276
r = requests.get(url, timeout=60) - Network accessline 292
# FASTA sequence for an entry r = requests.get("https://www.rcsb.org/fasta/entry/4HHB", timeout=30) - Network accessline 292
# FASTA sequence for an entry r = requests.get("https://www.rcsb.org/fasta/entry/4HHB", timeout=30) - Network accessline 293
r = requests.get("https://www.rcsb.org/fasta/entry/4HHB", timeout=30) - Network accessline 293
r = requests.get("https://www.rcsb.org/fasta/entry/4HHB", timeout=30) - Network accessline 303
import requests, datetime SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 303
import requests, datetime SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 304
SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 304
SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 352
payload = {"query": q_recent_hq, "return_type": "entry", "request_options": {"paginate": {"rows": 5}}} r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 352
payload = {"query": q_recent_hq, "return_type": "entry", "request_options": {"paginate": {"rows": 5}}} r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 353
"request_options": {"paginate": {"rows": 5}}} r = requests.post(SEARCH, json=payload, timeout=30) print(f"Recent high-quality: {r.json()['total_count']} structures") - Network accessline 353
"request_options": {"paginate": {"rows": 5}}} r = requests.post(SEARCH, json=payload, timeout=30) print(f"Recent high-quality: {r.json()['total_count']} structures") - Network accessline 354
r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 354
r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 363
import requests, time SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 363
import requests, time SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 364
SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 364
SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 370
payload = {"query": query_node, "return_type": return_type, "request_options": {"paginate": {"start": start, "rows": page}}} r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 370
payload = {"query": query_node, "return_type": return_type, "request_options": {"paginate": {"start": start, "rows": page}}} r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 371
"request_options": {"paginate": {"start": start, "rows": page}}} r = requests.post(SEARCH, json=payload, timeout=30) r.raise_for_status() - Network accessline 371
"request_options": {"paginate": {"start": start, "rows": page}}} r = requests.post(SEARCH, json=payload, timeout=30) r.raise_for_status() - Network accessline 372
r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 372
r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 396
# Batch metadata fetch via GraphQL `entries(...)` to avoid one round-trip per ID DATA = "https://data.rcsb.org/graphql" - Network accessline 396
# Batch metadata fetch via GraphQL `entries(...)` to avoid one round-trip per ID DATA = "https://data.rcsb.org/graphql" - Network accessline 397
DATA = "https://data.rcsb.org/graphql" - Network accessline 397
DATA = "https://data.rcsb.org/graphql" - Network accessline 399
def batch_metadata(pdb_ids, chunk=50): """Fetch (title, method, resolution) for many entries with one POST per chunk.""" - Network accessline 399
def batch_metadata(pdb_ids, chunk=50): """Fetch (title, method, resolution) for many entries with one POST per chunk.""" - Network accessline 400
"""Fetch (title, method, resolution) for many entries with one POST per chunk.""" - Network accessline 400
"""Fetch (title, method, resolution) for many entries with one POST per chunk.""" - Network accessline 409
rcsb_entry_info {{ resolution_combined }} }} }}""" r = requests.post(DATA, json={"query": gql}, timeout=60) - Network accessline 409
rcsb_entry_info {{ resolution_combined }} }} }}""" r = requests.post(DATA, json={"query": gql}, timeout=60) - Network accessline 410
}} }}""" r = requests.post(DATA, json={"query": gql}, timeout=60) r.raise_for_status() - Network accessline 410
}} }}""" r = requests.post(DATA, json={"query": gql}, timeout=60) r.raise_for_status() - Network accessline 411
r = requests.post(DATA, json={"query": gql}, timeout=60) - Network accessline 411
r = requests.post(DATA, json={"query": gql}, timeout=60) - Network accessline 479
| Format | URL pattern | Notes | |---|---|---| | mmCIF | `https://files.rcsb.org/download/{id}.cif` | Recommended; no atom-count limit | - Network accessline 479
| Format | URL pattern | Notes | |---|---|---| | mmCIF | `https://files.rcsb.org/download/{id}.cif` | Recommended; no atom-count limit | - Network accessline 480
|---|---|---| | mmCIF | `https://files.rcsb.org/download/{id}.cif` | Recommended; no atom-count limit | | PDB | `https://files.rcsb.org/download/{id}.pdb` | Legacy; 99,999 atom limit | - Network accessline 480
|---|---|---| | mmCIF | `https://files.rcsb.org/download/{id}.cif` | Recommended; no atom-count limit | | PDB | `https://files.rcsb.org/download/{id}.pdb` | Legacy; 99,999 atom limit | - Network accessline 481
| mmCIF | `https://files.rcsb.org/download/{id}.cif` | Recommended; no atom-count limit | - Network accessline 481
| mmCIF | `https://files.rcsb.org/download/{id}.cif` | Recommended; no atom-count limit | - Network accessline 482
| PDB | `https://files.rcsb.org/download/{id}.pdb` | Legacy; 99,999 atom limit | - Network accessline 482
| PDB | `https://files.rcsb.org/download/{id}.pdb` | Legacy; 99,999 atom limit | - Network accessline 483
| Assembly (mmCIF) | `https://files.rcsb.org/download/{id}-assembly{N}.cif` | Biological unit | - Network accessline 483
| Assembly (mmCIF) | `https://files.rcsb.org/download/{id}-assembly{N}.cif` | Biological unit | - Network accessline 484
| FASTA | `https://www.rcsb.org/fasta/entry/{id}` | Sequence only | - Network accessline 484
| FASTA | `https://www.rcsb.org/fasta/entry/{id}` | Sequence only | - Network accessline 495
SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 495
SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 496
DATA = "https://data.rcsb.org/graphql" - Network accessline 496
DATA = "https://data.rcsb.org/graphql" - Network accessline 513
"request_options": {"paginate": {"rows": 50}}, } r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 513
"request_options": {"paginate": {"rows": 50}}, } r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 514
} r = requests.post(SEARCH, json=payload, timeout=30) r.raise_for_status() - Network accessline 514
} r = requests.post(SEARCH, json=payload, timeout=30) r.raise_for_status() - Network accessline 515
r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 515
r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 525
rcsb_entry_info {{ resolution_combined nonpolymer_entity_count }} }} }}""" r2 = requests.post(DATA, json={"query": gql}, timeout=60) - Network accessline 525
rcsb_entry_info {{ resolution_combined nonpolymer_entity_count }} }} }}""" r2 = requests.post(DATA, json={"query": gql}, timeout=60) - Network accessline 526
}} }}""" r2 = requests.post(DATA, json={"query": gql}, timeout=60) for e in r2.json()["data"]["entries"]: - Network accessline 526
}} }}""" r2 = requests.post(DATA, json={"query": gql}, timeout=60) for e in r2.json()["data"]["entries"]: - Network accessline 527
r2 = requests.post(DATA, json={"query": gql}, timeout=60) - Network accessline 527
r2 = requests.post(DATA, json={"query": gql}, timeout=60) - Network accessline 544
SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 544
SEARCH = "https://search.rcsb.org/rcsbsearch/v2/query" - Network accessline 545
DATA = "https://data.rcsb.org/graphql" - Network accessline 545
DATA = "https://data.rcsb.org/graphql" - Network accessline 558
"request_options": {"paginate": {"rows": 20}}, } r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 558
"request_options": {"paginate": {"rows": 20}}, } r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 559
} r = requests.post(SEARCH, json=payload, timeout=30) hits = r.json()["result_set"] - Network accessline 559
} r = requests.post(SEARCH, json=payload, timeout=30) hits = r.json()["result_set"] - Network accessline 560
r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 560
r = requests.post(SEARCH, json=payload, timeout=30) - Network accessline 573
rcsb_entry_info {{ resolution_combined }} }} }}""" r2 = requests.post(DATA, json={"query": gql}, timeout=60) - Network accessline 573
rcsb_entry_info {{ resolution_combined }} }} }}""" r2 = requests.post(DATA, json={"query": gql}, timeout=60) - Network accessline 574
}} }}""" r2 = requests.post(DATA, json={"query": gql}, timeout=60) for e in sorted(r2.json()["data"]["entries"], - Network accessline 574
}} }}""" r2 = requests.post(DATA, json={"query": gql}, timeout=60) for e in sorted(r2.json()["data"]["entries"], - Network accessline 575
r2 = requests.post(DATA, json={"query": gql}, timeout=60) - Network accessline 575
r2 = requests.post(DATA, json={"query": gql}, timeout=60) - Network accessline 591
pdb_id = "4HHB" r = requests.get(f"https://files.rcsb.org/download/{pdb_id}.cif", timeout=60) - Network accessline 591
pdb_id = "4HHB" r = requests.get(f"https://files.rcsb.org/download/{pdb_id}.cif", timeout=60) - Network accessline 592
r = requests.get(f"https://files.rcsb.org/download/{pdb_id}.cif", timeout=60) - Network accessline 592
r = requests.get(f"https://files.rcsb.org/download/{pdb_id}.cif", timeout=60) - Network accessline 641
import requests r = requests.get("https://www.rcsb.org/fasta/entry/4HHB", timeout=30) - Network accessline 641
import requests r = requests.get("https://www.rcsb.org/fasta/entry/4HHB", timeout=30) - Network accessline 642
r = requests.get("https://www.rcsb.org/fasta/entry/4HHB", timeout=30) - Network accessline 642
r = requests.get("https://www.rcsb.org/fasta/entry/4HHB", timeout=30) - Network accessline 649
import requests DATA = "https://data.rcsb.org/graphql" - Network accessline 649
import requests DATA = "https://data.rcsb.org/graphql" - Network accessline 650
DATA = "https://data.rcsb.org/graphql" - Network accessline 650
DATA = "https://data.rcsb.org/graphql" - Network accessline 656
} } }""" r = requests.post(DATA, json={"query": gql}, timeout=30) - Network accessline 656
} } }""" r = requests.post(DATA, json={"query": gql}, timeout=30) - Network accessline 657
} }""" r = requests.post(DATA, json={"query": gql}, timeout=30) for pe in r.json()["data"]["entry"]["polymer_entities"]: - Network accessline 657
} }""" r = requests.post(DATA, json={"query": gql}, timeout=30) for pe in r.json()["data"]["entry"]["polymer_entities"]: - Network accessline 658
r = requests.post(DATA, json={"query": gql}, timeout=30) - Network accessline 658
r = requests.post(DATA, json={"query": gql}, timeout=30) - Network accessline 668
import requests DATA = "https://data.rcsb.org/graphql" - Network accessline 668
import requests DATA = "https://data.rcsb.org/graphql" - Network accessline 669
DATA = "https://data.rcsb.org/graphql" - Network accessline 669
DATA = "https://data.rcsb.org/graphql" - Network accessline 674
} } }""" r = requests.post(DATA, json={"query": gql}, timeout=30) - Network accessline 674
} } }""" r = requests.post(DATA, json={"query": gql}, timeout=30) - Network accessline 675
} }""" r = requests.post(DATA, json={"query": gql}, timeout=30) for npe in r.json()["data"]["entry"]["nonpolymer_entities"]: - Network accessline 675
} }""" r = requests.post(DATA, json={"query": gql}, timeout=30) for npe in r.json()["data"]["entry"]["nonpolymer_entities"]: - Network accessline 676
r = requests.post(DATA, json={"query": gql}, timeout=30) - Network accessline 676
r = requests.post(DATA, json={"query": gql}, timeout=30) - Network accessline 684
The Search API exposes a JSON schema at `https://search.rcsb.org/rcsbsearch/v2/metadata/schema`. Use it to look up valid attribute paths. - Network accessline 684
The Search API exposes a JSON schema at `https://search.rcsb.org/rcsbsearch/v2/metadata/schema`. Use it to look up valid attribute paths. - Network accessline 687
import requests r = requests.get("https://search.rcsb.org/rcsbsearch/v2/metadata/schema", timeout=30) - Network accessline 687
import requests r = requests.get("https://search.rcsb.org/rcsbsearch/v2/metadata/schema", timeout=30) - Network accessline 688
r = requests.get("https://search.rcsb.org/rcsbsearch/v2/metadata/schema", timeout=30) - Network accessline 688
r = requests.get("https://search.rcsb.org/rcsbsearch/v2/metadata/schema", timeout=30) - Network accessline 704
| `HTTP 500` from search | Server-side glitch | Retry after 5–10 s; check `status.rcsb.org` | | Downloaded `.pdb` file truncated | >99,999 atoms (legacy format limit) | Download `.cif` instead | | GraphQL response has `errors` array | Field - Network accessline 704
| `HTTP 500` from search | Server-side glitch | Retry after 5–10 s; check `status.rcsb.org` | | Downloaded `.pdb` file truncated | >99,999 atoms (legacy format limit) | Download `.cif` instead | | GraphQL response has `errors` array | Field - Network accessline 706
| GraphQL response has `errors` array | Field name typo or wrong root | Read the error message; the API is strict about field names — check the schema browser at <https://data.rcsb.org/index.html#graphql-api> | - Network accessline 706
| GraphQL response has `errors` array | Field name typo or wrong root | Read the error message; the API is strict about field names — check the schema browser at <https://data.rcsb.org/index.html#graphql-api> | - Network accessline 718
- [RCSB PDB](https://www.rcsb.org) — main portal and web search - Network accessline 718
- [RCSB PDB](https://www.rcsb.org) — main portal and web search - Network accessline 719
- [Search API v2 docs](https://search.rcsb.org/) — full JSON-payload reference and Swagger - Network accessline 719
- [Search API v2 docs](https://search.rcsb.org/) — full JSON-payload reference and Swagger - Network accessline 720
- [Data API GraphQL browser](https://data.rcsb.org/index.html#graphql-api) — schema explorer - Network accessline 720
- [Data API GraphQL browser](https://data.rcsb.org/index.html#graphql-api) — schema explorer - Network accessline 721
- [Search API attribute schema](https://search.rcsb.org/rcsbsearch/v2/metadata/schema) — JSON of every searchable attribute - Network accessline 721
- [Search API attribute schema](https://search.rcsb.org/rcsbsearch/v2/metadata/schema) — JSON of every searchable attribute - Network accessline 722
- [RCSB PDB Web APIs overview](https://www.rcsb.org/docs/programmatic-access/web-apis-overview) — index of all programmatic surfaces - Network accessline 722
- [RCSB PDB Web APIs overview](https://www.rcsb.org/docs/programmatic-access/web-apis-overview) — index of all programmatic surfaces
skills/structural-biology-drug-discovery/pubchem-compound-search/SKILL.md
- Network accessline 16
https://pubchem.ncbi.nlm.nih.gov/rest/pug/<input>/<operation>/<output> - Network accessline 16
https://pubchem.ncbi.nlm.nih.gov/rest/pug/<input>/<operation>/<output> - Network accessline 55
BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 55
BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 57
# name → CID cid = requests.get(f"{BASE}/compound/name/aspirin/cids/JSON").json()["IdentifierList"]["CID"][0] - Network accessline 57
# name → CID cid = requests.get(f"{BASE}/compound/name/aspirin/cids/JSON").json()["IdentifierList"]["CID"][0] - Network accessline 58
cid = requests.get(f"{BASE}/compound/name/aspirin/cids/JSON").json()["IdentifierList"]["CID"][0] - Network accessline 58
cid = requests.get(f"{BASE}/compound/name/aspirin/cids/JSON").json()["IdentifierList"]["CID"][0] - Network accessline 60
# CID → properties (single call, many fields) r = requests.get( - Network accessline 60
# CID → properties (single call, many fields) r = requests.get( - Network accessline 61
r = requests.get( - Network accessline 61
r = requests.get( - Network accessline 81
BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 81
BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 83
# By name (returns all matching CIDs as a list) cids = requests.get(f"{BASE}/compound/name/caffeine/cids/JSON").json()["IdentifierList"]["CID"] - Network accessline 83
# By name (returns all matching CIDs as a list) cids = requests.get(f"{BASE}/compound/name/caffeine/cids/JSON").json()["IdentifierList"]["CID"] - Network accessline 84
cids = requests.get(f"{BASE}/compound/name/caffeine/cids/JSON").json()["IdentifierList"]["CID"] - Network accessline 84
cids = requests.get(f"{BASE}/compound/name/caffeine/cids/JSON").json()["IdentifierList"]["CID"] - Network accessline 87
# By canonical SMILES (URL-encode!) smi = quote("CC(=O)OC1=CC=CC=C1C(=O)O", safe="") cid = requests.get(f"{BASE}/compound/smiles/{smi}/cids/JSON").json()["IdentifierList"]["CID"][0] - Network accessline 87
# By canonical SMILES (URL-encode!) smi = quote("CC(=O)OC1=CC=CC=C1C(=O)O", safe="") cid = requests.get(f"{BASE}/compound/smiles/{smi}/cids/JSON").json()["IdentifierList"]["CID"][0] - Network accessline 88
smi = quote("CC(=O)OC1=CC=CC=C1C(=O)O", safe="") cid = requests.get(f"{BASE}/compound/smiles/{smi}/cids/JSON").json()["IdentifierList"]["CID"][0] print(f"aspirin SMILES → CID {cid}") - Network accessline 88
smi = quote("CC(=O)OC1=CC=CC=C1C(=O)O", safe="") cid = requests.get(f"{BASE}/compound/smiles/{smi}/cids/JSON").json()["IdentifierList"]["CID"][0] print(f"aspirin SMILES → CID {cid}") - Network accessline 89
cid = requests.get(f"{BASE}/compound/smiles/{smi}/cids/JSON").json()["IdentifierList"]["CID"][0] - Network accessline 89
cid = requests.get(f"{BASE}/compound/smiles/{smi}/cids/JSON").json()["IdentifierList"]["CID"][0] - Network accessline 92
# By InChIKey (exact match, fastest if you already have one) ikey = "BSYNRYMUTXBXSQ-UHFFFAOYSA-N" cid = requests.get(f"{BASE}/compound/inchikey/{ikey}/cids/JSON").json()["IdentifierList"]["CID"][0] - Network accessline 92
# By InChIKey (exact match, fastest if you already have one) ikey = "BSYNRYMUTXBXSQ-UHFFFAOYSA-N" cid = requests.get(f"{BASE}/compound/inchikey/{ikey}/cids/JSON").json()["IdentifierList"]["CID"][0] - Network accessline 93
ikey = "BSYNRYMUTXBXSQ-UHFFFAOYSA-N" cid = requests.get(f"{BASE}/compound/inchikey/{ikey}/cids/JSON").json()["IdentifierList"]["CID"][0] print(f"InChIKey → CID {cid}") - Network accessline 93
ikey = "BSYNRYMUTXBXSQ-UHFFFAOYSA-N" cid = requests.get(f"{BASE}/compound/inchikey/{ikey}/cids/JSON").json()["IdentifierList"]["CID"][0] print(f"InChIKey → CID {cid}") - Network accessline 94
cid = requests.get(f"{BASE}/compound/inchikey/{ikey}/cids/JSON").json()["IdentifierList"]["CID"][0] - Network accessline 94
cid = requests.get(f"{BASE}/compound/inchikey/{ikey}/cids/JSON").json()["IdentifierList"]["CID"][0] - Network accessline 105
BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 105
BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 109
"MolecularWeight,XLogP,TPSA,HBondDonorCount,HBondAcceptorCount," "RotatableBondCount,SMILES,InChIKey,IUPACName,MolecularFormula/JSON") p = requests.get(url).json()["PropertyTable"]["Properties"][0] - Network accessline 109
"MolecularWeight,XLogP,TPSA,HBondDonorCount,HBondAcceptorCount," "RotatableBondCount,SMILES,InChIKey,IUPACName,MolecularFormula/JSON") p = requests.get(url).json()["PropertyTable"]["Properties"][0] - Network accessline 110
"RotatableBondCount,SMILES,InChIKey,IUPACName,MolecularFormula/JSON") p = requests.get(url).json()["PropertyTable"]["Properties"][0] print(f"{p['IUPACName']} formula={p['MolecularFormula']}") - Network accessline 110
"RotatableBondCount,SMILES,InChIKey,IUPACName,MolecularFormula/JSON") p = requests.get(url).json()["PropertyTable"]["Properties"][0] print(f"{p['IUPACName']} formula={p['MolecularFormula']}") - Network accessline 111
p = requests.get(url).json()["PropertyTable"]["Properties"][0] - Network accessline 111
p = requests.get(url).json()["PropertyTable"]["Properties"][0] - Network accessline 120
# Batch: 4 CIDs, 3 properties — one request, one round trip cids = "2244,3672,2157,2662" # aspirin, ibuprofen, naproxen, celecoxib r = requests.get( - Network accessline 120
# Batch: 4 CIDs, 3 properties — one request, one round trip cids = "2244,3672,2157,2662" # aspirin, ibuprofen, naproxen, celecoxib r = requests.get( - Network accessline 121
cids = "2244,3672,2157,2662" # aspirin, ibuprofen, naproxen, celecoxib r = requests.get( f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/property/" - Network accessline 121
cids = "2244,3672,2157,2662" # aspirin, ibuprofen, naproxen, celecoxib r = requests.get( f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/property/" - Network accessline 122
r = requests.get( - Network accessline 122
r = requests.get( - Network accessline 123
f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/property/" - Network accessline 123
f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/property/" - Network accessline 136
BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 136
BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 138
# Full synonym list (aspirin has ~700) info = requests.get(f"{BASE}/compound/cid/2244/synonyms/JSON").json()["InformationList"]["Information"][0] - Network accessline 138
# Full synonym list (aspirin has ~700) info = requests.get(f"{BASE}/compound/cid/2244/synonyms/JSON").json()["InformationList"]["Information"][0] - Network accessline 139
info = requests.get(f"{BASE}/compound/cid/2244/synonyms/JSON").json()["InformationList"]["Information"][0] - Network accessline 139
info = requests.get(f"{BASE}/compound/cid/2244/synonyms/JSON").json()["InformationList"]["Information"][0] - Network accessline 148
# Curated descriptions (NCBI MeSH, CAMEO, etc.) r = requests.get("https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/aspirin/description/JSON") - Network accessline 148
# Curated descriptions (NCBI MeSH, CAMEO, etc.) r = requests.get("https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/aspirin/description/JSON") - Network accessline 149
r = requests.get("https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/aspirin/description/JSON") - Network accessline 149
r = requests.get("https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/aspirin/description/JSON") - Network accessline 165
BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 165
BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 169
for _ in range(max_polls): time.sleep(interval) j = requests.get(f"{BASE}/compound/listkey/{listkey}/cids/JSON", timeout=20).json() - Network accessline 169
for _ in range(max_polls): time.sleep(interval) j = requests.get(f"{BASE}/compound/listkey/{listkey}/cids/JSON", timeout=20).json() - Network accessline 170
time.sleep(interval) j = requests.get(f"{BASE}/compound/listkey/{listkey}/cids/JSON", timeout=20).json() if "IdentifierList" in j: - Network accessline 170
time.sleep(interval) j = requests.get(f"{BASE}/compound/listkey/{listkey}/cids/JSON", timeout=20).json() if "IdentifierList" in j: - Network accessline 171
j = requests.get(f"{BASE}/compound/listkey/{listkey}/cids/JSON", timeout=20).json() - Network accessline 171
j = requests.get(f"{BASE}/compound/listkey/{listkey}/cids/JSON", timeout=20).json() - Network accessline 176
# Tanimoto similarity (90% threshold, max 20 hits) — starting from aspirin SMILES smi = quote("CC(=O)OC1=CC=CC=C1C(=O)O", safe="") init = requests.get( - Network accessline 176
# Tanimoto similarity (90% threshold, max 20 hits) — starting from aspirin SMILES smi = quote("CC(=O)OC1=CC=CC=C1C(=O)O", safe="") init = requests.get( - Network accessline 177
smi = quote("CC(=O)OC1=CC=CC=C1C(=O)O", safe="") init = requests.get( f"{BASE}/compound/similarity/smiles/{smi}/JSON?Threshold=90&MaxRecords=20").json() - Network accessline 177
smi = quote("CC(=O)OC1=CC=CC=C1C(=O)O", safe="") init = requests.get( f"{BASE}/compound/similarity/smiles/{smi}/JSON?Threshold=90&MaxRecords=20").json() - Network accessline 178
init = requests.get( - Network accessline 178
init = requests.get( - Network accessline 189
BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 189
BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 191
# Substructure search — all compounds containing a sulfonamide group smi = quote("S(=O)(=O)N", safe="") init = requests.get( - Network accessline 191
# Substructure search — all compounds containing a sulfonamide group smi = quote("S(=O)(=O)N", safe="") init = requests.get( - Network accessline 192
smi = quote("S(=O)(=O)N", safe="") init = requests.get( f"{BASE}/compound/substructure/smiles/{smi}/JSON?MaxRecords=20").json() - Network accessline 192
smi = quote("S(=O)(=O)N", safe="") init = requests.get( f"{BASE}/compound/substructure/smiles/{smi}/JSON?MaxRecords=20").json() - Network accessline 193
init = requests.get( - Network accessline 193
init = requests.get( - Network accessline 207
BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 207
BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 209
r = requests.get(f"{BASE}/compound/cid/2244/assaysummary/JSON", timeout=30) - Network accessline 209
r = requests.get(f"{BASE}/compound/cid/2244/assaysummary/JSON", timeout=30) - Network accessline 226
BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 226
BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 229
# 2D SDF for downstream RDKit / OpenBabel sdf = requests.get(f"{BASE}/compound/cid/{cid}/SDF", timeout=15).text - Network accessline 229
# 2D SDF for downstream RDKit / OpenBabel sdf = requests.get(f"{BASE}/compound/cid/{cid}/SDF", timeout=15).text - Network accessline 230
sdf = requests.get(f"{BASE}/compound/cid/{cid}/SDF", timeout=15).text - Network accessline 230
sdf = requests.get(f"{BASE}/compound/cid/{cid}/SDF", timeout=15).text - Network accessline 235
# PNG structure image png = requests.get(f"{BASE}/compound/cid/{cid}/PNG?image_size=large", timeout=15).content - Network accessline 235
# PNG structure image png = requests.get(f"{BASE}/compound/cid/{cid}/PNG?image_size=large", timeout=15).content - Network accessline 236
png = requests.get(f"{BASE}/compound/cid/{cid}/PNG?image_size=large", timeout=15).content - Network accessline 236
png = requests.get(f"{BASE}/compound/cid/{cid}/PNG?image_size=large", timeout=15).content - Network accessline 290
BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 290
BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 295
cids = [] for d in drugs: cid = requests.get(f"{BASE}/compound/name/{quote(d)}/cids/JSON", - Network accessline 295
cids = [] for d in drugs: cid = requests.get(f"{BASE}/compound/name/{quote(d)}/cids/JSON", - Network accessline 296
for d in drugs: cid = requests.get(f"{BASE}/compound/name/{quote(d)}/cids/JSON", timeout=15).json()["IdentifierList"]["CID"][0] - Network accessline 296
for d in drugs: cid = requests.get(f"{BASE}/compound/name/{quote(d)}/cids/JSON", timeout=15).json()["IdentifierList"]["CID"][0] - Network accessline 297
cid = requests.get(f"{BASE}/compound/name/{quote(d)}/cids/JSON", - Network accessline 297
cid = requests.get(f"{BASE}/compound/name/{quote(d)}/cids/JSON", - Network accessline 302
# Single batched property pull cid_csv = ",".join(str(c) for c in cids) r = requests.get( - Network accessline 302
# Single batched property pull cid_csv = ",".join(str(c) for c in cids) r = requests.get( - Network accessline 303
cid_csv = ",".join(str(c) for c in cids) r = requests.get( f"{BASE}/compound/cid/{cid_csv}/property/" - Network accessline 303
cid_csv = ",".join(str(c) for c in cids) r = requests.get( f"{BASE}/compound/cid/{cid_csv}/property/" - Network accessline 304
r = requests.get( - Network accessline 304
r = requests.get( - Network accessline 322
BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 322
BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 325
for _ in range(max_polls): time.sleep(interval) j = requests.get(f"{BASE}/compound/listkey/{listkey}/cids/JSON", - Network accessline 325
for _ in range(max_polls): time.sleep(interval) j = requests.get(f"{BASE}/compound/listkey/{listkey}/cids/JSON", - Network accessline 326
time.sleep(interval) j = requests.get(f"{BASE}/compound/listkey/{listkey}/cids/JSON", timeout=20).json() - Network accessline 326
time.sleep(interval) j = requests.get(f"{BASE}/compound/listkey/{listkey}/cids/JSON", timeout=20).json() - Network accessline 327
j = requests.get(f"{BASE}/compound/listkey/{listkey}/cids/JSON", - Network accessline 327
j = requests.get(f"{BASE}/compound/listkey/{listkey}/cids/JSON", - Network accessline 333
# 1. lead → CID → canonical SMILES ref_cid = requests.get( - Network accessline 333
# 1. lead → CID → canonical SMILES ref_cid = requests.get( - Network accessline 334
ref_cid = requests.get( - Network accessline 334
ref_cid = requests.get( - Network accessline 335
f"{BASE}/compound/name/gefitinib/cids/JSON").json()["IdentifierList"]["CID"][0] ref_smi = requests.get( f"{BASE}/compound/cid/{ref_cid}/property/SMILES/JSON" - Network accessline 335
f"{BASE}/compound/name/gefitinib/cids/JSON").json()["IdentifierList"]["CID"][0] ref_smi = requests.get( f"{BASE}/compound/cid/{ref_cid}/property/SMILES/JSON" - Network accessline 336
ref_smi = requests.get( - Network accessline 336
ref_smi = requests.get( - Network accessline 341
# 2. similarity search smi_q = quote(ref_smi, safe="") init = requests.get( - Network accessline 341
# 2. similarity search smi_q = quote(ref_smi, safe="") init = requests.get( - Network accessline 342
smi_q = quote(ref_smi, safe="") init = requests.get( f"{BASE}/compound/similarity/smiles/{smi_q}/JSON?Threshold=85&MaxRecords=15" - Network accessline 342
smi_q = quote(ref_smi, safe="") init = requests.get( f"{BASE}/compound/similarity/smiles/{smi_q}/JSON?Threshold=85&MaxRecords=15" - Network accessline 343
init = requests.get( - Network accessline 343
init = requests.get( - Network accessline 350
# 3. batch-pull properties for top 5 analogs cid_csv = ",".join(str(c) for c in sim_cids[:5]) r = requests.get( - Network accessline 350
# 3. batch-pull properties for top 5 analogs cid_csv = ",".join(str(c) for c in sim_cids[:5]) r = requests.get( - Network accessline 351
cid_csv = ",".join(str(c) for c in sim_cids[:5]) r = requests.get( f"{BASE}/compound/cid/{cid_csv}/property/" - Network accessline 351
cid_csv = ",".join(str(c) for c in sim_cids[:5]) r = requests.get( f"{BASE}/compound/cid/{cid_csv}/property/" - Network accessline 352
r = requests.get( - Network accessline 352
r = requests.get( - Network accessline 367
BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 367
BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 370
for _ in range(max_polls): time.sleep(interval) j = requests.get(f"{BASE}/compound/listkey/{listkey}/cids/JSON", - Network accessline 370
for _ in range(max_polls): time.sleep(interval) j = requests.get(f"{BASE}/compound/listkey/{listkey}/cids/JSON", - Network accessline 371
time.sleep(interval) j = requests.get(f"{BASE}/compound/listkey/{listkey}/cids/JSON", timeout=20).json() - Network accessline 371
time.sleep(interval) j = requests.get(f"{BASE}/compound/listkey/{listkey}/cids/JSON", timeout=20).json() - Network accessline 372
j = requests.get(f"{BASE}/compound/listkey/{listkey}/cids/JSON", - Network accessline 372
j = requests.get(f"{BASE}/compound/listkey/{listkey}/cids/JSON", - Network accessline 378
smi = quote("S(=O)(=O)N", safe="") init = requests.get( - Network accessline 378
smi = quote("S(=O)(=O)N", safe="") init = requests.get( - Network accessline 379
init = requests.get( - Network accessline 379
init = requests.get( - Network accessline 385
# Bioactivity row counts for the first few hits for cid in cids[:3]: rows = requests.get(f"{BASE}/compound/cid/{cid}/assaysummary/JSON", - Network accessline 385
# Bioactivity row counts for the first few hits for cid in cids[:3]: rows = requests.get(f"{BASE}/compound/cid/{cid}/assaysummary/JSON", - Network accessline 386
for cid in cids[:3]: rows = requests.get(f"{BASE}/compound/cid/{cid}/assaysummary/JSON", timeout=30).json().get("Table", {}).get("Row", []) - Network accessline 386
for cid in cids[:3]: rows = requests.get(f"{BASE}/compound/cid/{cid}/assaysummary/JSON", timeout=30).json().get("Table", {}).get("Row", []) - Network accessline 387
rows = requests.get(f"{BASE}/compound/cid/{cid}/assaysummary/JSON", - Network accessline 387
rows = requests.get(f"{BASE}/compound/cid/{cid}/assaysummary/JSON", - Network accessline 419
7. **For 100+ CIDs use POST.** GET URLs over ~2000 chars get truncated by some HTTP proxies. PubChem also accepts `POST` with `cid` in the form body: `requests.post(f"{BASE}/compound/cid/property/MolecularWeight/JSON", data={"cid": cid_csv} - Network accessline 419
7. **For 100+ CIDs use POST.** GET URLs over ~2000 chars get truncated by some HTTP proxies. PubChem also accepts `POST` with `cid` in the form body: `requests.post(f"{BASE}/compound/cid/property/MolecularWeight/JSON", data={"cid": cid_csv} - Network accessline 431
BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 431
BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 433
def check_lipinski(name): cid = requests.get(f"{BASE}/compound/name/{quote(name)}/cids/JSON" - Network accessline 433
def check_lipinski(name): cid = requests.get(f"{BASE}/compound/name/{quote(name)}/cids/JSON" - Network accessline 434
cid = requests.get(f"{BASE}/compound/name/{quote(name)}/cids/JSON" - Network accessline 434
cid = requests.get(f"{BASE}/compound/name/{quote(name)}/cids/JSON" - Network accessline 435
).json()["IdentifierList"]["CID"][0] p = requests.get( f"{BASE}/compound/cid/{cid}/property/" - Network accessline 435
).json()["IdentifierList"]["CID"][0] p = requests.get( f"{BASE}/compound/cid/{cid}/property/" - Network accessline 436
p = requests.get( - Network accessline 436
p = requests.get( - Network accessline 457
r = requests.get("https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/aspirin/synonyms/JSON") - Network accessline 457
r = requests.get("https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/aspirin/synonyms/JSON") - Network accessline 469
cid = 2519 # caffeine png = requests.get( - Network accessline 469
cid = 2519 # caffeine png = requests.get( - Network accessline 470
png = requests.get( - Network accessline 470
png = requests.get( - Network accessline 471
f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cid}/PNG?image_size=large" - Network accessline 471
f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cid}/PNG?image_size=large" - Network accessline 485
s = requests.Session() - Network accessline 485
s = requests.Session() - Network accessline 492
r = s.get( "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/2244/property/MolecularWeight/JSON", - Network accessline 492
r = s.get( "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/2244/property/MolecularWeight/JSON", - Network accessline 493
"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/2244/property/MolecularWeight/JSON", - Network accessline 493
"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/2244/property/MolecularWeight/JSON", - Network accessline 517
| `KeyError: 'CanonicalSMILES'` / `KeyError: 'IsomericSMILES'` | Requested old name; URL returns 200 but 2025 JSON is keyed `ConnectivitySMILES` / `SMILES` | Read `p["ConnectivitySMILES"]` (connectivity, no stereo) or `p[ - Network accessline 517
| `KeyError: 'CanonicalSMILES'` / `KeyError: 'IsomericSMILES'` | Requested old name; URL returns 200 but 2025 JSON is keyed `ConnectivitySMILES` / `SMILES` | Read `p["ConnectivitySMILES"]` (connectivity, no stereo) or `p[ - Network accessline 518
| `TypeError: '>' not supported between instances of 'str' and 'int'` | `MolecularWeight` is a string | `float(p["MolecularWeight"])` before any arithmetic comparison - Network accessline 518
| `TypeError: '>' not supported between instances of 'str' and 'int'` | `MolecularWeight` is a string | `float(p["MolecularWeight"])` before any arithmetic comparison - Network accessline 519
| Batch `cid/2244,3672,...` returns only some rows | URL exceeded server limit | Switch to `requests.post(url, data={"cid": "2244,3672,..."})`; same URL - Network accessline 519
| Batch `cid/2244,3672,...` returns only some rows | URL exceeded server limit | Switch to `requests.post(url, data={"cid": "2244,3672,..."})`; same URL - Network accessline 531
- [PubChem PUG-REST documentation](https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest) — Full URL grammar reference - Network accessline 531
- [PubChem PUG-REST documentation](https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest) — Full URL grammar reference - Network accessline 532
- [PUG-REST tutorial with worked examples](https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest-tutorial) — Step-by-step API walk-through - Network accessline 532
- [PUG-REST tutorial with worked examples](https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest-tutorial) — Step-by-step API walk-through - Network accessline 533
- [PubChem property table](https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest#section=Compound-Property-Tables) — Authoritative list of `/property/<name>` values and their semantics - Network accessline 533
- [PubChem property table](https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest#section=Compound-Property-Tables) — Authoritative list of `/property/<name>` values and their semantics - Network accessline 534
- [PubChem main site](https://pubchem.ncbi.nlm.nih.gov/) — Web interface for spot-checks - Network accessline 534
- [PubChem main site](https://pubchem.ncbi.nlm.nih.gov/) — Web interface for spot-checks - Network accessline 535
- [Kim et al. (2023) Nucleic Acids Res — PubChem 2023 update](https://doi.org/10.1093/nar/gkac956) - Network accessline 535
- [Kim et al. (2023) Nucleic Acids Res — PubChem 2023 update](https://doi.org/10.1093/nar/gkac956)
skills/structural-biology-drug-discovery/pytdc-therapeutics-data-commons/SKILL.md
- Network accessline 420
- TDC Official Website: https://tdcommons.ai - Network accessline 420
- TDC Official Website: https://tdcommons.ai - Network accessline 421
- TDC Documentation: https://tdc.readthedocs.io - Network accessline 421
- TDC Documentation: https://tdc.readthedocs.io - Network accessline 422
- GitHub Repository: https://github.com/mims-harvard/TDC - Network accessline 422
- GitHub Repository: https://github.com/mims-harvard/TDC
skills/structural-biology-drug-discovery/rdkit-chemdraw-cdxml/SKILL.md
- Network accessline 205
Render the PNG next to the CDXML (same basename), look at it, then deliver both. [Indigo](https://lifescience.opensource.epam.com/indigo/) (`epam.indigo`) loads a CDXML — a scheme with arrows as a *reaction*, a lone structure as a *molecule - Network accessline 205
Render the PNG next to the CDXML (same basename), look at it, then deliver both. [Indigo](https://lifescience.opensource.epam.com/indigo/) (`epam.indigo`) loads a CDXML — a scheme with arrows as a *reaction*, a lone structure as a *molecule - Network accessline 444
- [RDKit `rdkit.Chem.rdChemDraw`](https://www.rdkit.org/docs/source/rdkit.Chem.rdChemDraw.html) — `MolsFromChemDraw*`, `ReactionsFromChemDraw*`, `MolToChemDrawBlock`, `CDXFormat` - Network accessline 444
- [RDKit `rdkit.Chem.rdChemDraw`](https://www.rdkit.org/docs/source/rdkit.Chem.rdChemDraw.html) — `MolsFromChemDraw*`, `ReactionsFromChemDraw*`, `MolToChemDrawBlock`, `CDXFormat` - Network accessline 445
- [RDKit depiction docs](https://www.rdkit.org/docs/source/rdkit.Chem.Draw.rdMolDraw2D.html) — 2D coordinates and CoordGen - Network accessline 445
- [RDKit depiction docs](https://www.rdkit.org/docs/source/rdkit.Chem.Draw.rdMolDraw2D.html) — 2D coordinates and CoordGen - Network accessline 446
- [CDX/CDXML format specification (CambridgeSoft SDK mirror)](https://chemapps.stolaf.edu/iupac/cdx/sdk/) — arrows, graphics, reactions, text - Network accessline 446
- [CDX/CDXML format specification (CambridgeSoft SDK mirror)](https://chemapps.stolaf.edu/iupac/cdx/sdk/) — arrows, graphics, reactions, text - Network accessline 447
- [RDKit CDXML test fixtures](https://github.com/rdkit/rdkit/tree/master/Code/GraphMol/test_data/CDXML) — real ChemDraw-authored `.cdxml` - Network accessline 447
- [RDKit CDXML test fixtures](https://github.com/rdkit/rdkit/tree/master/Code/GraphMol/test_data/CDXML) — real ChemDraw-authored `.cdxml` - Network accessline 448
- [Indigo toolkit (`epam.indigo`)](https://lifescience.opensource.epam.com/indigo/) — CDXML loading and PNG rendering - Network accessline 448
- [Indigo toolkit (`epam.indigo`)](https://lifescience.opensource.epam.com/indigo/) — CDXML loading and PNG rendering
skills/structural-biology-drug-discovery/rdkit-chemdraw-cdxml/references/cdxml-schema-reference.md
- Network accessline 3
Distilled from the CambridgeSoft/Revvity CDX/CDXML SDK specification (<https://chemapps.stolaf.edu/iupac/cdx/sdk/>) and cross-checked against real - Network accessline 3
Distilled from the CambridgeSoft/Revvity CDX/CDXML SDK specification (<https://chemapps.stolaf.edu/iupac/cdx/sdk/>) and cross-checked against real - Network accessline 4
(<https://chemapps.stolaf.edu/iupac/cdx/sdk/>) and cross-checked against real - Network accessline 4
(<https://chemapps.stolaf.edu/iupac/cdx/sdk/>) and cross-checked against real - Network accessline 40
<?xml version="1.0" encoding="UTF-8" ?> <!DOCTYPE CDXML SYSTEM "http://www.cambridgesoft.com/xml/cdxml.dtd" > - Network accessline 40
<?xml version="1.0" encoding="UTF-8" ?> <!DOCTYPE CDXML SYSTEM "http://www.cambridgesoft.com/xml/cdxml.dtd" > - Network accessline 41
<!DOCTYPE CDXML SYSTEM "http://www.cambridgesoft.com/xml/cdxml.dtd" > - Network accessline 41
<!DOCTYPE CDXML SYSTEM "http://www.cambridgesoft.com/xml/cdxml.dtd" >
skills/structural-biology-drug-discovery/rdkit-chemdraw-cdxml/scripts/build_reaction_scheme.py
skills/structural-biology-drug-discovery/rdkit-chemdraw-cdxml/scripts/check_scheme.py
skills/structural-biology-drug-discovery/rdkit-cheminformatics/SKILL.md
- Network accessline 461
- [RDKit Documentation](https://www.rdkit.org/docs/) — Official documentation and Getting Started guide - Network accessline 461
- [RDKit Documentation](https://www.rdkit.org/docs/) — Official documentation and Getting Started guide - Network accessline 462
- [RDKit Cookbook](https://www.rdkit.org/docs/Cookbook.html) — Recipes and common workflow patterns - Network accessline 462
- [RDKit Cookbook](https://www.rdkit.org/docs/Cookbook.html) — Recipes and common workflow patterns - Network accessline 463
- [Getting Started with RDKit in Python](https://www.rdkit.org/docs/GettingStartedInPython.html) — Comprehensive tutorial - Network accessline 463
- [Getting Started with RDKit in Python](https://www.rdkit.org/docs/GettingStartedInPython.html) — Comprehensive tutorial - Network accessline 464
- [Lipinski, C.A. et al. (2001) Advanced Drug Delivery Reviews 46:3-26](https://doi.org/10.1016/S0169-409X(00)00129-0) — Rule of Five - Network accessline 464
- [Lipinski, C.A. et al. (2001) Advanced Drug Delivery Reviews 46:3-26](https://doi.org/10.1016/S0169-409X(00)00129-0) — Rule of Five - Network accessline 465
- [Veber, D.F. et al. (2002) J. Med. Chem. 45:2615-2623](https://doi.org/10.1021/jm020017n) — Oral bioavailability criteria - Network accessline 465
- [Veber, D.F. et al. (2002) J. Med. Chem. 45:2615-2623](https://doi.org/10.1021/jm020017n) — Oral bioavailability criteria - Network accessline 466
- [Morgan, H.L. (1965) J. Chem. Doc. 5:107-113](https://doi.org/10.1021/c160017a018) — Circular fingerprint algorithm - Network accessline 466
- [Morgan, H.L. (1965) J. Chem. Doc. 5:107-113](https://doi.org/10.1021/c160017a018) — Circular fingerprint algorithm
skills/structural-biology-drug-discovery/rowan/SKILL.md
- Network accessline 26
- **Python packages**: `rowan` (official Python SDK) - **Account**: Free account at https://rowan.chem.ucla.edu/ (academic) or https://rowanquantum.com/ - Network accessline 26
- **Python packages**: `rowan` (official Python SDK) - **Account**: Free account at https://rowan.chem.ucla.edu/ (academic) or https://rowanquantum.com/ - Network accessline 27
- **Account**: Free account at https://rowan.chem.ucla.edu/ (academic) or https://rowanquantum.com/ - Network accessline 27
- **Account**: Free account at https://rowan.chem.ucla.edu/ (academic) or https://rowanquantum.com/ - Access to secretsline 28
[Credential-related source omitted] - Access to secretsline 28
[Credential-related source omitted] - Access to secretsline 35
[Credential-related source omitted] - Access to secretsline 35
[Credential-related source omitted] - Access to secretsline 43
[Credential-related source omitted] - Access to secretsline 43
[Credential-related source omitted] - Access to secretsline 68
[Credential-related source omitted] - Access to secretsline 68
[Credential-related source omitted] - Access to secretsline 72
[Credential-related source omitted] - Access to secretsline 72
[Credential-related source omitted] - Access to secretsline 406
[Credential-related source omitted] - Access to secretsline 406
[Credential-related source omitted] - Network accessline 415
- [Rowan Documentation](https://docs.rowanquantum.com/) — official SDK and API reference - Network accessline 415
- [Rowan Documentation](https://docs.rowanquantum.com/) — official SDK and API reference - Network accessline 416
- [Rowan Python SDK (GitHub)](https://github.com/rowansci/rowan-python) — source code and examples - Network accessline 416
- [Rowan Python SDK (GitHub)](https://github.com/rowansci/rowan-python) — source code and examples - Network accessline 417
- [GFN2-xTB paper: Bannwarth et al. (2019), J. Chem. Theory Comput.](https://doi.org/10.1021/acs.jctc.8b01176) — xTB semiempirical method used by Rowan - Network accessline 417
- [GFN2-xTB paper: Bannwarth et al. (2019), J. Chem. Theory Comput.](https://doi.org/10.1021/acs.jctc.8b01176) — xTB semiempirical method used by Rowan - Network accessline 418
- [B3LYP functional reference: Becke (1993), J. Chem. Phys.](https://doi.org/10.1063/1.464913) — foundational DFT method - Network accessline 418
- [B3LYP functional reference: Becke (1993), J. Chem. Phys.](https://doi.org/10.1063/1.464913) — foundational DFT method
skills/structural-biology-drug-discovery/sar-analysis/SKILL.md
- Network accessline 303
- RDKit documentation — Maximum Common Substructure: https://www.rdkit.org/docs/source/rdkit.Chem.rdFMCS.html - Network accessline 303
- RDKit documentation — Maximum Common Substructure: https://www.rdkit.org/docs/source/rdkit.Chem.rdFMCS.html - Network accessline 304
- RDKit documentation — R-Group Decomposition: https://www.rdkit.org/docs/source/rdkit.Chem.rdRGroupDecomposition.html - Network accessline 304
- RDKit documentation — R-Group Decomposition: https://www.rdkit.org/docs/source/rdkit.Chem.rdRGroupDecomposition.html - Network accessline 305
- RDKit documentation — `MolDraw2D` and `DrawMoleculeACS1996`: https://www.rdkit.org/docs/source/rdkit.Chem.Draw.rdMolDraw2D.html - Network accessline 305
- RDKit documentation — `MolDraw2D` and `DrawMoleculeACS1996`: https://www.rdkit.org/docs/source/rdkit.Chem.Draw.rdMolDraw2D.html - Network accessline 306
- Dalke A, Hastings J. "FMCS: a novel algorithm for the multiple MCS problem." J Cheminform. 2013;5(Suppl 1):O6. https://doi.org/10.1186/1758-2946-5-S1-O6 - Network accessline 306
- Dalke A, Hastings J. "FMCS: a novel algorithm for the multiple MCS problem." J Cheminform. 2013;5(Suppl 1):O6. https://doi.org/10.1186/1758-2946-5-S1-O6 - Network accessline 307
- Lewell XQ, Judd DB, Watson SP, Hann MM. "RECAP — Retrosynthetic Combinatorial Analysis Procedure." J Chem Inf Comput Sci. 1998;38(3):511-522. https://doi.org/10.1021/ci970429i - Network accessline 307
- Lewell XQ, Judd DB, Watson SP, Hann MM. "RECAP — Retrosynthetic Combinatorial Analysis Procedure." J Chem Inf Comput Sci. 1998;38(3):511-522. https://doi.org/10.1021/ci970429i - Network accessline 308
- Stumpfe D, Bajorath J. "Exploring activity cliffs in medicinal chemistry." J Med Chem. 2012;55(7):2932-2942. https://doi.org/10.1021/jm201706b - Network accessline 308
- Stumpfe D, Bajorath J. "Exploring activity cliffs in medicinal chemistry." J Med Chem. 2012;55(7):2932-2942. https://doi.org/10.1021/jm201706b - Network accessline 309
- Allen FH, Bellard S, Brice MD, et al. ACS document standards (ACS1996 drawing style reference): https://pubs.acs.org/doi/10.1021/ci00027a005 - Network accessline 309
- Allen FH, Bellard S, Brice MD, et al. ACS document standards (ACS1996 drawing style reference): https://pubs.acs.org/doi/10.1021/ci00027a005
skills/structural-biology-drug-discovery/smina-molecular-docking/SKILL.md
- Network accessline 34
command -v smina || conda install -c conda-forge smina openbabel pip install rdkit prody pandas py3Dmol # ADFR Suite: https://ccsb.scripps.edu/adfr/downloads/ - Network accessline 34
command -v smina || conda install -c conda-forge smina openbabel pip install rdkit prody pandas py3Dmol # ADFR Suite: https://ccsb.scripps.edu/adfr/downloads/ - Network accessline 36
# ADFR Suite: https://ccsb.scripps.edu/adfr/downloads/ - Network accessline 36
# ADFR Suite: https://ccsb.scripps.edu/adfr/downloads/ - Network accessline 370
- [smina on SourceForge](https://sourceforge.net/projects/smina/) — original distribution - Network accessline 370
- [smina on SourceForge](https://sourceforge.net/projects/smina/) — original distribution - Network accessline 371
- [smina GitHub mirror](https://github.com/mwojcikowski/smina) — maintained builds - Network accessline 371
- [smina GitHub mirror](https://github.com/mwojcikowski/smina) — maintained builds - Network accessline 372
- [Koes, Baumgartner, Camacho (2013)](https://doi.org/10.1021/ci300604z) — smina + dkoes scoring paper, *J Chem Inf Model* - Network accessline 372
- [Koes, Baumgartner, Camacho (2013)](https://doi.org/10.1021/ci300604z) — smina + dkoes scoring paper, *J Chem Inf Model* - Network accessline 373
- [Trott & Olson (2010)](https://doi.org/10.1002/jcc.21334) — upstream Vina paper, *J Comput Chem* - Network accessline 373
- [Trott & Olson (2010)](https://doi.org/10.1002/jcc.21334) — upstream Vina paper, *J Comput Chem* - Network accessline 374
- [ADFR Suite](https://ccsb.scripps.edu/adfr/downloads/) — `prepare_receptor` for receptor PDBQT - Network accessline 374
- [ADFR Suite](https://ccsb.scripps.edu/adfr/downloads/) — `prepare_receptor` for receptor PDBQT - Network accessline 375
- [RDKit documentation](https://www.rdkit.org/docs/) — ligand 3D generation and SDF I/O - Network accessline 375
- [RDKit documentation](https://www.rdkit.org/docs/) — ligand 3D generation and SDF I/O - Network accessline 376
- [Open Babel](https://openbabel.org/docs/) — alternative ligand format conversion - Network accessline 376
- [Open Babel](https://openbabel.org/docs/) — alternative ligand format conversion
skills/structural-biology-drug-discovery/torchdrug/SKILL.md
- Network accessline 31
pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu118 - Network accessline 31
pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu118 - Network accessline 499
- [TorchDrug Documentation](https://torchdrug.ai/docs/) — official docs and tutorials - Network accessline 499
- [TorchDrug Documentation](https://torchdrug.ai/docs/) — official docs and tutorials - Network accessline 500
- [TorchDrug GitHub (DeepGraphLearning/torchdrug)](https://github.com/DeepGraphLearning/torchdrug) — source code - Network accessline 500
- [TorchDrug GitHub (DeepGraphLearning/torchdrug)](https://github.com/DeepGraphLearning/torchdrug) — source code - Network accessline 501
- [Zhu et al. (2022), arXiv — TorchDrug paper](https://arxiv.org/abs/2202.08320) — original platform paper - Network accessline 501
- [Zhu et al. (2022), arXiv — TorchDrug paper](https://arxiv.org/abs/2202.08320) — original platform paper - Network accessline 502
- [MoleculeNet Benchmark](https://moleculenet.org/) — standardized datasets used in TorchDrug - Network accessline 502
- [MoleculeNet Benchmark](https://moleculenet.org/) — standardized datasets used in TorchDrug
skills/structural-biology-drug-discovery/unichem-database/SKILL.md
- Network accessline 11
UniChem is a chemical structure cross-referencing service from EMBL-EBI that links compound records across 20+ public chemistry databases using InChI-based identifiers. It maps a single chemical entity to its corresponding IDs in ChEMBL, Dr - Network accessline 11
UniChem is a chemical structure cross-referencing service from EMBL-EBI that links compound records across 20+ public chemistry databases using InChI-based identifiers. It maps a single chemical entity to its corresponding IDs in ChEMBL, Dr - Network accessline 12
It maps a single chemical entity to its corresponding IDs in ChEMBL, DrugBank, PubChem, ChEBI, PDB (RCSB and PDBe), SureChEMBL, HMDB, DrugCentral, BindingDB, and others. Access is via a free REST API at https://www.ebi.ac.uk/unichem/api/v1/ - Network accessline 12
It maps a single chemical entity to its corresponding IDs in ChEMBL, DrugBank, PubChem, ChEBI, PDB (RCSB and PDBe), SureChEMBL, HMDB, DrugCentral, BindingDB, and others. Access is via a free REST API at https://www.ebi.ac.uk/unichem/api/v1/ - Network accessline 13
Access is via a free REST API at https://www.ebi.ac.uk/unichem/api/v1/ - no API key required. - Network accessline 13
Access is via a free REST API at https://www.ebi.ac.uk/unichem/api/v1/ - no API key required. - Network accessline 45
UNICHEM_API = "https://www.ebi.ac.uk/unichem/api/v1" - Network accessline 45
UNICHEM_API = "https://www.ebi.ac.uk/unichem/api/v1" - Network accessline 47
def unichem_post(endpoint: str, body: dict) -> dict: """POST request to UniChem API; raise on HTTP errors.""" r = requests.post(f"{UNICHEM_API}/{endpoint}", json=body, timeout=20) - Network accessline 47
def unichem_post(endpoint: str, body: dict) -> dict: """POST request to UniChem API; raise on HTTP errors.""" r = requests.post(f"{UNICHEM_API}/{endpoint}", json=body, timeout=20) - Network accessline 48
"""POST request to UniChem API; raise on HTTP errors.""" r = requests.post(f"{UNICHEM_API}/{endpoint}", json=body, timeout=20) r.raise_for_status() - Network accessline 48
"""POST request to UniChem API; raise on HTTP errors.""" r = requests.post(f"{UNICHEM_API}/{endpoint}", json=body, timeout=20) r.raise_for_status() - Network accessline 49
r = requests.post(f"{UNICHEM_API}/{endpoint}", json=body, timeout=20) - Network accessline 49
r = requests.post(f"{UNICHEM_API}/{endpoint}", json=body, timeout=20) - Network accessline 85
UNICHEM_API = "https://www.ebi.ac.uk/unichem/api/v1" - Network accessline 85
UNICHEM_API = "https://www.ebi.ac.uk/unichem/api/v1" - Network accessline 95
def lookup_by_inchikey(inchikey: str) -> pd.DataFrame: """Return all database cross-references for an InChIKey.""" r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 95
def lookup_by_inchikey(inchikey: str) -> pd.DataFrame: """Return all database cross-references for an InChIKey.""" r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 96
"""Return all database cross-references for an InChIKey.""" r = requests.post(f"{UNICHEM_API}/compounds", json={"type": "inchikey", "compound": inchikey}, timeout=20) - Network accessline 96
"""Return all database cross-references for an InChIKey.""" r = requests.post(f"{UNICHEM_API}/compounds", json={"type": "inchikey", "compound": inchikey}, timeout=20) - Network accessline 97
r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 97
r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 125
def get_id_for_source(inchikey: str, source_id: int) -> str | None: """Return the compound ID in a specific database, or None if not found.""" r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 125
def get_id_for_source(inchikey: str, source_id: int) -> str | None: """Return the compound ID in a specific database, or None if not found.""" r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 126
"""Return the compound ID in a specific database, or None if not found.""" r = requests.post(f"{UNICHEM_API}/compounds", json={"type": "inchikey", "compound": inchikey}, timeout=20) - Network accessline 126
"""Return the compound ID in a specific database, or None if not found.""" r = requests.post(f"{UNICHEM_API}/compounds", json={"type": "inchikey", "compound": inchikey}, timeout=20) - Network accessline 127
r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 127
r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 153
UNICHEM_API = "https://www.ebi.ac.uk/unichem/api/v1" - Network accessline 153
UNICHEM_API = "https://www.ebi.ac.uk/unichem/api/v1" - Network accessline 161
""" body = {"type": "sourceID", "compound": compound_id, "sourceID": source_id} r = requests.post(f"{UNICHEM_API}/compounds", json=body, timeout=20) - Network accessline 161
""" body = {"type": "sourceID", "compound": compound_id, "sourceID": source_id} r = requests.post(f"{UNICHEM_API}/compounds", json=body, timeout=20) - Network accessline 162
body = {"type": "sourceID", "compound": compound_id, "sourceID": source_id} r = requests.post(f"{UNICHEM_API}/compounds", json=body, timeout=20) r.raise_for_status() - Network accessline 162
body = {"type": "sourceID", "compound": compound_id, "sourceID": source_id} r = requests.post(f"{UNICHEM_API}/compounds", json=body, timeout=20) r.raise_for_status() - Network accessline 163
r = requests.post(f"{UNICHEM_API}/compounds", json=body, timeout=20) - Network accessline 163
r = requests.post(f"{UNICHEM_API}/compounds", json=body, timeout=20) - Network accessline 194
UNICHEM_API = "https://www.ebi.ac.uk/unichem/api/v1" - Network accessline 194
UNICHEM_API = "https://www.ebi.ac.uk/unichem/api/v1" - Network accessline 200
""" body = {"type": "inchikey", "compound": inchikey} r = requests.post(f"{UNICHEM_API}/connectivity", json=body, timeout=30) - Network accessline 200
""" body = {"type": "inchikey", "compound": inchikey} r = requests.post(f"{UNICHEM_API}/connectivity", json=body, timeout=30) - Network accessline 201
body = {"type": "inchikey", "compound": inchikey} r = requests.post(f"{UNICHEM_API}/connectivity", json=body, timeout=30) r.raise_for_status() - Network accessline 201
body = {"type": "inchikey", "compound": inchikey} r = requests.post(f"{UNICHEM_API}/connectivity", json=body, timeout=30) r.raise_for_status() - Network accessline 202
r = requests.post(f"{UNICHEM_API}/connectivity", json=body, timeout=30) - Network accessline 202
r = requests.post(f"{UNICHEM_API}/connectivity", json=body, timeout=30) - Network accessline 248
UNICHEM_API = "https://www.ebi.ac.uk/unichem/api/v1" - Network accessline 248
UNICHEM_API = "https://www.ebi.ac.uk/unichem/api/v1" - Network accessline 250
def list_sources() -> pd.DataFrame: """Return all UniChem data sources as a DataFrame.""" r = requests.get(f"{UNICHEM_API}/sources", timeout=15) - Network accessline 250
def list_sources() -> pd.DataFrame: """Return all UniChem data sources as a DataFrame.""" r = requests.get(f"{UNICHEM_API}/sources", timeout=15) - Network accessline 251
"""Return all UniChem data sources as a DataFrame.""" r = requests.get(f"{UNICHEM_API}/sources", timeout=15) r.raise_for_status() - Network accessline 251
"""Return all UniChem data sources as a DataFrame.""" r = requests.get(f"{UNICHEM_API}/sources", timeout=15) r.raise_for_status() - Network accessline 252
r = requests.get(f"{UNICHEM_API}/sources", timeout=15) - Network accessline 252
r = requests.get(f"{UNICHEM_API}/sources", timeout=15) - Network accessline 283
UNICHEM_API = "https://www.ebi.ac.uk/unichem/api/v1" - Network accessline 283
UNICHEM_API = "https://www.ebi.ac.uk/unichem/api/v1" - Network accessline 297
row[SOURCE_NAMES.get(sid, f"src_{sid}")] = None try: r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 297
row[SOURCE_NAMES.get(sid, f"src_{sid}")] = None try: r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 298
try: r = requests.post(f"{UNICHEM_API}/compounds", json={"type": "inchikey", "compound": ik}, timeout=20) - Network accessline 298
try: r = requests.post(f"{UNICHEM_API}/compounds", json={"type": "inchikey", "compound": ik}, timeout=20) - Network accessline 299
r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 299
r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 335
UNICHEM_API = "https://www.ebi.ac.uk/unichem/api/v1" - Network accessline 335
UNICHEM_API = "https://www.ebi.ac.uk/unichem/api/v1" - Network accessline 347
for cid in ids: body = {"type": "sourceID", "compound": cid, "sourceID": from_source} r = requests.post(f"{UNICHEM_API}/compounds", json=body, timeout=20) - Network accessline 347
for cid in ids: body = {"type": "sourceID", "compound": cid, "sourceID": from_source} r = requests.post(f"{UNICHEM_API}/compounds", json=body, timeout=20) - Network accessline 348
body = {"type": "sourceID", "compound": cid, "sourceID": from_source} r = requests.post(f"{UNICHEM_API}/compounds", json=body, timeout=20) row = {"input": cid} - Network accessline 348
body = {"type": "sourceID", "compound": cid, "sourceID": from_source} r = requests.post(f"{UNICHEM_API}/compounds", json=body, timeout=20) row = {"input": cid} - Network accessline 349
r = requests.post(f"{UNICHEM_API}/compounds", json=body, timeout=20) - Network accessline 349
r = requests.post(f"{UNICHEM_API}/compounds", json=body, timeout=20) - Network accessline 423
UNICHEM_API = "https://www.ebi.ac.uk/unichem/api/v1" - Network accessline 423
UNICHEM_API = "https://www.ebi.ac.uk/unichem/api/v1" - Network accessline 424
CHEMBL_API = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 424
CHEMBL_API = "https://www.ebi.ac.uk/chembl/api/data" - Network accessline 429
def chembl_to_inchikey(chembl_id: str) -> str | None: """Look up the standard InChIKey for a ChEMBL compound ID.""" r = requests.get(f"{CHEMBL_API}/molecule/{chembl_id}.json", timeout=15) - Network accessline 429
def chembl_to_inchikey(chembl_id: str) -> str | None: """Look up the standard InChIKey for a ChEMBL compound ID.""" r = requests.get(f"{CHEMBL_API}/molecule/{chembl_id}.json", timeout=15) - Network accessline 430
"""Look up the standard InChIKey for a ChEMBL compound ID.""" r = requests.get(f"{CHEMBL_API}/molecule/{chembl_id}.json", timeout=15) if r.status_code == 404: - Network accessline 430
"""Look up the standard InChIKey for a ChEMBL compound ID.""" r = requests.get(f"{CHEMBL_API}/molecule/{chembl_id}.json", timeout=15) if r.status_code == 404: - Network accessline 431
r = requests.get(f"{CHEMBL_API}/molecule/{chembl_id}.json", timeout=15) - Network accessline 431
r = requests.get(f"{CHEMBL_API}/molecule/{chembl_id}.json", timeout=15) - Network accessline 437
def inchikey_to_sources(inchikey: str) -> dict: """Return source_id -> compound_id dict for an InChIKey (first hit per source).""" r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 437
def inchikey_to_sources(inchikey: str) -> dict: """Return source_id -> compound_id dict for an InChIKey (first hit per source).""" r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 438
"""Return source_id -> compound_id dict for an InChIKey (first hit per source).""" r = requests.post(f"{UNICHEM_API}/compounds", json={"type": "inchikey", "compound": inchikey}, timeout=20) - Network accessline 438
"""Return source_id -> compound_id dict for an InChIKey (first hit per source).""" r = requests.post(f"{UNICHEM_API}/compounds", json={"type": "inchikey", "compound": inchikey}, timeout=20) - Network accessline 439
r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 439
r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 485
UNICHEM_API = "https://www.ebi.ac.uk/unichem/api/v1" - Network accessline 485
UNICHEM_API = "https://www.ebi.ac.uk/unichem/api/v1" - Network accessline 494
r = requests.post(f"{UNICHEM_API}/connectivity", - Network accessline 494
r = requests.post(f"{UNICHEM_API}/connectivity", - Network accessline 535
UNICHEM_API = "https://www.ebi.ac.uk/unichem/api/v1" - Network accessline 535
UNICHEM_API = "https://www.ebi.ac.uk/unichem/api/v1" - Network accessline 542
for ik in unique_keys: try: r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 542
for ik in unique_keys: try: r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 543
try: r = requests.post(f"{UNICHEM_API}/compounds", json={"type": "inchikey", "compound": ik}, timeout=15) - Network accessline 543
try: r = requests.post(f"{UNICHEM_API}/compounds", json={"type": "inchikey", "compound": ik}, timeout=15) - Network accessline 544
r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 544
r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 603
def load_source_map() -> dict: r = requests.get(f"{UNICHEM_API}/sources", timeout=15) - Network accessline 603
def load_source_map() -> dict: r = requests.get(f"{UNICHEM_API}/sources", timeout=15) - Network accessline 604
r = requests.get(f"{UNICHEM_API}/sources", timeout=15) - Network accessline 604
r = requests.get(f"{UNICHEM_API}/sources", timeout=15) - Network accessline 621
UNICHEM_API = "https://www.ebi.ac.uk/unichem/api/v1" - Network accessline 621
UNICHEM_API = "https://www.ebi.ac.uk/unichem/api/v1" - Network accessline 622
PUBCHEM_API = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 622
PUBCHEM_API = "https://pubchem.ncbi.nlm.nih.gov/rest/pug" - Network accessline 624
def pubchem_cid_to_inchikey(cid) -> str | None: """Resolve a PubChem CID to a standard InChIKey via PubChem.""" r = requests.get(f"{PUBCHEM_API}/compound/cid/{cid}/property/InChIKey/JSON", timeout=10) - Network accessline 624
def pubchem_cid_to_inchikey(cid) -> str | None: """Resolve a PubChem CID to a standard InChIKey via PubChem.""" r = requests.get(f"{PUBCHEM_API}/compound/cid/{cid}/property/InChIKey/JSON", timeout=10) - Network accessline 625
"""Resolve a PubChem CID to a standard InChIKey via PubChem.""" r = requests.get(f"{PUBCHEM_API}/compound/cid/{cid}/property/InChIKey/JSON", timeout=10) if r.status_code == 404: - Network accessline 625
"""Resolve a PubChem CID to a standard InChIKey via PubChem.""" r = requests.get(f"{PUBCHEM_API}/compound/cid/{cid}/property/InChIKey/JSON", timeout=10) if r.status_code == 404: - Network accessline 626
r = requests.get(f"{PUBCHEM_API}/compound/cid/{cid}/property/InChIKey/JSON", timeout=10) - Network accessline 626
r = requests.get(f"{PUBCHEM_API}/compound/cid/{cid}/property/InChIKey/JSON", timeout=10) - Network accessline 636
if not ik: return [] r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 636
if not ik: return [] r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 637
return [] r = requests.post(f"{UNICHEM_API}/compounds", json={"type": "inchikey", "compound": ik}, timeout=20) - Network accessline 637
return [] r = requests.post(f"{UNICHEM_API}/compounds", json={"type": "inchikey", "compound": ik}, timeout=20) - Network accessline 638
r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 638
r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 664
UNICHEM_API = "https://www.ebi.ac.uk/unichem/api/v1" - Network accessline 664
UNICHEM_API = "https://www.ebi.ac.uk/unichem/api/v1" - Network accessline 666
def is_approved_drug(inchikey: str) -> tuple[bool, str | None]: """Check if compound appears in DrugBank (id=2). Returns (is_drug, DrugBank_ID).""" r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 666
def is_approved_drug(inchikey: str) -> tuple[bool, str | None]: """Check if compound appears in DrugBank (id=2). Returns (is_drug, DrugBank_ID).""" r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 667
"""Check if compound appears in DrugBank (id=2). Returns (is_drug, DrugBank_ID).""" r = requests.post(f"{UNICHEM_API}/compounds", json={"type": "inchikey", "compound": inchikey}, timeout=20) - Network accessline 667
"""Check if compound appears in DrugBank (id=2). Returns (is_drug, DrugBank_ID).""" r = requests.post(f"{UNICHEM_API}/compounds", json={"type": "inchikey", "compound": inchikey}, timeout=20) - Network accessline 668
r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 668
r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 698
UNICHEM_API = "https://www.ebi.ac.uk/unichem/api/v1" - Network accessline 698
UNICHEM_API = "https://www.ebi.ac.uk/unichem/api/v1" - Network accessline 704
rows = [] for ik in inchikeys: r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 704
rows = [] for ik in inchikeys: r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 705
for ik in inchikeys: r = requests.post(f"{UNICHEM_API}/compounds", json={"type": "inchikey", "compound": ik}, timeout=15) - Network accessline 705
for ik in inchikeys: r = requests.post(f"{UNICHEM_API}/compounds", json={"type": "inchikey", "compound": ik}, timeout=15) - Network accessline 706
r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 706
r = requests.post(f"{UNICHEM_API}/compounds", - Network accessline 735
| Problem | Cause | Solution | |---------|-------|----------| | 405 Method Not Allowed on /compounds | Hitting the endpoint with GET | UniChem /compounds is POST-only. Send requests.post(url, json={type:inchikey, compound:ik}). | - Network accessline 735
| Problem | Cause | Solution | |---------|-------|----------| | 405 Method Not Allowed on /compounds | Hitting the endpoint with GET | UniChem /compounds is POST-only. Send requests.post(url, json={type:inchikey, compound:ik}). | - Network accessline 736
|---------|-------|----------| | 405 Method Not Allowed on /compounds | Hitting the endpoint with GET | UniChem /compounds is POST-only. Send requests.post(url, json={type:inchikey, compound:ik}). | | KeyError: sourceID (or sourceId) on per - Network accessline 736
|---------|-------|----------| | 405 Method Not Allowed on /compounds | Hitting the endpoint with GET | UniChem /compounds is POST-only. Send requests.post(url, json={type:inchikey, compound:ik}). | | KeyError: sourceID (or sourceId) on per - Network accessline 737
| 405 Method Not Allowed on /compounds | Hitting the endpoint with GET | UniChem /compounds is POST-only. Send requests.post(url, json={type:inchikey, compound:ik}). | - Network accessline 737
| 405 Method Not Allowed on /compounds | Hitting the endpoint with GET | UniChem /compounds is POST-only. Send requests.post(url, json={type:inchikey, compound:ik}). | - Network accessline 755
- [UniChem API documentation](https://www.ebi.ac.uk/unichem/info/webservices) - Official REST API reference with all endpoint descriptions - Network accessline 755
- [UniChem API documentation](https://www.ebi.ac.uk/unichem/info/webservices) - Official REST API reference with all endpoint descriptions - Network accessline 756
- [UniChem home page](https://www.ebi.ac.uk/unichem/) - Interactive search interface and database listing - Network accessline 756
- [UniChem home page](https://www.ebi.ac.uk/unichem/) - Interactive search interface and database listing - Network accessline 757
- [Chambers et al., J Cheminform 2013](https://doi.org/10.1186/1758-2946-5-3) - Original UniChem publication describing the InChI-based cross-referencing methodology - Network accessline 757
- [Chambers et al., J Cheminform 2013](https://doi.org/10.1186/1758-2946-5-3) - Original UniChem publication describing the InChI-based cross-referencing methodology - Network accessline 758
- [InChI Trust](https://www.inchi-trust.org/) - InChI standard specification and algorithm documentation - Network accessline 758
- [InChI Trust](https://www.inchi-trust.org/) - InChI standard specification and algorithm documentation
skills/structural-biology-drug-discovery/zinc-database/SKILL.md
- Network accessline 49
BASE = "https://cartblanche22.docking.org" - Network accessline 49
BASE = "https://cartblanche22.docking.org" - Network accessline 52
# Look up a substance by ZINC ID (synchronous, returns JSON immediately) r = requests.get(f"{BASE}/substance/ZINC000000029632.json", headers=HEADERS, timeout=30) - Network accessline 52
# Look up a substance by ZINC ID (synchronous, returns JSON immediately) r = requests.get(f"{BASE}/substance/ZINC000000029632.json", headers=HEADERS, timeout=30) - Network accessline 53
r = requests.get(f"{BASE}/substance/ZINC000000029632.json", headers=HEADERS, timeout=30) - Network accessline 53
r = requests.get(f"{BASE}/substance/ZINC000000029632.json", headers=HEADERS, timeout=30) - Network accessline 82
BASE = "https://cartblanche22.docking.org" - Network accessline 82
BASE = "https://cartblanche22.docking.org" - Network accessline 85
def get_substance(zinc_id): """Fetch full record for one ZINC ID (synchronous).""" r = requests.get(f"{BASE}/substance/{zinc_id}.json", headers=HEADERS, timeout=30) - Network accessline 85
def get_substance(zinc_id): """Fetch full record for one ZINC ID (synchronous).""" r = requests.get(f"{BASE}/substance/{zinc_id}.json", headers=HEADERS, timeout=30) - Network accessline 86
"""Fetch full record for one ZINC ID (synchronous).""" r = requests.get(f"{BASE}/substance/{zinc_id}.json", headers=HEADERS, timeout=30) r.raise_for_status() - Network accessline 86
"""Fetch full record for one ZINC ID (synchronous).""" r = requests.get(f"{BASE}/substance/{zinc_id}.json", headers=HEADERS, timeout=30) r.raise_for_status() - Network accessline 87
r = requests.get(f"{BASE}/substance/{zinc_id}.json", headers=HEADERS, timeout=30) - Network accessline 87
r = requests.get(f"{BASE}/substance/{zinc_id}.json", headers=HEADERS, timeout=30) - Network accessline 98
files = {"smiles": (None, smiles), "dist": (None, str(dist)), "adist": (None, str(adist)), "database": (None, database)} sub = requests.post(f"{BASE}/smiles.json", files=files, headers=HEADERS, timeout=60) - Network accessline 98
files = {"smiles": (None, smiles), "dist": (None, str(dist)), "adist": (None, str(adist)), "database": (None, database)} sub = requests.post(f"{BASE}/smiles.json", files=files, headers=HEADERS, timeout=60) - Network accessline 99
"adist": (None, str(adist)), "database": (None, database)} sub = requests.post(f"{BASE}/smiles.json", files=files, headers=HEADERS, timeout=60) sub.raise_for_status() - Network accessline 99
"adist": (None, str(adist)), "database": (None, database)} sub = requests.post(f"{BASE}/smiles.json", files=files, headers=HEADERS, timeout=60) sub.raise_for_status() - Network accessline 100
sub = requests.post(f"{BASE}/smiles.json", files=files, headers=HEADERS, timeout=60) - Network accessline 100
sub = requests.post(f"{BASE}/smiles.json", files=files, headers=HEADERS, timeout=60) - Network accessline 104
deadline = time.time() + timeout while time.time() < deadline: res = requests.get(f"{BASE}/search/result/{task}", headers=HEADERS, timeout=30).json() - Network accessline 104
deadline = time.time() + timeout while time.time() < deadline: res = requests.get(f"{BASE}/search/result/{task}", headers=HEADERS, timeout=30).json() - Network accessline 105
while time.time() < deadline: res = requests.get(f"{BASE}/search/result/{task}", headers=HEADERS, timeout=30).json() if res.get("status") == "SUCCESS": - Network accessline 105
while time.time() < deadline: res = requests.get(f"{BASE}/search/result/{task}", headers=HEADERS, timeout=30).json() if res.get("status") == "SUCCESS": - Network accessline 106
res = requests.get(f"{BASE}/search/result/{task}", headers=HEADERS, timeout=30).json() - Network accessline 106
res = requests.get(f"{BASE}/search/result/{task}", headers=HEADERS, timeout=30).json() - Network accessline 407
- [CartBlanche / ZINC22 web app](https://cartblanche22.docking.org/) — Substance lookup, SMILES search, tranche browser - [ZINC22 paper](https://doi.org/10.1021/acs.jcim.2c01253) — Tingle et al., "ZINC-22, A Free Multi-Billion-Scale Databas - Network accessline 407
- [CartBlanche / ZINC22 web app](https://cartblanche22.docking.org/) — Substance lookup, SMILES search, tranche browser - [ZINC22 paper](https://doi.org/10.1021/acs.jcim.2c01253) — Tingle et al., "ZINC-22, A Free Multi-Billion-Scale Databas - Network accessline 408
- [ZINC22 paper](https://doi.org/10.1021/acs.jcim.2c01253) — Tingle et al., "ZINC-22, A Free Multi-Billion-Scale Database of Tangible Compounds for Ligand Discovery," J. Chem. Inf. Model. 2023 - [docking.org wiki](https://wiki.docking.org/) - Network accessline 408
- [ZINC22 paper](https://doi.org/10.1021/acs.jcim.2c01253) — Tingle et al., "ZINC-22, A Free Multi-Billion-Scale Database of Tangible Compounds for Ligand Discovery," J. Chem. Inf. Model. 2023 - [docking.org wiki](https://wiki.docking.org/) - Network accessline 409
- [docking.org wiki](https://wiki.docking.org/) — CartBlanche usage and ZINC22 download documentation - Network accessline 409
- [docking.org wiki](https://wiki.docking.org/) — CartBlanche usage and ZINC22 download documentation - Network accessline 410
- [files.docking.org](https://files.docking.org/) — Bulk tranche file downloads for ZINC22 - Network accessline 410
- [files.docking.org](https://files.docking.org/) — Bulk tranche file downloads for ZINC22
skills/systems-biology-multiomics/brenda-database/SKILL.md
- Network accessline 25
- **Python packages**: `zeep` (SOAP client), `pandas`, `requests` - **Data requirements**: EC numbers (e.g., `1.1.1.1`), enzyme names, or organism names - **Environment**: internet connection; free academic registration at https://www.brend - Network accessline 25
- **Python packages**: `zeep` (SOAP client), `pandas`, `requests` - **Data requirements**: EC numbers (e.g., `1.1.1.1`), enzyme names, or organism names - **Environment**: internet connection; free academic registration at https://www.brend - Network accessline 26
- **Data requirements**: EC numbers (e.g., `1.1.1.1`), enzyme names, or organism names - **Environment**: internet connection; free academic registration at https://www.brenda-enzymes.org/register.php - **Rate limits**: no explicit limit st - Network accessline 26
- **Data requirements**: EC numbers (e.g., `1.1.1.1`), enzyme names, or organism names - **Environment**: internet connection; free academic registration at https://www.brenda-enzymes.org/register.php - **Rate limits**: no explicit limit st - Network accessline 27
- **Environment**: internet connection; free academic registration at https://www.brenda-enzymes.org/register.php - Network accessline 27
- **Environment**: internet connection; free academic registration at https://www.brenda-enzymes.org/register.php - Network accessline 31
pip install zeep pandas requests # Register at https://www.brenda-enzymes.org/register.php to obtain API credentials - Network accessline 31
pip install zeep pandas requests # Register at https://www.brenda-enzymes.org/register.php to obtain API credentials - Network accessline 32
# Register at https://www.brenda-enzymes.org/register.php to obtain API credentials - Network accessline 32
# Register at https://www.brenda-enzymes.org/register.php to obtain API credentials - Network accessline 40
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl" - Network accessline 40
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl" - Network accessline 66
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl" - Network accessline 66
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl" - Network accessline 120
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl" - Network accessline 120
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl" - Network accessline 155
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl" - Network accessline 155
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl" - Network accessline 196
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl" - Network accessline 196
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl" - Network accessline 222
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl" - Network accessline 222
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl" - Network accessline 259
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl" - Network accessline 259
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl" - Network accessline 301
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl" - Network accessline 301
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl" - Network accessline 349
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl" - Network accessline 349
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl" - Access to secretsline 397
[Credential-related source omitted] - Access to secretsline 397
[Credential-related source omitted] - Network accessline 415
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl" - Network accessline 415
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl" - Network accessline 455
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl" - Network accessline 455
WSDL = "https://www.brenda-enzymes.org/soap/brenda_zeep.wsdl" - Network accessline 489
- [BRENDA database](https://www.brenda-enzymes.org/) — Main BRENDA portal and manual search - Network accessline 489
- [BRENDA database](https://www.brenda-enzymes.org/) — Main BRENDA portal and manual search - Network accessline 490
- [BRENDA web service documentation](https://www.brenda-enzymes.org/soap.php) — SOAP API reference and parameter descriptions - Network accessline 490
- [BRENDA web service documentation](https://www.brenda-enzymes.org/soap.php) — SOAP API reference and parameter descriptions - Network accessline 491
- [zeep Python SOAP client](https://docs.python-zeep.org/) — Python library for SOAP web services - Network accessline 491
- [zeep Python SOAP client](https://docs.python-zeep.org/) — Python library for SOAP web services - Network accessline 492
- [Chang et al. (2021) BRENDA update](https://doi.org/10.1093/nar/gkaa1025) — BRENDA 2021 database update paper - Network accessline 492
- [Chang et al. (2021) BRENDA update](https://doi.org/10.1093/nar/gkaa1025) — BRENDA 2021 database update paper
skills/systems-biology-multiomics/cellchat-cell-communication/SKILL.md
- Network accessline 578
- [CellChat GitHub — jinworks/CellChat](https://github.com/jinworks/CellChat) — source code, tutorials, and vignettes - Network accessline 578
- [CellChat GitHub — jinworks/CellChat](https://github.com/jinworks/CellChat) — source code, tutorials, and vignettes - Network accessline 579
- [CellChat vignette (HTML)](https://htmlpreview.github.io/?https://github.com/jinworks/CellChat/blob/master/tutorial/CellChat-vignette.html) — official step-by-step tutorial - Network accessline 579
- [CellChat vignette (HTML)](https://htmlpreview.github.io/?https://github.com/jinworks/CellChat/blob/master/tutorial/CellChat-vignette.html) — official step-by-step tutorial - Network accessline 580
- [Jin et al. (2021) Nature Communications — CellChat original paper](https://doi.org/10.1038/s41467-021-21246-9) — algorithmic description, benchmarking, and case studies - Network accessline 580
- [Jin et al. (2021) Nature Communications — CellChat original paper](https://doi.org/10.1038/s41467-021-21246-9) — algorithmic description, benchmarking, and case studies - Network accessline 581
- [CellChat comparison vignette](https://htmlpreview.github.io/?https://github.com/jinworks/CellChat/blob/master/tutorial/CellChat_comparison_with_different_databases.html) — comparing CellChatDB with CellPhoneDB and other databases - Network accessline 581
- [CellChat comparison vignette](https://htmlpreview.github.io/?https://github.com/jinworks/CellChat/blob/master/tutorial/CellChat_comparison_with_different_databases.html) — comparing CellChatDB with CellPhoneDB and other databases - Network accessline 582
- [liana Python package documentation](https://liana-py.readthedocs.io/) — pure-Python alternative with consensus LR ranking across CellChat, CellPhoneDB, NATMI, and Connectome - Network accessline 582
- [liana Python package documentation](https://liana-py.readthedocs.io/) — pure-Python alternative with consensus LR ranking across CellChat, CellPhoneDB, NATMI, and Connectome
skills/systems-biology-multiomics/cobrapy-metabolic-modeling/SKILL.md
- Network accessline 527
- [COBRApy documentation](https://cobrapy.readthedocs.io/) — official API reference and tutorials - Network accessline 527
- [COBRApy documentation](https://cobrapy.readthedocs.io/) — official API reference and tutorials - Network accessline 528
- [BiGG Models](http://bigg.ucsd.edu/) — curated genome-scale metabolic model repository - Network accessline 528
- [BiGG Models](http://bigg.ucsd.edu/) — curated genome-scale metabolic model repository - Network accessline 529
- [COBRApy GitHub](https://github.com/opencobra/cobrapy) — source code and issue tracker - Network accessline 529
- [COBRApy GitHub](https://github.com/opencobra/cobrapy) — source code and issue tracker - Network accessline 530
- Ebrahim et al. (2013) "COBRApy: COnstraints-Based Reconstruction and Analysis for Python" — [BMC Systems Biology](https://doi.org/10.1186/1752-0509-7-74) - Network accessline 530
- Ebrahim et al. (2013) "COBRApy: COnstraints-Based Reconstruction and Analysis for Python" — [BMC Systems Biology](https://doi.org/10.1186/1752-0509-7-74)
skills/systems-biology-multiomics/kegg-pathway-analysis/SKILL.md
- Network accessline 243
pathway_map = json.load(f) else: resp = requests.get( - Network accessline 243
pathway_map = json.load(f) else: resp = requests.get( - Network accessline 244
else: resp = requests.get( f"https://rest.kegg.jp/list/pathway/{organism_code}", timeout=30 - Network accessline 244
else: resp = requests.get( f"https://rest.kegg.jp/list/pathway/{organism_code}", timeout=30 - Network accessline 245
resp = requests.get( - Network accessline 245
resp = requests.get( - Network accessline 246
f"https://rest.kegg.jp/list/pathway/{organism_code}", timeout=30 - Network accessline 246
f"https://rest.kegg.jp/list/pathway/{organism_code}", timeout=30 - Network accessline 353
- [KEGG: Kyoto Encyclopedia of Genes and Genomes](https://www.kegg.jp/) -- Official KEGG database with pathway maps, organism lists, and REST API documentation - Network accessline 353
- [KEGG: Kyoto Encyclopedia of Genes and Genomes](https://www.kegg.jp/) -- Official KEGG database with pathway maps, organism lists, and REST API documentation - Network accessline 354
- [clusterProfiler: an R package for comparing biological themes among gene clusters](https://doi.org/10.1089/omi.2011.0118) -- Original publication by Guangchuang Yu describing the enrichKEGG and gseKEGG functions - Network accessline 354
- [clusterProfiler: an R package for comparing biological themes among gene clusters](https://doi.org/10.1089/omi.2011.0118) -- Original publication by Guangchuang Yu describing the enrichKEGG and gseKEGG functions - Network accessline 355
- [gseapy documentation](https://gseapy.readthedocs.io/) -- Python package for gene set enrichment analysis with offline KEGG gene set support - Network accessline 355
- [gseapy documentation](https://gseapy.readthedocs.io/) -- Python package for gene set enrichment analysis with offline KEGG gene set support - Network accessline 356
- [KEGG REST API reference](https://rest.kegg.jp/kegg/rest_api.html) -- Technical documentation for programmatic access to KEGG pathway data - Network accessline 356
- [KEGG REST API reference](https://rest.kegg.jp/kegg/rest_api.html) -- Technical documentation for programmatic access to KEGG pathway data
skills/systems-biology-multiomics/lamindb-data-management/SKILL.md
- Network accessline 470
- [LaminDB documentation](https://docs.lamin.ai) — official user guide and API reference - Network accessline 470
- [LaminDB documentation](https://docs.lamin.ai) — official user guide and API reference - Network accessline 471
- [LaminDB tutorial](https://docs.lamin.ai/tutorial) — step-by-step introduction - Network accessline 471
- [LaminDB tutorial](https://docs.lamin.ai/tutorial) — step-by-step introduction - Network accessline 472
- [Bionty documentation](https://docs.lamin.ai/bionty) — biological ontology management - Network accessline 472
- [Bionty documentation](https://docs.lamin.ai/bionty) — biological ontology management - Network accessline 473
- [LaminDB GitHub](https://github.com/laminlabs/lamindb) — source code and issues - Network accessline 473
- [LaminDB GitHub](https://github.com/laminlabs/lamindb) — source code and issues
skills/systems-biology-multiomics/libsbml-network-modeling/SKILL.md
- Network accessline 26
- **Python packages**: `python-libsbml`, `numpy`, `pandas` (optional, for matrix extraction) - **Optional packages**: `cobra` (COBRApy, for FBA after SBML load), `tellurium` (for SBML↔Antimony conversion and simulation) - **Data requirement - Network accessline 26
- **Python packages**: `python-libsbml`, `numpy`, `pandas` (optional, for matrix extraction) - **Optional packages**: `cobra` (COBRApy, for FBA after SBML load), `tellurium` (for SBML↔Antimony conversion and simulation) - **Data requirement - Network accessline 28
- **Data requirements**: SBML files (`.xml`), or built from scratch in Python; BioModels Database SBML files are freely available at https://www.ebi.ac.uk/biomodels/ - Network accessline 28
- **Data requirements**: SBML files (`.xml`), or built from scratch in Python; BioModels Database SBML files are freely available at https://www.ebi.ac.uk/biomodels/ - Network accessline 486
# 1. Download SBML from BioModels Database (BIOMD0000000012 = Tyson 1991 cell cycle) url = "https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000012?filename=BIOMD0000000012_url.xml" - Network accessline 486
# 1. Download SBML from BioModels Database (BIOMD0000000012 = Tyson 1991 cell cycle) url = "https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000012?filename=BIOMD0000000012_url.xml" - Network accessline 487
url = "https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000012?filename=BIOMD0000000012_url.xml" - Network accessline 487
url = "https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000012?filename=BIOMD0000000012_url.xml" - Network accessline 488
urllib.request.urlretrieve(url, "BIOMD0000000012.xml") - Network accessline 488
urllib.request.urlretrieve(url, "BIOMD0000000012.xml") - Network accessline 826
- [libSBML Python documentation](https://sbml.org/software/libsbml/libsbml-docs/api/python/) — complete Python API reference - Network accessline 826
- [libSBML Python documentation](https://sbml.org/software/libsbml/libsbml-docs/api/python/) — complete Python API reference - Network accessline 827
- [SBML specification portal](https://sbml.org/documents/specifications/) — official SBML Level 3 core and package specifications - Network accessline 827
- [SBML specification portal](https://sbml.org/documents/specifications/) — official SBML Level 3 core and package specifications - Network accessline 828
- [libSBML GitHub repository](https://github.com/sbmlteam/libsbml) — source code, issue tracker, and release notes - Network accessline 828
- [libSBML GitHub repository](https://github.com/sbmlteam/libsbml) — source code, issue tracker, and release notes - Network accessline 829
- [BioModels Database](https://www.ebi.ac.uk/biomodels/) — curated SBML model repository (1,000+ manually curated models) - Network accessline 829
- [BioModels Database](https://www.ebi.ac.uk/biomodels/) — curated SBML model repository (1,000+ manually curated models) - Network accessline 830
- [Hucka et al. (2003) Bioinformatics 19(4):524–531](https://doi.org/10.1093/bioinformatics/btg015) — original SBML specification paper - Network accessline 830
- [Hucka et al. (2003) Bioinformatics 19(4):524–531](https://doi.org/10.1093/bioinformatics/btg015) — original SBML specification paper - Network accessline 831
- [Keating et al. (2020) Molecular Systems Biology 16(8):e9110](https://doi.org/10.15252/msb.20199110) — SBML Level 3 and the current extension ecosystem overview - Network accessline 831
- [Keating et al. (2020) Molecular Systems Biology 16(8):e9110](https://doi.org/10.15252/msb.20199110) — SBML Level 3 and the current extension ecosystem overview
skills/systems-biology-multiomics/mofaplus-multi-omics/SKILL.md
- Network accessline 560
- [MOFA2 GitHub repository](https://github.com/bioFAM/MOFA2) — Source code for mofapy2 (Python) and MOFA2 R package - Network accessline 560
- [MOFA2 GitHub repository](https://github.com/bioFAM/MOFA2) — Source code for mofapy2 (Python) and MOFA2 R package - Network accessline 561
- [MOFA2 documentation and tutorials](https://biofam.github.io/MOFA2/) — Official documentation with vignettes for Python and R - Network accessline 561
- [MOFA2 documentation and tutorials](https://biofam.github.io/MOFA2/) — Official documentation with vignettes for Python and R - Network accessline 562
- [Argelaguet et al. (2020)](https://doi.org/10.1186/s13059-020-02015-1) — "MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data", *Genome Biology* - Network accessline 562
- [Argelaguet et al. (2020)](https://doi.org/10.1186/s13059-020-02015-1) — "MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data", *Genome Biology* - Network accessline 563
- [Argelaguet et al. (2018)](https://doi.org/10.15252/msb.20178124) — Original MOFA paper, *Molecular Systems Biology* - Network accessline 563
- [Argelaguet et al. (2018)](https://doi.org/10.15252/msb.20178124) — Original MOFA paper, *Molecular Systems Biology* - Network accessline 564
- [mofapy2 PyPI package](https://pypi.org/project/mofapy2/) — Installation and version history - Network accessline 564
- [mofapy2 PyPI package](https://pypi.org/project/mofapy2/) — Installation and version history
skills/systems-biology-multiomics/muon-multiomics-singlecell/SKILL.md
- Network accessline 702
- [muon documentation](https://muon.readthedocs.io/) — official docs, tutorials, API reference - Network accessline 702
- [muon documentation](https://muon.readthedocs.io/) — official docs, tutorials, API reference - Network accessline 703
- [muon GitHub (scverse)](https://github.com/scverse/muon) — source code, issues, examples - Network accessline 703
- [muon GitHub (scverse)](https://github.com/scverse/muon) — source code, issues, examples - Network accessline 704
- [Bredikhin et al. Genome Biology 2022](https://doi.org/10.1186/s13059-021-02577-8) — muon/MuData publication - Network accessline 704
- [Bredikhin et al. Genome Biology 2022](https://doi.org/10.1186/s13059-021-02577-8) — muon/MuData publication - Network accessline 705
- [10x Genomics Multiome analysis guide](https://muon-tutorials.readthedocs.io/en/latest/single-cell-rna-atac/pbmc10k/1-Gene-Expression-Processing.html) — step-by-step Multiome tutorial in muon - Network accessline 705
- [10x Genomics Multiome analysis guide](https://muon-tutorials.readthedocs.io/en/latest/single-cell-rna-atac/pbmc10k/1-Gene-Expression-Processing.html) — step-by-step Multiome tutorial in muon - Network accessline 706
- [WNN method (Hao et al. Cell 2021)](https://doi.org/10.1016/j.cell.2021.04.048) — original Weighted Nearest Neighbor paper from Seurat v4 - Network accessline 706
- [WNN method (Hao et al. Cell 2021)](https://doi.org/10.1016/j.cell.2021.04.048) — original Weighted Nearest Neighbor paper from Seurat v4
skills/systems-biology-multiomics/omics-analysis-guide/SKILL.md
- Network accessline 530
### Pipelines and Tools - **DESeq2**: https://bioconductor.org/packages/release/bioc/html/DESeq2.html — Love MI, Huber W, Anders S. *Genome Biology* 2014; 15:550. PMID: 25516281 - Network accessline 530
### Pipelines and Tools - **DESeq2**: https://bioconductor.org/packages/release/bioc/html/DESeq2.html — Love MI, Huber W, Anders S. *Genome Biology* 2014; 15:550. PMID: 25516281 - Network accessline 531
- **DESeq2**: https://bioconductor.org/packages/release/bioc/html/DESeq2.html — Love MI, Huber W, Anders S. *Genome Biology* 2014; 15:550. PMID: 25516281 - Network accessline 531
- **DESeq2**: https://bioconductor.org/packages/release/bioc/html/DESeq2.html — Love MI, Huber W, Anders S. *Genome Biology* 2014; 15:550. PMID: 25516281 - Network accessline 532
- **edgeR**: https://bioconductor.org/packages/release/bioc/html/edgeR.html — Robinson MD, McCarthy DJ, Smyth GK. *Bioinformatics* 2010; 26(1):139-40. - Network accessline 532
- **edgeR**: https://bioconductor.org/packages/release/bioc/html/edgeR.html — Robinson MD, McCarthy DJ, Smyth GK. *Bioinformatics* 2010; 26(1):139-40. - Network accessline 533
- **STAR aligner**: https://github.com/alexdobin/STAR — Dobin A, et al. *Bioinformatics* 2013; 29(1):15-21. PMID: 23104886 - Network accessline 533
- **STAR aligner**: https://github.com/alexdobin/STAR — Dobin A, et al. *Bioinformatics* 2013; 29(1):15-21. PMID: 23104886 - Network accessline 534
- **MaxQuant**: https://www.maxquant.org/ — Cox J, Mann M. *Nat Biotechnol* 2008; 26(12):1367-72. PMID: 19029910 - Network accessline 534
- **MaxQuant**: https://www.maxquant.org/ — Cox J, Mann M. *Nat Biotechnol* 2008; 26(12):1367-72. PMID: 19029910 - Network accessline 535
- **limma**: https://bioconductor.org/packages/release/bioc/html/limma.html — Ritchie ME, et al. *Nucleic Acids Res* 2015; 43(7):e47. - Network accessline 535
- **limma**: https://bioconductor.org/packages/release/bioc/html/limma.html — Ritchie ME, et al. *Nucleic Acids Res* 2015; 43(7):e47. - Network accessline 536
- **ComBat (sva package)**: https://bioconductor.org/packages/release/bioc/html/sva.html — Johnson WE, Li C, Rabinovic A. *Biostatistics* 2007; 8(1):118-27. - Network accessline 536
- **ComBat (sva package)**: https://bioconductor.org/packages/release/bioc/html/sva.html — Johnson WE, Li C, Rabinovic A. *Biostatistics* 2007; 8(1):118-27. - Network accessline 538
### Consortium Best Practices - **ENCODE RNA-seq pipeline**: https://www.encodeproject.org/data-standards/rna-seq/ - Network accessline 538
### Consortium Best Practices - **ENCODE RNA-seq pipeline**: https://www.encodeproject.org/data-standards/rna-seq/ - Network accessline 539
- **ENCODE RNA-seq pipeline**: https://www.encodeproject.org/data-standards/rna-seq/ - Network accessline 539
- **ENCODE RNA-seq pipeline**: https://www.encodeproject.org/data-standards/rna-seq/ - Network accessline 540
- **GTEx Analysis Protocol**: https://gtexportal.org/home/methods - Network accessline 540
- **GTEx Analysis Protocol**: https://gtexportal.org/home/methods - Network accessline 541
- **TCGA Analysis Protocols**: https://docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/ - Network accessline 541
- **TCGA Analysis Protocols**: https://docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/ - Network accessline 544
- **Benjamini-Hochberg FDR**: Benjamini Y, Hochberg Y. *J. R. Stat. Soc. B* 1995; 57(1):289-300. - **Multiple imputation in proteomics review**: Lazar C, et al. *J Proteome Res* 2016; 15(4):1116-25. - **scipy.stats**: https://docs.scipy.org - Network accessline 544
- **Benjamini-Hochberg FDR**: Benjamini Y, Hochberg Y. *J. R. Stat. Soc. B* 1995; 57(1):289-300. - **Multiple imputation in proteomics review**: Lazar C, et al. *J Proteome Res* 2016; 15(4):1116-25. - **scipy.stats**: https://docs.scipy.org - Network accessline 545
- **Multiple imputation in proteomics review**: Lazar C, et al. *J Proteome Res* 2016; 15(4):1116-25. - **scipy.stats**: https://docs.scipy.org/doc/scipy/reference/stats.html - **statsmodels multiple testing**: https://www.statsmodels.org/s - Network accessline 545
- **Multiple imputation in proteomics review**: Lazar C, et al. *J Proteome Res* 2016; 15(4):1116-25. - **scipy.stats**: https://docs.scipy.org/doc/scipy/reference/stats.html - **statsmodels multiple testing**: https://www.statsmodels.org/s - Network accessline 546
- **scipy.stats**: https://docs.scipy.org/doc/scipy/reference/stats.html - Network accessline 546
- **scipy.stats**: https://docs.scipy.org/doc/scipy/reference/stats.html - Network accessline 547
- **statsmodels multiple testing**: https://www.statsmodels.org/stable/stats.html - Network accessline 547
- **statsmodels multiple testing**: https://www.statsmodels.org/stable/stats.html - Network accessline 549
### Data Repositories for Validation - **GEO**: https://www.ncbi.nlm.nih.gov/geo/ - Network accessline 549
### Data Repositories for Validation - **GEO**: https://www.ncbi.nlm.nih.gov/geo/ - Network accessline 550
- **GEO**: https://www.ncbi.nlm.nih.gov/geo/ - Network accessline 550
- **GEO**: https://www.ncbi.nlm.nih.gov/geo/ - Network accessline 551
- **ArrayExpress**: https://www.ebi.ac.uk/biostudies/arrayexpress - Network accessline 551
- **ArrayExpress**: https://www.ebi.ac.uk/biostudies/arrayexpress - Network accessline 552
- **PRIDE (proteomics)**: https://www.ebi.ac.uk/pride/ - Network accessline 552
- **PRIDE (proteomics)**: https://www.ebi.ac.uk/pride/
skills/systems-biology-multiomics/reactome-database/SKILL.md
- Network accessline 34
- **No authentication required** — all endpoints are public - **No documented hard rate limit** — add `time.sleep(0.5)` between batch requests to be respectful - **Content Service base URL**: `https://reactome.org/ContentService` - Network accessline 34
- **No authentication required** — all endpoints are public - **No documented hard rate limit** — add `time.sleep(0.5)` between batch requests to be respectful - **Content Service base URL**: `https://reactome.org/ContentService` - Network accessline 35
- **No documented hard rate limit** — add `time.sleep(0.5)` between batch requests to be respectful - **Content Service base URL**: `https://reactome.org/ContentService` - **Analysis Service base URL**: `https://reactome.org/AnalysisService - Network accessline 35
- **No documented hard rate limit** — add `time.sleep(0.5)` between batch requests to be respectful - **Content Service base URL**: `https://reactome.org/ContentService` - **Analysis Service base URL**: `https://reactome.org/AnalysisService - Network accessline 36
- **Content Service base URL**: `https://reactome.org/ContentService` - Network accessline 36
- **Content Service base URL**: `https://reactome.org/ContentService` - Network accessline 37
- **Analysis Service base URL**: `https://reactome.org/AnalysisService` - Network accessline 37
- **Analysis Service base URL**: `https://reactome.org/AnalysisService` - Network accessline 46
CONTENT = "https://reactome.org/ContentService" - Network accessline 46
CONTENT = "https://reactome.org/ContentService" - Network accessline 47
ANALYSIS = "https://reactome.org/AnalysisService" - Network accessline 47
ANALYSIS = "https://reactome.org/AnalysisService" - Network accessline 49
def reactome_get(base, path, params=None): """Generic Reactome REST API caller. Returns JSON or raises.""" resp = requests.get(f"{base}{path}", params=params) - Network accessline 49
def reactome_get(base, path, params=None): """Generic Reactome REST API caller. Returns JSON or raises.""" resp = requests.get(f"{base}{path}", params=params) - Network accessline 50
"""Generic Reactome REST API caller. Returns JSON or raises.""" resp = requests.get(f"{base}{path}", params=params) resp.raise_for_status() - Network accessline 50
"""Generic Reactome REST API caller. Returns JSON or raises.""" resp = requests.get(f"{base}{path}", params=params) resp.raise_for_status() - Network accessline 51
resp = requests.get(f"{base}{path}", params=params) - Network accessline 51
resp = requests.get(f"{base}{path}", params=params) - Network accessline 141
ANALYSIS = "https://reactome.org/AnalysisService" - Network accessline 141
ANALYSIS = "https://reactome.org/AnalysisService" - Network accessline 146
# Submit for enrichment (POST with text body) resp = requests.post( - Network accessline 146
# Submit for enrichment (POST with text body) resp = requests.post( - Network accessline 147
resp = requests.post( - Network accessline 147
resp = requests.post( - Network accessline 177
ANALYSIS = "https://reactome.org/AnalysisService" - Network accessline 177
ANALYSIS = "https://reactome.org/AnalysisService" - Network accessline 182
# Get results with filtering results = requests.get(f"{ANALYSIS}/token/{token}", params={ - Network accessline 182
# Get results with filtering results = requests.get(f"{ANALYSIS}/token/{token}", params={ - Network accessline 183
results = requests.get(f"{ANALYSIS}/token/{token}", params={ - Network accessline 183
results = requests.get(f"{ANALYSIS}/token/{token}", params={ - Network accessline 196
# Get identifiers found in a specific pathway pathway_detail = requests.get( - Network accessline 196
# Get identifiers found in a specific pathway pathway_detail = requests.get( - Network accessline 197
pathway_detail = requests.get( - Network accessline 197
pathway_detail = requests.get( - Network accessline 299
- **Re-fetching** results without re-running analysis (`GET /token/{token}`) - **Filtering** results by species or resource after initial analysis - **Sharing** results with collaborators via URL: `https://reactome.org/PathwayBrowser/#/DTAB - Network accessline 299
- **Re-fetching** results without re-running analysis (`GET /token/{token}`) - **Filtering** results by species or resource after initial analysis - **Sharing** results with collaborators via URL: `https://reactome.org/PathwayBrowser/#/DTAB - Network accessline 300
- **Filtering** results by species or resource after initial analysis - **Sharing** results with collaborators via URL: `https://reactome.org/PathwayBrowser/#/DTAB=AN&ANALYSIS={token}` - Tokens expire after several hours; re-submit the gene - Network accessline 300
- **Filtering** results by species or resource after initial analysis - **Sharing** results with collaborators via URL: `https://reactome.org/PathwayBrowser/#/DTAB=AN&ANALYSIS={token}` - Tokens expire after several hours; re-submit the gene - Network accessline 301
- **Sharing** results with collaborators via URL: `https://reactome.org/PathwayBrowser/#/DTAB=AN&ANALYSIS={token}` - Network accessline 301
- **Sharing** results with collaborators via URL: `https://reactome.org/PathwayBrowser/#/DTAB=AN&ANALYSIS={token}` - Network accessline 314
CONTENT = "https://reactome.org/ContentService" - Network accessline 314
CONTENT = "https://reactome.org/ContentService" - Network accessline 315
ANALYSIS = "https://reactome.org/AnalysisService" - Network accessline 315
ANALYSIS = "https://reactome.org/AnalysisService" - Network accessline 317
# Step 1: Submit gene list genes = "TP53\nBRCA1\nBRCA2\nATM\nCHEK2\nCDK2\nRB1\nMDM2\nCDKN1A\nBAX" resp = requests.post( - Network accessline 317
# Step 1: Submit gene list genes = "TP53\nBRCA1\nBRCA2\nATM\nCHEK2\nCDK2\nRB1\nMDM2\nCDKN1A\nBAX" resp = requests.post( - Network accessline 318
genes = "TP53\nBRCA1\nBRCA2\nATM\nCHEK2\nCDK2\nRB1\nMDM2\nCDKN1A\nBAX" resp = requests.post( f"{ANALYSIS}/identifiers/", - Network accessline 318
genes = "TP53\nBRCA1\nBRCA2\nATM\nCHEK2\nCDK2\nRB1\nMDM2\nCDKN1A\nBAX" resp = requests.post( f"{ANALYSIS}/identifiers/", - Network accessline 319
resp = requests.post( - Network accessline 319
resp = requests.post( - Network accessline 337
# Step 3: Get details on top pathway top_id = result["pathways"][0]["stId"] detail = requests.get(f"{CONTENT}/data/query/{top_id}").json() - Network accessline 337
# Step 3: Get details on top pathway top_id = result["pathways"][0]["stId"] detail = requests.get(f"{CONTENT}/data/query/{top_id}").json() - Network accessline 338
top_id = result["pathways"][0]["stId"] detail = requests.get(f"{CONTENT}/data/query/{top_id}").json() print(f"\nTop pathway: {detail['displayName']}") - Network accessline 338
top_id = result["pathways"][0]["stId"] detail = requests.get(f"{CONTENT}/data/query/{top_id}").json() print(f"\nTop pathway: {detail['displayName']}") - Network accessline 339
detail = requests.get(f"{CONTENT}/data/query/{top_id}").json() - Network accessline 339
detail = requests.get(f"{CONTENT}/data/query/{top_id}").json() - Network accessline 382
ANALYSIS = "https://reactome.org/AnalysisService" - Network accessline 382
ANALYSIS = "https://reactome.org/AnalysisService" - Network accessline 395
resp = requests.post( - Network accessline 395
resp = requests.post( - Network accessline 442
CONTENT = "https://reactome.org/ContentService" - Network accessline 442
CONTENT = "https://reactome.org/ContentService" - Network accessline 444
pathway_id = "R-HSA-69620" # Cell Cycle refs = requests.get(f"{CONTENT}/data/participants/{pathway_id}/referenceEntities").json() - Network accessline 444
pathway_id = "R-HSA-69620" # Cell Cycle refs = requests.get(f"{CONTENT}/data/participants/{pathway_id}/referenceEntities").json() - Network accessline 445
refs = requests.get(f"{CONTENT}/data/participants/{pathway_id}/referenceEntities").json() - Network accessline 445
refs = requests.get(f"{CONTENT}/data/participants/{pathway_id}/referenceEntities").json() - Network accessline 458
# Generate a direct link to the Reactome pathway diagram pathway_id = "R-HSA-69620" diagram_url = f"https://reactome.org/PathwayBrowser/#/{pathway_id}" - Network accessline 458
# Generate a direct link to the Reactome pathway diagram pathway_id = "R-HSA-69620" diagram_url = f"https://reactome.org/PathwayBrowser/#/{pathway_id}" - Network accessline 459
pathway_id = "R-HSA-69620" diagram_url = f"https://reactome.org/PathwayBrowser/#/{pathway_id}" print(f"View diagram: {diagram_url}") - Network accessline 459
pathway_id = "R-HSA-69620" diagram_url = f"https://reactome.org/PathwayBrowser/#/{pathway_id}" print(f"View diagram: {diagram_url}") - Network accessline 460
diagram_url = f"https://reactome.org/PathwayBrowser/#/{pathway_id}" - Network accessline 460
diagram_url = f"https://reactome.org/PathwayBrowser/#/{pathway_id}" - Network accessline 463
[Credential-related source omitted] - Network accessline 463
[Credential-related source omitted] - Network accessline 464
[Credential-related source omitted] - Network accessline 464
[Credential-related source omitted] - Network accessline 465
overlay_url = f"https://reactome.org/PathwayBrowser/#/{pathway_id}&DTAB=AN&ANALYSIS={token}" - Network accessline 465
overlay_url = f"https://reactome.org/PathwayBrowser/#/{pathway_id}&DTAB=AN&ANALYSIS={token}" - Network accessline 475
CONTENT = "https://reactome.org/ContentService" - Network accessline 475
CONTENT = "https://reactome.org/ContentService" - Network accessline 478
summaries = [] for pid in pathway_ids: resp = requests.get(f"{CONTENT}/data/query/{pid}") - Network accessline 478
summaries = [] for pid in pathway_ids: resp = requests.get(f"{CONTENT}/data/query/{pid}") - Network accessline 479
for pid in pathway_ids: resp = requests.get(f"{CONTENT}/data/query/{pid}") resp.raise_for_status() - Network accessline 479
for pid in pathway_ids: resp = requests.get(f"{CONTENT}/data/query/{pid}") resp.raise_for_status() - Network accessline 480
resp = requests.get(f"{CONTENT}/data/query/{pid}") - Network accessline 480
resp = requests.get(f"{CONTENT}/data/query/{pid}") - Network accessline 523
- [Reactome Content Service API](https://reactome.org/ContentService/) — interactive API documentation (Swagger) - Network accessline 523
- [Reactome Content Service API](https://reactome.org/ContentService/) — interactive API documentation (Swagger) - Network accessline 524
- [Reactome Analysis Service API](https://reactome.org/AnalysisService/) — enrichment analysis API documentation - Network accessline 524
- [Reactome Analysis Service API](https://reactome.org/AnalysisService/) — enrichment analysis API documentation - Network accessline 525
- [Reactome website](https://reactome.org/) — pathway browser, diagram viewer, species comparison - Network accessline 525
- [Reactome website](https://reactome.org/) — pathway browser, diagram viewer, species comparison - Network accessline 526
- [reactome2py](https://github.com/reactome/reactome2py) — official Python wrapper for Reactome APIs - Network accessline 526
- [reactome2py](https://github.com/reactome/reactome2py) — official Python wrapper for Reactome APIs
skills/systems-biology-multiomics/string-database-ppi/SKILL.md
- Network accessline 30
**Rate limiting**: No strict rate limit, but wait ~1 second between API calls. For proteome-scale analyses, use bulk downloads from https://string-db.org/cgi/download instead of the API. - Network accessline 30
**Rate limiting**: No strict rate limit, but wait ~1 second between API calls. For proteome-scale analyses, use bulk downloads from https://string-db.org/cgi/download instead of the API. - Network accessline 38
STRING_API = "https://string-db.org/api" - Network accessline 38
STRING_API = "https://string-db.org/api" - Network accessline 42
url = f"{STRING_API}/{fmt}/{endpoint}" params.setdefault("caller_identity", "python_script") response = requests.get(url, params=params) - Network accessline 42
url = f"{STRING_API}/{fmt}/{endpoint}" params.setdefault("caller_identity", "python_script") response = requests.get(url, params=params) - Network accessline 43
params.setdefault("caller_identity", "python_script") response = requests.get(url, params=params) response.raise_for_status() - Network accessline 43
params.setdefault("caller_identity", "python_script") response = requests.get(url, params=params) response.raise_for_status() - Network accessline 44
response = requests.get(url, params=params) - Network accessline 44
response = requests.get(url, params=params) - Network accessline 82
Full species list: https://string-db.org/cgi/input?input_page_active_form=organisms - Network accessline 82
Full species list: https://string-db.org/cgi/input?input_page_active_form=organisms - Network accessline 179
"caller_identity": "python_script" } response = requests.get(url, params=params) - Network accessline 179
"caller_identity": "python_script" } response = requests.get(url, params=params) - Network accessline 180
} response = requests.get(url, params=params) with open("network.png", "wb") as f: - Network accessline 180
} response = requests.get(url, params=params) with open("network.png", "wb") as f: - Network accessline 181
response = requests.get(url, params=params) - Network accessline 181
response = requests.get(url, params=params) - Network accessline 260
STRING_API = "https://string-db.org/api" - Network accessline 260
STRING_API = "https://string-db.org/api" - Network accessline 262
url = f"{STRING_API}/{fmt}/{endpoint}" params.setdefault("caller_identity", "python_script") response = requests.get(url, params=params) - Network accessline 262
url = f"{STRING_API}/{fmt}/{endpoint}" params.setdefault("caller_identity", "python_script") response = requests.get(url, params=params) - Network accessline 263
params.setdefault("caller_identity", "python_script") response = requests.get(url, params=params) response.raise_for_status() - Network accessline 263
params.setdefault("caller_identity", "python_script") response = requests.get(url, params=params) response.raise_for_status() - Network accessline 264
response = requests.get(url, params=params) - Network accessline 264
response = requests.get(url, params=params) - Network accessline 289
# Step 5: Save network image img_resp = requests.get(f"{STRING_API}/image/network", params={ - Network accessline 289
# Step 5: Save network image img_resp = requests.get(f"{STRING_API}/image/network", params={ - Network accessline 290
img_resp = requests.get(f"{STRING_API}/image/network", params={ - Network accessline 290
img_resp = requests.get(f"{STRING_API}/image/network", params={ - Network accessline 412
- STRING website: https://string-db.org - API documentation: https://string-db.org/help/api/ - Download page: https://string-db.org/cgi/download - Network accessline 412
- STRING website: https://string-db.org - API documentation: https://string-db.org/help/api/ - Download page: https://string-db.org/cgi/download - Network accessline 413
- API documentation: https://string-db.org/help/api/ - Download page: https://string-db.org/cgi/download - Publications: https://string-db.org/cgi/about - Network accessline 413
- API documentation: https://string-db.org/help/api/ - Download page: https://string-db.org/cgi/download - Publications: https://string-db.org/cgi/about - Network accessline 414
- Download page: https://string-db.org/cgi/download - Network accessline 414
- Download page: https://string-db.org/cgi/download - Network accessline 415
- Publications: https://string-db.org/cgi/about - Network accessline 415
- Publications: https://string-db.org/cgi/about
skills/systems-biology-multiomics/string-database-ppi/references/api_advanced.md
- Network accessline 14
[Credential-related source omitted] - Network accessline 14
[Credential-related source omitted] - Access to secretsline 14
[Credential-related source omitted] - Access to secretsline 14
[Credential-related source omitted] - Access to secretsline 15
[Credential-related source omitted] - Access to secretsline 15
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Network accessline 27
response = requests.post( - Network accessline 27
response = requests.post( - Network accessline 28
"https://string-db.org/api/json/valuesranks_submit", - Network accessline 28
"https://string-db.org/api/json/valuesranks_submit", - Access to secretsline 32
[Credential-related source omitted] - Access to secretsline 32
[Credential-related source omitted] - Network accessline 44
while True: status = requests.get( - Network accessline 44
while True: status = requests.get( - Network accessline 45
status = requests.get( - Network accessline 45
status = requests.get( - Network accessline 46
"https://string-db.org/api/json/valuesranks_enrichment_status", - Network accessline 46
"https://string-db.org/api/json/valuesranks_enrichment_status", - Network accessline 54
# Retrieve enrichment results enrichment = requests.get( - Network accessline 54
# Retrieve enrichment results enrichment = requests.get( - Network accessline 55
enrichment = requests.get( - Network accessline 55
enrichment = requests.get( - Network accessline 56
"https://string-db.org/api/tsv/valuesranks_enrichment", - Network accessline 56
"https://string-db.org/api/tsv/valuesranks_enrichment", - Network accessline 68
1. Navigate to https://string-db.org/ - Network accessline 68
1. Navigate to https://string-db.org/ - Network accessline 69
2. Select "Upload proteome" option 3. Upload FASTA file — STRING generates complete interaction network and predicts functions 4. Alternatively, download pre-computed networks from https://string-db.org/cgi/download - Network accessline 69
2. Select "Upload proteome" option 3. Upload FASTA file — STRING generates complete interaction network and predicts functions 4. Alternatively, download pre-computed networks from https://string-db.org/cgi/download - Network accessline 71
4. Alternatively, download pre-computed networks from https://string-db.org/cgi/download - Network accessline 71
4. Alternatively, download pre-computed networks from https://string-db.org/cgi/download - Network accessline 80
# Version-specific (recommended for publications) STRING_API = "https://version-12-0.string-db.org/api" - Network accessline 80
# Version-specific (recommended for publications) STRING_API = "https://version-12-0.string-db.org/api" - Network accessline 81
STRING_API = "https://version-12-0.string-db.org/api" - Network accessline 81
STRING_API = "https://version-12-0.string-db.org/api" - Network accessline 83
# Current version (always latest) STRING_API = "https://string-db.org/api" - Network accessline 83
# Current version (always latest) STRING_API = "https://string-db.org/api" - Network accessline 84
STRING_API = "https://string-db.org/api" - Network accessline 84
STRING_API = "https://string-db.org/api" - Network accessline 100
# POST method for large protein lists response = requests.post( - Network accessline 100
# POST method for large protein lists response = requests.post( - Network accessline 101
response = requests.post( - Network accessline 101
response = requests.post( - Network accessline 102
"https://string-db.org/api/tsv/network", - Network accessline 102
"https://string-db.org/api/tsv/network", - Network accessline 187
**Citation format**: "Protein-protein interactions were obtained from the STRING database v{version} (Szklarczyk et al., {year}; https://string-db.org)." - Network accessline 187
**Citation format**: "Protein-protein interactions were obtained from the STRING database v{version} (Szklarczyk et al., {year}; https://string-db.org)."
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.
Signal confidence: 500 facts
- Network access in prose of the SKILL.mdopentrons-integration/SKILL.md:429Low confidence
- Network access in prose of the SKILL.mdopentrons-integration/SKILL.md:430Low confidence
- Network access in prose of the SKILL.mdopentrons-integration/SKILL.md:431Low confidence
- Network access in prose of the SKILL.mdplotly-interactive-visualization/SKILL.md:482Low confidence
- Network access in prose of the SKILL.mdplotly-interactive-visualization/SKILL.md:483Low confidence
- Network access in prose of the SKILL.mdplotly-interactive-visualization/SKILL.md:484Low confidence
- Network access in prose of the SKILL.mdplotly-interactive-visualization/SKILL.md:485Low confidence
- Network access in prose of the SKILL.mdseaborn-statistical-visualization/SKILL.md:466Low confidence
- Network access in prose of the SKILL.mdseaborn-statistical-visualization/SKILL.md:467Low confidence
- Network access in prose of the SKILL.mdseaborn-statistical-visualization/SKILL.md:468Low confidence
- Network access in prose of the SKILL.mdsingle-cell-annotation/SKILL.md:27Low confidence
- Network access in prose of the SKILL.mdsingle-cell-annotation/SKILL.md:248Low confidence
- Network access in prose of the SKILL.mdsingle-cell-annotation/SKILL.md:249Low confidence
- Network access in prose of the SKILL.mdsingle-cell-annotation/SKILL.md:250Low confidence
- Network access in prose of the SKILL.mdsingle-cell-annotation/SKILL.md:251Low confidence
- Network access in prose of the SKILL.mdsingle-cell-annotation/SKILL.md:252Low confidence
- Network access in prose of the SKILL.mdsingle-cell-annotation/SKILL.md:254Low confidence
- Network access in prose of the SKILL.mdsingle-cell-annotation/SKILL.md:255Low confidence
- Network access in prose of the SKILL.mdsingle-cell-annotation/SKILL.md:256Low confidence
- Network access in prose of the SKILL.mdsingle-cell-annotation/SKILL.md:257Low confidence
- Network access in prose of the SKILL.mdsingle-cell-annotation/SKILL.md:258Low confidence
- Network access in prose of the SKILL.mdskills/biostatistics/pymc-bayesian-modeling/SKILL.md:466Low confidence
- Network access in prose of the SKILL.mdpymc-bayesian-modeling/SKILL.md:466Low confidence
- Network access in prose of the SKILL.mdskills/biostatistics/pymc-bayesian-modeling/SKILL.md:467Low confidence
- Network access in prose of the SKILL.mdpymc-bayesian-modeling/SKILL.md:467Low confidence
- Network access in prose of the SKILL.mdskills/biostatistics/pymc-bayesian-modeling/SKILL.md:468Low confidence
- Network access in prose of the SKILL.mdpymc-bayesian-modeling/SKILL.md:468Low confidence
- Network access in prose of the SKILL.mdskills/biostatistics/pymc-bayesian-modeling/SKILL.md:469Low confidence
- Network access in prose of the SKILL.mdpymc-bayesian-modeling/SKILL.md:469Low confidence
- Network access in prose of the SKILL.mdskills/biostatistics/pymc-bayesian-modeling/SKILL.md:470Low confidence
- Network access in prose of the SKILL.mdpymc-bayesian-modeling/SKILL.md:470Low confidence
- Network access in prose of the SKILL.mdskills/biostatistics/scikit-survival-analysis/SKILL.md:666Low confidence
- Network access in prose of the SKILL.mdscikit-survival-analysis/SKILL.md:666Low confidence
- Network access in prose of the SKILL.mdskills/biostatistics/scikit-survival-analysis/SKILL.md:667Low confidence
- Network access in prose of the SKILL.mdscikit-survival-analysis/SKILL.md:667Low confidence
- Network access in prose of the SKILL.mdskills/biostatistics/scikit-survival-analysis/SKILL.md:668Low confidence
- Network access in prose of the SKILL.mdscikit-survival-analysis/SKILL.md:668Low confidence
- Network access in prose of the SKILL.mdskills/biostatistics/scikit-survival-analysis/SKILL.md:669Low confidence
- Network access in prose of the SKILL.mdscikit-survival-analysis/SKILL.md:669Low confidence
- Network access in prose of the SKILL.mdskills/biostatistics/statistical-analysis/SKILL.md:290Low confidence
- Network access in prose of the SKILL.mdstatistical-analysis/SKILL.md:290Low confidence
- Network access in prose of the SKILL.mdskills/biostatistics/statistical-analysis/SKILL.md:291Low confidence
- Network access in prose of the SKILL.mdstatistical-analysis/SKILL.md:291Low confidence
- Network access in prose of the SKILL.mdskills/biostatistics/statistical-analysis/SKILL.md:292Low confidence
- Network access in prose of the SKILL.mdstatistical-analysis/SKILL.md:292Low confidence
- Network access in prose of the SKILL.mdskills/biostatistics/statistical-analysis/SKILL.md:293Low confidence
- Network access in prose of the SKILL.mdstatistical-analysis/SKILL.md:293Low confidence
- Network access in prose of the SKILL.mdskills/biostatistics/statsmodels-statistical-modeling/SKILL.md:494Low confidence
- Network access in prose of the SKILL.mdstatsmodels-statistical-modeling/SKILL.md:494Low confidence
- Network access in prose of the SKILL.mdskills/biostatistics/statsmodels-statistical-modeling/SKILL.md:495Low confidence
and 450 more
Only the first 500 facts are listed.
Works with
| Agent | Status | Evidence | Install to |
|---|---|---|---|
| Claude Code | Native | Manifest .claude-plugin/marketplace.json | .claude/skills/<skill-name>/ |
| Codex | Native | Manifest AGENTS.md | .agents/skills/<skill-name>/ |
| Cursor | Compatible | Standard SKILL.md legacy/opentrons-integration/SKILL.md | .cursor/skills/<skill-name>/ |
| Gemini CLI | Compatible | Standard SKILL.md legacy/opentrons-integration/SKILL.md | .gemini/skills/<skill-name>/ |
| GitHub Copilot | Compatible | Standard SKILL.md legacy/opentrons-integration/SKILL.md | .github/skills/<skill-name>/ |
| OpenCode | Compatible | Standard SKILL.md legacy/opentrons-integration/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.
