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ammawla/encode-toolkit

MCP server and Claude Code plugin for the ENCODE Project: search, download and analyze ChIP-seq, ATAC-seq, RNA-seq, WGBS, Hi-C and CUT&RUN data with 20 tools, 47 skills and 7 Nextflow pipelines

Claude CodeData

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

plugin/skills/accessibility-aggregation/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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plugin/skills/batch-analysis/SKILL.md

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plugin/skills/bioinformatics-installer/SKILL.md

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plugin/skills/cellxgene-context/SKILL.md

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plugin/skills/cite-encode/SKILL.md

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plugin/skills/clinvar-annotation/SKILL.md

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plugin/skills/compare-biosamples/SKILL.md

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plugin/skills/cross-reference/SKILL.md

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plugin/skills/data-provenance/SKILL.md

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plugin/skills/disease-research/SKILL.md

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plugin/skills/download-encode/SKILL.md

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plugin/skills/ensembl-annotation/SKILL.md

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plugin/skills/epigenome-profiling/SKILL.md

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plugin/skills/functional-screen-analysis/SKILL.md

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plugin/skills/geo-connector/SKILL.md

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plugin/skills/gnomad-variants/SKILL.md

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plugin/skills/gtex-expression/SKILL.md

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plugin/skills/gwas-catalog/SKILL.md

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plugin/skills/hic-aggregation/SKILL.md

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plugin/skills/histone-aggregation/SKILL.md

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plugin/skills/integrative-analysis/SKILL.md

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plugin/skills/jaspar-motifs/SKILL.md

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plugin/skills/liftover-coordinates/SKILL.md

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plugin/skills/methylation-aggregation/SKILL.md

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plugin/skills/motif-analysis/SKILL.md

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plugin/skills/multi-omics-integration/SKILL.md

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plugin/skills/peak-annotation/SKILL.md

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plugin/skills/pipeline-atacseq/SKILL.md

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plugin/skills/pipeline-chipseq/SKILL.md

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plugin/skills/pipeline-cutandrun/SKILL.md

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plugin/skills/pipeline-dnaseseq/SKILL.md

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plugin/skills/pipeline-guide/SKILL.md

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plugin/skills/pipeline-hic/SKILL.md

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plugin/skills/pipeline-rnaseq/SKILL.md

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plugin/skills/pipeline-wgbs/SKILL.md

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plugin/skills/publication-trust/SKILL.md

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plugin/skills/quality-assessment/SKILL.md

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plugin/skills/regulatory-elements/SKILL.md

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plugin/skills/scientific-writing/SKILL.md

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plugin/skills/scrna-meta-analysis/SKILL.md

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plugin/skills/search-encode/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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plugin/skills/setup/SKILL.md

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plugin/skills/single-cell-encode/SKILL.md

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plugin/skills/track-experiments/SKILL.md

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plugin/skills/ucsc-browser/SKILL.md

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plugin/skills/variant-annotation/SKILL.md

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plugin/skills/visualization-workflow/SKILL.md

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skills/accessibility-aggregation/SKILL.md

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skills/batch-analysis/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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skills/bioinformatics-installer/SKILL.md

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skills/cellxgene-context/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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skills/cite-encode/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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skills/clinvar-annotation/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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skills/compare-biosamples/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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skills/cross-reference/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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skills/data-provenance/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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skills/disease-research/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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skills/download-encode/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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skills/ensembl-annotation/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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skills/epigenome-profiling/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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skills/functional-screen-analysis/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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skills/geo-connector/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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skills/gnomad-variants/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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skills/gtex-expression/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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skills/gwas-catalog/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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skills/hic-aggregation/SKILL.md

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skills/histone-aggregation/SKILL.md

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skills/integrative-analysis/SKILL.md

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skills/jaspar-motifs/SKILL.md

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skills/liftover-coordinates/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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skills/methylation-aggregation/SKILL.md

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skills/motif-analysis/SKILL.md

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skills/multi-omics-integration/SKILL.md

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skills/peak-annotation/SKILL.md

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skills/pipeline-atacseq/SKILL.md

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skills/pipeline-chipseq/SKILL.md

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skills/pipeline-cutandrun/SKILL.md

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skills/pipeline-dnaseseq/SKILL.md

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skills/pipeline-guide/SKILL.md

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skills/pipeline-hic/SKILL.md

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skills/pipeline-rnaseq/SKILL.md

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skills/pipeline-wgbs/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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skills/publication-trust/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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skills/quality-assessment/SKILL.md

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skills/regulatory-elements/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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skills/scientific-writing/SKILL.md

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skills/scrna-meta-analysis/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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skills/search-encode/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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skills/setup/SKILL.md

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skills/single-cell-encode/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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skills/track-experiments/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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skills/ucsc-browser/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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skills/variant-annotation/SKILL.md

Week of Nov 2, 2026 · Line counts unavailable

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skills/visualization-workflow/SKILL.md

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View on GitHubAdd to my skills

Verified commit 36836c8725fd4d20d9c851ce314f5151aea5f57c

git clone --filter=blob:none --no-checkout https://github.com/ammawla/encode-toolkit.git .hotskills/ammawla-encode-toolkit && git -C .hotskills/ammawla-encode-toolkit checkout --detach 36836c8725fd4d20d9c851ce314f5151aea5f57c # Copy each selected skill directory from .hotskills/ammawla-encode-toolkit into .claude/skills/<skill-name>/
21GitHub stars
-1star in 7 days
94skills in this repository
0commits in 30 days

Heat

0.2
  • Star growth, last 7 daysNot available
  • Star growth, last 24 hoursNot available
  • Rank movement, last 7 daysNot available
  • Commits, last 30 daysNot available
  • Accounts asking about it through MCP, last 7 daysNot available
  • Hot votes this weekNot available

Heat never changes the order by stars, and sponsorship never touches it. How heat works

Stars over time

Oct 25, 2026Nov 5, 202622

30-day change not yet available: not enough history.

Individual skills in this repository

How it works

accessibility-aggregation

When your agent uses it

Build comprehensive chromatin accessibility maps by aggregating ATAC-seq and DNase-seq narrowPeak data across multiple ENCODE experiments, donors, and labs. Use when the user wants to answer "where is chromatin accessible in my tissue?" by combining peak calls into a union peak set. Handles cross-lab variation, ATAC vs DNase platform differences, and ENCODE blocklist filtering.

What it loads into context

Frontmatter

88 tokens

Always available to the agent

SKILL.md

3,846 tokens

When the skill is activated

Reference documents

3,628 tokens

Loaded on request

Token counts are estimates. Scripts run separately; they are not counted as reference context.

The playbook

  1. When to Use
  2. Scientific Rationale
  3. Step 1: Find All Available Accessibility Data
  4. Step 2: Quality-Gate Each Experiment
  5. Step 3: Download Peak Files
  6. Step 4: Per-Sample Noise Filtering
  7. Step 5: Union Merge
  8. Step 6: Confidence Annotation
  9. Step 7: Log Provenance
  10. Step 8: Summary Statistics
  11. Pitfalls Specific to Accessibility Data
  12. Walkthrough: Building a Pan-Donor Accessibility Map for Brain Cortex
  13. Code Examples
  14. Integration
  15. Related Skills
  16. Presenting Results
  17. For the request: "$ARGUMENTS"

Scripts executed separately

  • scripts/validate_peaks.py

Files

  • ../../../.claude-plugin/marketplace.jsonasset556 bytes
  • ../../../.claude-plugin/plugin.jsonasset3,957 bytes
  • ../../../.claude-plugin/plugin.jsonasset3,957 bytes
  • ../../../.claude-plugin/plugin.jsonasset3,957 bytes
  • ../../../docs/icon.svgasset4,593 bytes
  • ../../.claude-plugin/plugin.jsonasset559 bytes
  • SKILL.mdcore14,988 bytes
  • SKILL.mdcore14,988 bytes
  • SKILL.mdcore14,988 bytes
  • SKILL.mdcore14,988 bytes
  • SKILL.mdcore14,988 bytes
  • SKILL.mdcore14,988 bytes
  • SKILL.mdcore14,988 bytes
  • SKILL.mdcore14,988 bytes
  • SKILL.mdcore14,988 bytes
  • references/atac-vs-dnase.mddoc5,830 bytes
  • references/literature.mddoc8,481 bytes
  • scripts/validate_peaks.pyruns12,661 bytes

No excerpt is shown for this license. Read the source on GitHub.

View the complete source on GitHub

Skills included

  • accessibility-aggregation
  • batch-analysis
  • bioinformatics-installer
  • cellxgene-context
  • cite-encode
  • clinvar-annotation
  • compare-biosamples
  • cross-reference
  • data-provenance
  • disease-research
  • download-encode
  • ensembl-annotation
  • epigenome-profiling
  • functional-screen-analysis
  • geo-connector
  • gnomad-variants
  • gtex-expression
  • gwas-catalog
  • hic-aggregation
  • histone-aggregation
  • integrative-analysis
  • jaspar-motifs
  • liftover-coordinates
  • methylation-aggregation
  • motif-analysis
  • multi-omics-integration
  • peak-annotation
  • pipeline-atacseq
  • pipeline-chipseq
  • pipeline-cutandrun
  • pipeline-dnaseseq
  • pipeline-guide
  • pipeline-hic
  • pipeline-rnaseq
  • pipeline-wgbs
  • publication-trust
  • quality-assessment
  • regulatory-elements
  • scientific-writing
  • scrna-meta-analysis
  • search-encode
  • setup
  • single-cell-encode
  • track-experiments
  • ucsc-browser
  • variant-annotation
  • visualization-workflow
  • accessibility-aggregation
  • batch-analysis
  • bioinformatics-installer
  • cellxgene-context
  • cite-encode
  • clinvar-annotation
  • compare-biosamples
  • cross-reference
  • data-provenance
  • disease-research
  • download-encode
  • ensembl-annotation
  • epigenome-profiling
  • functional-screen-analysis
  • geo-connector
  • gnomad-variants
  • gtex-expression
  • gwas-catalog
  • hic-aggregation
  • histone-aggregation
  • integrative-analysis
  • jaspar-motifs
  • liftover-coordinates
  • methylation-aggregation
  • motif-analysis
  • multi-omics-integration
  • peak-annotation
  • pipeline-atacseq
  • pipeline-chipseq
  • pipeline-cutandrun
  • pipeline-dnaseseq
  • pipeline-guide
  • pipeline-hic
  • pipeline-rnaseq
  • pipeline-wgbs
  • publication-trust
  • quality-assessment
  • regulatory-elements
  • scientific-writing
  • scrna-meta-analysis
  • search-encode
  • setup
  • single-cell-encode
  • track-experiments
  • ucsc-browser
  • variant-annotation
  • visualization-workflow

Repository facts

License
AGPL-3.0
Created
Mar 8, 2026
Last pushed
Sep 27, 2026
Forks
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Before you install

Network or broad shell

Automated signals, not a guarantee

Checked Nov 6, 2026

Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.

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  • .claude-plugin/plugin.json:1 Analysis incomplete: Referenced executable target unavailable; not analyzed
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Show all 4,213 signals by file

.claude-plugin/marketplace.json

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    "metadata": {     "description": "ENCODE Project genomics research infrastructure for Claude Code",     "homepage": "https://github.com/ammawla/encode-toolkit"
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    "description": "ENCODE Project genomics research infrastructure for Claude Code",     "homepage": "https://github.com/ammawla/encode-toolkit"   },
  • Network accessline 8
    "homepage": "https://github.com/ammawla/encode-toolkit"

.claude-plugin/plugin.json

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    "email": "ammawla@ucdavis.edu"   },   "homepage": "https://github.com/ammawla/encode-toolkit",
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    },   "homepage": "https://github.com/ammawla/encode-toolkit",   "repository": "https://github.com/ammawla/encode-toolkit",
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    "homepage": "https://github.com/ammawla/encode-toolkit",
  • Network accessline 10
    "repository": "https://github.com/ammawla/encode-toolkit",

docs/icon.svg

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    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 256 256" width="256" height="256">

plugin/.claude-plugin/plugin.json

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    "name": "Dr. Alex M. Mawla, PhD"   },   "repository": "https://github.com/ammawla/encode-toolkit",
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    },   "repository": "https://github.com/ammawla/encode-toolkit",   "license": "AGPL-3.0-only",
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    "repository": "https://github.com/ammawla/encode-toolkit",

plugin/skills/accessibility-aggregation/SKILL.md

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    ### Literature Support - **Corces et al. 2017** (Nature Methods, 733 citations): Established that ATAC-seq and DNase-seq identify largely overlapping accessible regions, with ATAC capturing ~75% of DNase sites. Both are valid for union maps
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    - **Corces et al. 2017** (Nature Methods, 733 citations): Established that ATAC-seq and DNase-seq identify largely overlapping accessible regions, with ATAC capturing ~75% of DNase sites. Both are valid for union maps. - **ENCODE Blacklist*
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    - **ENCODE Blacklist** (Amemiya et al. 2019, Scientific Reports, 1,372 citations): Comprehensive set of problematic genomic regions to filter. Essential for all functional genomics analyses. [DOI](https://doi.org/10.1038/s41598-019-45839-z)
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    # Download from: https://github.com/Boyle-Lab/Blacklist/blob/master/lists/hg38-blacklist.v2.bed.gz

plugin/skills/accessibility-aggregation/references/literature.md

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    - **Citation:** Corces MR, Trevino AE, Hamilton EG, Greenside PG, Sinnott-Armstrong NA, Vesuna S, Satpathy AT, Rubin AJ, Montine KS, Wu B, Kathiria A, Cho SW, Mumbach MR, Carter AC, Kasowski M, Orloff LA, Risca VI, Kundaje A, Khavari PA, Mo
  • Network accessline 15
    - **DOI:** [10.1038/nmeth.4396](https://doi.org/10.1038/nmeth.4396)
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    - **Citation:** Thurman RE, Rynes E, Humbert R, et al. The accessible chromatin landscape of the human genome. Nature, 489(7414):75-82, 2012. - **DOI:** [10.1038/nature11232](https://doi.org/10.1038/nature11232)
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    - **DOI:** [10.1038/nature11232](https://doi.org/10.1038/nature11232)
  • Network accessline 34
    - **Citation:** Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nature Methods, 10(12)
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    - **DOI:** [10.1038/nmeth.2688](https://doi.org/10.1038/nmeth.2688)
  • Network accessline 48
    - **Citation:** Zhao Z, Boyle AP. F-Seq2: improving the feature density based peak caller with dynamic statistics. NAR Genomics and Bioinformatics, 3(1):lqab012, 2021. - **DOI:** [10.1093/nargab/lqab012](https://doi.org/10.1093/nargab/lqab0
  • Network accessline 49
    - **DOI:** [10.1093/nargab/lqab012](https://doi.org/10.1093/nargab/lqab012)
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    - **Citation:** Amemiya HM, Kundaje A, Boyle AP. The ENCODE Blacklist: Identification of Problematic Regions of the Genome. Scientific Reports, 9:9354, 2019. - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z
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    - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z)
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    - **Citation:** Orchard P, Kyono Y, Hensley J, Kitzman JO, Parker SCJ. Quantification, Dynamic Visualization, and Validation of Bias in ATAC-Seq Data with ataqv. Cell Systems, 10(3):298-306.e4, 2020. - **DOI:** [10.1016/j.cels.2020.02.009](
  • Network accessline 69
    - **DOI:** [10.1016/j.cels.2020.02.009](https://doi.org/10.1016/j.cels.2020.02.009)
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    - **Citation:** ENCODE Project Consortium et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature, 583(7818):699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
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    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
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    - **Citation:** Gorkin DU, Barozzi I, Zhao Y, et al. An atlas of dynamic chromatin landscapes in mouse fetal development. Nature, 583(7818):744-751, 2020. - **DOI:** [10.1038/s41586-020-2093-3](https://doi.org/10.1038/s41586-020-2093-3)
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    - **DOI:** [10.1038/s41586-020-2093-3](https://doi.org/10.1038/s41586-020-2093-3)
  • Network accessline 106
    - **Citation:** Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics, 26(6):841-842, 2010. - **DOI:** [10.1093/bioinformatics/btq033](https://doi.org/10.1093/bioinformatics/btq033)
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    - **DOI:** [10.1093/bioinformatics/btq033](https://doi.org/10.1093/bioinformatics/btq033)

plugin/skills/accessibility-aggregation/scripts/validate_peaks.py

  • Executable scriptline 1
    #!/usr/bin/env python3

plugin/skills/batch-analysis/SKILL.md

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    | Reference | Journal | Key Contribution | DOI | Citations | |-----------|---------|-----------------|-----|-----------| | ENCODE Project Consortium (2020) | Nature | Expanded encyclopedia of 926,535 candidate cis-regulatory elements across
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    |-----------|---------|-----------------|-----|-----------| | ENCODE Project Consortium (2020) | Nature | Expanded encyclopedia of 926,535 candidate cis-regulatory elements across 1,698 cell types; framework for large-scale integrative anal
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    | ENCODE Project Consortium (2020) | Nature | Expanded encyclopedia of 926,535 candidate cis-regulatory elements across 1,698 cell types; framework for large-scale integrative analysis | [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s
  • Network accessline 23
    | Hitz et al. (2023) | Nucleic Acids Research | The ENCODE Uniform Processing Pipelines: standardized processing enables large-scale batch comparisons | [10.1093/nar/gkac1067](https://doi.org/10.1093/nar/gkac1067) | ~50 |
  • Network accessline 24
    | Landt et al. (2012) | Genome Research | ChIP-seq guidelines of ENCODE/modENCODE: QC metrics (FRiP, NSC, RSC, NRF) for batch quality assessment | [10.1101/gr.136184.111](https://doi.org/10.1101/gr.136184.111) | ~4,000 |
  • Network accessline 25
    | Leek et al. (2010) | Nature Reviews Genetics | Tackling batch effects: detection via PCA, correction via ComBat/SVA; essential for multi-lab analyses | [10.1038/nrg2825](https://doi.org/10.1038/nrg2825) | ~1,200 |
  • Network accessline 26
    | Amemiya et al. (2019) | Scientific Reports | ENCODE Blacklist: artifact regions to exclude across all experiments in batch analyses | [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z) | ~1,372 |

plugin/skills/batch-analysis/references/literature.md

  • Network accessline 16
    impact of batch effects in high-throughput data. Nature Reviews Genetics,   11(10):733-739, 2010. - **DOI:** [10.1038/nrg2825](https://doi.org/10.1038/nrg2825)
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    11(10):733-739, 2010. - **DOI:** [10.1038/nrg2825](https://doi.org/10.1038/nrg2825) - **PMID:** 20838408 | **PMC:** PMC3880143
  • Network accessline 18
    - **DOI:** [10.1038/nrg2825](https://doi.org/10.1038/nrg2825)
  • Network accessline 38
    - **Citation:** Goh WWB, Wang W, Wong L. Why batch effects matter in omics   data, and how to avoid them. Trends in Biotechnology, 35(6):498-507, 2017. - **DOI:** [10.1016/j.tibtech.2017.02.012](https://doi.org/10.1016/j.tibtech.2
  • Network accessline 39
    data, and how to avoid them. Trends in Biotechnology, 35(6):498-507, 2017. - **DOI:** [10.1016/j.tibtech.2017.02.012](https://doi.org/10.1016/j.tibtech.2   017.02.012)
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    - **DOI:** [10.1016/j.tibtech.2017.02.012](https://doi.org/10.1016/j.tibtech.2
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    for microarray gene expression data integration: a survey. Briefings in   Bioinformatics, 14(4):469-490, 2013. - **DOI:** [10.1093/bib/bbs037](https://doi.org/10.1093/bib/bbs037)
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    Bioinformatics, 14(4):469-490, 2013. - **DOI:** [10.1093/bib/bbs037](https://doi.org/10.1093/bib/bbs037) - **PMID:** 22851511
  • Network accessline 69
    - **DOI:** [10.1093/bib/bbs037](https://doi.org/10.1093/bib/bbs037)
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    microarray expression data using empirical Bayes methods. Biostatistics,   8(1):118-127, 2007. - **DOI:** [10.1093/biostatistics/kxj037](https://doi.org/10.1093/biostatistic
  • Network accessline 97
    8(1):118-127, 2007. - **DOI:** [10.1093/biostatistics/kxj037](https://doi.org/10.1093/biostatistic   s/kxj037)
  • Network accessline 98
    - **DOI:** [10.1093/biostatistics/kxj037](https://doi.org/10.1093/biostatistic
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    single-cell RNA-sequencing data are corrected by matching mutual nearest   neighbors. Nature Biotechnology, 36(5):421-427, 2018. - **DOI:** [10.1038/nbt.4091](https://doi.org/10.1038/nbt.4091)
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    neighbors. Nature Biotechnology, 36(5):421-427, 2018. - **DOI:** [10.1038/nbt.4091](https://doi.org/10.1038/nbt.4091) - **PMID:** 29608177 | **PMC:** PMC6152897
  • Network accessline 128
    - **DOI:** [10.1038/nbt.4091](https://doi.org/10.1038/nbt.4091)
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    integration of single-cell data with Harmony. Nature Methods,   16(12):1289-1296, 2019. - **DOI:** [10.1038/s41592-019-0619-0](https://doi.org/10.1038/s41592-019-0619-0)
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    16(12):1289-1296, 2019. - **DOI:** [10.1038/s41592-019-0619-0](https://doi.org/10.1038/s41592-019-0619-0) - **PMID:** 31740819 | **PMC:** PMC6884693
  • Network accessline 154
    - **DOI:** [10.1038/s41592-019-0619-0](https://doi.org/10.1038/s41592-019-0619-0)
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    A benchmark of batch-effect correction methods for single-cell RNA   sequencing data. Genome Biology, 21(1):12, 2020. - **DOI:** [10.1186/s13059-019-1850-9](https://doi.org/10.1186/s13059-019-1850-9)
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    sequencing data. Genome Biology, 21(1):12, 2020. - **DOI:** [10.1186/s13059-019-1850-9](https://doi.org/10.1186/s13059-019-1850-9) - **PMID:** 31948481 | **PMC:** PMC6965632
  • Network accessline 182
    - **DOI:** [10.1186/s13059-019-1850-9](https://doi.org/10.1186/s13059-019-1850-9)

plugin/skills/bioinformatics-installer/SKILL.md

  • Dangerous shell operationline 495
    sudo apt-get update
  • Dangerous shell operationline 496
    sudo apt-get install -y docker-ce docker-ce-cli containerd.io
  • Dangerous shell operationline 499
    sudo usermod -aG docker $USER
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    mkdir -p ~/software/homer cd ~/software/homer wget http://homer.ucsd.edu/homer/configureHomer.pl
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    cd ~/software/homer wget http://homer.ucsd.edu/homer/configureHomer.pl perl configureHomer.pl -install homer
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    wget http://homer.ucsd.edu/homer/configureHomer.pl
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    # Or from source wget https://meme-suite.org/meme/meme-software/5.5.5/meme-5.5.5.tar.gz
  • Network accessline 566
    wget https://meme-suite.org/meme/meme-software/5.5.5/meme-5.5.5.tar.gz

plugin/skills/bioinformatics-installer/environments/cutandrun-env.yml

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    - pigz   - wget   - curl

plugin/skills/bioinformatics-installer/references/literature.md

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    - **Citation:** Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Köster J; Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7):475-476, 2018
  • Network accessline 15
    - **DOI:** [10.1038/s41592-018-0046-7](https://doi.org/10.1038/s41592-018-0046-7)
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    - **Citation:** Kurtzer GM, Sochat V, Bauer MW. Singularity: Scientific containers for mobility of compute. PLoS ONE, 12(5):e0177459, 2017. - **DOI:** [10.1371/journal.pone.0177459](https://doi.org/10.1371/journal.pone.0177459)
  • Network accessline 33
    - **DOI:** [10.1371/journal.pone.0177459](https://doi.org/10.1371/journal.pone.0177459)
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    - **Citation:** Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4):316-319, 2017. - **DOI:** [10.1038/nbt.3820](https://doi.org/10.1038/nb
  • Network accessline 43
    - **DOI:** [10.1038/nbt.3820](https://doi.org/10.1038/nbt.3820)
  • Network accessline 52
    - **Citation:** Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3):276-278, 2020. - **DOI:** [
  • Network accessline 53
    - **DOI:** [10.1038/s41587-020-0439-x](https://doi.org/10.1038/s41587-020-0439-x)
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    - **Citation:** Huber W, Carey VJ, Gentleman R, Anders S, Brainard M, Davis S, Dudoit S, Ellis B, Gatto L, Girke T, Gottardo R, Hahne F, Hansen KD, Irizarry RA, Lawrence M, Love MI, MacDonald J, Obenchain V, Oleś AK, Pagès H, Reyes A, Shann
  • Network accessline 67
    - **DOI:** [10.1038/nmeth.3252](https://doi.org/10.1038/nmeth.3252)
  • Network accessline 80
    - **Citation:** Wolf FA, Angerer P, Theis FJ. SCANPY: large-scale single-cell gene expression data analysis. Genome Biology, 19:15, 2018. - **DOI:** [10.1186/s13059-017-1382-0](https://doi.org/10.1186/s13059-017-1382-0)
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    - **DOI:** [10.1186/s13059-017-1382-0](https://doi.org/10.1186/s13059-017-1382-0)
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    - **deepTools** (Ramírez et al. 2016): DOI 10.1093/nar/gkw257 — Signal tracks, heatmaps - **MultiQC** (Ewels et al. 2016): DOI 10.1093/bioinformatics/btw354 — QC report aggregation - **FastQC** (Andrews 2010): URL https://www.bioinformatics
  • Network accessline 113
    - **MultiQC** (Ewels et al. 2016): DOI 10.1093/bioinformatics/btw354 — QC report aggregation - **FastQC** (Andrews 2010): URL https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ - **Picard** (Broad Institute): URL https://broadinstit
  • Network accessline 114
    - **FastQC** (Andrews 2010): URL https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
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    - **Picard** (Broad Institute): URL https://broadinstitute.github.io/picard/
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    ### Methylation Specific - **MethylDackel** (Ryan): URL https://github.com/dpryan79/MethylDackel — Methylation extraction
  • Network accessline 123
    - **MethylDackel** (Ryan): URL https://github.com/dpryan79/MethylDackel — Methylation extraction

plugin/skills/bioinformatics-installer/scripts/install-nextflow.sh

  • Executable scriptline 1
    #!/usr/bin/env bash
  • Network accessline 14
    # with `nextflow lint` and `nextflow run -preview`. To move to another release, change the # version and replace the checksum with the sha256 published for the # `nextflow-<version>-dist` asset on https://github.com/nextflow-io/nextflow/rel
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    # `nextflow-<version>-dist` asset on https://github.com/nextflow-io/nextflow/releases
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    NEXTFLOW_VERSION="26.04.6" NEXTFLOW_SHA256="182a63c74074e2dc7956ffa3c8cd59de952ed2c44394e21faf5e1736b945444c" NEXTFLOW_URL="https://github.com/nextflow-io/nextflow/releases/download/v${NEXTFLOW_VERSION}/nextflow-${NEXTFLOW_VERSION}-dist"
  • Network accessline 19
    NEXTFLOW_URL="https://github.com/nextflow-io/nextflow/releases/download/v${NEXTFLOW_VERSION}/nextflow-${NEXTFLOW_VERSION}-dist"
  • Dangerous shell operationline 57
    echo "  Ubuntu: sudo apt-get install -y openjdk-17-jdk"
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    tmp_file="$(mktemp)"         echo "Downloading Nextflow ${NEXTFLOW_VERSION}..."         curl -fsSL "$NEXTFLOW_URL" -o "$tmp_file"
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    curl -fsSL "$NEXTFLOW_URL" -o "$tmp_file"
  • Dangerous shell operationline 79
    rm -f "$tmp_file"
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    echo "macOS detected. Install Docker Desktop:"                 echo "  brew install --cask docker"                 echo "  OR download from https://www.docker.com/products/docker-desktop/"
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    echo "  brew install --cask docker"                 echo "  OR download from https://www.docker.com/products/docker-desktop/"                 ;;
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    echo "  OR download from https://www.docker.com/products/docker-desktop/"
  • Network accessline 125
    Linux)                 echo "Linux detected. Install Docker Engine:"                 echo "  curl -fsSL https://get.docker.com | sh"
  • Network accessline 126
    echo "Linux detected. Install Docker Engine:"                 echo "  curl -fsSL https://get.docker.com | sh"                 echo "  sudo usermod -aG docker \$USER"
  • Dangerous shell operationline 127
    echo "  curl -fsSL https://get.docker.com | sh"
  • Network accessline 127
    echo "  curl -fsSL https://get.docker.com | sh"
  • Dangerous shell operationline 128
    echo "  sudo usermod -aG docker \$USER"
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    *)                 echo "Unsupported OS: $OS"                 echo "See https://docs.docker.com/get-docker/"
  • Network accessline 132
    echo "Unsupported OS: $OS"                 echo "See https://docs.docker.com/get-docker/"                 ;;
  • Network accessline 133
    echo "See https://docs.docker.com/get-docker/"
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    echo "Install options:"         echo "  conda: conda install -c conda-forge singularity"         echo "  Linux: See https://apptainer.org/docs/admin/main/installation.html"
  • Network accessline 152
    echo "  conda: conda install -c conda-forge singularity"         echo "  Linux: See https://apptainer.org/docs/admin/main/installation.html"         echo ""
  • Network accessline 153
    echo "  Linux: See https://apptainer.org/docs/admin/main/installation.html"

plugin/skills/bioinformatics-installer/scripts/install-python-packages.sh

  • Executable scriptline 1
    #!/usr/bin/env bash

plugin/skills/bioinformatics-installer/scripts/install-r-packages.R

  • Executable scriptline 1
    #!/usr/bin/env Rscript
  • Network accessline 25
    if (!requireNamespace("BiocManager", quietly = TRUE)) {   cat("Installing BiocManager...\n")   install.packages("BiocManager", repos = "https://cloud.r-project.org")
  • Network accessline 26
    cat("Installing BiocManager...\n")   install.packages("BiocManager", repos = "https://cloud.r-project.org") }
  • Network accessline 27
    install.packages("BiocManager", repos = "https://cloud.r-project.org")

plugin/skills/cellxgene-context/SKILL.md

  • Network accessline 36
    - **Megill et al. 2021** "cellxgene: a performant, scalable exploration platform for high dimensional sparse matrices" (bioRxiv preprint). Describes the CellxGene platform architecture and exploration capabilities. [DOI: 10.1101/2021.04.05.
  • Network accessline 37
    - **CZ CELLxGENE Discover** (Chan Zuckerberg Initiative, 2023). CellxGene Census provides programmatic access to the entire CellxGene data corpus as a single unified dataset. [https://cellxgene.cziscience.com/](https://cellxgene.cziscience.
  • Network accessline 38
    - **Regev et al. 2017** "The Human Cell Atlas" (eLife, ~1,500 citations). The vision paper for comprehensive single-cell reference maps of all human cells. CellxGene Census is the largest realization of this vision. [DOI: 10.7554/eLife.2704
  • Network accessline 39
    - **Tabula Sapiens Consortium 2022** "The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans" (Science, ~800 citations). Multi-organ human cell atlas contributing to CellxGene Census. [DOI: 10.1126/science.abl4896]
  • Network accessline 40
    - **ENCODE Project Consortium 2020** (Nature, ~1,656 citations). The bulk regulatory element catalog that CellxGene single-cell data contextualizes. [DOI: 10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)

plugin/skills/cellxgene-context/references/literature.md

  • Network accessline 15
    Bodenmiller B, Campbell P, Carninci P, Clatworthy M, et al. The Human Cell   Atlas. eLife, 6:e27041, 2017. - **DOI:** [10.7554/eLife.27041](https://doi.org/10.7554/eLife.27041)
  • Network accessline 16
    Atlas. eLife, 6:e27041, 2017. - **DOI:** [10.7554/eLife.27041](https://doi.org/10.7554/eLife.27041) - **PMID:** 29206104 | **PMC:** PMC5762154
  • Network accessline 17
    - **DOI:** [10.7554/eLife.27041](https://doi.org/10.7554/eLife.27041)
  • Network accessline 40
    Heumos L, Pisco AO, et al. CellxGene: a performant, scalable exploration   platform for high dimensional sparse matrices. bioRxiv, 2021. - **DOI:** [10.1101/2021.04.05.438318](https://doi.org/10.1101/2021.04.05.438318)
  • Network accessline 41
    platform for high dimensional sparse matrices. bioRxiv, 2021. - **DOI:** [10.1101/2021.04.05.438318](https://doi.org/10.1101/2021.04.05.438318) - **PMID:** N/A | **PMC:** N/A
  • Network accessline 42
    - **DOI:** [10.1101/2021.04.05.438318](https://doi.org/10.1101/2021.04.05.438318)
  • Network accessline 68
    organ, single-cell transcriptomic atlas of humans. Science,   376(6594):eabl4896, 2022. - **DOI:** [10.1126/science.abl4896](https://doi.org/10.1126/science.abl4896)
  • Network accessline 69
    376(6594):eabl4896, 2022. - **DOI:** [10.1126/science.abl4896](https://doi.org/10.1126/science.abl4896) - **PMID:** 35549404 | **PMC:** PMC9812015
  • Network accessline 70
    - **DOI:** [10.1126/science.abl4896](https://doi.org/10.1126/science.abl4896)
  • Network accessline 96
    - **Citation:** Tabula Muris Consortium. Single-cell transcriptomics of 20   mouse organs creates a Tabula Muris. Nature, 562(7727):367-372, 2018. - **DOI:** [10.1038/s41586-018-0590-4](https://doi.org/10.1038/s41586-018-0590-4)
  • Network accessline 97
    mouse organs creates a Tabula Muris. Nature, 562(7727):367-372, 2018. - **DOI:** [10.1038/s41586-018-0590-4](https://doi.org/10.1038/s41586-018-0590-4) - **PMID:** 30283141 | **PMC:** PMC6642641
  • Network accessline 98
    - **DOI:** [10.1038/s41586-018-0590-4](https://doi.org/10.1038/s41586-018-0590-4)
  • Network accessline 124
    immune cell analysis reveals tissue-specific features in humans. Science,   376(6594):eabl5197, 2022. - **DOI:** [10.1126/science.abl5197](https://doi.org/10.1126/science.abl5197)
  • Network accessline 125
    376(6594):eabl5197, 2022. - **DOI:** [10.1126/science.abl5197](https://doi.org/10.1126/science.abl5197) - **PMID:** 35549406 | **PMC:** PMC7613554
  • Network accessline 126
    - **DOI:** [10.1126/science.abl5197](https://doi.org/10.1126/science.abl5197)
  • Network accessline 155
    Benchmarking atlas-level data integration in single-cell genomics. Nature   Methods, 19(1):41-50, 2022. - **DOI:** [10.1038/s41592-021-01336-8](https://doi.org/10.1038/s41592-021-01336-8)
  • Network accessline 156
    Methods, 19(1):41-50, 2022. - **DOI:** [10.1038/s41592-021-01336-8](https://doi.org/10.1038/s41592-021-01336-8) - **PMID:** 34949812 | **PMC:** PMC8762698
  • Network accessline 157
    - **DOI:** [10.1038/s41592-021-01336-8](https://doi.org/10.1038/s41592-021-01336-8)
  • Network accessline 181
    Flexible comparison of batch correction methods for single-cell RNA-seq   using BatchBench. Genome Biology, 22(1):189, 2021. - **DOI:** [10.1186/s13059-021-02393-y](https://doi.org/10.1186/s13059-021-02393-y)
  • Network accessline 182
    using BatchBench. Genome Biology, 22(1):189, 2021. - **DOI:** [10.1186/s13059-021-02393-y](https://doi.org/10.1186/s13059-021-02393-y) - **PMID:** 34183031
  • Network accessline 183
    - **DOI:** [10.1186/s13059-021-02393-y](https://doi.org/10.1186/s13059-021-02393-y)

plugin/skills/cite-encode/SKILL.md

  • Network accessline 89
    Template: > "[Assay type] data for [biosample] were obtained from the ENCODE Project (https://www.encodeproject.org). Experiment accessions: [list ENCSR accessions]. All ENCODE data are freely available under unrestricted use policy."
  • Network accessline 90
    > "[Assay type] data for [biosample] were obtained from the ENCODE Project (https://www.encodeproject.org). Experiment accessions: [list ENCSR accessions]. All ENCODE data are freely available under unrestricted use policy."
  • Network accessline 149
    | HOMER | Heinz et al. Mol Cell 2010 | 10.1016/j.molcel.2010.05.004 | | IGV | Robinson et al. Nat Biotechnol 2011 | 10.1038/nbt.2754 | | Picard | Broad Institute (URL-only citation) | https://broadinstitute.github.io/picard/ |
  • Network accessline 150
    | IGV | Robinson et al. Nat Biotechnol 2011 | 10.1038/nbt.2754 | | Picard | Broad Institute (URL-only citation) | https://broadinstitute.github.io/picard/ | | BWA-MEM | Li. arXiv 2013 | arXiv:1303.3997 |
  • Network accessline 151
    | Picard | Broad Institute (URL-only citation) | https://broadinstitute.github.io/picard/ |
  • Network accessline 188
    > "Histone ChIP-seq (H3K27ac, H3K4me1, H3K4me3, H3K27me3, H3K36me3), ATAC-seq, RNA-seq, and whole-genome bisulfite sequencing data were obtained from the ENCODE Project (https://www.encodeproject.org). Experiment accessions are listed in Su
  • Network accessline 444
    | ATAC-seq, human pancreatic islets | ENCODE | ENCSR345MNO | | RNA-seq, human pancreatic islets | ENCODE | ENCSR678PQR | | GRCh38 reference genome | UCSC | https://hgdownload.soe.ucsc.edu/goldenPath/hg38/ |
  • Network accessline 445
    | RNA-seq, human pancreatic islets | ENCODE | ENCSR678PQR | | GRCh38 reference genome | UCSC | https://hgdownload.soe.ucsc.edu/goldenPath/hg38/ | | GENCODE v41 gene annotation | GENCODE | https://www.gencodegenes.org/human/release_41.html |
  • Network accessline 446
    | GRCh38 reference genome | UCSC | https://hgdownload.soe.ucsc.edu/goldenPath/hg38/ |
  • Network accessline 447
    | GENCODE v41 gene annotation | GENCODE | https://www.gencodegenes.org/human/release_41.html |
  • Network accessline 448
    | ENCODE Blacklist v2 | Amemiya et al., 2019 | https://github.com/Boyle-Lab/Blacklist/ |
  • Network accessline 449
    | **Software and Algorithms** | | | | MACS2 v2.2.9.1 | Zhang et al., 2008 | https://github.com/macs3-project/MACS | | STAR v2.7.11b | Dobin et al., 2013 | https://github.com/alexdobin/STAR |
  • Network accessline 450
    | MACS2 v2.2.9.1 | Zhang et al., 2008 | https://github.com/macs3-project/MACS |
  • Network accessline 451
    | STAR v2.7.11b | Dobin et al., 2013 | https://github.com/alexdobin/STAR |
  • Network accessline 452
    | DESeq2 v1.38 | Love et al., 2014 | https://bioconductor.org/packages/DESeq2 |
  • Network accessline 453
    | bedtools v2.31.0 | Quinlan & Hall, 2010 | https://github.com/arq5x/bedtools2 |
  • Network accessline 454
    | deepTools v3.5.5 | Ramirez et al., 2016 | https://github.com/deeptools/deepTools |
  • Network accessline 455
    | samtools v1.19 | Danecek et al., 2021 | https://github.com/samtools/samtools |
  • Network accessline 456
    | Custom analysis scripts | This study | https://github.com/[your-repo] |
  • Network accessline 489
    ### 9. Stale DOIs DOI links can break if a publisher migrates. Always verify DOIs resolve correctly before submitting a manuscript. Use https://doi.org/[DOI] to check.
  • Network accessline 490
    DOI links can break if a publisher migrates. Always verify DOIs resolve correctly before submitting a manuscript. Use https://doi.org/[DOI] to check.

plugin/skills/cite-encode/references/literature.md

  • Network accessline 10
    - **Citation:** ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. *Nature*, 489(7414), 57-74, 2012. - **DOI:** [10.1038/nature11247](https://doi.org/10.1038/nature11247)
  • Network accessline 11
    - **DOI:** [10.1038/nature11247](https://doi.org/10.1038/nature11247)
  • Network accessline 20
    - **Citation:** ENCODE Project Consortium, Moore JE, Purcaro MJ, Pratt HE, Epstein CB, Shoresh N, Adrian J, Kawli T, Davis CA, Dobin A, Kaul R, Halow J, Van Nostrand EL, Freese P, Gorkin DU, Shen Y, He Y, Mackiewicz M, Pauli-Behn F, William
  • Network accessline 21
    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 30
    - **Citation:** Davis CA, Hitz BC, Sloan CA, Chan ET, Davidson JM, Gabdank I, Hilton JA, Jain K, Baymuradov UK, Narayanan AK, Onate KC, Graham K, Miyasato SR, Dreszer TR, Strattan JS, Jolanki O, Tanaka FY, Cherry JM. The Encyclopedia of DNA
  • Network accessline 31
    - **DOI:** [10.1093/nar/gkx1081](https://doi.org/10.1093/nar/gkx1081)
  • Network accessline 40
    - **Citation:** Luo Y, Hitz BC, Gabdank I, Hilton JA, Kagda MS, Lam B, Myers Z, Sud P, Jou J, Lin K, Baymuradov UK, Graham K, Litton C, Miyasato SR, Strattan JS, Jolanki O, Lee JW, Tanaka FY, Batchelder JE, Takeo I, Cherry JM. New developme
  • Network accessline 41
    - **DOI:** [10.1093/nar/gkz1062](https://doi.org/10.1093/nar/gkz1062)
  • Network accessline 50
    - **Citation:** Toronto International Data Release Workshop Authors. Prepublication data sharing. *Nature*, 461(7261), 168-170, 2009. - **DOI:** [10.1038/461168a](https://doi.org/10.1038/461168a)
  • Network accessline 51
    - **DOI:** [10.1038/461168a](https://doi.org/10.1038/461168a)

plugin/skills/clinvar-annotation/SKILL.md

  • Network accessline 37
    - **Landrum et al. 2018** "ClinVar: improving access to variant interpretations and supporting evidence" (Nucleic Acids Research, ~2,000 citations). Describes the ClinVar database architecture, submission standards, and the star-rating revi
  • Network accessline 38
    - **Riggs et al. 2020** "Technical standards for the interpretation and reporting of constitutional copy-number variants: a joint consensus recommendation of the ACMG and ClinGen" (Genetics in Medicine, ~500 citations). Framework for interp
  • Network accessline 39
    - **Richards et al. 2015** "Standards and guidelines for the interpretation of sequence variants: ACMG/AMP joint consensus recommendation" (Genetics in Medicine, ~12,000 citations). The ACMG variant classification framework (pathogenic thro
  • Network accessline 40
    - **ENCODE Project Consortium 2020** (Nature, ~1,656 citations). Registry of 926,535 human cCREs — the functional annotation layer for interpreting non-coding ClinVar variants. [DOI: 10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586
  • Network accessline 68
    **Base URL**: `https://eutils.ncbi.nlm.nih.gov/entrez/eutils/`
  • Network accessline 83
    For bulk intersection with ENCODE peaks, download the ClinVar VCF: - GRCh38: `https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz`
  • Network accessline 84
    - GRCh38: `https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz`
  • Network accessline 85
    - GRCh37: `https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh37/clinvar.vcf.gz`
  • Network accessline 121
    # Search ClinVar for pathogenic variants in a gene url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi"
  • Network accessline 122
    url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi"
  • Network accessline 127
    "retmode": "json" } response = requests.get(url, params=params)
  • Network accessline 128
    } response = requests.get(url, params=params) result = response.json()
  • Network accessline 129
    response = requests.get(url, params=params)
  • Network accessline 142
    def search_clinvar(gene_symbol, significance="pathogenic"):     """Search ClinVar for variants in a gene with given clinical significance."""     url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi"
  • Network accessline 143
    """Search ClinVar for variants in a gene with given clinical significance."""     url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi"     term = f"{gene_symbol}[gene] AND {significance}[clinical significance]"
  • Network accessline 144
    url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi"
  • Network accessline 150
    "retmode": "json"     }     response = requests.get(url, params=params)
  • Network accessline 151
    }     response = requests.get(url, params=params)     time.sleep(0.34)  # Rate limit: 3/sec
  • Network accessline 152
    response = requests.get(url, params=params)
  • Network accessline 160
    def get_clinvar_summary(variant_ids):     """Get summary for ClinVar variant IDs."""     url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi"
  • Network accessline 161
    """Get summary for ClinVar variant IDs."""     url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi"     params = {
  • Network accessline 162
    url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi"
  • Network accessline 166
    "retmode": "json"     }     response = requests.get(url, params=params)
  • Network accessline 167
    }     response = requests.get(url, params=params)     time.sleep(0.34)
  • Network accessline 168
    response = requests.get(url, params=params)
  • Network accessline 185
    # Download ClinVar VCF (GRCh38) wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz
  • Network accessline 186
    wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz
  • Network accessline 187
    wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz.tbi
  • Network accessline 215
    # Find ClinVar variants in region for record in vcf.fetch(chrom, start, end):
  • Network accessline 216
    for record in vcf.fetch(chrom, start, end):
  • Network accessline 338
    GET https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=clinvar&term=chr1[chr]+AND+10000:20000[chrpos]+AND+pathogenic[clnsig]

plugin/skills/clinvar-annotation/references/literature.md

  • Network accessline 16
    variant interpretations and supporting evidence. Nucleic Acids Research,   46(D1):D1062-D1067, 2018. - **DOI:** [10.1093/nar/gkx1153](https://doi.org/10.1093/nar/gkx1153)
  • Network accessline 17
    46(D1):D1062-D1067, 2018. - **DOI:** [10.1093/nar/gkx1153](https://doi.org/10.1093/nar/gkx1153) - **PMID:** 29165669 | **PMC:** PMC5753237
  • Network accessline 18
    - **DOI:** [10.1093/nar/gkx1153](https://doi.org/10.1093/nar/gkx1153)
  • Network accessline 42
    track and share variant interpretations. Current Protocols, 1(12):e315,   2021. - **DOI:** [10.1002/cpz1.315](https://doi.org/10.1002/cpz1.315)
  • Network accessline 43
    2021. - **DOI:** [10.1002/cpz1.315](https://doi.org/10.1002/cpz1.315) - **PMID:** 34964606 | **PMC:** PMC8858745
  • Network accessline 44
    - **DOI:** [10.1002/cpz1.315](https://doi.org/10.1002/cpz1.315)
  • Network accessline 74
    American College of Medical Genetics and Genomics and the Association for   Molecular Pathology. Genetics in Medicine, 17(5):405-424, 2015. - **DOI:** [10.1038/gim.2015.30](https://doi.org/10.1038/gim.2015.30)
  • Network accessline 75
    Molecular Pathology. Genetics in Medicine, 17(5):405-424, 2015. - **DOI:** [10.1038/gim.2015.30](https://doi.org/10.1038/gim.2015.30) - **PMID:** 25741868 | **PMC:** PMC4544753
  • Network accessline 76
    - **DOI:** [10.1038/gim.2015.30](https://doi.org/10.1038/gim.2015.30)
  • Network accessline 100
    refinement of the ACMG-AMP variant classification criteria. Genetics in   Medicine, 19(10):1105-1117, 2017. - **DOI:** [10.1038/gim.2017.37](https://doi.org/10.1038/gim.2017.37)
  • Network accessline 101
    Medicine, 19(10):1105-1117, 2017. - **DOI:** [10.1038/gim.2017.37](https://doi.org/10.1038/gim.2017.37) - **PMID:** 28492532 | **PMC:** PMC5632834
  • Network accessline 102
    - **DOI:** [10.1038/gim.2017.37](https://doi.org/10.1038/gim.2017.37)
  • Network accessline 128
    improving the interpretation of cancer susceptibility genetic test results.   Human Mutation, 29(11):1282-1291, 2008. - **DOI:** [10.1002/humu.20880](https://doi.org/10.1002/humu.20880)
  • Network accessline 129
    Human Mutation, 29(11):1282-1291, 2008. - **DOI:** [10.1002/humu.20880](https://doi.org/10.1002/humu.20880) - **PMID:** 18951446 | **PMC:** PMC3075918
  • Network accessline 130
    - **DOI:** [10.1002/humu.20880](https://doi.org/10.1002/humu.20880)
  • Network accessline 161
    Clinical Genome Resource. New England Journal of Medicine,   372(23):2235-2242, 2015. - **DOI:** [10.1056/NEJMsr1406261](https://doi.org/10.1056/NEJMsr1406261)
  • Network accessline 162
    372(23):2235-2242, 2015. - **DOI:** [10.1056/NEJMsr1406261](https://doi.org/10.1056/NEJMsr1406261) - **PMID:** 26014595 | **PMC:** PMC4474187
  • Network accessline 163
    - **DOI:** [10.1056/NEJMsr1406261](https://doi.org/10.1056/NEJMsr1406261)
  • Network accessline 190
    and Genomics (ACMG) and the Clinical Genome Resource (ClinGen). Genetics in   Medicine, 22(2):245-257, 2020. - **DOI:** [10.1038/s41436-019-0686-8](https://doi.org/10.1038/s41436-019-0686-8)
  • Network accessline 191
    Medicine, 22(2):245-257, 2020. - **DOI:** [10.1038/s41436-019-0686-8](https://doi.org/10.1038/s41436-019-0686-8) - **PMID:** 31690835 | **PMC:** PMC7313390
  • Network accessline 192
    - **DOI:** [10.1038/s41436-019-0686-8](https://doi.org/10.1038/s41436-019-0686-8)

plugin/skills/compare-biosamples/SKILL.md

  • Network accessline 238
    **ENCODE Blacklist filtering (required before comparison)**: Before any cross-tissue comparison, remove peaks and signal in ENCODE Blacklist regions (Amemiya et al. 2019, Scientific Reports, 1,372 citations). Blacklisted regions produce art
  • Network accessline 239
    - Human GRCh38: `hg38-blacklist.v2.bed.gz` from [Boyle-Lab/Blacklist](https://github.com/Boyle-Lab/Blacklist)

plugin/skills/compare-biosamples/references/literature.md

  • Network accessline 33
    - **Citation:** Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, et al. Integrative analysis   of 111 reference human epigenomes. *Nature*, 518(7539), 317-330, 2015. - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248)
  • Network accessline 34
    of 111 reference human epigenomes. *Nature*, 518(7539), 317-330, 2015. - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248) - **PMID:** 25693563 | **PMC:** PMC4530010
  • Network accessline 35
    - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248)
  • Network accessline 55
    analyses implicate candidate causal variants at inherited risk loci for Alzheimer's and   Parkinson's diseases. *Nature Genetics*, 52(11), 1158-1168, 2020. - **DOI:** [10.1038/s41588-020-00721-x](https://doi.org/10.1038/s41588-020-00721-x)
  • Network accessline 56
    Parkinson's diseases. *Nature Genetics*, 52(11), 1158-1168, 2020. - **DOI:** [10.1038/s41588-020-00721-x](https://doi.org/10.1038/s41588-020-00721-x) - **PMID:** 33106633 | **PMC:** PMC7887218
  • Network accessline 57
    - **DOI:** [10.1038/s41588-020-00721-x](https://doi.org/10.1038/s41588-020-00721-x)
  • Network accessline 74
    encyclopaedias of DNA elements in the human and mouse genomes. *Nature*, 583(7818),   699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 75
    699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4) - **PMID:** 32728249 | **PMC:** PMC7410828
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    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
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    - **Citation:** Thurman RE, Rynes E, Humbert R, Vierstra J, Maurano MT, et al. The accessible   chromatin landscape of the human genome. *Nature*, 489(7414), 75-82, 2012. - **DOI:** [10.1038/nature11232](https://doi.org/10.1038/nature11232)
  • Network accessline 102
    chromatin landscape of the human genome. *Nature*, 489(7414), 75-82, 2012. - **DOI:** [10.1038/nature11232](https://doi.org/10.1038/nature11232) - **PMID:** 22955617 | **PMC:** PMC3721348
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    - **DOI:** [10.1038/nature11232](https://doi.org/10.1038/nature11232)
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    - **Citation:** Bernstein BE, Stamatoyannopoulos JA, Costello JF, et al. The NIH Roadmap   Epigenomics Mapping Consortium. *Nature Biotechnology*, 28(10), 1045-1048, 2010. - **DOI:** [10.1038/nbt1010-1045](https://doi.org/10.1038/nbt1010-10
  • Network accessline 132
    Epigenomics Mapping Consortium. *Nature Biotechnology*, 28(10), 1045-1048, 2010. - **DOI:** [10.1038/nbt1010-1045](https://doi.org/10.1038/nbt1010-1045) - **PMID:** 20944595 | **PMC:** PMC3607281
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    - **DOI:** [10.1038/nbt1010-1045](https://doi.org/10.1038/nbt1010-1045)
  • Network accessline 159
    - **Citation:** Zhu J, Adli M, Zou JY, Verstappen G, et al. Tissue-specific enhancers   identified by H3K27ac across reference epigenomes. *Genome Biology*, 17, 32, 2016. - **DOI:** [10.1186/s13059-016-0974-4](https://doi.org/10.1186/s13059
  • Network accessline 160
    identified by H3K27ac across reference epigenomes. *Genome Biology*, 17, 32, 2016. - **DOI:** [10.1186/s13059-016-0974-4](https://doi.org/10.1186/s13059-016-0974-4) - **PMID:** 27040513 | **PMC:** PMC4818977
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    - **DOI:** [10.1186/s13059-016-0974-4](https://doi.org/10.1186/s13059-016-0974-4)
  • Network accessline 175
    - **Citation:** Hnisz D, Abraham BJ, Lee TI, et al. Super-enhancers in the control of cell   identity and disease. *Cell*, 155(4), 934-947, 2013. - **DOI:** [10.1016/j.cell.2013.09.053](https://doi.org/10.1016/j.cell.2013.09.053)
  • Network accessline 176
    identity and disease. *Cell*, 155(4), 934-947, 2013. - **DOI:** [10.1016/j.cell.2013.09.053](https://doi.org/10.1016/j.cell.2013.09.053) - **PMID:** 24119843 | **PMC:** PMC3841062
  • Network accessline 177
    - **DOI:** [10.1016/j.cell.2013.09.053](https://doi.org/10.1016/j.cell.2013.09.053)
  • Network accessline 196
    cell type-specific enhancers. *Nature Reviews Molecular Cell Biology*, 16(3),   144-154, 2015. - **DOI:** [10.1038/nrm3949](https://doi.org/10.1038/nrm3949)
  • Network accessline 197
    144-154, 2015. - **DOI:** [10.1038/nrm3949](https://doi.org/10.1038/nrm3949) - **PMID:** 25650801 | **PMC:** PMC4517609
  • Network accessline 198
    - **DOI:** [10.1038/nrm3949](https://doi.org/10.1038/nrm3949)

plugin/skills/cross-reference/SKILL.md

  • Network accessline 70
    curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=gds&db=sra&id=GDS_UID&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 606
    2. **DOI format inconsistency**: Normalize DOIs to the bare format "10.xxxx/..." without the "https://doi.org/" prefix. ENCODE sometimes stores DOIs with the URL prefix; strip it before passing to `encode_link_reference`. Both formats work

plugin/skills/cross-reference/references/literature.md

  • Network accessline 31
    - **Citation:** The GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects   across human tissues. *Science*, 369(6509), 1318-1330, 2020. - **DOI:** [10.1126/science.aaz1776](https://doi.org/10.1126/science.aaz1776)
  • Network accessline 32
    across human tissues. *Science*, 369(6509), 1318-1330, 2020. - **DOI:** [10.1126/science.aaz1776](https://doi.org/10.1126/science.aaz1776) - **PMID:** 32913098 | **PMC:** PMC7737656
  • Network accessline 33
    - **DOI:** [10.1126/science.aaz1776](https://doi.org/10.1126/science.aaz1776)
  • Network accessline 58
    access to variant interpretations and supporting evidence. *Nucleic Acids Research*,   46(D1), D1062-D1067, 2018. - **DOI:** [10.1093/nar/gkx1153](https://doi.org/10.1093/nar/gkx1153)
  • Network accessline 59
    46(D1), D1062-D1067, 2018. - **DOI:** [10.1093/nar/gkx1153](https://doi.org/10.1093/nar/gkx1153) - **PMID:** 29165669 | **PMC:** PMC5753237
  • Network accessline 60
    - **DOI:** [10.1093/nar/gkx1153](https://doi.org/10.1093/nar/gkx1153)
  • Network accessline 84
    Catalog of published genome-wide association studies, targeted arrays and summary   statistics 2019. *Nucleic Acids Research*, 47(D1), D1005-D1012, 2019. - **DOI:** [10.1093/nar/gky1120](https://doi.org/10.1093/nar/gky1120)
  • Network accessline 85
    statistics 2019. *Nucleic Acids Research*, 47(D1), D1005-D1012, 2019. - **DOI:** [10.1093/nar/gky1120](https://doi.org/10.1093/nar/gky1120) - **PMID:** 30445434 | **PMC:** PMC6323933
  • Network accessline 86
    - **DOI:** [10.1093/nar/gky1120](https://doi.org/10.1093/nar/gky1120)
  • Network accessline 110
    - **Citation:** Barrett T, Wilhite SE, Ledoux P, Evangelista C, et al. NCBI GEO: archive for   functional genomics data sets — update. *Nucleic Acids Research*, 41(D1), D991-D995, 2013. - **DOI:** [10.1093/nar/gks1193](https://doi.org/10.10
  • Network accessline 111
    functional genomics data sets — update. *Nucleic Acids Research*, 41(D1), D991-D995, 2013. - **DOI:** [10.1093/nar/gks1193](https://doi.org/10.1093/nar/gks1193) - **PMID:** 23193258 | **PMC:** PMC3531084
  • Network accessline 112
    - **DOI:** [10.1093/nar/gks1193](https://doi.org/10.1093/nar/gks1193)
  • Network accessline 139
    the 9th release of the open-access database of transcription factor binding profiles.   *Nucleic Acids Research*, 50(D1), D580-D587, 2022. - **DOI:** [10.1093/nar/gkab1113](https://doi.org/10.1093/nar/gkab1113)
  • Network accessline 140
    *Nucleic Acids Research*, 50(D1), D580-D587, 2022. - **DOI:** [10.1093/nar/gkab1113](https://doi.org/10.1093/nar/gkab1113) - **PMID:** 34850907 | **PMC:** PMC8728201
  • Network accessline 141
    - **DOI:** [10.1093/nar/gkab1113](https://doi.org/10.1093/nar/gkab1113)
  • Network accessline 166
    constraint spectrum quantified from variation in 141,456 humans. *Nature*, 581(7809),   434-443, 2020. - **DOI:** [10.1038/s41586-020-2308-7](https://doi.org/10.1038/s41586-020-2308-7)
  • Network accessline 167
    434-443, 2020. - **DOI:** [10.1038/s41586-020-2308-7](https://doi.org/10.1038/s41586-020-2308-7) - **PMID:** 32461654 | **PMC:** PMC7334197
  • Network accessline 168
    - **DOI:** [10.1038/s41586-020-2308-7](https://doi.org/10.1038/s41586-020-2308-7)
  • Network accessline 194
    - **Citation:** Cunningham F, Allen JE, Allen J, et al. Ensembl 2022. *Nucleic Acids   Research*, 50(D1), D988-D995, 2022. - **DOI:** [10.1093/nar/gkab1049](https://doi.org/10.1093/nar/gkab1049)
  • Network accessline 195
    Research*, 50(D1), D988-D995, 2022. - **DOI:** [10.1093/nar/gkab1049](https://doi.org/10.1093/nar/gkab1049) - **PMID:** 34791404 | **PMC:** PMC8728283
  • Network accessline 196
    - **DOI:** [10.1093/nar/gkab1049](https://doi.org/10.1093/nar/gkab1049)

plugin/skills/data-provenance/SKILL.md

  • Network accessline 42
    > "Genomic coordinates were lifted from GRCh37/hg19 to GRCh38/hg38 using UCSC liftOver (v377, Kent et al. 2002, PMID: 12045153). The chain file hg19ToHg38.over.chain.gz was obtained from UCSC Genome Browser (https://hgdownload.soe.ucsc.edu/
  • Network accessline 134
    "file": "hg38-blacklist.v2.bed.gz",       "source": "ENCODE Blacklist v2 (Amemiya et al. 2019, Sci Rep, DOI:10.1038/s41598-019-45839-z)",       "url": "https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz",
  • Network accessline 135
    "source": "ENCODE Blacklist v2 (Amemiya et al. 2019, Sci Rep, DOI:10.1038/s41598-019-45839-z)",       "url": "https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz",       "md5": "def456..."
  • Network accessline 136
    "url": "https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz",
  • Network accessline 336
    **Data Acquisition** > [Assay] data for [biosample] were obtained from the ENCODE Project (ENCODE Project Consortium 2020) via the ENCODE portal (https://www.encodeproject.org). [N] experiments were included (accessions: [list]). All experi
  • Network accessline 337
    > [Assay] data for [biosample] were obtained from the ENCODE Project (ENCODE Project Consortium 2020) via the ENCODE portal (https://www.encodeproject.org). [N] experiments were included (accessions: [list]). All experiments used [sequencer

plugin/skills/data-provenance/references/literature.md

  • Network accessline 10
    - **Citation:** Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten JW, da Silva Santos LB, Bourne PE, Bouwman J, Brookes AJ, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo CT, Finkers R, Gonzal
  • Network accessline 11
    - **DOI:** [10.1038/sdata.2016.18](https://doi.org/10.1038/sdata.2016.18)
  • Network accessline 20
    - **Citation:** Baker M. 1,500 scientists lift the lid on reproducibility. *Nature*, 533(7604), 452-454, 2016. - **DOI:** [10.1038/533452a](https://doi.org/10.1038/533452a)
  • Network accessline 21
    - **DOI:** [10.1038/533452a](https://doi.org/10.1038/533452a)
  • Network accessline 30
    - **Citation:** Stodden V, McNutt M, Bailey DH, Deelman E, Gil Y, Hanson B, Heroux MA, Ioannidis JPA, Taufer M. Enhancing reproducibility for computational methods. *Science*, 354(6317), 1240-1241, 2016. - **DOI:** [10.1126/science.aah6168]
  • Network accessline 31
    - **DOI:** [10.1126/science.aah6168](https://doi.org/10.1126/science.aah6168)
  • Network accessline 40
    - **Citation:** Grüning B, Dale R, Sjodin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Koester J, Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. *Nature Methods*, 15(7), 475-476,
  • Network accessline 41
    - **DOI:** [10.1038/s41592-018-0046-7](https://doi.org/10.1038/s41592-018-0046-7)
  • Network accessline 50
    - **Citation:** Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. *Nature Biotechnology*, 35(4), 316-319, 2017. - **DOI:** [10.1038/nbt.3820](https://doi.org/10.1038
  • Network accessline 51
    - **DOI:** [10.1038/nbt.3820](https://doi.org/10.1038/nbt.3820)
  • Network accessline 60
    - **Citation:** Amstutz P, Crusoe MR, Tijanic N, Chapman B, Chilton J, Heuer M, Kartashov A, Leehr D, Menager H, Nedeljkovich M, Scales M, Soiland-Reyes S, Stojanovic L. Common Workflow Language, v1.0. *Specification*, Common Workflow Langu
  • Network accessline 61
    - **DOI:** [10.6084/m9.figshare.3115156.v2](https://doi.org/10.6084/m9.figshare.3115156.v2)
  • Network accessline 69
    - **Citation:** Goecks J, Nekrutenko A, Taylor J, Galaxy Team. Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. *Genome Biology*, 11(8), R86, 2010. - **DO
  • Network accessline 70
    - **DOI:** [10.1186/gb-2010-11-8-r86](https://doi.org/10.1186/gb-2010-11-8-r86)

plugin/skills/disease-research/SKILL.md

  • Network accessline 26
    | Reference | Year | Journal | Key Contribution | Citations | DOI | |-----------|------|---------|-----------------|-----------|-----| | ENCODE Phase 3 | 2020 | Nature | 926,535 human cCREs across 400+ biosamples, SCREEN portal | ~1,656 | [
  • Network accessline 27
    |-----------|------|---------|-----------------|-----------|-----| | ENCODE Phase 3 | 2020 | Nature | 926,535 human cCREs across 400+ biosamples, SCREEN portal | ~1,656 | [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4
  • Network accessline 28
    | ENCODE Phase 3 | 2020 | Nature | 926,535 human cCREs across 400+ biosamples, SCREEN portal | ~1,656 | [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4) |
  • Network accessline 29
    | Maurano et al. | 2012 | Science | Disease variants enriched in regulatory DNA; DNase hotspots explain 76.6% of GWAS SNPs | ~3,500 | [10.1126/science.1222794](https://doi.org/10.1126/science.1222794) |
  • Network accessline 30
    | Finucane et al. | 2015 | Nat Genet | S-LDSC partitions heritability into functional annotations; ENCODE categories explain disproportionate heritability | ~2,253 | [10.1038/ng.3404](https://doi.org/10.1038/ng.3404) |
  • Network accessline 31
    | Nasser et al. | 2021 | Nature | ABC model links enhancers to genes in 131 cell types; connected 5,036 GWAS signals to 2,249 genes | ~468 | [10.1038/s41586-021-03446-x](https://doi.org/10.1038/s41586-021-03446-x) |
  • Network accessline 32
    | Roadmap Epigenomics | 2015 | Nature | 111 reference epigenomes; tissue-specific chromatin states; disease variant enrichment in tissue-specific marks | ~5,810 | [10.1038/nature14248](https://doi.org/10.1038/nature14248) |
  • Network accessline 33
    | Visscher et al. | 2017 | Am J Hum Genet | GWAS review — 10 years of discoveries, statistical frameworks, shift toward functional interpretation | ~2,500 | [10.1016/j.ajhg.2017.06.005](https://doi.org/10.1016/j.ajhg.2017.06.005) |
  • Network accessline 34
    | Buniello et al. | 2019 | Nucleic Acids Res | GWAS Catalog — curated repository; >250,000 SNP-trait associations | ~3,000 | [10.1093/nar/gky1120](https://doi.org/10.1093/nar/gky1120) |
  • Network accessline 35
    | Ochoa et al. | 2021 | Nucleic Acids Res | Open Targets Platform — integrates GWAS, functional genomics, drugs for systematic target identification | ~600 | [10.1093/nar/gkaa1027](https://doi.org/10.1093/nar/gkaa1027) |
  • Network accessline 355
    # Query GWAS Catalog for Alzheimer's disease associations GET https://www.ebi.ac.uk/gwas/rest/api/associations?efoTrait=EFO_0000249
  • Network accessline 356
    GET https://www.ebi.ac.uk/gwas/rest/api/associations?efoTrait=EFO_0000249

plugin/skills/disease-research/references/literature.md

  • Network accessline 12
    - **Citation:** Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, Wang H, Reynolds AP, Sandstrom R, Qu H, Brody J, Shafer A, Neri F, Lee K, Kutyavin T, Stehling-Sun S, Johnson AK, Canfield TK, Giste E, Diegel M, Bates D, Hansen RS, Neph
  • Network accessline 13
    - **DOI:** [10.1126/science.1222794](https://doi.org/10.1126/science.1222794)
  • Network accessline 22
    - **Citation:** Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. *Proceedings of the National A
  • Network accessline 23
    - **DOI:** [10.1073/pnas.0903103106](https://doi.org/10.1073/pnas.0903103106)
  • Network accessline 34
    - **Citation:** Roadmap Epigenomics Consortium, Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, Heravi-Moussavi A, Kheradpour P, Zhang Z, Wang J, Ziller MJ, Amin V, Whitaker JW, Schultz MD, Ward LD, Sarkar A, Quon G, Sandstrom RS, Eaton M
  • Network accessline 35
    - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248)
  • Network accessline 44
    - **Citation:** Ernst J, Kellis M. ChromHMM: automating chromatin-state discovery and characterization. *Nature Methods*, 9(3), 215-216, 2012. - **DOI:** [10.1038/nmeth.1906](https://doi.org/10.1038/nmeth.1906)
  • Network accessline 45
    - **DOI:** [10.1038/nmeth.1906](https://doi.org/10.1038/nmeth.1906)
  • Network accessline 56
    - **Citation:** Finucane HK, Bulik-Sullivan B, Gusev A, Trynka G, Reshef Y, Loh PR, Anttila V, Xu H, Zang C, Farh K, Ripke S, Day FR, ReproGen Consortium, Schizophrenia Working Group of the Psychiatric Genomics Consortium, RACI Consortium,
  • Network accessline 57
    - **DOI:** [10.1038/ng.3404](https://doi.org/10.1038/ng.3404)
  • Network accessline 66
    - **Citation:** Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Patterson N, Daly MJ, Price AL, Neale BM. LD Score regression distinguishes confounding from polyge
  • Network accessline 67
    - **DOI:** [10.1038/ng.3211](https://doi.org/10.1038/ng.3211)
  • Network accessline 78
    - **Citation:** Nasser J, Bergman DT, Fulco CP, Gusev A, Engreitz JM. Genome-wide enhancer maps link risk variants to disease genes. *Nature*, 593(7858), 238-243, 2021. - **DOI:** [10.1038/s41586-021-03446-x](https://doi.org/10.1038/s41586-
  • Network accessline 79
    - **DOI:** [10.1038/s41586-021-03446-x](https://doi.org/10.1038/s41586-021-03446-x)
  • Network accessline 88
    - **Citation:** Gasperini M, Hill AJ, McFaline-Figueroa JL, Martin B, Kim S, Zhang MD, Jackson D, Leith A, Schreiber J, Noble WS, Trapnell C, Ahfeldt T, Shendure J. A genome-scale screen for enhancers at single-cell resolution. *Cell*, 176(
  • Network accessline 89
    - **DOI:** [10.1016/j.cell.2018.12.038](https://doi.org/10.1016/j.cell.2018.12.038)

plugin/skills/download-encode/references/literature.md

  • Network accessline 32
    Blomberg N, Boiten JW, da Silva Santos LB, Bourne PE, et al. The FAIR Guiding Principles   for scientific data management and stewardship. *Scientific Data*, 3, 160018, 2016. - **DOI:** [10.1038/sdata.2016.18](https://doi.org/10.1038/sdata.
  • Network accessline 33
    for scientific data management and stewardship. *Scientific Data*, 3, 160018, 2016. - **DOI:** [10.1038/sdata.2016.18](https://doi.org/10.1038/sdata.2016.18) - **PMID:** 26978244 | **PMC:** PMC4792175
  • Network accessline 34
    - **DOI:** [10.1038/sdata.2016.18](https://doi.org/10.1038/sdata.2016.18)
  • Network accessline 65
    Strattan JS, Jolanki O, Tanaka FY, Cherry JM. The Encyclopedia of DNA elements (ENCODE):   data portal update. *Nucleic Acids Research*, 46(D1), D794-D801, 2018. - **DOI:** [10.1093/nar/gkx1081](https://doi.org/10.1093/nar/gkx1081)
  • Network accessline 66
    data portal update. *Nucleic Acids Research*, 46(D1), D794-D801, 2018. - **DOI:** [10.1093/nar/gkx1081](https://doi.org/10.1093/nar/gkx1081) - **PMID:** 29126249 | **PMC:** PMC5753278
  • Network accessline 67
    - **DOI:** [10.1093/nar/gkx1081](https://doi.org/10.1093/nar/gkx1081)
  • Network accessline 94
    guidelines and practices of the ENCODE and modENCODE consortia. *Genome Research*,   22(9), 1813-1831, 2012. - **DOI:** [10.1101/gr.136184.111](https://doi.org/10.1101/gr.136184.111)
  • Network accessline 95
    22(9), 1813-1831, 2012. - **DOI:** [10.1101/gr.136184.111](https://doi.org/10.1101/gr.136184.111) - **PMID:** 22955991 | **PMC:** PMC3431496
  • Network accessline 96
    - **DOI:** [10.1101/gr.136184.111](https://doi.org/10.1101/gr.136184.111)
  • Network accessline 115
    - **Citation:** Amemiya HM, Kundaje A, Boyle AP. The ENCODE Blacklist: identification of   problematic regions of the genome. *Scientific Reports*, 9(1), 9354, 2019. - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019
  • Network accessline 116
    problematic regions of the genome. *Scientific Reports*, 9(1), 9354, 2019. - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z) - **PMID:** 31249361 | **PMC:** PMC6597582
  • Network accessline 117
    - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z)
  • Network accessline 147
    - **Citation:** Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, et al. Integrative analysis   of 111 reference human epigenomes. *Nature*, 518(7539), 317-330, 2015. - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248)
  • Network accessline 148
    of 111 reference human epigenomes. *Nature*, 518(7539), 317-330, 2015. - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248) - **PMID:** 25693563 | **PMC:** PMC4530010
  • Network accessline 149
    - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248)

plugin/skills/ensembl-annotation/SKILL.md

  • Network accessline 30
    - **Cunningham et al. 2022** (Nucleic Acids Research): Ensembl 2022 update. [DOI](https://doi.org/10.1093/nar/gkab1049)
  • Network accessline 31
    - **McLaren et al. 2016** (Genome Biology, ~4,500 citations): The Ensembl Variant Effect Predictor. [DOI](https://doi.org/10.1186/s13059-016-0974-4)
  • Network accessline 32
    - **Zerbino et al. 2015** (Genome Biology): The Ensembl Regulatory Build. [DOI](https://doi.org/10.1186/s13059-015-0621-5)
  • Network accessline 36
    **Base URL**: `https://rest.ensembl.org`
  • Network accessline 49
    # Get regulatory features in a region curl -H "Content-type: application/json" \
  • Network accessline 50
    curl -H "Content-type: application/json" \
  • Network accessline 51
    "https://rest.ensembl.org/overlap/region/human/7:140424943-140624564?feature=regulatory"
  • Network accessline 53
    # Also get TF binding motifs curl -H "Content-type: application/json" \
  • Network accessline 54
    curl -H "Content-type: application/json" \
  • Network accessline 55
    "https://rest.ensembl.org/overlap/region/human/7:140424943-140624564?feature=regulatory;feature=motif"
  • Network accessline 76
    # VEP annotation for a variant curl -H "Content-type: application/json" \
  • Network accessline 77
    curl -H "Content-type: application/json" \
  • Network accessline 78
    "https://rest.ensembl.org/vep/human/region/9:22125503-22125502:1/C"
  • Network accessline 80
    # By rs ID curl -H "Content-type: application/json" \
  • Network accessline 81
    curl -H "Content-type: application/json" \
  • Network accessline 82
    "https://rest.ensembl.org/vep/human/id/rs699"
  • Network accessline 88
    curl -X POST -H "Content-type: application/json" \
  • Network accessline 89
    "https://rest.ensembl.org/vep/human/region" \
  • Network accessline 126
    # GRCh37 → GRCh38 curl -H "Content-type: application/json" \
  • Network accessline 127
    curl -H "Content-type: application/json" \
  • Network accessline 128
    "https://rest.ensembl.org/map/human/GRCh37/17:1000000..1000100:1/GRCh38"
  • Network accessline 130
    # GRCh38 → GRCh37 curl -H "Content-type: application/json" \
  • Network accessline 131
    curl -H "Content-type: application/json" \
  • Network accessline 132
    "https://rest.ensembl.org/map/human/GRCh38/17:1000000..1000100:1/GRCh37"
  • Network accessline 142
    # By Ensembl ID curl -H "Content-type: application/json" \
  • Network accessline 143
    curl -H "Content-type: application/json" \
  • Network accessline 144
    "https://rest.ensembl.org/lookup/id/ENSG00000157764?expand=1"
  • Network accessline 146
    # By symbol curl -H "Content-type: application/json" \
  • Network accessline 147
    curl -H "Content-type: application/json" \
  • Network accessline 148
    "https://rest.ensembl.org/lookup/symbol/homo_sapiens/BRAF"
  • Network accessline 154
    # Get all external references for a gene curl -H "Content-type: application/json" \
  • Network accessline 155
    curl -H "Content-type: application/json" \
  • Network accessline 156
    "https://rest.ensembl.org/xrefs/id/ENSG00000157764"
  • Network accessline 158
    # Filter by external DB curl -H "Content-type: application/json" \
  • Network accessline 159
    curl -H "Content-type: application/json" \
  • Network accessline 160
    "https://rest.ensembl.org/xrefs/id/ENSG00000157764?external_db=HGNC"
  • Network accessline 168
    # Get phenotype associations for a gene curl -H "Content-type: application/json" \
  • Network accessline 169
    curl -H "Content-type: application/json" \
  • Network accessline 170
    "https://rest.ensembl.org/phenotype/gene/homo_sapiens/BRCA2"
  • Network accessline 172
    # Get phenotype associations for a region curl -H "Content-type: application/json" \
  • Network accessline 173
    curl -H "Content-type: application/json" \
  • Network accessline 174
    "https://rest.ensembl.org/phenotype/region/homo_sapiens/9:22125500-22136000"
  • Network accessline 183
    2. Annotate with VEP:    curl "https://rest.ensembl.org/vep/human/id/rs699?CADD=1;regulatory=1;REVEL=1"
  • Network accessline 184
    curl "https://rest.ensembl.org/vep/human/id/rs699?CADD=1;regulatory=1;REVEL=1"
  • Network accessline 187
    3. Check Ensembl Regulatory Build for independent confirmation:    curl "https://rest.ensembl.org/overlap/region/human/CHR:START-END?feature=regulatory"
  • Network accessline 188
    curl "https://rest.ensembl.org/overlap/region/human/CHR:START-END?feature=regulatory"
  • Network accessline 191
    4. If working with GRCh37 data, liftOver:    curl "https://rest.ensembl.org/map/human/GRCh37/CHR:POS..POS:1/GRCh38"
  • Network accessline 192
    curl "https://rest.ensembl.org/map/human/GRCh37/CHR:POS..POS:1/GRCh38"
  • Network accessline 194
    5. Resolve gene identifiers for ENCODE targets:    curl "https://rest.ensembl.org/lookup/symbol/homo_sapiens/GENE_SYMBOL"
  • Network accessline 195
    curl "https://rest.ensembl.org/lookup/symbol/homo_sapiens/GENE_SYMBOL"
  • Network accessline 197
    6. Check disease associations:    curl "https://rest.ensembl.org/phenotype/gene/homo_sapiens/GENE"
  • Network accessline 198
    curl "https://rest.ensembl.org/phenotype/gene/homo_sapiens/GENE"
  • Network accessline 245
    POST https://rest.ensembl.org/vep/human/region
  • Network accessline 278
    GET https://rest.ensembl.org/regulatory/species/homo_sapiens/id/ENSR00000123456

plugin/skills/ensembl-annotation/references/literature.md

  • Network accessline 15
    Armean IM, Austine-Orimoloye O, Azov AG, Barnes I, Bennett R, et al. Ensembl   2022. Nucleic Acids Research, 50(D1):D988-D995, 2022. - **DOI:** [10.1093/nar/gkab1049](https://doi.org/10.1093/nar/gkab1049)
  • Network accessline 16
    2022. Nucleic Acids Research, 50(D1):D988-D995, 2022. - **DOI:** [10.1093/nar/gkab1049](https://doi.org/10.1093/nar/gkab1049) - **PMID:** 34791404 | **PMC:** PMC8728283
  • Network accessline 17
    - **DOI:** [10.1093/nar/gkab1049](https://doi.org/10.1093/nar/gkab1049)
  • Network accessline 40
    Jarreta J, Amode MR, Armean IM, Azov AG, Bennett R, et al. Ensembl 2020.   Nucleic Acids Research, 48(D1):D682-D688, 2020. - **DOI:** [10.1093/nar/gkz966](https://doi.org/10.1093/nar/gkz966)
  • Network accessline 41
    Nucleic Acids Research, 48(D1):D682-D688, 2020. - **DOI:** [10.1093/nar/gkz966](https://doi.org/10.1093/nar/gkz966) - **PMID:** 31691826 | **PMC:** PMC7145704
  • Network accessline 42
    - **DOI:** [10.1093/nar/gkz966](https://doi.org/10.1093/nar/gkz966)
  • Network accessline 65
    Amode MR, Armean IM, Azov AG, Bennett R, Bhai J, et al. Ensembl 2021.   Nucleic Acids Research, 49(D1):D884-D891, 2021. - **DOI:** [10.1093/nar/gkaa942](https://doi.org/10.1093/nar/gkaa942)
  • Network accessline 66
    Nucleic Acids Research, 49(D1):D884-D891, 2021. - **DOI:** [10.1093/nar/gkaa942](https://doi.org/10.1093/nar/gkaa942) - **PMID:** 33137190 | **PMC:** PMC7778937
  • Network accessline 67
    - **DOI:** [10.1093/nar/gkaa942](https://doi.org/10.1093/nar/gkaa942)
  • Network accessline 88
    - **Citation:** Martin FJ, Amode MR, Anber A, Apweiler R, Arkinson RG, et al.   Ensembl 2023. Nucleic Acids Research, 51(D1):D933-D941, 2023. - **DOI:** [10.1093/nar/gkac958](https://doi.org/10.1093/nar/gkac958)
  • Network accessline 89
    Ensembl 2023. Nucleic Acids Research, 51(D1):D933-D941, 2023. - **DOI:** [10.1093/nar/gkac958](https://doi.org/10.1093/nar/gkac958) - **PMID:** 36318249 | **PMC:** PMC9825606
  • Network accessline 90
    - **DOI:** [10.1093/nar/gkac958](https://doi.org/10.1093/nar/gkac958)
  • Network accessline 117
    Flicek P, Cunningham F. The Ensembl Variant Effect Predictor. Genome   Biology, 17(1):122, 2016. - **DOI:** [10.1186/s13059-016-0974-4](https://doi.org/10.1186/s13059-016-0974-4)
  • Network accessline 118
    Biology, 17(1):122, 2016. - **DOI:** [10.1186/s13059-016-0974-4](https://doi.org/10.1186/s13059-016-0974-4) - **PMID:** 27268795 | **PMC:** PMC4893825
  • Network accessline 119
    - **DOI:** [10.1186/s13059-016-0974-4](https://doi.org/10.1186/s13059-016-0974-4)
  • Network accessline 150
    - **Citation:** Zerbino DR, Wilder SP, Johnson N, Juettemann T, Flicek P. The   Ensembl Regulatory Build. Genome Biology, 16(1):56, 2015. - **DOI:** [10.1186/s13059-015-0621-5](https://doi.org/10.1186/s13059-015-0621-5)
  • Network accessline 151
    Ensembl Regulatory Build. Genome Biology, 16(1):56, 2015. - **DOI:** [10.1186/s13059-015-0621-5](https://doi.org/10.1186/s13059-015-0621-5) - **PMID:** 25887522 | **PMC:** PMC4407537
  • Network accessline 152
    - **DOI:** [10.1186/s13059-015-0621-5](https://doi.org/10.1186/s13059-015-0621-5)
  • Network accessline 180
    Sheppard D, Parton A, Armean IM, Trevanion SJ, Flicek P, Cunningham F.   Ensembl variation resources. Database, 2018:bay119, 2018. - **DOI:** [10.1093/database/bay119](https://doi.org/10.1093/database/bay119)
  • Network accessline 181
    Ensembl variation resources. Database, 2018:bay119, 2018. - **DOI:** [10.1093/database/bay119](https://doi.org/10.1093/database/bay119) - **PMID:** 30576484 | **PMC:** PMC6305438
  • Network accessline 182
    - **DOI:** [10.1093/database/bay119](https://doi.org/10.1093/database/bay119)

plugin/skills/epigenome-profiling/SKILL.md

  • Network accessline 21
    | Reference | Year | Journal | DOI | Citations | Contribution | |-----------|------|---------|-----|-----------|-------------| | Roadmap Epigenomics Consortium (Kundaje et al.) | 2015 | *Nature* | [10.1038/nature14248](https://doi.org/10.10
  • Network accessline 22
    |-----------|------|---------|-----|-----------|-------------| | Roadmap Epigenomics Consortium (Kundaje et al.) | 2015 | *Nature* | [10.1038/nature14248](https://doi.org/10.1038/nature14248) | ~5,810 | 111 reference epigenomes; 5-mark core
  • Network accessline 23
    | Roadmap Epigenomics Consortium (Kundaje et al.) | 2015 | *Nature* | [10.1038/nature14248](https://doi.org/10.1038/nature14248) | ~5,810 | 111 reference epigenomes; 5-mark core model; 15/18/25-state ChromHMM |
  • Network accessline 24
    | ENCODE Phase 3 (ENCODE Project Consortium) | 2020 | *Nature* | [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4) | ~1,656 | Registry of candidate cis-regulatory elements (cCREs) across 1,310+ experiments |
  • Network accessline 25
    | Ernst & Kellis | 2012 | *Nat Methods* | [10.1038/nmeth.1906](https://doi.org/10.1038/nmeth.1906) | ~2,294 | ChromHMM: multivariate HMM for chromatin state discovery and characterization |
  • Network accessline 26
    | Barski et al. | 2007 | *Cell* | [10.1016/j.cell.2007.05.009](https://doi.org/10.1016/j.cell.2007.05.009) | ~4,800 | First genome-wide ChIP-Seq of 20 histone methylations in human CD4+ T cells |
  • Network accessline 27
    | Mikkelsen et al. | 2007 | *Nature* | [10.1038/nature06008](https://doi.org/10.1038/nature06008) | ~4,289 | Chromatin state maps in pluripotent and lineage-committed cells; H3K4me3/H3K27me3 discriminate expressed, poised, and repressed gen
  • Network accessline 28
    | Bernstein et al. | 2006 | *Cell* | [10.1016/j.cell.2006.02.041](https://doi.org/10.1016/j.cell.2006.02.041) | ~3,500 | Discovery of bivalent chromatin domains (H3K4me3+H3K27me3) in embryonic stem cells |
  • Network accessline 29
    | Creyghton et al. | 2010 | *PNAS* | [10.1073/pnas.1016071107](https://doi.org/10.1073/pnas.1016071107) | ~2,800 | H3K27ac distinguishes active enhancers from poised (H3K4me1-only) enhancers |
  • Network accessline 30
    | Whyte et al. | 2013 | *Cell* | [10.1016/j.cell.2013.03.035](https://doi.org/10.1016/j.cell.2013.03.035) | ~2,500 | Master transcription factors and super-enhancer identification via ROSE algorithm |
  • Network accessline 31
    | Buenrostro et al. | 2013 | *Nat Methods* | [10.1038/nmeth.2688](https://doi.org/10.1038/nmeth.2688) | ~5,000 | ATAC-seq: transposase-based chromatin accessibility profiling |
  • Network accessline 32
    | Heintzman et al. | 2007 | *Nat Genet* | [10.1038/ng1966](https://doi.org/10.1038/ng1966) | ~2,300 | H3K4me1 marks enhancers, H3K4me3 marks promoters — foundational chromatin signature for regulatory element classification |
  • Network accessline 33
    | Rada-Iglesias et al. | 2011 | *Nature* | [10.1038/nature09692](https://doi.org/10.1038/nature09692) | ~1,200 | Discovered "poised enhancers" (H3K4me1+H3K27me3, no H3K27ac) that activate during differentiation |
  • Network accessline 34
    | ENCODE Blacklist (Amemiya et al.) | 2019 | *Sci Rep* | [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z) | ~1,372 | Comprehensive set of problematic genomic regions to exclude from all analyses |
  • Network accessline 429
    **Always filter with the ENCODE blacklist** (Amemiya et al. 2019) before any downstream analysis. Download from: `https://github.com/Boyle-Lab/Blacklist/blob/master/lists/hg38-blacklist.v2.bed.gz`

plugin/skills/epigenome-profiling/references/literature.md

  • Network accessline 30
    - **Citation:** Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, et al. Integrative analysis   of 111 reference human epigenomes. *Nature*, 518(7539), 317-330, 2015. - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248)
  • Network accessline 31
    of 111 reference human epigenomes. *Nature*, 518(7539), 317-330, 2015. - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248) - **PMID:** 25693563 | **PMC:** PMC4530010
  • Network accessline 32
    - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248)
  • Network accessline 49
    - **Citation:** The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in   the human genome. *Nature*, 489(7414), 57-74, 2012. - **DOI:** [10.1038/nature11247](https://doi.org/10.1038/nature11247)
  • Network accessline 50
    the human genome. *Nature*, 489(7414), 57-74, 2012. - **DOI:** [10.1038/nature11247](https://doi.org/10.1038/nature11247) - **PMID:** 22955616 | **PMC:** PMC3439153
  • Network accessline 51
    - **DOI:** [10.1038/nature11247](https://doi.org/10.1038/nature11247)
  • Network accessline 75
    - **Citation:** Ernst J, Kellis M. ChromHMM: automating chromatin-state discovery and   characterization. *Nature Methods*, 9(3), 215-216, 2012. - **DOI:** [10.1038/nmeth.1906](https://doi.org/10.1038/nmeth.1906)
  • Network accessline 76
    characterization. *Nature Methods*, 9(3), 215-216, 2012. - **DOI:** [10.1038/nmeth.1906](https://doi.org/10.1038/nmeth.1906) - **PMID:** 22373907 | **PMC:** PMC3577932
  • Network accessline 77
    - **DOI:** [10.1038/nmeth.1906](https://doi.org/10.1038/nmeth.1906)
  • Network accessline 104
    Zhao K. High-resolution profiling of histone methylations in the human genome. *Cell*,   129(4), 823-837, 2007. - **DOI:** [10.1016/j.cell.2007.05.009](https://doi.org/10.1016/j.cell.2007.05.009)
  • Network accessline 105
    129(4), 823-837, 2007. - **DOI:** [10.1016/j.cell.2007.05.009](https://doi.org/10.1016/j.cell.2007.05.009) - **PMID:** 17512414
  • Network accessline 106
    - **DOI:** [10.1016/j.cell.2007.05.009](https://doi.org/10.1016/j.cell.2007.05.009)
  • Network accessline 125
    structure marks key developmental genes in embryonic stem cells. *Cell*, 125(2),   315-326, 2006. - **DOI:** [10.1016/j.cell.2006.02.041](https://doi.org/10.1016/j.cell.2006.02.041)
  • Network accessline 126
    315-326, 2006. - **DOI:** [10.1016/j.cell.2006.02.041](https://doi.org/10.1016/j.cell.2006.02.041) - **PMID:** 16630819
  • Network accessline 127
    - **DOI:** [10.1016/j.cell.2006.02.041](https://doi.org/10.1016/j.cell.2006.02.041)
  • Network accessline 144
    active enhancers from poised enhancers and predicts developmental state. *PNAS*, 107(50),   21931-21936, 2010. - **DOI:** [10.1073/pnas.1016071107](https://doi.org/10.1073/pnas.1016071107)
  • Network accessline 145
    21931-21936, 2010. - **DOI:** [10.1073/pnas.1016071107](https://doi.org/10.1073/pnas.1016071107) - **PMID:** 21106759 | **PMC:** PMC3003124
  • Network accessline 146
    - **DOI:** [10.1073/pnas.1016071107](https://doi.org/10.1073/pnas.1016071107)
  • Network accessline 170
    activity and timing of transcriptome reprogramming during differentiation. *Molecular Cell*,   56(2), 286-297, 2014. - **DOI:** [10.1016/j.molcel.2014.08.026](https://doi.org/10.1016/j.molcel.2014.08.026)
  • Network accessline 171
    56(2), 286-297, 2014. - **DOI:** [10.1016/j.molcel.2014.08.026](https://doi.org/10.1016/j.molcel.2014.08.026) - **PMID:** 25263596 | **PMC:** PMC4291692
  • Network accessline 172
    - **DOI:** [10.1016/j.molcel.2014.08.026](https://doi.org/10.1016/j.molcel.2014.08.026)
  • Network accessline 191
    International Human Epigenome Consortium: a blueprint for scientific collaboration and   discovery. *Cell*, 167(5), 1145-1149, 2016. - **DOI:** [10.1016/j.cell.2016.11.007](https://doi.org/10.1016/j.cell.2016.11.007)
  • Network accessline 192
    discovery. *Cell*, 167(5), 1145-1149, 2016. - **DOI:** [10.1016/j.cell.2016.11.007](https://doi.org/10.1016/j.cell.2016.11.007) - **PMID:** 27863232
  • Network accessline 193
    - **DOI:** [10.1016/j.cell.2016.11.007](https://doi.org/10.1016/j.cell.2016.11.007)
  • Network accessline 206
    - **Citation:** Xie W, Schultz MD, Lister R, Hou Z, et al. Epigenomic analysis of multilineage   differentiation of human embryonic stem cells. *Cell*, 153(5), 1134-1148, 2013. - **DOI:** [10.1016/j.cell.2013.04.022](https://doi.org/10.1016
  • Network accessline 207
    differentiation of human embryonic stem cells. *Cell*, 153(5), 1134-1148, 2013. - **DOI:** [10.1016/j.cell.2013.04.022](https://doi.org/10.1016/j.cell.2013.04.022) - **PMID:** 23664764 | **PMC:** PMC3786220
  • Network accessline 208
    - **DOI:** [10.1016/j.cell.2013.04.022](https://doi.org/10.1016/j.cell.2013.04.022)
  • Network accessline 223
    - **Citation:** Zhang D, Huang P, Lam CS, et al. ENCODE Phase 3 histone modification   analysis. *Nature*, 583(7818), 706-710, 2020. - **DOI:** [10.1038/s41586-020-2489-0](https://doi.org/10.1038/s41586-020-2489-0)
  • Network accessline 224
    analysis. *Nature*, 583(7818), 706-710, 2020. - **DOI:** [10.1038/s41586-020-2489-0](https://doi.org/10.1038/s41586-020-2489-0) - **PMID:** 32728246 | **PMC:** PMC7410825
  • Network accessline 225
    - **DOI:** [10.1038/s41586-020-2489-0](https://doi.org/10.1038/s41586-020-2489-0)

plugin/skills/functional-screen-analysis/SKILL.md

  • Network accessline 44
    - **Gasperini et al. 2019** (Cell, ~800 citations): CRISPRi screen of 5,920 candidate enhancers with single-cell RNA-seq readout in K562 cells. Identified 664 enhancer-gene pairs. Established the "crisprQTL" framework linking perturbation e
  • Network accessline 45
    - **Fulco et al. 2019** (Nature Genetics, ~1,200 citations): CRISPRi tiling screen combined with the Activity-By-Contact (ABC) model. Demonstrated that ABC predictions outperform distance-based enhancer-gene assignment. Quantitative relatio
  • Network accessline 46
    - **Shalem et al. 2014** (Science, ~5,500 citations): Genome-scale CRISPR-Cas9 knockout screening. The foundational paper for CRISPR loss-of-function screens. Established library design principles and analytical frameworks. [DOI](https://do
  • Network accessline 47
    - **Arnold et al. 2013** (Science, ~1,100 citations): STARR-seq — Self-Transcribing Active Regulatory Region sequencing. First genome-wide quantitative enhancer activity assay. Tests millions of fragments simultaneously in Drosophila; adapt
  • Network accessline 48
    - **Inoue et al. 2017** (Genome Research, ~400 citations): MPRA for systematic variant effect prediction. Tested thousands of regulatory variants for allele-specific enhancer activity. Demonstrated that GWAS risk alleles frequently alter en
  • Network accessline 49
    - **Li et al. 2014** (Genome Biology, ~2,800 citations): MAGeCK — Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout. The standard computational tool for CRISPR screen analysis. Robust negative binomial model for guide RNA count data.
  • Network accessline 50
    - **Gordon et al. 2020** (Nature Protocols, ~200 citations): MPRAflow — standardized computational pipeline for MPRA data analysis. Reproducible barcode counting, normalization, and activity scoring. [DOI](https://doi.org/10.1038/s41596-020
  • Network accessline 51
    - **Tewhey et al. 2016** (Cell, ~600 citations): High-throughput identification of regulatory variants using MPRA. Tested >30,000 allelic pairs across 3,642 GWAS loci. Identified hundreds of variants with allele-specific regulatory activity
  • Network accessline 52
    - **Nasser et al. 2021** (Nature, ~700 citations): ABC model for enhancer-gene prediction using ENCODE data. Linked 5,036 GWAS signals to 2,249 genes across 131 cell types. Validated predictions against CRISPRi perturbation data. [DOI](http
  • Network accessline 53
    - **Klein et al. 2020** (Nature Genetics, ~350 citations): CRISPRi screen design principles for non-coding regulatory elements. Established that guide RNA positioning relative to regulatory element boundaries is critical. Quantified the "sh
  • Network accessline 54
    - **Yao et al. 2024** (Nature Methods, ~26 citations): ENCODE4 Functional Characterization Centers — 108 CRISPRi screens, >540,000 perturbations across multiple cell types. Pre-designed sgRNA library targeting 3.27M ENCODE SCREEN cCREs. Est
  • Network accessline 55
    - **Lee et al. 2020** (Genome Biology, ~150 citations): STARRPeaker — peak caller designed specifically for STARR-seq data. Handles input library normalization and identifies significant enhancer peaks from STARR-seq enrichment. [DOI](https
  • Network accessline 56
    - **Kim & Hart 2021** (Genome Medicine, ~200 citations): BAGEL2 — Bayesian Analysis of Gene Essentiality. Updated framework for identifying essential genes and functional elements from CRISPR screen data. [DOI](https://doi.org/10.1186/s1307
  • Network accessline 269
    Both commands run inside a clone of https://github.com/shendurelab/MPRAflow; `nextflow run count.nf --help`

plugin/skills/functional-screen-analysis/references/literature.md

  • Network accessline 14
    - **Citation:** Shalem O, Sanjana NE, Hartenian E, Shi X, Scott DA, Mikkelson T, Heckl D, Ebert BL, Root DE, Doench JG, Zhang F. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science, 343(6166):84-87, 2014. - **DOI:** [10.1126
  • Network accessline 15
    - **DOI:** [10.1126/science.1247005](https://doi.org/10.1126/science.1247005)
  • Network accessline 24
    - **Citation:** Fulco CP, Nasser J, Jones TR, Munson G, Bergman DT, Subramanian V, Grossman SR, Anyoha R, Doughty BR, Patwardhan TA, Nguyen TH, Kane M, Perez EM, Duber NC, Lander ES, Engreitz JM. Activity-by-contact model of enhancer-gene s
  • Network accessline 25
    - **DOI:** [10.1038/s41588-019-0538-0](https://doi.org/10.1038/s41588-019-0538-0)
  • Network accessline 34
    - **Citation:** Gasperini M, Hill AJ, McFaline-Figueroa JL, Martin B, Kim S, Zhang MD, Jackson D, Leith A, Schreiber J, Noble WS, Trapnell C, Ahfeldt T, Shendure J. A genome-wide framework for mapping gene regulation via cellular genetic sc
  • Network accessline 35
    - **DOI:** [10.1016/j.cell.2018.11.029](https://doi.org/10.1016/j.cell.2018.11.029)
  • Network accessline 44
    - **Citation:** Li W, Xu H, Xiao T, Cong L, Love MI, Zhang F, Irizarry RA, Liu JS, Brown M, Liu XS. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biology, 15(12):554, 2014. -
  • Network accessline 45
    - **DOI:** [10.1186/s13059-014-0554-4](https://doi.org/10.1186/s13059-014-0554-4)
  • Network accessline 58
    - **Citation:** Melnikov A, Murugan A, Zhang X, Tesileanu T, Wang L, Rogov P, Feber S, Gnirke A, Callan CG Jr, Kinney JB, Kellis M, Lander ES, Mikkelsen TS. Systematic dissection and optimization of inducible enhancers in human cells using
  • Network accessline 59
    - **DOI:** [10.1038/nbt.2137](https://doi.org/10.1038/nbt.2137)
  • Network accessline 68
    - **Citation:** Ernst J, Melnikov A, Zhang X, Wang L, Rogov P, Mikkelsen TS, Kellis M. Genome-scale high-resolution mapping of activating and repressive nucleotides in regulatory regions. Nature Biotechnology, 34(11):1180-1190, 2016. - **DO
  • Network accessline 69
    - **DOI:** [10.1038/nbt.3678](https://doi.org/10.1038/nbt.3678)
  • Network accessline 82
    - **Citation:** Arnold CD, Gerlach D, Stelzer C, Boryń ŁM, Rath M, Stark A. Genome-wide quantitative enhancer activity maps identified by STARR-seq. Science, 339(6123):1074-1077, 2013. - **DOI:** [10.1126/science.1232542](https://doi.org/10
  • Network accessline 83
    - **DOI:** [10.1126/science.1232542](https://doi.org/10.1126/science.1232542)
  • Network accessline 96
    - **Citation:** ENCODE Project Consortium et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature, 583(7818):699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 97
    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 110
    - **Citation:** Hart T, Moffat J. BAGEL: a computational framework for identifying essential genes from pooled library screens. BMC Bioinformatics, 17:164, 2016. - **DOI:** [10.1186/s12859-016-1015-8](https://doi.org/10.1186/s12859-016-1015
  • Network accessline 111
    - **DOI:** [10.1186/s12859-016-1015-8](https://doi.org/10.1186/s12859-016-1015-8)

plugin/skills/geo-connector/SKILL.md

  • Network accessline 28
    - The ENCODE Portal is canonical; GEO is secondary archive - GEO accessions are stored in ENCODE's `dbxrefs` field as `GEO:GSExxxxx` - NCBI maintains a dedicated ENCODE listing: https://www.ncbi.nlm.nih.gov/geo/encode/
  • Network accessline 30
    - NCBI maintains a dedicated ENCODE listing: https://www.ncbi.nlm.nih.gov/geo/encode/
  • Network accessline 70
    # Via NCBI E-utilities curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=gds&term=ENCODE[KEYWORD]+AND+gse[ETYP]&retmax=100&usehistory=y&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 71
    curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=gds&term=ENCODE[KEYWORD]+AND+gse[ETYP]&retmax=100&usehistory=y&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 78
    **Base URL**: `https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi`
  • Network accessline 82
    **Rate limit**: 3 req/sec without API key, 10 req/sec with key. Get a key at https://www.ncbi.nlm.nih.gov/account/
  • Network accessline 97
    # Human pancreas ATAC-seq datasets with BED files curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=gds&term=pancreas+AND+ATAC-seq+AND+%22Homo+sapiens%22[ORGN]+AND+gse[ETYP]+AND+bed[suppFile]&retmax=50&tool=encode_mcp&emai
  • Network accessline 98
    curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=gds&term=pancreas+AND+ATAC-seq+AND+%22Homo+sapiens%22[ORGN]+AND+gse[ETYP]+AND+bed[suppFile]&retmax=50&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 100
    # ChIP-seq datasets from a specific year curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=gds&term=ChIP-seq+AND+H3K27ac+AND+gse[ETYP]+AND+2024[PDAT]&retmax=50&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 101
    curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=gds&term=ChIP-seq+AND+H3K27ac+AND+gse[ETYP]+AND+2024[PDAT]&retmax=50&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 103
    # Datasets associated with a PubMed ID curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&db=gds&id=PMID&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 104
    curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&db=gds&id=PMID&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 112
    # Step 1: Search (returns UIDs, NOT accessions) curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=gds&term=GSE12345[ACCN]&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 113
    curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=gds&term=GSE12345[ACCN]&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 115
    # Step 2: Get summary (use UID from step 1) curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=gds&id=UID&version=2.0&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 116
    curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=gds&id=UID&version=2.0&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 122
    # Get full SOFT-format record curl "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12345&targ=self&view=full&form=text"
  • Network accessline 123
    curl "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12345&targ=self&view=full&form=text"
  • Network accessline 125
    # Get XML (MINiML) format curl "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12345&targ=self&view=full&form=xml"
  • Network accessline 126
    curl "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12345&targ=self&view=full&form=xml"
  • Network accessline 128
    # Get all sample metadata for a series curl "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12345&targ=gsm&view=brief&form=text"
  • Network accessline 129
    curl "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12345&targ=gsm&view=brief&form=text"
  • Network accessline 156
    # Download series matrix (fastest for expression data) wget "https://ftp.ncbi.nlm.nih.gov/geo/series/GSE12nnn/GSE12345/matrix/GSE12345_series_matrix.txt.gz"
  • Network accessline 157
    wget "https://ftp.ncbi.nlm.nih.gov/geo/series/GSE12nnn/GSE12345/matrix/GSE12345_series_matrix.txt.gz"
  • Network accessline 159
    # Download all supplementary files wget "https://ftp.ncbi.nlm.nih.gov/geo/series/GSE12nnn/GSE12345/suppl/GSE12345_RAW.tar"
  • Network accessline 160
    wget "https://ftp.ncbi.nlm.nih.gov/geo/series/GSE12nnn/GSE12345/suppl/GSE12345_RAW.tar"
  • Network accessline 211
    # Link GEO to SRA curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=gds&db=sra&id=GDS_UID&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 212
    curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=gds&db=sra&id=GDS_UID&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 263
    GET https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=gds&term=HepG2[cell+line]+AND+RNA-seq[strategy]+AND+Homo+sapiens[organism]&retmax=10

plugin/skills/geo-connector/references/literature.md

  • Network accessline 16
    GEO: archive for functional genomics data sets — update. Nucleic Acids   Research, 41(D1):D991-D995, 2013. - **DOI:** [10.1093/nar/gks1193](https://doi.org/10.1093/nar/gks1193)
  • Network accessline 17
    Research, 41(D1):D991-D995, 2013. - **DOI:** [10.1093/nar/gks1193](https://doi.org/10.1093/nar/gks1193) - **PMID:** 23193258 | **PMC:** PMC3531084
  • Network accessline 18
    - **DOI:** [10.1093/nar/gks1193](https://doi.org/10.1093/nar/gks1193)
  • Network accessline 53
    gene expression and hybridization array data repository. Nucleic Acids   Research, 30(1):207-210, 2002. - **DOI:** [10.1093/nar/30.1.207](https://doi.org/10.1093/nar/30.1.207)
  • Network accessline 54
    Research, 30(1):207-210, 2002. - **DOI:** [10.1093/nar/30.1.207](https://doi.org/10.1093/nar/30.1.207) - **PMID:** 11752295 | **PMC:** PMC99122
  • Network accessline 55
    - **DOI:** [10.1093/nar/30.1.207](https://doi.org/10.1093/nar/30.1.207)
  • Network accessline 89
    - **Citation:** Clough E, Barrett T. The Gene Expression Omnibus Database.   Methods in Molecular Biology, 1418:93-110, 2016. - **DOI:** [10.1007/978-1-4939-3578-9_5](https://doi.org/10.1007/978-1-4939-3578-9_5)
  • Network accessline 90
    Methods in Molecular Biology, 1418:93-110, 2016. - **DOI:** [10.1007/978-1-4939-3578-9_5](https://doi.org/10.1007/978-1-4939-3578-9_5) - **PMID:** 27008011 | **PMC:** PMC4944384
  • Network accessline 91
    - **DOI:** [10.1007/978-1-4939-3578-9_5](https://doi.org/10.1007/978-1-4939-3578-9_5)
  • Network accessline 131
    Sequence Database Collaboration. The Sequence Read Archive. Nucleic Acids   Research, 39(Database issue):D19-D21, 2011. - **DOI:** [10.1093/nar/gkq1019](https://doi.org/10.1093/nar/gkq1019)
  • Network accessline 132
    Research, 39(Database issue):D19-D21, 2011. - **DOI:** [10.1093/nar/gkq1019](https://doi.org/10.1093/nar/gkq1019) - **PMID:** 21062823 | **PMC:** PMC3013647
  • Network accessline 133
    - **DOI:** [10.1093/nar/gkq1019](https://doi.org/10.1093/nar/gkq1019)
  • Network accessline 167
    update — simplifying data submissions. Nucleic Acids Research,   43(D1):D1113-D1116, 2015. - **DOI:** [10.1093/nar/gku1057](https://doi.org/10.1093/nar/gku1057)
  • Network accessline 168
    43(D1):D1113-D1116, 2015. - **DOI:** [10.1093/nar/gku1057](https://doi.org/10.1093/nar/gku1057) - **PMID:** 25361974 | **PMC:** PMC4383899
  • Network accessline 169
    - **DOI:** [10.1093/nar/gku1057](https://doi.org/10.1093/nar/gku1057)

plugin/skills/gnomad-variants/SKILL.md

  • Network accessline 37
    - **gnomAD v2.1.1** (GRCh37/hg19): Legacy dataset, still widely used - **ExAC** (GRCh37): Superseded by gnomAD. All ExAC samples included in gnomAD v2+. - **gnomAD browser**: https://gnomad.broadinstitute.org
  • Network accessline 39
    - **gnomAD browser**: https://gnomad.broadinstitute.org
  • Network accessline 43
    - **Karczewski et al. 2020** (Nature, ~5,000 citations): gnomAD — mutational constraint spectrum from 141,456 individuals. Defined LOEUF as primary constraint metric. [DOI](https://doi.org/10.1038/s41586-020-2308-7)
  • Network accessline 44
    - **Lek et al. 2016** (Nature, ~7,000 citations): ExAC — analysis of protein-coding variation in 60,706 humans. Established pLI for gene constraint. [DOI](https://doi.org/10.1038/nature19057)
  • Network accessline 45
    - **Maurano et al. 2012** (Science, ~2,800 citations): Disease variants concentrate in DNase hypersensitive sites. [DOI](https://doi.org/10.1126/science.1222794)
  • Network accessline 46
    - **Finucane et al. 2015** (Nature Genetics, ~2,253 citations): Stratified LD score regression partitioning heritability into ENCODE annotations. [DOI](https://doi.org/10.1038/ng.3404)
  • Network accessline 59
    **Endpoint**: `https://gnomad.broadinstitute.org/api`
  • Network accessline 67
    curl -X POST https://gnomad.broadinstitute.org/api \
  • Network accessline 79
    curl -X POST https://gnomad.broadinstitute.org/api \
  • Network accessline 89
    curl -X POST https://gnomad.broadinstitute.org/api \
  • Network accessline 179
    # gnomAD v4 genome VCFs (GRCh38) # Available at: https://gnomad.broadinstitute.org/downloads
  • Network accessline 180
    # Available at: https://gnomad.broadinstitute.org/downloads

plugin/skills/gnomad-variants/references/literature.md

  • Network accessline 16
    mutational constraint spectrum quantified from variation in 141,456 humans.   Nature, 581(7809):434-443, 2020. - **DOI:** [10.1038/s41586-020-2308-7](https://doi.org/10.1038/s41586-020-2308-7)
  • Network accessline 17
    Nature, 581(7809):434-443, 2020. - **DOI:** [10.1038/s41586-020-2308-7](https://doi.org/10.1038/s41586-020-2308-7) - **PMID:** 32461654 | **PMC:** PMC7334197
  • Network accessline 18
    - **DOI:** [10.1038/s41586-020-2308-7](https://doi.org/10.1038/s41586-020-2308-7)
  • Network accessline 43
    protein-coding genetic variation in 60,706 humans. Nature,   536(7616):285-291, 2016. - **DOI:** [10.1038/nature19057](https://doi.org/10.1038/nature19057)
  • Network accessline 44
    536(7616):285-291, 2016. - **DOI:** [10.1038/nature19057](https://doi.org/10.1038/nature19057) - **PMID:** 27535533 | **PMC:** PMC5018207
  • Network accessline 45
    - **DOI:** [10.1038/nature19057](https://doi.org/10.1038/nature19057)
  • Network accessline 69
    constraint map using variation in 76,156 human genomes. Nature,   625(7993):92-100, 2024. - **DOI:** [10.1038/s41586-023-06045-0](https://doi.org/10.1038/s41586-023-06045-0)
  • Network accessline 70
    625(7993):92-100, 2024. - **DOI:** [10.1038/s41586-023-06045-0](https://doi.org/10.1038/s41586-023-06045-0) - **PMID:** 38057664 | **PMC:** PMC10764060
  • Network accessline 71
    - **DOI:** [10.1038/s41586-023-06045-0](https://doi.org/10.1038/s41586-023-06045-0)
  • Network accessline 101
    variation reference for medical and population genetics. Nature,   581(7809):444-451, 2020. - **DOI:** [10.1038/s41586-020-2287-8](https://doi.org/10.1038/s41586-020-2287-8)
  • Network accessline 102
    581(7809):444-451, 2020. - **DOI:** [10.1038/s41586-020-2287-8](https://doi.org/10.1038/s41586-020-2287-8) - **PMID:** 32461652 | **PMC:** PMC7334194
  • Network accessline 103
    - **DOI:** [10.1038/s41586-020-2287-8](https://doi.org/10.1038/s41586-020-2287-8)
  • Network accessline 131
    Solomonson M, et al. Variant interpretation using population databases:   Lessons from gnomAD. Human Mutation, 43(8):1012-1030, 2022. - **DOI:** [10.1002/humu.24309](https://doi.org/10.1002/humu.24309)
  • Network accessline 132
    Lessons from gnomAD. Human Mutation, 43(8):1012-1030, 2022. - **DOI:** [10.1002/humu.24309](https://doi.org/10.1002/humu.24309) - **PMID:** 34859531 | **PMC:** PMC9160216
  • Network accessline 133
    - **DOI:** [10.1002/humu.24309](https://doi.org/10.1002/humu.24309)
  • Network accessline 159
    resolution variant frequencies to empower clinical genome interpretation.   Genetics in Medicine, 19(10):1151-1158, 2017. - **DOI:** [10.1038/gim.2017.26](https://doi.org/10.1038/gim.2017.26)
  • Network accessline 160
    Genetics in Medicine, 19(10):1151-1158, 2017. - **DOI:** [10.1038/gim.2017.26](https://doi.org/10.1038/gim.2017.26) - **PMID:** 28518168 | **PMC:** PMC5633148
  • Network accessline 161
    - **DOI:** [10.1038/gim.2017.26](https://doi.org/10.1038/gim.2017.26)
  • Network accessline 190
    to human disease in 281,104 UK Biobank exomes. Nature, 597(7877):527-532,   2021. - **DOI:** [10.1038/s41586-021-03855-y](https://doi.org/10.1038/s41586-021-03855-y)
  • Network accessline 191
    2021. - **DOI:** [10.1038/s41586-021-03855-y](https://doi.org/10.1038/s41586-021-03855-y) - **PMID:** 34375979 | **PMC:** PMC8459922
  • Network accessline 192
    - **DOI:** [10.1038/s41586-021-03855-y](https://doi.org/10.1038/s41586-021-03855-y)

plugin/skills/gtex-expression/SKILL.md

  • Network accessline 38
    - **GTEx Consortium 2020** "The GTEx Consortium atlas of genetic regulatory effects across human tissues" (Science, ~4,000 citations). The flagship publication describing the v8 release with 17,382 samples across 54 tissues from 948 donors.
  • Network accessline 39
    - **Aguet et al. 2017** "Genetic effects on gene expression across human tissues" (Nature, ~3,000 citations). The v6p analysis establishing the multi-tissue eQTL framework, demonstrating widespread tissue-specific genetic regulation. [DOI:
  • Network accessline 40
    - **ENCODE Project Consortium 2020** (Nature, ~1,656 citations). Registry of 926,535 human cCREs. Provides the regulatory element catalog to cross-reference with GTEx expression. [DOI: 10.1038/s41586-020-2493-4](https://doi.org/10.1038/s415
  • Network accessline 41
    - **Nasser et al. 2021** (Nature, ~468 citations). ABC model linking enhancers to genes using ENCODE data. GTEx expression validates ABC-predicted enhancer-gene pairs. [DOI: 10.1038/s41586-021-03446-x](https://doi.org/10.1038/s41586-021-034
  • Network accessline 56
    **Base URL**: `https://gtexportal.org/api/v2`
  • Network accessline 101
    gene_id = "ENSG00000254647"  # INS (insulin) url = f"https://gtexportal.org/api/v2/expression/geneExpression"
  • Network accessline 102
    url = f"https://gtexportal.org/api/v2/expression/geneExpression"
  • Network accessline 108
    response = requests.get(url, params=params)
  • Network accessline 119
    url = "https://gtexportal.org/api/v2/expression/medianTranscriptExpression"
  • Network accessline 122
    "datasetId": "gtex_v8" } response = requests.get(url, params=params)
  • Network accessline 124
    response = requests.get(url, params=params)
  • Network accessline 152
    url = "https://gtexportal.org/api/v2/dataset/tissueSiteDetail" response = requests.get(url)
  • Network accessline 153
    response = requests.get(url)
  • Network accessline 243
    url = "https://gtexportal.org/api/v2/eqtl/singleTissueEqtl"
  • Network accessline 247
    "datasetId": "gtex_v8" } response = requests.get(url, params=params)
  • Network accessline 248
    } response = requests.get(url, params=params) eqtls = response.json()["data"]
  • Network accessline 249
    response = requests.get(url, params=params)
  • Network accessline 271
    # Query GTEx API gene_id <- "ENSG00000254647" url <- paste0("https://gtexportal.org/api/v2/expression/geneExpression",
  • Network accessline 272
    gene_id <- "ENSG00000254647" url <- paste0("https://gtexportal.org/api/v2/expression/geneExpression",               "?geneId=", gene_id, "&datasetId=gtex_v8")
  • Network accessline 273
    url <- paste0("https://gtexportal.org/api/v2/expression/geneExpression",
  • Network accessline 323
    GET https://gtexportal.org/api/v2/expression/medianGeneExpression?gencodeId=ENSG00000134243&tissueSiteDetailId=Liver

plugin/skills/gtex-expression/references/literature.md

  • Network accessline 14
    - **Citation:** GTEx Consortium. The GTEx Consortium atlas of genetic   regulatory effects across human tissues. Science, 369(6509):1318-1330, 2020. - **DOI:** [10.1126/science.aaz1776](https://doi.org/10.1126/science.aaz1776)
  • Network accessline 15
    regulatory effects across human tissues. Science, 369(6509):1318-1330, 2020. - **DOI:** [10.1126/science.aaz1776](https://doi.org/10.1126/science.aaz1776) - **PMID:** 32913098 | **PMC:** PMC7737656
  • Network accessline 16
    - **DOI:** [10.1126/science.aaz1776](https://doi.org/10.1126/science.aaz1776)
  • Network accessline 45
    P, Park Y, Parsana P, Segre AV, et al. Genetic effects on gene expression   across human tissues. Nature, 550(7675):204-213, 2017. - **DOI:** [10.1038/nature24277](https://doi.org/10.1038/nature24277)
  • Network accessline 46
    across human tissues. Nature, 550(7675):204-213, 2017. - **DOI:** [10.1038/nature24277](https://doi.org/10.1038/nature24277) - **PMID:** 29022597 | **PMC:** PMC5776756
  • Network accessline 47
    - **DOI:** [10.1038/nature24277](https://doi.org/10.1038/nature24277)
  • Network accessline 68
    Expression (GTEx) pilot analysis: multitissue gene regulation in humans.   Science, 348(6235):648-660, 2015. - **DOI:** [10.1126/science.1262110](https://doi.org/10.1126/science.1262110)
  • Network accessline 69
    Science, 348(6235):648-660, 2015. - **DOI:** [10.1126/science.1262110](https://doi.org/10.1126/science.1262110) - **PMID:** 25954001 | **PMC:** PMC4547484
  • Network accessline 70
    - **DOI:** [10.1126/science.1262110](https://doi.org/10.1126/science.1262110)
  • Network accessline 98
    HK. A gene-based association method for mapping traits using reference   transcriptome data. Nature Genetics, 47(9):1091-1098, 2015. - **DOI:** [10.1038/ng.3367](https://doi.org/10.1038/ng.3367)
  • Network accessline 99
    transcriptome data. Nature Genetics, 47(9):1091-1098, 2015. - **DOI:** [10.1038/ng.3367](https://doi.org/10.1038/ng.3367) - **PMID:** 26258848 | **PMC:** PMC4552594
  • Network accessline 100
    - **DOI:** [10.1038/ng.3367](https://doi.org/10.1038/ng.3367)
  • Network accessline 125
    inferred from GWAS summary statistics. Nature Communications, 9(1):1825,   2018. - **DOI:** [10.1038/s41467-018-03621-1](https://doi.org/10.1038/s41467-018-03621-1)
  • Network accessline 126
    2018. - **DOI:** [10.1038/s41467-018-03621-1](https://doi.org/10.1038/s41467-018-03621-1) - **PMID:** 29739930 | **PMC:** PMC5940825
  • Network accessline 127
    - **DOI:** [10.1038/s41467-018-03621-1](https://doi.org/10.1038/s41467-018-03621-1)
  • Network accessline 158
    specific genetic regulation of gene expression across human tissues.   Science, 369(6509):eaaz8528, 2020. - **DOI:** [10.1126/science.aaz8528](https://doi.org/10.1126/science.aaz8528)
  • Network accessline 159
    Science, 369(6509):eaaz8528, 2020. - **DOI:** [10.1126/science.aaz8528](https://doi.org/10.1126/science.aaz8528) - **PMID:** 32913075 | **PMC:** PMC7668455
  • Network accessline 160
    - **DOI:** [10.1126/science.aaz8528](https://doi.org/10.1126/science.aaz8528)
  • Network accessline 181
    nucleus cross-tissue molecular reference maps toward understanding disease   gene function. Science, 376(6594):eabl4290, 2022. - **DOI:** [10.1126/science.abl4290](https://doi.org/10.1126/science.abl4290)
  • Network accessline 182
    gene function. Science, 376(6594):eabl4290, 2022. - **DOI:** [10.1126/science.abl4290](https://doi.org/10.1126/science.abl4290) - **PMID:** 35549429 | **PMC:** PMC9744718
  • Network accessline 183
    - **DOI:** [10.1126/science.abl4290](https://doi.org/10.1126/science.abl4290)

plugin/skills/gwas-catalog/SKILL.md

  • Network accessline 35
    - **Sollis et al. 2023** "The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource" (Nucleic Acids Research). The current GWAS Catalog publication describing the REST API, summary statistics hosting, and expanded annotation pipelin
  • Network accessline 36
    - **Buniello et al. 2019** "The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019" (Nucleic Acids Research, ~3,500 citations). The widely-cited GWAS Catalog reference describing
  • Network accessline 37
    - **Maurano et al. 2012** "Systematic localization of common disease-associated variation in regulatory DNA" (Science, ~3,000 citations). The foundational demonstration that GWAS variants concentrate in DNase I hypersensitive sites, with ce
  • Network accessline 38
    - **ENCODE Project Consortium 2020** (Nature, ~1,656 citations). Registry of 926,535 human cCREs that provides the regulatory annotation layer for GWAS variant interpretation. [DOI: 10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-
  • Network accessline 39
    - **Finucane et al. 2015** (Nature Genetics, ~2,253 citations). Stratified LD Score Regression (S-LDSC) for partitioning heritability into ENCODE-defined functional categories. [DOI: 10.1038/ng.3404](https://doi.org/10.1038/ng.3404)
  • Network accessline 40
    - **Nasser et al. 2021** (Nature, ~468 citations). ABC model linked 5,036 GWAS signals to 2,249 genes using ENCODE data. [DOI: 10.1038/s41586-021-03446-x](https://doi.org/10.1038/s41586-021-03446-x)
  • Network accessline 44
    **Base URL**: `https://www.ebi.ac.uk/gwas/rest/api`
  • Network accessline 67
    For genome-wide analysis, use the GWAS Catalog downloads (faster than API): - All associations: `https://www.ebi.ac.uk/gwas/api/search/downloads/full`
  • Network accessline 68
    - All associations: `https://www.ebi.ac.uk/gwas/api/search/downloads/full`
  • Network accessline 69
    - Alternative: `https://www.ebi.ac.uk/gwas/docs/file-downloads`
  • Network accessline 79
    # Search by trait name trait = "type 2 diabetes" url = "https://www.ebi.ac.uk/gwas/rest/api/studies"
  • Network accessline 80
    trait = "type 2 diabetes" url = "https://www.ebi.ac.uk/gwas/rest/api/studies" params = {"diseaseTrait": trait}
  • Network accessline 81
    url = "https://www.ebi.ac.uk/gwas/rest/api/studies" params = {"diseaseTrait": trait} response = requests.get(url, params=params)
  • Network accessline 82
    params = {"diseaseTrait": trait} response = requests.get(url, params=params) studies = response.json()["_embedded"]["studies"]
  • Network accessline 83
    response = requests.get(url, params=params)
  • Network accessline 105
    # Query by EFO ID (more precise) efo_id = "EFO_0001360" url = f"https://www.ebi.ac.uk/gwas/rest/api/efoTraits/{efo_id}/associations"
  • Network accessline 106
    efo_id = "EFO_0001360" url = f"https://www.ebi.ac.uk/gwas/rest/api/efoTraits/{efo_id}/associations" params = {"size": 500}
  • Network accessline 107
    url = f"https://www.ebi.ac.uk/gwas/rest/api/efoTraits/{efo_id}/associations" params = {"size": 500} response = requests.get(url, params=params)
  • Network accessline 109
    response = requests.get(url, params=params)
  • Network accessline 137
    def get_gwas_associations(rs_id):     """Get all GWAS associations for a variant."""     url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}/associations"
  • Network accessline 138
    """Get all GWAS associations for a variant."""     url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}/associations"     response = requests.get(url)
  • Network accessline 139
    url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}/associations"     response = requests.get(url)     if response.status_code == 200:
  • Network accessline 140
    response = requests.get(url)
  • Network accessline 156
    def get_study_associations(study_id):     """Get all associations from a GWAS study."""     url = f"https://www.ebi.ac.uk/gwas/rest/api/studies/{study_id}/associations"
  • Network accessline 157
    """Get all associations from a GWAS study."""     url = f"https://www.ebi.ac.uk/gwas/rest/api/studies/{study_id}/associations"     params = {"size": 500}
  • Network accessline 158
    url = f"https://www.ebi.ac.uk/gwas/rest/api/studies/{study_id}/associations"     params = {"size": 500}     response = requests.get(url, params=params)
  • Network accessline 159
    params = {"size": 500}     response = requests.get(url, params=params)     return response.json()["_embedded"]["associations"]
  • Network accessline 160
    response = requests.get(url, params=params)
  • Network accessline 178
    rs_id = gene.get("riskAlleleName", "").split("-")[0]         # Get location from the SNP endpoint         snp_url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}"
  • Network accessline 179
    # Get location from the SNP endpoint         snp_url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}"         snp_resp = requests.get(snp_url)
  • Network accessline 180
    snp_url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}"         snp_resp = requests.get(snp_url)         if snp_resp.status_code == 200:
  • Network accessline 181
    snp_resp = requests.get(snp_url)
  • Network accessline 205
    - **LDlink** (NIH): `https://ldlink.nih.gov/?tab=ldproxy` — Web and API
  • Network accessline 206
    [Credential-related source omitted]
  • Network accessline 318
    Activity-By-Contact predictions link enhancers to genes using ENCODE ATAC-seq + H3K27ac + Hi-C: - Pre-computed for 131 cell types - Download from: https://www.engreitzlab.org/abc/
  • Network accessline 319
    - Pre-computed for 131 cell types - Download from: https://www.engreitzlab.org/abc/ - Linked 5,036 GWAS signals to 2,249 target genes
  • Network accessline 320
    - Download from: https://www.engreitzlab.org/abc/
  • Network accessline 419
    GET https://www.ebi.ac.uk/gwas/rest/api/associations?efoTrait=EFO_0001360&pvalueFilter=5e-8

plugin/skills/gwas-catalog/references/literature.md

  • Network accessline 17
    arrays and summary statistics 2019. Nucleic Acids Research,   47(D1):D1005-D1012, 2019. - **DOI:** [10.1093/nar/gky1120](https://doi.org/10.1093/nar/gky1120)
  • Network accessline 18
    47(D1):D1005-D1012, 2019. - **DOI:** [10.1093/nar/gky1120](https://doi.org/10.1093/nar/gky1120) - **PMID:** 30445434 | **PMC:** PMC6323933
  • Network accessline 19
    - **DOI:** [10.1093/nar/gky1120](https://doi.org/10.1093/nar/gky1120)
  • Network accessline 42
    knowledgebase and deposition resource. Nucleic Acids Research,   51(D1):D1299-D1307, 2023. - **DOI:** [10.1093/nar/gkac1010](https://doi.org/10.1093/nar/gkac1010)
  • Network accessline 43
    51(D1):D1299-D1307, 2023. - **DOI:** [10.1093/nar/gkac1010](https://doi.org/10.1093/nar/gkac1010) - **PMID:** 36350656 | **PMC:** PMC9825413
  • Network accessline 44
    - **DOI:** [10.1093/nar/gkac1010](https://doi.org/10.1093/nar/gkac1010)
  • Network accessline 71
    common disease-associated variation in regulatory DNA. Science,   337(6099):1190-1195, 2012. - **DOI:** [10.1126/science.1222794](https://doi.org/10.1126/science.1222794)
  • Network accessline 72
    337(6099):1190-1195, 2012. - **DOI:** [10.1126/science.1222794](https://doi.org/10.1126/science.1222794) - **PMID:** 22955828 | **PMC:** PMC3629424
  • Network accessline 73
    - **DOI:** [10.1126/science.1222794](https://doi.org/10.1126/science.1222794)
  • Network accessline 98
    functional annotation using genome-wide association summary statistics.   Nature Genetics, 47(11):1228-1235, 2015. - **DOI:** [10.1038/ng.3404](https://doi.org/10.1038/ng.3404)
  • Network accessline 99
    Nature Genetics, 47(11):1228-1235, 2015. - **DOI:** [10.1038/ng.3404](https://doi.org/10.1038/ng.3404) - **PMID:** 26414678 | **PMC:** PMC4626285
  • Network accessline 100
    - **DOI:** [10.1038/ng.3404](https://doi.org/10.1038/ng.3404)
  • Network accessline 123
    Genome-wide enhancer maps link risk variants to disease target genes.   Nature, 593(7858):238-243, 2021. - **DOI:** [10.1038/s41586-021-03446-x](https://doi.org/10.1038/s41586-021-03446-x)
  • Network accessline 124
    Nature, 593(7858):238-243, 2021. - **DOI:** [10.1038/s41586-021-03446-x](https://doi.org/10.1038/s41586-021-03446-x) - **PMID:** 33828297 | **PMC:** PMC8516355
  • Network accessline 125
    - **DOI:** [10.1038/s41586-021-03446-x](https://doi.org/10.1038/s41586-021-03446-x)
  • Network accessline 152
    Yang J. 10 years of GWAS discovery: biology, function, and translation.   American Journal of Human Genetics, 101(1):5-22, 2017. - **DOI:** [10.1016/j.ajhg.2017.06.005](https://doi.org/10.1016/j.ajhg.2017.06.005)
  • Network accessline 153
    American Journal of Human Genetics, 101(1):5-22, 2017. - **DOI:** [10.1016/j.ajhg.2017.06.005](https://doi.org/10.1016/j.ajhg.2017.06.005) - **PMID:** 28686856 | **PMC:** PMC5501872
  • Network accessline 154
    - **DOI:** [10.1016/j.ajhg.2017.06.005](https://doi.org/10.1016/j.ajhg.2017.06.005)
  • Network accessline 177
    association to function. American Journal of Human Genetics, 102(5):717-730,   2018. - **DOI:** [10.1016/j.ajhg.2018.04.002](https://doi.org/10.1016/j.ajhg.2018.04.002)
  • Network accessline 178
    2018. - **DOI:** [10.1016/j.ajhg.2018.04.002](https://doi.org/10.1016/j.ajhg.2018.04.002) - **PMID:** 29727686 | **PMC:** PMC5986732
  • Network accessline 179
    - **DOI:** [10.1016/j.ajhg.2018.04.002](https://doi.org/10.1016/j.ajhg.2018.04.002)
  • Network accessline 206
    mapping and annotation of genetic associations with FUMA. Nature   Communications, 8(1):1826, 2017. - **DOI:** [10.1038/s41467-017-01261-5](https://doi.org/10.1038/s41467-017-01261-5)
  • Network accessline 207
    Communications, 8(1):1826, 2017. - **DOI:** [10.1038/s41467-017-01261-5](https://doi.org/10.1038/s41467-017-01261-5) - **PMID:** 29184056 | **PMC:** PMC5705698
  • Network accessline 208
    - **DOI:** [10.1038/s41467-017-01261-5](https://doi.org/10.1038/s41467-017-01261-5)
  • Network accessline 230
    AR, Martin HC, Lappalainen T, Posthuma D. Genome-wide association studies.   Nature Reviews Methods Primers, 1(1):59, 2021. - **DOI:** [10.1038/s43586-021-00056-9](https://doi.org/10.1038/s43586-021-00056-9)
  • Network accessline 231
    Nature Reviews Methods Primers, 1(1):59, 2021. - **DOI:** [10.1038/s43586-021-00056-9](https://doi.org/10.1038/s43586-021-00056-9) - **PMID:** 37325440 | **PMC:** PMC10272025
  • Network accessline 232
    - **DOI:** [10.1038/s43586-021-00056-9](https://doi.org/10.1038/s43586-021-00056-9)

plugin/skills/hic-aggregation/SKILL.md

  • Network accessline 41
    - **mariner** (Flores et al. 2024, Bioinformatics): R/Bioconductor package for BEDPE manipulation including merging loops across experiments with configurable anchor tolerance. - **ENCODE Phase 3** (Gorkin et al. 2020, Nature, 301 citations
  • Network accessline 42
    - **ENCODE Phase 3** (Gorkin et al. 2020, Nature, 301 citations): Integrated Hi-C data across tissues to define regulatory loops connecting enhancers to promoters. - **ENCODE Blacklist** (Amemiya et al. 2019, Scientific Reports, 1,372 citat
  • Network accessline 43
    - **ENCODE Blacklist** (Amemiya et al. 2019, Scientific Reports, 1,372 citations): Problematic genomic regions to filter from loop anchors. [DOI](https://doi.org/10.1038/s41598-019-45839-z)
  • Network accessline 180
    ### 5a. ENCODE Blocklist Filtering (Amemiya et al. 2019) Remove loops with anchors in artifact-prone regions (download from https://github.com/Boyle-Lab/Blacklist/blob/master/lists/hg38-blacklist.v2.bed.gz):
  • Network accessline 181
    Remove loops with anchors in artifact-prone regions (download from https://github.com/Boyle-Lab/Blacklist/blob/master/lists/hg38-blacklist.v2.bed.gz):

plugin/skills/hic-aggregation/references/literature.md

  • Network accessline 22
    - **Citation:** Wolff J, Bhardwaj V, Nothjunge S, Richard G, Renschler G, Gilsbach R, Manke T, Backofen R, Ramírez F, Grüning BA. Galaxy HiCExplorer 3: a web server for reproducible Hi-C, capture Hi-C and single-cell Hi-C data analysis, qua
  • Network accessline 23
    - **DOI:** [10.1093/nar/gkac407](https://doi.org/10.1093/nar/gkac407)
  • Network accessline 32
    - **Citation:** Roayaei Ardakany A, Gezer HT, Lonardi S, Ay F. Mustache: multi-scale detection of chromatin loops from Hi-C and Micro-C maps using scale-space representation. Genome Biology, 21:256, 2020. - **DOI:** [10.1186/s13059-020-0216
  • Network accessline 33
    - **DOI:** [10.1186/s13059-020-02167-0](https://doi.org/10.1186/s13059-020-02167-0)
  • Network accessline 46
    - **Citation:** Rao SSP, Huntley MH, Durand NC, Stamenova EK, et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell, 159(7):1665-1680, 2014. - **DOI:** [10.1016/j.cell.2014.11.021](https://
  • Network accessline 47
    - **DOI:** [10.1016/j.cell.2014.11.021](https://doi.org/10.1016/j.cell.2014.11.021)
  • Network accessline 56
    - **Citation:** Durand NC, Shamim MS, Machol I, et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Systems, 3(1):95-98, 2016. - **DOI:** [10.1016/j.cels.2016.07.002](https://doi.org/10.1016/j.cels
  • Network accessline 57
    - **DOI:** [10.1016/j.cels.2016.07.002](https://doi.org/10.1016/j.cels.2016.07.002)
  • Network accessline 88
    - **Citation:** Yardimci GG, Ozadam H, Sauria MEG, et al. Measuring the reproducibility and quality of Hi-C data. Genome Biology, 20:57, 2019. - **DOI:** [10.1186/s13059-019-1658-7](https://doi.org/10.1186/s13059-019-1658-7)
  • Network accessline 89
    - **DOI:** [10.1186/s13059-019-1658-7](https://doi.org/10.1186/s13059-019-1658-7)
  • Network accessline 98
    - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z) | **PMID:** 31249361 | **Citations:** ~1,372

plugin/skills/hic-aggregation/scripts/validate_loops.py

  • Executable scriptline 1
    #!/usr/bin/env python3

plugin/skills/histone-aggregation/SKILL.md

  • Network accessline 27
    - **ChIP-Atlas** (Oki et al. 2018, EMBO Reports, 597 citations): Integrated >70,000 public ChIP-seq datasets using union of all peak calls - **ENCODE Phase 3** (Gorkin et al. 2020, Nature, 301 citations): Created unified chromatin state ann
  • Network accessline 28
    - **ENCODE Phase 3** (Gorkin et al. 2020, Nature, 301 citations): Created unified chromatin state annotations by integrating all peaks across 1,128 ChIP-seq experiments - **ENCODE Blacklist** (Amemiya et al. 2019, Scientific Reports, 1,372
  • Network accessline 29
    - **ENCODE Blacklist** (Amemiya et al. 2019, Scientific Reports, 1,372 citations): Defined the comprehensive set of problematic genomic regions to filter from all functional genomics analyses. Essential quality step. [DOI](https://doi.org/1
  • Network accessline 126
    # Download ENCODE blocklist for GRCh38 from: # https://github.com/Boyle-Lab/Blacklist/blob/master/lists/hg38-blacklist.v2.bed.gz
  • Network accessline 127
    # https://github.com/Boyle-Lab/Blacklist/blob/master/lists/hg38-blacklist.v2.bed.gz
  • Network accessline 128
    # For mm10: https://github.com/Boyle-Lab/Blacklist/blob/master/lists/mm10-blacklist.v2.bed.gz

plugin/skills/histone-aggregation/references/histone-marks-reference.md

  • Network accessline 36
    **Barski et al. 2007** -- The first genome-wide ChIP-Seq study of histone modifications. - **Citation:** Barski A, Cuddapah S, Cui K, Roh TY, Schones DE, Wang Z, Wei G, Chepelev I, Zhao K. "High-resolution profiling of histone methylations
  • Network accessline 37
    - **Citation:** Barski A, Cuddapah S, Cui K, Roh TY, Schones DE, Wang Z, Wei G, Chepelev I, Zhao K. "High-resolution profiling of histone methylations in the human genome." *Cell*. 2007;129(4):823-37. - **DOI:** [10.1016/j.cell.2007.05.009]
  • Network accessline 38
    - **DOI:** [10.1016/j.cell.2007.05.009](https://doi.org/10.1016/j.cell.2007.05.009)
  • Network accessline 43
    **Wang et al. 2008** -- First systematic study of combinatorial histone modification patterns. - **Citation:** Wang Z, Zang C, Rosenfeld JA, Schones DE, Barski A, Cuddapah S, Cui K, Roh TY, Peng W, Zhang MQ, Zhao K. "Combinatorial patterns
  • Network accessline 44
    - **Citation:** Wang Z, Zang C, Rosenfeld JA, Schones DE, Barski A, Cuddapah S, Cui K, Roh TY, Peng W, Zhang MQ, Zhao K. "Combinatorial patterns of histone acetylations and methylations in the human genome." *Nat Genet*. 2008;40(7):897-903.
  • Network accessline 45
    - **DOI:** [10.1038/ng.154](https://doi.org/10.1038/ng.154)
  • Network accessline 50
    **Mikkelsen et al. 2007** -- Genome-wide chromatin state maps in pluripotent and differentiated cells. - **Citation:** Mikkelsen TS, Ku M, Jaffe DB, Issac B, Lieberman E, Giannoukos G, Alvarez P, Brockman W, Kim TK, Koche RP, Lee W, Mendenh
  • Network accessline 51
    - **Citation:** Mikkelsen TS, Ku M, Jaffe DB, Issac B, Lieberman E, Giannoukos G, Alvarez P, Brockman W, Kim TK, Koche RP, Lee W, Mendenhall E, O'Donovan A, Presser A, Russ C, Xie X, Meissner A, Wernig M, Jaenisch R, Nusbaum C, Lander ES, B
  • Network accessline 52
    - **DOI:** [10.1038/nature06008](https://doi.org/10.1038/nature06008)
  • Network accessline 58
    **Kouzarides 2007** -- Definitive review of histone modification classes and functions. - **Citation:** Kouzarides T. "Chromatin modifications and their function." *Cell*. 2007;128(4):693-705. - **DOI:** [10.1016/j.cell.2007.02.005](https:/
  • Network accessline 59
    - **Citation:** Kouzarides T. "Chromatin modifications and their function." *Cell*. 2007;128(4):693-705. - **DOI:** [10.1016/j.cell.2007.02.005](https://doi.org/10.1016/j.cell.2007.02.005) - **Citations:** ~10,700
  • Network accessline 60
    - **DOI:** [10.1016/j.cell.2007.02.005](https://doi.org/10.1016/j.cell.2007.02.005)
  • Network accessline 64
    **Bannister & Kouzarides 2011** -- Updated review of histone modification biology. - **Citation:** Bannister AJ, Kouzarides T. "Regulation of chromatin by histone modifications." *Cell Res*. 2011;21(3):381-395. - **DOI:** [10.1038/cr.2011.2
  • Network accessline 65
    - **Citation:** Bannister AJ, Kouzarides T. "Regulation of chromatin by histone modifications." *Cell Res*. 2011;21(3):381-395. - **DOI:** [10.1038/cr.2011.22](https://doi.org/10.1038/cr.2011.22) - **Citations:** ~5,300
  • Network accessline 66
    - **DOI:** [10.1038/cr.2011.22](https://doi.org/10.1038/cr.2011.22)
  • Network accessline 86
    **Key papers:** - Heintzman ND et al. (2007) *Nat Genet* 39:311-318. [DOI: 10.1038/ng1966](https://doi.org/10.1038/ng1966) (~3,400 cit.) -- First demonstration that H3K4me1 (without me3) distinguishes enhancers from promoters.
  • Network accessline 87
    - Heintzman ND et al. (2007) *Nat Genet* 39:311-318. [DOI: 10.1038/ng1966](https://doi.org/10.1038/ng1966) (~3,400 cit.) -- First demonstration that H3K4me1 (without me3) distinguishes enhancers from promoters.
  • Network accessline 88
    - Creyghton MP et al. (2010) *PNAS* 107:21931-6. [DOI: 10.1073/pnas.1016071107](https://doi.org/10.1073/pnas.1016071107) (~3,000 cit.) -- Showed H3K27ac separates active from poised H3K4me1-marked enhancers.
  • Network accessline 89
    - Rada-Iglesias A et al. (2011) *Nature* 470:279-83. [DOI: 10.1038/nature09692](https://doi.org/10.1038/nature09692) (~1,500 cit.) -- Defined two enhancer classes: active (H3K4me1+H3K27ac) and poised (H3K4me1+H3K27me3).
  • Network accessline 103
    **Key papers:** - Barski et al. (2007) *Cell* 129:823-37. [DOI: 10.1016/j.cell.2007.05.009](https://doi.org/10.1016/j.cell.2007.05.009) -- Mapped H3K4me2 distribution; enriched at promoters and enhancers.
  • Network accessline 104
    - Barski et al. (2007) *Cell* 129:823-37. [DOI: 10.1016/j.cell.2007.05.009](https://doi.org/10.1016/j.cell.2007.05.009) -- Mapped H3K4me2 distribution; enriched at promoters and enhancers.
  • Network accessline 105
    - Wang et al. (2008) *Nat Genet* 40:897-903. [DOI: 10.1038/ng.154](https://doi.org/10.1038/ng.154) -- Part of the 17-modification active module.
  • Network accessline 121
    **Key papers:** - Bernstein BE et al. (2005) *Cell* 120:169-181. [DOI: 10.1016/j.cell.2005.01.001](https://doi.org/10.1016/j.cell.2005.01.001) -- Early ChIP-chip study linking H3K4me3 to active genes.
  • Network accessline 122
    - Bernstein BE et al. (2005) *Cell* 120:169-181. [DOI: 10.1016/j.cell.2005.01.001](https://doi.org/10.1016/j.cell.2005.01.001) -- Early ChIP-chip study linking H3K4me3 to active genes.
  • Network accessline 123
    - Heintzman ND et al. (2007) *Nat Genet* 39:311-318. [DOI: 10.1038/ng1966](https://doi.org/10.1038/ng1966) -- H3K4me3 at promoters vs. H3K4me1 at enhancers.
  • Network accessline 124
    - Bernstein BE et al. (2006) *Cell* 125:315-326. [DOI: 10.1016/j.cell.2006.02.041](https://doi.org/10.1016/j.cell.2006.02.041) (~5,400 cit.) -- Discovered bivalent domains (H3K4me3 + H3K27me3) in ESCs.
  • Network accessline 138
    **Key papers:** - Barski et al. (2007) *Cell* 129:823-37. [DOI: 10.1016/j.cell.2007.05.009](https://doi.org/10.1016/j.cell.2007.05.009) -- First genome-wide demonstration that H3K9me1 is associated with gene activation, in contrast to H3K9m
  • Network accessline 139
    - Barski et al. (2007) *Cell* 129:823-37. [DOI: 10.1016/j.cell.2007.05.009](https://doi.org/10.1016/j.cell.2007.05.009) -- First genome-wide demonstration that H3K9me1 is associated with gene activation, in contrast to H3K9me2/me3.
  • Network accessline 155
    **Key papers:** - Wen B et al. (2009) *Genome Res* 19:1639-1645. [DOI: 10.1101/gr.092643.109](https://doi.org/10.1101/gr.092643.109) -- Described LOCKs (large organized chromatin K9 modifications) domains.
  • Network accessline 156
    - Wen B et al. (2009) *Genome Res* 19:1639-1645. [DOI: 10.1101/gr.092643.109](https://doi.org/10.1101/gr.092643.109) -- Described LOCKs (large organized chromatin K9 modifications) domains.
  • Network accessline 157
    - Padeken J et al. (2022) *Nat Rev Mol Cell Biol* 23:623-640. [DOI: 10.1038/s41580-022-00483-2](https://doi.org/10.1038/s41580-022-00483-2) (~314 cit.) -- Comprehensive review of H3K9 methylation in tissue differentiation.
  • Network accessline 171
    **Key papers:** - Rea S et al. (2000) *Nature* 406:593-599. [DOI: 10.1038/35020506](https://doi.org/10.1038/35020506) (~3,500 cit.) -- Identified SUV39H1 as the first histone methyltransferase; established H3K9me3 as HP1 binding platform.
  • Network accessline 172
    - Rea S et al. (2000) *Nature* 406:593-599. [DOI: 10.1038/35020506](https://doi.org/10.1038/35020506) (~3,500 cit.) -- Identified SUV39H1 as the first histone methyltransferase; established H3K9me3 as HP1 binding platform.
  • Network accessline 173
    - Lachner M et al. (2001) *Nature* 410:116-120. [DOI: 10.1038/35065132](https://doi.org/10.1038/35065132) (~2,000 cit.) -- Showed HP1 chromodomain specifically recognizes H3K9me3.
  • Network accessline 174
    - Padeken J et al. (2022) *Nat Rev Mol Cell Biol* 23:623-640. [DOI: 10.1038/s41580-022-00483-2](https://doi.org/10.1038/s41580-022-00483-2) (~314 cit.) -- Comprehensive review of H3K9me and its HMTs.
  • Network accessline 191
    **Key papers:** - Cao R et al. (2002) *Science* 298:1039-1043. [DOI: 10.1126/science.1076997](https://doi.org/10.1126/science.1076997) (~3,000 cit.) -- Identified EZH2 as the H3K27 methyltransferase.
  • Network accessline 192
    - Cao R et al. (2002) *Science* 298:1039-1043. [DOI: 10.1126/science.1076997](https://doi.org/10.1126/science.1076997) (~3,000 cit.) -- Identified EZH2 as the H3K27 methyltransferase.
  • Network accessline 193
    - Boyer LA et al. (2006) *Nature* 441:349-353. [DOI: 10.1038/nature04733](https://doi.org/10.1038/nature04733) (~3,000 cit.) -- Mapped PRC2/H3K27me3 targets in ESCs; found occupancy at developmental TF genes.
  • Network accessline 194
    - Bernstein BE et al. (2006) *Cell* 125:315-326. [DOI: 10.1016/j.cell.2006.02.041](https://doi.org/10.1016/j.cell.2006.02.041) (~5,400 cit.) -- Co-discovery of bivalent domains (H3K4me3+H3K27me3).
  • Network accessline 210
    **Key papers:** - Bannister AJ et al. (2005) *Nature* 438:1181-1185. [DOI: 10.1038/nature04219](https://doi.org/10.1038/nature04219) (~800 cit.) -- Demonstrated Set2-mediated H3K36me in gene bodies during elongation.
  • Network accessline 211
    - Bannister AJ et al. (2005) *Nature* 438:1181-1185. [DOI: 10.1038/nature04219](https://doi.org/10.1038/nature04219) (~800 cit.) -- Demonstrated Set2-mediated H3K36me in gene bodies during elongation.
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    **Key papers:** - Feng Q et al. (2002) *Curr Biol* 12:1052-1058. [DOI: 10.1016/S0960-9822(02)00901-6](https://doi.org/10.1016/S0960-9822(02)00901-6) (~700 cit.) -- Identified DOT1L as H3K79 methyltransferase.
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    - Barski et al. (2007) *Cell* 129:823-37. [DOI: 10.1016/j.cell.2007.05.009](https://doi.org/10.1016/j.cell.2007.05.009) -- H3K79me1 linked to activation, H3K79me3 linked to repression.
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    **Key papers:** - Creyghton MP et al. (2010) *PNAS* 107:21931-6. [DOI: 10.1073/pnas.1016071107](https://doi.org/10.1073/pnas.1016071107) -- Established H3K27ac as THE distinguishing mark between active and poised enhancers.
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    - Creyghton MP et al. (2010) *PNAS* 107:21931-6. [DOI: 10.1073/pnas.1016071107](https://doi.org/10.1073/pnas.1016071107) -- Established H3K27ac as THE distinguishing mark between active and poised enhancers.
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    - Rada-Iglesias A et al. (2011) *Nature* 470:279-83. [DOI: 10.1038/nature09692](https://doi.org/10.1038/nature09692) -- Active enhancers = H3K4me1+H3K27ac; poised enhancers = H3K4me1+H3K27me3.
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    - Whyte WA et al. (2013) *Cell* 153:307-319. [DOI: 10.1016/j.cell.2013.03.035](https://doi.org/10.1016/j.cell.2013.03.035) (~3,600 cit.) -- Super-enhancers defined by exceptional H3K27ac/Mediator signal.
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    **Key papers:** - Wang et al. (2008) *Nat Genet* 40:897-903. [DOI: 10.1038/ng.154](https://doi.org/10.1038/ng.154) -- Part of the 17-mark active promoter module.
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    - Wang et al. (2008) *Nat Genet* 40:897-903. [DOI: 10.1038/ng.154](https://doi.org/10.1038/ng.154) -- Part of the 17-mark active promoter module.
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    **Key papers:** - Wang et al. (2008) *Nat Genet* 40:897-903. [DOI: 10.1038/ng.154](https://doi.org/10.1038/ng.154) -- Part of the 17-mark active promoter module.
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    - Wang et al. (2008) *Nat Genet* 40:897-903. [DOI: 10.1038/ng.154](https://doi.org/10.1038/ng.154) -- Part of the 17-mark active promoter module.
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    **Key papers:** - Wang et al. (2008) *Nat Genet* 40:897-903. [DOI: 10.1038/ng.154](https://doi.org/10.1038/ng.154) -- Identified in combinatorial analysis.
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    - Wang et al. (2008) *Nat Genet* 40:897-903. [DOI: 10.1038/ng.154](https://doi.org/10.1038/ng.154) -- Identified in combinatorial analysis.
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    **Key papers:** - Wang et al. (2008) *Nat Genet* 40:897-903. [DOI: 10.1038/ng.154](https://doi.org/10.1038/ng.154) -- Part of combinatorial patterns at active promoters.
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    - Wang et al. (2008) *Nat Genet* 40:897-903. [DOI: 10.1038/ng.154](https://doi.org/10.1038/ng.154) -- Part of combinatorial patterns at active promoters.
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    - Rice JC et al. (2002) *Genes Dev* 16:2225-30. [DOI: 10.1101/gad.986602](https://doi.org/10.1101/gad.986602) (~257 cit.) -- H4K20me1 is mitotic-specific, deposited by PR-Set7, inversely correlated with H4K16ac.
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    - Barski et al. (2007) *Cell* 129:823-37. [DOI: 10.1016/j.cell.2007.05.009](https://doi.org/10.1016/j.cell.2007.05.009) -- H4K20me1 genome-wide linked to gene activation.
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    **Key papers:** - Wang et al. (2008) *Nat Genet* 40:897-903. [DOI: 10.1038/ng.154](https://doi.org/10.1038/ng.154) -- Part of the active chromatin module.
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    - Wang et al. (2008) *Nat Genet* 40:897-903. [DOI: 10.1038/ng.154](https://doi.org/10.1038/ng.154) -- Part of the active chromatin module.
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    **Key papers:** - Wang et al. (2008) *Nat Genet* 40:897-903. [DOI: 10.1038/ng.154](https://doi.org/10.1038/ng.154).
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    - Wang et al. (2008) *Nat Genet* 40:897-903. [DOI: 10.1038/ng.154](https://doi.org/10.1038/ng.154).
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    **Key papers:** - Shogren-Knaak M et al. (2006) *Science* 311:844-847. [DOI: 10.1126/science.1124000](https://doi.org/10.1126/science.1124000) (~1,000 cit.) -- H4K16ac inhibits 30nm fiber formation (in vitro reconstitution).
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    - Shogren-Knaak M et al. (2006) *Science* 311:844-847. [DOI: 10.1126/science.1124000](https://doi.org/10.1126/science.1124000) (~1,000 cit.) -- H4K16ac inhibits 30nm fiber formation (in vitro reconstitution).
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    - Fraga MF et al. (2005) *Nat Genet* 37:391-400. [DOI: 10.1038/ng1531](https://doi.org/10.1038/ng1531) (~2,000 cit.) -- Global loss of H4K16ac (and H4K20me3) is a hallmark of cancer.
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    - Rice JC et al. (2002) *Genes Dev* 16:2225-30. [DOI: 10.1101/gad.986602](https://doi.org/10.1101/gad.986602) -- H4K16ac in early S-phase inversely correlated with H4K20me1 at mitosis.
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    **Key papers:** - Ye J et al. (2005) *Mol Cell* 20:199-209. [DOI: 10.1016/j.molcel.2005.08.029](https://doi.org/10.1016/j.molcel.2005.08.029) -- H4K91ac affects nucleosome stability and chromatin assembly.
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    - Ye J et al. (2005) *Mol Cell* 20:199-209. [DOI: 10.1016/j.molcel.2005.08.029](https://doi.org/10.1016/j.molcel.2005.08.029) -- H4K91ac affects nucleosome stability and chromatin assembly.
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    **Key papers:** - Barski et al. (2007) *Cell* 129:823-37. [DOI: 10.1016/j.cell.2007.05.009](https://doi.org/10.1016/j.cell.2007.05.009) -- H2A.Z at functional regulatory elements.
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    - Barski et al. (2007) *Cell* 129:823-37. [DOI: 10.1016/j.cell.2007.05.009](https://doi.org/10.1016/j.cell.2007.05.009) -- H2A.Z at functional regulatory elements.
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    - Ku M et al. (2012) *Genes Dev* 26:1326-1338. [DOI: 10.1101/gad.187609.112](https://doi.org/10.1101/gad.187609.112) -- H2A.Z enriched at bivalent promoters in ESCs.
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    - Jin C et al. (2009) *Nat Genet* 41:941-945. [DOI: 10.1038/ng.409](https://doi.org/10.1038/ng.409) -- H2A.Z+H3.3 double-variant nucleosomes mark active regulatory elements.
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    **Key papers:** - Kim J et al. (2009) *Cell* 137:459-471. [DOI: 10.1016/j.cell.2009.02.027](https://doi.org/10.1016/j.cell.2009.02.027) (~700 cit.) -- H2BK120ub required for H3K4me3 by COMPASS and H3K79me by DOT1L.
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    - Kim J et al. (2009) *Cell* 137:459-471. [DOI: 10.1016/j.cell.2009.02.027](https://doi.org/10.1016/j.cell.2009.02.027) (~700 cit.) -- H2BK120ub required for H3K4me3 by COMPASS and H3K79me by DOT1L.
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    - McGinty RK et al. (2008) *Nature* 453:812-816. [DOI: 10.1038/nature06906](https://doi.org/10.1038/nature06906) (~500 cit.) -- Structural basis for H2Bub1 stimulation of DOT1L.
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    |-------|------|---------|--------|-------|-----|-----------| | Ernst & Kellis | 2010 | *Nat Biotechnol* | 51 | 8+ marks | [10.1038/nbt.1662](https://doi.org/10.1038/nbt.1662) | ~1,100 | | Ernst et al. | 2011 | *Nature* | 15 | 9 marks | [10
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    | Ernst et al. | 2011 | *Nature* | 15 | 9 marks | [10.1038/nature09906](https://doi.org/10.1038/nature09906) | ~2,000 |
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    | Ernst & Kellis | 2012 | *Nat Methods* | variable | flexible | [10.1038/nmeth.1906](https://doi.org/10.1038/nmeth.1906) | ~2,300 |
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    | Kundaje et al. | 2015 | *Nature* | 15/18/25 | 5 core + | [10.1038/nature14248](https://doi.org/10.1038/nature14248) | ~5,000 |
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    | Ernst & Kellis | 2017 | *Nat Protocols* | variable | flexible | [10.1038/nprot.2017.124](https://doi.org/10.1038/nprot.2017.124) | ~711 |
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    | ENCODE Phase 3 | 2020 | *Nature* | 15+ | 5 core + | [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4) | ~2,000 |
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    **Source:** Ernst J et al. (2011) *Nature* 473:43-49. [DOI: 10.1038/nature09906](https://doi.org/10.1038/nature09906)
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    | Feature | Description | Key Reference | |---------|-------------|---------------| | H3K4me1 (not me3) | Enhancer-specific vs. promoter-specific | Heintzman et al. 2007 Nat Genet [DOI: 10.1038/ng1966](https://doi.org/10.1038/ng1966) |
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    |---------|-------------|---------------| | H3K4me1 (not me3) | Enhancer-specific vs. promoter-specific | Heintzman et al. 2007 Nat Genet [DOI: 10.1038/ng1966](https://doi.org/10.1038/ng1966) | | H3K27ac distinguishes active | Active enhanc
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    | H3K4me1 (not me3) | Enhancer-specific vs. promoter-specific | Heintzman et al. 2007 Nat Genet [DOI: 10.1038/ng1966](https://doi.org/10.1038/ng1966) |
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    | H3K27ac distinguishes active | Active enhancer = H3K4me1+H3K27ac | Creyghton et al. 2010 PNAS [DOI: 10.1073/pnas.1016071107](https://doi.org/10.1073/pnas.1016071107) |
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    | p300/CBP binding | Co-activator occupancy marks enhancers | Visel A et al. 2009 Nature [DOI: 10.1038/nature07730](https://doi.org/10.1038/nature07730) |
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    | eRNA transcription | Active enhancers produce bidirectional non-coding RNAs | Kim TK et al. 2010 Nature [DOI: 10.1038/nature09014](https://doi.org/10.1038/nature09014) |
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    | Cell-type specificity | Enhancer marks are highly cell-type specific | Ernst et al. 2011 Nature [DOI: 10.1038/nature09906](https://doi.org/10.1038/nature09906) |
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    | Feature | Description | Key Reference | |---------|-------------|---------------| | Bivalent promoters | H3K4me3+H3K27me3 at developmental genes in ESCs | Bernstein et al. 2006 Cell [DOI: 10.1016/j.cell.2006.02.041](https://doi.org/10.101
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    |---------|-------------|---------------| | Bivalent promoters | H3K4me3+H3K27me3 at developmental genes in ESCs | Bernstein et al. 2006 Cell [DOI: 10.1016/j.cell.2006.02.041](https://doi.org/10.1016/j.cell.2006.02.041) | | Resolution on di
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    | Bivalent promoters | H3K4me3+H3K27me3 at developmental genes in ESCs | Bernstein et al. 2006 Cell [DOI: 10.1016/j.cell.2006.02.041](https://doi.org/10.1016/j.cell.2006.02.041) |
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    | Resolution on differentiation | Resolve to H3K4me3-only (active) or H3K27me3-only (repressed) | Mikkelsen et al. 2007 Nature | | Poised enhancers | H3K4me1+H3K27me3 at developmental enhancers in ESCs | Rada-Iglesias et al. 2011 Nature [DO
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    | Poised enhancers | H3K4me1+H3K27me3 at developmental enhancers in ESCs | Rada-Iglesias et al. 2011 Nature [DOI: 10.1038/nature09692](https://doi.org/10.1038/nature09692) |
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    | Same-allele co-occurrence | Sequential ChIP confirmed both marks on same nucleosome/allele | Bernstein et al. 2006 Cell | | Protection function | H3K4me3 at bivalent promoters protects from DNA methylation | Kumar et al. 2021 Genome Res [
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    | Protection function | H3K4me3 at bivalent promoters protects from DNA methylation | Kumar et al. 2021 Genome Res [DOI: 10.1101/gr.266924.120](https://doi.org/10.1101/gr.266924.120) |
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    | Feature | Description | Key Reference | |---------|-------------|---------------| | Definition | Large clusters of enhancers with disproportionately high H3K27ac/Mediator | Whyte et al. 2013 Cell [DOI: 10.1016/j.cell.2013.03.035](https://
  • Network accessline 568
    |---------|-------------|---------------| | Definition | Large clusters of enhancers with disproportionately high H3K27ac/Mediator | Whyte et al. 2013 Cell [DOI: 10.1016/j.cell.2013.03.035](https://doi.org/10.1016/j.cell.2013.03.035) | | Ce
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    | Definition | Large clusters of enhancers with disproportionately high H3K27ac/Mediator | Whyte et al. 2013 Cell [DOI: 10.1016/j.cell.2013.03.035](https://doi.org/10.1016/j.cell.2013.03.035) |
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    | Cell identity genes | Associated with genes controlling cell type identity | Hnisz et al. 2013 Cell [DOI: 10.1016/j.cell.2013.09.053](https://doi.org/10.1016/j.cell.2013.09.053) |
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    | Sensitivity to perturbation | Disproportionately affected by BET inhibitors (JQ1) | Loven et al. 2013 Cell [DOI: 10.1016/j.cell.2013.03.036](https://doi.org/10.1016/j.cell.2013.03.036) (~2,700 cit.) |
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    | Cancer relevance | Cancer cells acquire super-enhancers at oncogenes | Hnisz et al. 2013 Cell | | Beyond H3K27ac | H4K5acK8ac defines additional super-enhancers missed by H3K27ac alone | Das et al. 2023 BMC Genomics | | Phase separation |
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    | Phase separation | Super-enhancers may form phase-separated condensates | Sabari et al. 2018 Science [DOI: 10.1126/science.aar3958](https://doi.org/10.1126/science.aar3958) |
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    | H3K36me3 gradient | Increases from 5' to 3'; co-transcriptional via SETD2 | Bannister et al. 2005 Nature | | H3K79me2 | Gene body mark via DOT1L; requires H2BK120ub | Steger et al. 2008 Mol Cell Biol | | Cryptic transcription prevention |
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    | H3K79me2 | Gene body mark via DOT1L; requires H2BK120ub | Steger et al. 2008 Mol Cell Biol | | Cryptic transcription prevention | H3K36me3 recruits HDACs to prevent spurious initiation | Carrozza MJ et al. 2005 Cell [DOI: 10.1016/j.cell.2
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    | Cryptic transcription prevention | H3K36me3 recruits HDACs to prevent spurious initiation | Carrozza MJ et al. 2005 Cell [DOI: 10.1016/j.cell.2005.10.023](https://doi.org/10.1016/j.cell.2005.10.023) |
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    | Gene body DNA methylation | H3K36me3 recruits DNMT3B for intragenic CpG methylation | Baubec T et al. 2015 Nature [DOI: 10.1038/nature14456](https://doi.org/10.1038/nature14456) |
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    | Feature | Description | Key Reference | |---------|-------------|---------------| | CTCF binding | Zinc-finger protein that defines TAD boundaries | Dixon JR et al. 2012 Nature [DOI: 10.1038/nature11082](https://doi.org/10.1038/nature1108
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    |---------|-------------|---------------| | CTCF binding | Zinc-finger protein that defines TAD boundaries | Dixon JR et al. 2012 Nature [DOI: 10.1038/nature11082](https://doi.org/10.1038/nature11082) (~4,000 cit.) | | CTCF essential for TA
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    | CTCF binding | Zinc-finger protein that defines TAD boundaries | Dixon JR et al. 2012 Nature [DOI: 10.1038/nature11082](https://doi.org/10.1038/nature11082) (~4,000 cit.) |
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    | CTCF essential for TADs | Auxin-mediated CTCF depletion eliminates TADs | Nora EP et al. 2017 Cell [DOI: 10.1016/j.cell.2017.09.026](https://doi.org/10.1016/j.cell.2017.09.026) (~1,437 cit.) |
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    | Cohesin co-occupancy | CTCF+cohesin define chromatin loops | Rao SSP et al. 2014 Cell [DOI: 10.1016/j.cell.2014.11.021](https://doi.org/10.1016/j.cell.2014.11.021) (~5,000 cit.) |
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    - H3K4me1 is also found downstream of active TSSs and in gene bodies. - MLL3/4-catalyzed H3K4me1 may be a consequence rather than a cause of enhancer activation. - Dorighi KM et al. (2017) *Mol Cell* 66:568-576 [DOI: 10.1016/j.molcel.2017.0
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    - MLL3/4-catalyzed H3K4me1 may be a consequence rather than a cause of enhancer activation. - Dorighi KM et al. (2017) *Mol Cell* 66:568-576 [DOI: 10.1016/j.molcel.2017.04.018](https://doi.org/10.1016/j.molcel.2017.04.018) showed that catal
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    - Dorighi KM et al. (2017) *Mol Cell* 66:568-576 [DOI: 10.1016/j.molcel.2017.04.018](https://doi.org/10.1016/j.molcel.2017.04.018) showed that catalytically dead MLL3/4 still supports enhancer function, suggesting H3K4me1 itself is not requ
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    - Rickels R et al. (2017) *Genes Dev* 31:1412-1424 [DOI: 10.1101/gad.300592.117](https://doi.org/10.1101/gad.300592.117) corroborated this finding.
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    **Current view:** Both models may be correct. Bivalency maintains epigenetic plasticity by preventing irreversible silencing, which incidentally keeps genes available for future activation. - **Macrae et al. (2022) *Nat Rev Mol Cell Biol* 2
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    - **Macrae et al. (2022) *Nat Rev Mol Cell Biol* 24:6-26** [DOI: 10.1038/s41580-022-00544-w](https://doi.org/10.1038/s41580-022-00544-w) (~117 cit.) provides the most comprehensive review, concluding that bivalency is a feature of both germ
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    |------------|-------------|---------------| | Global H4K16ac loss | One of the earliest epigenetic changes in cancer | Fraga et al. 2005 Nat Genet | | H3K27me3 redistribution | Loss at tumor suppressor promoters, gain at other loci | Berma
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    | Global H4K16ac loss | One of the earliest epigenetic changes in cancer | Fraga et al. 2005 Nat Genet | | H3K27me3 redistribution | Loss at tumor suppressor promoters, gain at other loci | Berman BP et al. 2012 Nat Genet [DOI: 10.1038/ng.1
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    | H3K27me3 redistribution | Loss at tumor suppressor promoters, gain at other loci | Berman BP et al. 2012 Nat Genet [DOI: 10.1038/ng.1090](https://doi.org/10.1038/ng.1090) |
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    | De novo super-enhancers | Cancer cells create super-enhancers at oncogenes | Hnisz et al. 2013 Cell | | Bivalent-to-methylated switch | Bivalent promoters in ESCs become DNA hypermethylated in cancer | Ohm JE et al. 2007 Nat Genet [DOI: 1
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    | Bivalent-to-methylated switch | Bivalent promoters in ESCs become DNA hypermethylated in cancer | Ohm JE et al. 2007 Nat Genet [DOI: 10.1038/ng1972](https://doi.org/10.1038/ng1972) |
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    | H3K9me3 expansion | Aberrant spreading into euchromatic regions in some cancers | McDonald OG et al. 2017 Nat Genet [DOI: 10.1038/ng.3861](https://doi.org/10.1038/ng.3861) |
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    | EZH2 gain-of-function | Y641 mutations in lymphoma increase H3K27me3 | Morin RD et al. 2010 Nat Genet [DOI: 10.1038/ng.518](https://doi.org/10.1038/ng.518) |
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    | Oncohistone mutations | H3.3K27M (glioma) globally reduces H3K27me3; H3.3K36M (chondrosarcoma) reduces H3K36me3 | Lewis PW et al. 2013 Science [DOI: 10.1126/science.1232245](https://doi.org/10.1126/science.1232245); Lu C et al. 2016 Scien
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    | H3K4me3 breadth changes | Broad H3K4me3 domains mark tumor suppressors; narrowing in cancer | Chen K et al. 2015 Cell Rep [DOI: 10.1016/j.celrep.2015.02.003](https://doi.org/10.1016/j.celrep.2015.02.003) |
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    **H3K4me3 breadth matters:** Benayoun BA et al. (2014) *Cell* 158:673-688 [DOI: 10.1016/j.cell.2014.07.026](https://doi.org/10.1016/j.cell.2014.07.026) showed that broad H3K4me3 domains (spanning >5kb) mark cell identity genes and differ fr
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    **Rao et al. 2014** -- Kilobase-resolution 3D genome map identifying CTCF/cohesin-anchored chromatin loops. - **Citation:** Rao SS, Huntley MH, Durand NC, Stamenova EK, Bochkov ID, Robinson JT, Sanborn AL, Machol I, Omer AD, Lander ES, Aide
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    - **DOI:** [10.1093/bioinformatics/bty117](https://doi.org/10.1093/bioinformatics/bty117)
  • Network accessline 92
    - **Citation:** Landt SG, Marinov GK, Kundaje A, Kheradpour P, Pauli F, Batzoglou S, Bernstein BE, Bickel P, Brown JB, Cayting P, Chen Y, DeSalvo G, Epstein C, Fisher-Aylor KI, Euskirchen G, Gerstein M, Gertz J, Hartemink AJ, Hoffman MM, Iy
  • Network accessline 93
    - **DOI:** [10.1101/gr.136184.111](https://doi.org/10.1101/gr.136184.111)
  • Network accessline 102
    - **Citation:** Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics, 26(6):841-842, 2010. - **DOI:** [10.1093/bioinformatics/btq033](https://doi.org/10.1093/bioinformatics/btq033)
  • Network accessline 103
    - **DOI:** [10.1093/bioinformatics/btq033](https://doi.org/10.1093/bioinformatics/btq033)

plugin/skills/histone-aggregation/scripts/validate_peaks.py

  • Executable scriptline 1
    #!/usr/bin/env python3

plugin/skills/integrative-analysis/SKILL.md

  • Network accessline 21
    | Reference | Journal | Key Contribution | DOI | Citations | |-----------|---------|-----------------|-----|-----------| | ENCODE Phase 3 (2020) | Nature | Registry of 926,535 candidate cis-regulatory elements; integrative analysis framewor
  • Network accessline 22
    |-----------|---------|-----------------|-----|-----------| | ENCODE Phase 3 (2020) | Nature | Registry of 926,535 candidate cis-regulatory elements; integrative analysis framework across 5,992 experiments | [10.1038/s41586-020-2493-4](http
  • Network accessline 23
    | ENCODE Phase 3 (2020) | Nature | Registry of 926,535 candidate cis-regulatory elements; integrative analysis framework across 5,992 experiments | [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4) | ~1,656 |
  • Network accessline 24
    | Gorkin et al. (2020) | Nature | Integrative analysis of 3,158 mouse epigenomes; cross-tissue chromatin state annotation | [10.1038/s41586-020-2093-3](https://doi.org/10.1038/s41586-020-2093-3) | ~301 |
  • Network accessline 25
    | Ernst & Kellis (2012) | Nature Methods | ChromHMM: chromatin state discovery from combinatorial histone mark patterns | [10.1038/nmeth.1906](https://doi.org/10.1038/nmeth.1906) | ~2,294 |
  • Network accessline 26
    | Nasser et al. (2021) | Nature | Activity-by-Contact (ABC) model for enhancer-gene linkage; outperforms proximity assignment | [10.1038/s41586-021-03446-x](https://doi.org/10.1038/s41586-021-03446-x) | ~468 |
  • Network accessline 27
    | Quinlan & Hall (2010) | Bioinformatics | BEDTools: genome arithmetic for interval comparisons, intersections, and merges | [10.1093/bioinformatics/btq033](https://doi.org/10.1093/bioinformatics/btq033) | ~10,000 |
  • Network accessline 28
    | Ramirez et al. (2016) | Nucleic Acids Res | deepTools: signal normalization, correlation, and visualization for multi-sample genomic data | [10.1093/nar/gkw257](https://doi.org/10.1093/nar/gkw257) | ~3,000 |
  • Network accessline 29
    | Love et al. (2014) | Genome Biology | DESeq2: differential analysis of count data with shrinkage estimation | [10.1186/s13059-014-0550-8](https://doi.org/10.1186/s13059-014-0550-8) | ~40,000 |
  • Network accessline 30
    | Ross-Innes et al. (2012) | Nature | DiffBind: differential binding analysis of ChIP-seq peak data across conditions | [10.1038/nature10730](https://doi.org/10.1038/nature10730) | ~1,200 |
  • Network accessline 31
    | Leek et al. (2010) | Nature Rev Genetics | Tackling batch effects: PCA-based detection, SVA/ComBat correction, experimental design | [10.1038/nrg2825](https://doi.org/10.1038/nrg2825) | ~1,200 |

plugin/skills/integrative-analysis/references/literature.md

  • Network accessline 32
    - **Citation:** Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, et al. Integrative analysis   of 111 reference human epigenomes. *Nature*, 518(7539), 317-330, 2015. - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248)
  • Network accessline 33
    of 111 reference human epigenomes. *Nature*, 518(7539), 317-330, 2015. - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248) - **PMID:** 25693563 | **PMC:** PMC4530010
  • Network accessline 34
    - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248)
  • Network accessline 51
    - **Citation:** Ernst J, Kellis M. ChromHMM: automating chromatin-state discovery and   characterization. *Nature Methods*, 9(3), 215-216, 2012. - **DOI:** [10.1038/nmeth.1906](https://doi.org/10.1038/nmeth.1906)
  • Network accessline 52
    characterization. *Nature Methods*, 9(3), 215-216, 2012. - **DOI:** [10.1038/nmeth.1906](https://doi.org/10.1038/nmeth.1906) - **PMID:** 22373907 | **PMC:** PMC3577932
  • Network accessline 53
    - **DOI:** [10.1038/nmeth.1906](https://doi.org/10.1038/nmeth.1906)
  • Network accessline 79
    - **Citation:** Sheffield NC, Bock C. LOLA: enrichment analysis for genomic region sets and   regulatory elements in R and Bioconductor. *Bioinformatics*, 32(4), 587-589, 2016. - **DOI:** [10.1093/bioinformatics/btv612](https://doi.org/10.1
  • Network accessline 80
    regulatory elements in R and Bioconductor. *Bioinformatics*, 32(4), 587-589, 2016. - **DOI:** [10.1093/bioinformatics/btv612](https://doi.org/10.1093/bioinformatics/btv612) - **PMID:** 26508757 | **PMC:** PMC4743626
  • Network accessline 81
    - **DOI:** [10.1093/bioinformatics/btv612](https://doi.org/10.1093/bioinformatics/btv612)
  • Network accessline 108
    - **Citation:** Yu G, Wang LG, He QY. ChIPseeker: an R/Bioconductor package for ChIP peak   annotation, comparison and visualization. *Bioinformatics*, 31(14), 2382-2383, 2015. - **DOI:** [10.1093/bioinformatics/btv145](https://doi.org/10.1
  • Network accessline 109
    annotation, comparison and visualization. *Bioinformatics*, 31(14), 2382-2383, 2015. - **DOI:** [10.1093/bioinformatics/btv145](https://doi.org/10.1093/bioinformatics/btv145) - **PMID:** 25765347
  • Network accessline 110
    - **DOI:** [10.1093/bioinformatics/btv145](https://doi.org/10.1093/bioinformatics/btv145)
  • Network accessline 126
    Bejerano G. GREAT improves functional interpretation of cis-regulatory regions.   *Nature Biotechnology*, 28(5), 495-501, 2010. - **DOI:** [10.1038/nbt.1630](https://doi.org/10.1038/nbt.1630)
  • Network accessline 127
    *Nature Biotechnology*, 28(5), 495-501, 2010. - **DOI:** [10.1038/nbt.1630](https://doi.org/10.1038/nbt.1630) - **PMID:** 20436461 | **PMC:** PMC4840234
  • Network accessline 128
    - **DOI:** [10.1038/nbt.1630](https://doi.org/10.1038/nbt.1630)
  • Network accessline 154
    kilobase resolution reveals principles of chromatin looping. *Cell*, 159(7),   1665-1680, 2014. - **DOI:** [10.1016/j.cell.2014.11.021](https://doi.org/10.1016/j.cell.2014.11.021)
  • Network accessline 155
    1665-1680, 2014. - **DOI:** [10.1016/j.cell.2014.11.021](https://doi.org/10.1016/j.cell.2014.11.021) - **PMID:** 25497547 | **PMC:** PMC5635824
  • Network accessline 156
    - **DOI:** [10.1016/j.cell.2014.11.021](https://doi.org/10.1016/j.cell.2014.11.021)
  • Network accessline 180
    - **Citation:** Corces MR, Granja JM, Shams S, Louber BH, Banber CE, et al. The chromatin   accessibility landscape of primary human cancers. *Science*, 362(6413), eaav1898, 2018. - **DOI:** [10.1126/science.aav1898](https://doi.org/10.1126
  • Network accessline 181
    accessibility landscape of primary human cancers. *Science*, 362(6413), eaav1898, 2018. - **DOI:** [10.1126/science.aav1898](https://doi.org/10.1126/science.aav1898) - **PMID:** 30361341 | **PMC:** PMC6408149
  • Network accessline 182
    - **DOI:** [10.1126/science.aav1898](https://doi.org/10.1126/science.aav1898)
  • Network accessline 196
    - **Citation:** Regev A, Teichmann SA, Lander ES, Amit I, et al. The Human Cell Atlas.   *eLife*, 6, e27041, 2017. - **DOI:** [10.7554/eLife.27041](https://doi.org/10.7554/eLife.27041)
  • Network accessline 197
    *eLife*, 6, e27041, 2017. - **DOI:** [10.7554/eLife.27041](https://doi.org/10.7554/eLife.27041) - **PMID:** 29206104 | **PMC:** PMC5762154
  • Network accessline 198
    - **DOI:** [10.7554/eLife.27041](https://doi.org/10.7554/eLife.27041)
  • Network accessline 213
    single-cell atlas of in vivo mammalian chromatin accessibility. *Cell*, 174(5),   1309-1324, 2018. - **DOI:** [10.1016/j.cell.2018.06.052](https://doi.org/10.1016/j.cell.2018.06.052)
  • Network accessline 214
    1309-1324, 2018. - **DOI:** [10.1016/j.cell.2018.06.052](https://doi.org/10.1016/j.cell.2018.06.052) - **PMID:** 30078704 | **PMC:** PMC6104449
  • Network accessline 215
    - **DOI:** [10.1016/j.cell.2018.06.052](https://doi.org/10.1016/j.cell.2018.06.052)
  • Network accessline 230
    - **Citation:** Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, et al.   Comprehensive integration of single-cell data. *Cell*, 177(7), 1888-1902, 2019. - **DOI:** [10.1016/j.cell.2019.05.031](https://doi.org/10.1016/j.cell.2019.0
  • Network accessline 231
    Comprehensive integration of single-cell data. *Cell*, 177(7), 1888-1902, 2019. - **DOI:** [10.1016/j.cell.2019.05.031](https://doi.org/10.1016/j.cell.2019.05.031) - **PMID:** 31178118 | **PMC:** PMC6687398
  • Network accessline 232
    - **DOI:** [10.1016/j.cell.2019.05.031](https://doi.org/10.1016/j.cell.2019.05.031)

plugin/skills/jaspar-motifs/SKILL.md

  • Network accessline 40
    - **Castro-Mondragon et al. 2022** "JASPAR 2022: the 9th release of the open-access database of transcription factor binding profiles" (Nucleic Acids Research, ~1,400 citations). The current JASPAR release with 1,956 profiles across 7 taxon
  • Network accessline 41
    - **Sandelin et al. 2004** "JASPAR: an open-access database for eukaryotic transcription factor binding profiles" (Nucleic Acids Research, ~2,000 citations). The founding JASPAR publication establishing the curated, open-access model for TF
  • Network accessline 42
    - **Grant et al. 2011** "FIMO: scanning for occurrences of a given motif" (Bioinformatics, ~2,500 citations). FIMO (Find Individual Motif Occurrences) — the standard tool for scanning sequences with PWMs. Part of the MEME Suite. [DOI: 10.10
  • Network accessline 43
    - **Heinz et al. 2010** "Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities" (Molecular Cell, ~5,000 citations). Introduced HOMER motif analysis — the
  • Network accessline 44
    - **ENCODE Project Consortium 2020** (Nature, ~1,656 citations). The TF ChIP-seq experiments that JASPAR motifs validate and enrich. [DOI: 10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 59
    **Base URL**: `https://jaspar.genereg.net/api/v1/`
  • Network accessline 126
    def get_jaspar_matrix(tf_name, tax_group="vertebrates", collection="CORE"):     """Get JASPAR matrix for a TF."""     url = "https://jaspar.genereg.net/api/v1/matrix/"
  • Network accessline 127
    """Get JASPAR matrix for a TF."""     url = "https://jaspar.genereg.net/api/v1/matrix/"     params = {
  • Network accessline 128
    url = "https://jaspar.genereg.net/api/v1/matrix/"
  • Network accessline 133
    "format": "json"     }     response = requests.get(url, params=params)
  • Network accessline 134
    }     response = requests.get(url, params=params)     results = response.json()["results"]
  • Network accessline 135
    response = requests.get(url, params=params)
  • Network accessline 149
    def get_pfm(matrix_id):     """Get Position Frequency Matrix from JASPAR."""     url = f"https://jaspar.genereg.net/api/v1/matrix/{matrix_id}/"
  • Network accessline 150
    """Get Position Frequency Matrix from JASPAR."""     url = f"https://jaspar.genereg.net/api/v1/matrix/{matrix_id}/"     params = {"format": "json"}
  • Network accessline 151
    url = f"https://jaspar.genereg.net/api/v1/matrix/{matrix_id}/"     params = {"format": "json"}     response = requests.get(url, params=params)
  • Network accessline 152
    params = {"format": "json"}     response = requests.get(url, params=params)     data = response.json()
  • Network accessline 153
    response = requests.get(url, params=params)
  • Network accessline 168
    def get_meme_format(matrix_id):     """Get motif in MEME format for use with FIMO."""     url = f"https://jaspar.genereg.net/api/v1/matrix/{matrix_id}/"
  • Network accessline 169
    """Get motif in MEME format for use with FIMO."""     url = f"https://jaspar.genereg.net/api/v1/matrix/{matrix_id}/"     params = {"format": "meme"}
  • Network accessline 170
    url = f"https://jaspar.genereg.net/api/v1/matrix/{matrix_id}/"     params = {"format": "meme"}     response = requests.get(url, params=params)
  • Network accessline 171
    params = {"format": "meme"}     response = requests.get(url, params=params)     return response.text
  • Network accessline 172
    response = requests.get(url, params=params)
  • Network accessline 188
    # Download reference genome (if not available) # GRCh38: https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz
  • Network accessline 189
    # GRCh38: https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz
  • Network accessline 424
    GET https://jaspar.elixir.no/api/v2/matrix/?tax_id=9606&collection=CORE&profile_class=liver

plugin/skills/jaspar-motifs/references/literature.md

  • Network accessline 17
    transcription factor binding profiles. Nucleic Acids Research,   50(D1):D165-D173, 2022. - **DOI:** [10.1093/nar/gkab1113](https://doi.org/10.1093/nar/gkab1113)
  • Network accessline 18
    50(D1):D165-D173, 2022. - **DOI:** [10.1093/nar/gkab1113](https://doi.org/10.1093/nar/gkab1113) - **PMID:** 34850907 | **PMC:** PMC8728201
  • Network accessline 19
    - **DOI:** [10.1093/nar/gkab1113](https://doi.org/10.1093/nar/gkab1113)
  • Network accessline 41
    JASPAR: an open-access database for eukaryotic transcription factor binding   profiles. Nucleic Acids Research, 32(Database issue):D91-D94, 2004. - **DOI:** [10.1093/nar/gkh012](https://doi.org/10.1093/nar/gkh012)
  • Network accessline 42
    profiles. Nucleic Acids Research, 32(Database issue):D91-D94, 2004. - **DOI:** [10.1093/nar/gkh012](https://doi.org/10.1093/nar/gkh012) - **PMID:** 14681366 | **PMC:** PMC308747
  • Network accessline 43
    - **DOI:** [10.1093/nar/gkh012](https://doi.org/10.1093/nar/gkh012)
  • Network accessline 67
    and update of the open-access database of transcription factor binding   profiles. Nucleic Acids Research, 44(D1):D110-D115, 2016. - **DOI:** [10.1093/nar/gkv1176](https://doi.org/10.1093/nar/gkv1176)
  • Network accessline 68
    profiles. Nucleic Acids Research, 44(D1):D110-D115, 2016. - **DOI:** [10.1093/nar/gkv1176](https://doi.org/10.1093/nar/gkv1176) - **PMID:** 26531826 | **PMC:** PMC4702842
  • Network accessline 69
    - **DOI:** [10.1093/nar/gkv1176](https://doi.org/10.1093/nar/gkv1176)
  • Network accessline 93
    profiles and its web framework. Nucleic Acids Research, 46(D1):D260-D266,   2018. - **DOI:** [10.1093/nar/gkx1126](https://doi.org/10.1093/nar/gkx1126)
  • Network accessline 94
    2018. - **DOI:** [10.1093/nar/gkx1126](https://doi.org/10.1093/nar/gkx1126) - **PMID:** 29140473 | **PMC:** PMC5753243
  • Network accessline 95
    - **DOI:** [10.1093/nar/gkx1126](https://doi.org/10.1093/nar/gkx1126)
  • Network accessline 119
    2020: update of the open-access database of transcription factor binding   profiles. Nucleic Acids Research, 48(D1):D87-D92, 2020. - **DOI:** [10.1093/nar/gkz1001](https://doi.org/10.1093/nar/gkz1001)
  • Network accessline 120
    profiles. Nucleic Acids Research, 48(D1):D87-D92, 2020. - **DOI:** [10.1093/nar/gkz1001](https://doi.org/10.1093/nar/gkz1001) - **PMID:** 31701148 | **PMC:** PMC7145627
  • Network accessline 121
    - **DOI:** [10.1093/nar/gkz1001](https://doi.org/10.1093/nar/gkz1001)
  • Network accessline 149
    inference of eukaryotic transcription factor sequence specificity. Cell,   158(6):1431-1443, 2014. - **DOI:** [10.1016/j.cell.2014.08.009](https://doi.org/10.1016/j.cell.2014.08.009)
  • Network accessline 150
    158(6):1431-1443, 2014. - **DOI:** [10.1016/j.cell.2014.08.009](https://doi.org/10.1016/j.cell.2014.08.009) - **PMID:** 25215497 | **PMC:** PMC4163041
  • Network accessline 151
    - **DOI:** [10.1016/j.cell.2014.08.009](https://doi.org/10.1016/j.cell.2014.08.009)
  • Network accessline 176
    Morgunova E, Enge M, Taipale M, Wei G, et al. DNA-binding specificities of   human transcription factors. Cell, 152(1-2):327-339, 2013. - **DOI:** [10.1016/j.cell.2012.12.009](https://doi.org/10.1016/j.cell.2012.12.009)
  • Network accessline 177
    human transcription factors. Cell, 152(1-2):327-339, 2013. - **DOI:** [10.1016/j.cell.2012.12.009](https://doi.org/10.1016/j.cell.2012.12.009) - **PMID:** 23332764
  • Network accessline 178
    - **DOI:** [10.1016/j.cell.2012.12.009](https://doi.org/10.1016/j.cell.2012.12.009)

plugin/skills/liftover-coordinates/SKILL.md

  • Network accessline 42
    - **Kent et al. 2002** (Genome Research, ~5,000 citations): UCSC Genome Browser and the liftOver tool. The original chain/net alignment framework for coordinate conversion between genome assemblies. [DOI](https://doi.org/10.1101/gr.229102)
  • Network accessline 43
    - **Zhao et al. 2014** (Bioinformatics, ~800 citations): CrossMap — a versatile tool for coordinate conversion between genome assemblies. Handles VCF, BAM, bigWig, GFF, and Wiggle formats that UCSC liftOver cannot process natively. [DOI](ht
  • Network accessline 44
    - **Hinrichs et al. 2006** (Nucleic Acids Research, ~1,200 citations): UCSC genome browser chain/net alignment methodology. Defines the reciprocal-best chain alignment that underpins coordinate conversion. [DOI](https://doi.org/10.1093/nar/
  • Network accessline 45
    - **Kuhn et al. 2013** (Nucleic Acids Research, ~600 citations): Assembly updates and the implications for re-annotation. Documents the biological impact of assembly changes on gene models and regulatory element coordinates. [DOI](https://d
  • Network accessline 46
    - **Schneider et al. 2017** (Genome Research, ~400 citations): GRCh38 improvements over GRCh37 — gap closures, centromere models, alternative haplotypes. Quantifies what changed and why liftover is necessary. [DOI](https://doi.org/10.1101/g
  • Network accessline 47
    - **Amemiya et al. 2019** (Scientific Reports, ~1,372 citations): ENCODE Blacklist regions — some blacklisted regions are assembly-specific. Liftover of blacklist files must use the correct version. [DOI](https://doi.org/10.1038/s41598-019-
  • Network accessline 75
    https://hgdownload.soe.ucsc.edu/goldenPath/{from}/liftOver/{from}To{To}.over.chain.gz
  • Network accessline 79
    | Conversion | Chain File | URL | |-----------|-----------|-----| | hg19 to hg38 | hg19ToHg38.over.chain.gz | `https://hgdownload.soe.ucsc.edu/goldenPath/hg19/liftOver/hg19ToHg38.over.chain.gz` |
  • Network accessline 80
    |-----------|-----------|-----| | hg19 to hg38 | hg19ToHg38.over.chain.gz | `https://hgdownload.soe.ucsc.edu/goldenPath/hg19/liftOver/hg19ToHg38.over.chain.gz` | | hg38 to hg19 | hg38ToHg19.over.chain.gz | `https://hgdownload.soe.ucsc.edu/g
  • Network accessline 81
    | hg19 to hg38 | hg19ToHg38.over.chain.gz | `https://hgdownload.soe.ucsc.edu/goldenPath/hg19/liftOver/hg19ToHg38.over.chain.gz` |
  • Network accessline 82
    | hg38 to hg19 | hg38ToHg19.over.chain.gz | `https://hgdownload.soe.ucsc.edu/goldenPath/hg38/liftOver/hg38ToHg19.over.chain.gz` |
  • Network accessline 83
    | mm9 to mm10 | mm9ToMm10.over.chain.gz | `https://hgdownload.soe.ucsc.edu/goldenPath/mm9/liftOver/mm9ToMm10.over.chain.gz` |
  • Network accessline 84
    | mm10 to mm39 | mm10ToMm39.over.chain.gz | `https://hgdownload.soe.ucsc.edu/goldenPath/mm10/liftOver/mm10ToMm39.over.chain.gz` |
  • Network accessline 85
    | mm10 to hg38 | mm10ToHg38.over.chain.gz | `https://hgdownload.soe.ucsc.edu/goldenPath/mm10/liftOver/mm10ToHg38.over.chain.gz` |
  • Network accessline 97
    NCBI Genome Remapping Service: `https://www.ncbi.nlm.nih.gov/genome/tools/remap`
  • Network accessline 106
    wget https://hgdownload.soe.ucsc.edu/goldenPath/hg19/liftOver/hg19ToHg38.over.chain.gz
  • Network accessline 240
    GET https://rest.ensembl.org/map/human/GRCh37/{region}/GRCh38?content-type=application/json
  • Network accessline 249
    """Convert coordinates using Ensembl REST API."""     region = f"{chrom}:{start}..{end}:1"     url = f"https://rest.ensembl.org/map/{species}/{source}/{region}/{target}"
  • Network accessline 250
    region = f"{chrom}:{start}..{end}:1"     url = f"https://rest.ensembl.org/map/{species}/{source}/{region}/{target}"     headers = {"Content-Type": "application/json"}
  • Network accessline 251
    url = f"https://rest.ensembl.org/map/{species}/{source}/{region}/{target}"     headers = {"Content-Type": "application/json"}     response = requests.get(url, headers=headers)
  • Network accessline 252
    headers = {"Content-Type": "application/json"}     response = requests.get(url, headers=headers)     if response.status_code == 200:
  • Network accessline 253
    response = requests.get(url, headers=headers)

plugin/skills/liftover-coordinates/references/literature.md

  • Network accessline 14
    - **Citation:** Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, Haussler D. The human genome browser at UCSC. Genome Research, 12(6):996-1006, 2002. - **DOI:** [10.1101/gr.229102](https://doi.org/10.1101/gr.229102)
  • Network accessline 15
    - **DOI:** [10.1101/gr.229102](https://doi.org/10.1101/gr.229102)
  • Network accessline 24
    - **Citation:** Zhao H, Sun Z, Wang J, Huang H, Kocher JP, Wang L. CrossMap: a versatile tool for coordinate conversion between genome assemblies. Bioinformatics, 30(7):1006-1007, 2014. - **DOI:** [10.1093/bioinformatics/btt730](https://doi
  • Network accessline 25
    - **DOI:** [10.1093/bioinformatics/btt730](https://doi.org/10.1093/bioinformatics/btt730)
  • Network accessline 34
    - **Citation:** Hinrichs AS, Karolchik D, Baertsch R, Barber GP, Bejerano G, Clawson H, Diekhans M, Furey TS, Harte RA, Hsu F, Hillman-Jackson J, Kuhn RM, Pedersen JS, Pohl A, Raney BJ, Rosenbloom KR, Siepel A, Smith KE, Sugnet CW, Sultan-Q
  • Network accessline 35
    - **DOI:** [10.1093/nar/gkj144](https://doi.org/10.1093/nar/gkj144)
  • Network accessline 48
    - **Citation:** Schneider VA, Graves-Lindsay T, Howe K, Bouk N, Chen HC, Kitts PA, Murphy TD, Pruitt KD, Tello-Ruiz MK, Erber J, Phan L, Robbertse B, Schoch CL, Vnencak-Jones CL, Renfree MB, Havlak P, Seidl CJ, Church DM. Evaluation of GRCh
  • Network accessline 49
    - **DOI:** [10.1101/gr.213611.116](https://doi.org/10.1101/gr.213611.116)
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    - **Citation:** ENCODE Project Consortium et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature, 583(7818):699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
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    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
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    - **Citation:** Castel SE, Levy-Moonshine A, Mohammadi P, Banks E, Lappalainen T. Tools and best practices for data processing in allelic expression analysis. Genome Biology, 16:195, 2015. - **DOI:** [10.1186/s13059-015-0762-6](https://doi.
  • Network accessline 73
    - **DOI:** [10.1186/s13059-015-0762-6](https://doi.org/10.1186/s13059-015-0762-6)

plugin/skills/methylation-aggregation/SKILL.md

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    - **DMRcate** (Peters et al. 2021, Nucleic Acids Research, 65 citations): Method for calling differentially methylated regions from multiple WGBS samples; uses kernel smoothing across CpG sites - **ENCODE Phase 3** (Gorkin et al. 2020, Natu
  • Network accessline 38
    - **ENCODE Phase 3** (Gorkin et al. 2020, Nature, 301 citations): Integrated methylation data with histone marks and accessibility to define chromatin states - **ENCODE Blacklist** (Amemiya et al. 2019, Scientific Reports, 1,372 citations):
  • Network accessline 39
    - **ENCODE Blacklist** (Amemiya et al. 2019, Scientific Reports, 1,372 citations): Problematic genomic regions to filter. [DOI](https://doi.org/10.1038/s41598-019-45839-z)
  • Network accessline 158
    # Download from: https://github.com/Boyle-Lab/Blacklist/blob/master/lists/hg38-blacklist.v2.bed.gz

plugin/skills/methylation-aggregation/references/literature.md

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    - **Citation:** Schultz MD, He Y, Whitaker JW, Hariharan M, Mukamel EA, Leung D, Rajagopal N, Nery JR, Urich MA, Chen H, Lin S, Lin Y, Jung I, Schmitt AD, Selvaraj S, Ren B, Sejnowski TJ, Wang W, Ecker JR. Human body epigenome maps reveal n
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    - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248)
  • Network accessline 24
    - **Citation:** Zhou W, Dinh HQ, Ramjan Z, Weisenberger DJ, Nicolet CM, Shen H, Laird PW, Berman BP. DNA methylation loss in late-replicating domains is linked to mitotic cell division. Nature Genetics, 50(4):591-602, 2018. - **DOI:** [10.1
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    - **DOI:** [10.1038/s41588-018-0073-4](https://doi.org/10.1038/s41588-018-0073-4)
  • Network accessline 38
    - **Citation:** Peters TJ, Buckley MJ, Statham AL, Pidsley R, Samaras K, Lord RV, Clark SJ, Molloy PL. De novo identification of differentially methylated regions in the human genome. Epigenetics & Chromatin, 8:6, 2015. - **DOI:** [10.1186/
  • Network accessline 39
    - **DOI:** [10.1186/1756-8935-8-6](https://doi.org/10.1186/1756-8935-8-6)
  • Network accessline 48
    - **Citation:** Hansen KD, Langmead B, Irizarry RA. BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biology, 13(10):R83, 2012. - **DOI:** [10.1186/gb-2012-13-10-r83](https://doi.org/10.1186
  • Network accessline 49
    - **DOI:** [10.1186/gb-2012-13-10-r83](https://doi.org/10.1186/gb-2012-13-10-r83)
  • Network accessline 76
    - **Citation:** Lister R, Pelizzola M, Dowen RH, et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature, 462(7271):315-322, 2009. - **DOI:** [10.1038/nature08514](https://doi.org/10.1038/nature08514)
  • Network accessline 77
    - **DOI:** [10.1038/nature08514](https://doi.org/10.1038/nature08514)
  • Network accessline 90
    - **Citation:** ENCODE Project Consortium et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature, 583(7818):699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 91
    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
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    - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z) | **PMID:** 31249361 | **Citations:** ~1,372

plugin/skills/methylation-aggregation/scripts/validate_methylation.py

  • Executable scriptline 1
    #!/usr/bin/env python3

plugin/skills/motif-analysis/SKILL.md

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    | Reference | Journal | Key Contribution | DOI | Citations | |-----------|---------|-----------------|-----|-----------| | Heinz et al. (2010) | Molecular Cell | HOMER: Simple combinations of lineage-determining TFs prime cis-regulatory ele
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    |-----------|---------|-----------------|-----|-----------| | Heinz et al. (2010) | Molecular Cell | HOMER: Simple combinations of lineage-determining TFs prime cis-regulatory elements; introduced findMotifsGenome.pl for ChIP-seq motif anal
  • Network accessline 22
    | Heinz et al. (2010) | Molecular Cell | HOMER: Simple combinations of lineage-determining TFs prime cis-regulatory elements; introduced findMotifsGenome.pl for ChIP-seq motif analysis | [10.1016/j.molcel.2010.05.004](https://doi.org/10.101
  • Network accessline 23
    | Bailey et al. (2009) | Nucleic Acids Research | MEME Suite: comprehensive tools for motif discovery (MEME), enrichment (AME), scanning (FIMO), and spacing (SpaMo) | [10.1093/nar/gkp335](https://doi.org/10.1093/nar/gkp335) | ~2,500 |
  • Network accessline 24
    | Bailey & Elkan (1994) | ISMB | Foundational MEME algorithm: expectation maximization for discovering ungapped motifs in biopolymers | PMID: 7584402 | ~4,000 | | Machanick & Bailey (2011) | Bioinformatics | MEME-ChIP: all-in-one motif anal
  • Network accessline 25
    | Machanick & Bailey (2011) | Bioinformatics | MEME-ChIP: all-in-one motif analysis pipeline optimized for large ChIP-seq datasets | [10.1093/bioinformatics/btr189](https://doi.org/10.1093/bioinformatics/btr189) | ~1,800 |
  • Network accessline 26
    | Fornes et al. (2020) | Nucleic Acids Research | JASPAR 2020: curated, non-redundant TF binding profile database; standard reference for known motifs | [10.1093/nar/gkz1001](https://doi.org/10.1093/nar/gkz1001) | ~2,200 |
  • Network accessline 27
    | Amemiya et al. (2019) | Scientific Reports | ENCODE Blacklist: regions producing artifact signal that can generate spurious motif hits | [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z) | ~1,372 |
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    # Download JASPAR 2024 core vertebrate motifs in MEME format wget https://jaspar.elixir.no/download/data/2024/CORE/JASPAR2024_CORE_vertebrates_non-redundant_pfms_meme.txt \
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    wget https://jaspar.elixir.no/download/data/2024/CORE/JASPAR2024_CORE_vertebrates_non-redundant_pfms_meme.txt \

plugin/skills/motif-analysis/references/literature.md

  • Network accessline 18
    factors prime cis-regulatory elements required for macrophage and B cell identities.   Molecular Cell, 38(4):576-589, 2010. - **DOI:** [10.1016/j.molcel.2010.05.004](https://doi.org/10.1016/j.molcel.2010.05.004)
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    Molecular Cell, 38(4):576-589, 2010. - **DOI:** [10.1016/j.molcel.2010.05.004](https://doi.org/10.1016/j.molcel.2010.05.004) - **PMID:** 20513432 | **PMC:** PMC2898526
  • Network accessline 20
    - **DOI:** [10.1016/j.molcel.2010.05.004](https://doi.org/10.1016/j.molcel.2010.05.004)
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    Li WW, Noble WS. MEME SUITE: tools for motif discovery and searching. Nucleic Acids   Research, 37(Web Server issue):W202-W208, 2009. - **DOI:** [10.1093/nar/gkp335](https://doi.org/10.1093/nar/gkp335)
  • Network accessline 43
    Research, 37(Web Server issue):W202-W208, 2009. - **DOI:** [10.1093/nar/gkp335](https://doi.org/10.1093/nar/gkp335) - **PMID:** 19458158 | **PMC:** PMC2703892
  • Network accessline 44
    - **DOI:** [10.1093/nar/gkp335](https://doi.org/10.1093/nar/gkp335)
  • Network accessline 65
    - **Citation:** Grant CE, Bailey TL, Noble WS. FIMO: scanning for occurrences of a   given motif. Bioinformatics, 27(7):1017-1018, 2011. - **DOI:** [10.1093/bioinformatics/btr064](https://doi.org/10.1093/bioinformatics/btr064)
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    given motif. Bioinformatics, 27(7):1017-1018, 2011. - **DOI:** [10.1093/bioinformatics/btr064](https://doi.org/10.1093/bioinformatics/btr064) - **PMID:** 21330290 | **PMC:** PMC3065696
  • Network accessline 67
    - **DOI:** [10.1093/bioinformatics/btr064](https://doi.org/10.1093/bioinformatics/btr064)
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    JASPAR 2022: the 9th release of the open-access database of transcription factor   binding profiles. Nucleic Acids Research, 50(D1):D165-D173, 2022. - **DOI:** [10.1093/nar/gkab1113](https://doi.org/10.1093/nar/gkab1113)
  • Network accessline 95
    binding profiles. Nucleic Acids Research, 50(D1):D165-D173, 2022. - **DOI:** [10.1093/nar/gkab1113](https://doi.org/10.1093/nar/gkab1113) - **PMID:** 34850907 | **PMC:** PMC8728201
  • Network accessline 96
    - **DOI:** [10.1093/nar/gkab1113](https://doi.org/10.1093/nar/gkab1113)
  • Network accessline 119
    human and mouse via large-scale ChIP-Seq analysis. Nucleic Acids Research,   46(D1):D252-D259, 2018. - **DOI:** [10.1093/nar/gkx1106](https://doi.org/10.1093/nar/gkx1106)
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    46(D1):D252-D259, 2018. - **DOI:** [10.1093/nar/gkx1106](https://doi.org/10.1093/nar/gkx1106) - **PMID:** 29140464 | **PMC:** PMC5753240
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    - **DOI:** [10.1093/nar/gkx1106](https://doi.org/10.1093/nar/gkx1106)
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    open-access database of transcription factor binding profiles. Nucleic Acids   Research, 42(D1):D142-D147, 2014. - **DOI:** [10.1093/nar/gkt997](https://doi.org/10.1093/nar/gkt997)
  • Network accessline 143
    Research, 42(D1):D142-D147, 2014. - **DOI:** [10.1093/nar/gkt997](https://doi.org/10.1093/nar/gkt997) - **PMID:** 24194598 | **PMC:** PMC3965078
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    - **DOI:** [10.1093/nar/gkt997](https://doi.org/10.1093/nar/gkt997)
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    Najafabadi HS, Lambert SA, Mann I, Cook K, et al. Determination and inference of   eukaryotic transcription factor sequence specificity. Cell, 158(6):1431-1443, 2014. - **DOI:** [10.1016/j.cell.2014.08.009](https://doi.org/10.1016/j.cell.20
  • Network accessline 169
    eukaryotic transcription factor sequence specificity. Cell, 158(6):1431-1443, 2014. - **DOI:** [10.1016/j.cell.2014.08.009](https://doi.org/10.1016/j.cell.2014.08.009) - **PMID:** 25215497 | **PMC:** PMC4163041
  • Network accessline 170
    - **DOI:** [10.1016/j.cell.2014.08.009](https://doi.org/10.1016/j.cell.2014.08.009)
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    Morgunova E, Enge M, Taipale M, Wei G, et al. DNA-binding specificities of human   transcription factors. Cell, 152(1-2):327-339, 2013. - **DOI:** [10.1016/j.cell.2012.12.009](https://doi.org/10.1016/j.cell.2012.12.009)
  • Network accessline 192
    transcription factors. Cell, 152(1-2):327-339, 2013. - **DOI:** [10.1016/j.cell.2012.12.009](https://doi.org/10.1016/j.cell.2012.12.009) - **PMID:** 23332764
  • Network accessline 193
    - **DOI:** [10.1016/j.cell.2012.12.009](https://doi.org/10.1016/j.cell.2012.12.009)
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    regulatory motifs in ENCODE TF binding experiments. Nucleic Acids Research,   42(5):2976-2987, 2014. - **DOI:** [10.1093/nar/gkt1249](https://doi.org/10.1093/nar/gkt1249)
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    42(5):2976-2987, 2014. - **DOI:** [10.1093/nar/gkt1249](https://doi.org/10.1093/nar/gkt1249) - **PMID:** 24335146 | **PMC:** PMC3950668
  • Network accessline 221
    - **DOI:** [10.1093/nar/gkt1249](https://doi.org/10.1093/nar/gkt1249)
  • Network accessline 242
    - **Citation:** Stormo GD. Modeling the specificity of protein-DNA interactions.   Quantitative Biology, 1(2):115-130, 2013. - **DOI:** [10.1007/s40484-013-0012-4](https://doi.org/10.1007/s40484-013-0012-4)
  • Network accessline 243
    Quantitative Biology, 1(2):115-130, 2013. - **DOI:** [10.1007/s40484-013-0012-4](https://doi.org/10.1007/s40484-013-0012-4) - **PMID:** 25093161 | **PMC:** PMC4119722
  • Network accessline 244
    - **DOI:** [10.1007/s40484-013-0012-4](https://doi.org/10.1007/s40484-013-0012-4)

plugin/skills/multi-omics-integration/SKILL.md

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    - **Mawla, van der Meulen & Huising 2023** (BMC Genomics): Integrated ATAC-seq across alpha, beta, and delta cells. Identified common and cell type-specific enhancers. Demonstrated that chromatin accessibility patterns predict cell identity
  • Network accessline 41
    - **ChromHMM** (Ernst & Kellis 2017, Nature Protocols, 711 citations): The standard tool for chromatin state segmentation. Uses combinatorial patterns of histone marks to annotate genome into functional states (active promoter, enhancer, re
  • Network accessline 42
    - **ENCODE Phase 3** (Gorkin et al. 2020, Nature, 301 citations): Created unified chromatin state annotations across 66 mouse epigenomes. 18 chromatin states annotated. Demonstrated that bivalent chromatin is enriched in silencers and polyc
  • Network accessline 43
    - **ENCODE cCRE Registry** (ENCODE Project Consortium 2020, Nature): Defined ~926,000 candidate cis-regulatory elements (cCREs) in the human genome classified as promoter-like, enhancer-like, or CTCF-bound, using DNase, H3K4me3, H3K27ac, an
  • Network accessline 44
    - **SCENIC+** (Gonzalez-Blas et al. 2022, Nature Methods, 369 citations): Single-cell multi-omic inference of enhancers and gene regulatory networks. Predicts genomic enhancers, upstream TFs, and target genes from joint chromatin accessibil
  • Network accessline 45
    - **Minnoye et al. 2021** (Nature Reviews Methods Primers, 125 citations): Comprehensive review of chromatin accessibility profiling methods. Discusses orthogonal assays needed to interpret accessible regions — enhancer-promoter proximity,
  • Network accessline 46
    - **Roadmap Epigenomics** (Kundaje et al. 2015, Nature, 4,800+ citations): Mapped chromatin states across 111 human reference epigenomes. Established the canonical histone mark signatures for functional annotation. - **ENCODE Blacklist** (A
  • Network accessline 47
    - **ENCODE Blacklist** (Amemiya et al. 2019, Scientific Reports, 1,372 citations): Defined problematic genomic regions to filter from all functional genomics analyses. [DOI](https://doi.org/10.1038/s41598-019-45839-z)
  • Network accessline 48
    - **ABC Model** (Fulco et al. 2019, Nature Genetics, 800+ citations): Activity-By-Contact model for predicting enhancer-gene connections. Combines enhancer activity (H3K27ac) with Hi-C contact frequency. Outperforms proximity-based assignme
  • Network accessline 49
    - **ROSE** (Whyte et al. 2013, Cell, 3,000+ citations): Algorithm for identifying super-enhancers from H3K27ac or Med1 ChIP-seq signal. Regions above the inflection point in ranked signal are classified as super-enhancers. [DOI](https://doi
  • Network accessline 50
    - **GREAT** (McLean et al. 2010, Nature Biotechnology, 3,500+ citations): Genomic Regions Enrichment of Annotations Tool. Assigns biological meaning to sets of non-coding genomic regions (enhancers, accessible regions) by analyzing nearby g
  • Network accessline 51
    - **GRaNIE** (Kamal et al. 2023, Molecular Systems Biology): Enhancer-mediated gene regulatory network inference. Builds GRNs based on covariation of chromatin accessibility and RNA-seq across samples, connecting TFs → enhancers → target ge
  • Network accessline 52
    - **Enformer** (Avsec et al. 2021, Nature Methods): Deep learning model predicting gene expression from DNA sequence by integrating long-range interactions (up to 100kb). Accurately predicts variant effects and enhancer-promoter interaction
  • Network accessline 53
    - **scVI/scANVI** (Lopez et al. 2018; Xu et al. 2021, Nature Methods / MSB): Deep generative models for single-cell RNA-seq. scANVI extends scVI with semi-supervised cell type annotation. When used with scATAC-seq multiome data, provides pr
  • Network accessline 54
    - **Luecken et al. 2022** (Nature Methods, scIB benchmark): Atlas-level integration benchmark of 68 methods. Found scANVI, scVI, Scanorama, and scGen perform best. Provides the standard framework (14 metrics) for evaluating single-cell inte
  • Network accessline 194
    Always filter against ENCODE Blacklist regions before any analysis: - Human GRCh38: `hg38-blacklist.v2.bed.gz` from [Boyle-Lab/Blacklist](https://github.com/Boyle-Lab/Blacklist)
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    - Human GRCh38: `hg38-blacklist.v2.bed.gz` from [Boyle-Lab/Blacklist](https://github.com/Boyle-Lab/Blacklist)
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    ### Functional Enrichment of Identified Regions Use GREAT (McLean et al. 2010) to assign biological meaning to enhancer/regulatory region sets: - Upload enhancer BED file to GREAT (https://great.stanford.edu)
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    Use GREAT (McLean et al. 2010) to assign biological meaning to enhancer/regulatory region sets: - Upload enhancer BED file to GREAT (https://great.stanford.edu) - Assess GO term enrichment, pathway enrichment, disease association
  • Network accessline 359
    - Upload enhancer BED file to GREAT (https://great.stanford.edu)

plugin/skills/multi-omics-integration/references/literature.md

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    - **Citation:** ENCODE Project Consortium, Moore JE, Purcaro MJ, Pratt HE, Epstein CB, Shoresh N, Adrian J, Kawli T, Davis CA, Dobin A, et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. *Nature*, 583(7818), 699-
  • Network accessline 13
    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 22
    - **Citation:** Roadmap Epigenomics Consortium, Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, et al. Integrative analysis of 111 reference human epigenomes. *Nature*, 518(7539), 317-330, 2015. - **DOI:** [10.1038/nature14248](https://do
  • Network accessline 23
    - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248)
  • Network accessline 34
    - **Citation:** Ernst J, Kellis M. ChromHMM: automating chromatin-state discovery and characterization. *Nature Methods*, 9(3), 215-216, 2012. - **DOI:** [10.1038/nmeth.1906](https://doi.org/10.1038/nmeth.1906)
  • Network accessline 35
    - **DOI:** [10.1038/nmeth.1906](https://doi.org/10.1038/nmeth.1906)
  • Network accessline 46
    - **Citation:** Wang C, Sun D, Huang X, Wan C, Li Z, Han Y, Qin Q, Fan J, Qiu X, Xie Y, Meyer CA, Brown M, Tang M, Long H, Liu T, Liu XS. Integrative analyses of single-cell transcriptome and regulome using MAESTRO. *Genome Biology*, 21(1),
  • Network accessline 47
    - **DOI:** [10.1186/s13059-020-02116-x](https://doi.org/10.1186/s13059-020-02116-x)
  • Network accessline 56
    - **Citation:** Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM III, Hao Y, Stoeckius M, Smibert P, Satija R. Comprehensive integration of single-cell data. *Cell*, 177(7), 1888-1902.e21, 2019. - **DOI:** [10.1016/j.cell.
  • Network accessline 57
    - **DOI:** [10.1016/j.cell.2019.05.031](https://doi.org/10.1016/j.cell.2019.05.031)
  • Network accessline 66
    - **Citation:** Welch JD, Kozareva V, Ferreira A, Vanderburg C, Martin C, Macosko EZ. Single-cell multi-omic integration compares and contrasts features of brain cell identity. *Cell*, 177(7), 1873-1887.e17, 2019. - **DOI:** [10.1016/j.cell
  • Network accessline 67
    - **DOI:** [10.1016/j.cell.2019.05.006](https://doi.org/10.1016/j.cell.2019.05.006)
  • Network accessline 76
    - **Citation:** Argelaguet R, Arnol D, Ber D, Helber Y, Gonzalez-Blas CB, Mber I, Jawaid W, Cber W, Laber O, Gerstung M, Edber BT, Reik W, Stegle O. MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell dat
  • Network accessline 77
    - **DOI:** [10.1186/s13059-020-02015-1](https://doi.org/10.1186/s13059-020-02015-1)
  • Network accessline 88
    - **Citation:** Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expressi
  • Network accessline 89
    - **DOI:** [10.1073/pnas.0506580102](https://doi.org/10.1073/pnas.0506580102)
  • Network accessline 100
    - **Citation:** Corces MR, Granja JM, Shams S, Louber BH, Ber EH, Zhang J, Greenside PG, Ghber M, Atber A, Demir E, Cerber L, Frer JR, Mber K, Gao GF, Gaber M, Meyerson M, Kundaje A, Margolais EK, Chang HY, Greenleaf WJ. The chromatin acces
  • Network accessline 101
    - **DOI:** [10.1126/science.aav1898](https://doi.org/10.1126/science.aav1898)
  • Network accessline 110
    - **Citation:** Granja JM, Corces MR, Pierce SE, Bagdatli ST, Chouber H, Zheng H, Zhu M, Dumm RE, Chang HY, Greenleaf WJ. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. *Nature Genetics*,
  • Network accessline 111
    - **DOI:** [10.1038/s41588-021-00790-6](https://doi.org/10.1038/s41588-021-00790-6)

plugin/skills/peak-annotation/SKILL.md

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    | Reference | Journal | Key Contribution | DOI | Citations | |-----------|---------|-----------------|-----|-----------| | Yu et al. (2015) | Bioinformatics | ChIPseeker: R/Bioconductor package for ChIP peak annotation, comparison, and visu
  • Network accessline 21
    |-----------|---------|-----------------|-----|-----------| | Yu et al. (2015) | Bioinformatics | ChIPseeker: R/Bioconductor package for ChIP peak annotation, comparison, and visualization | [10.1093/bioinformatics/btv145](https://doi.org/1
  • Network accessline 22
    | Yu et al. (2015) | Bioinformatics | ChIPseeker: R/Bioconductor package for ChIP peak annotation, comparison, and visualization | [10.1093/bioinformatics/btv145](https://doi.org/10.1093/bioinformatics/btv145) | ~3,200 |
  • Network accessline 23
    | McLean et al. (2010) | Nature Biotechnology | GREAT: Genomic Regions Enrichment of Annotations Tool; assigns biological meaning to cis-regulatory regions using basal+extension gene association | [10.1038/nbt.1630](https://doi.org/10.1038/
  • Network accessline 24
    | Zhu et al. (2010) | BMC Bioinformatics | ChIPpeakAnno: Bioconductor package for ChIP-seq/ChIP-chip annotation; pioneered peak-gene association | [10.1186/1471-2105-11-237](https://doi.org/10.1186/1471-2105-11-237) | ~1,200 |
  • Network accessline 25
    | Tanigawa et al. (2022) | PLOS Computational Biology | rGREAT: R/Bioconductor interface to GREAT; enables programmatic enrichment analysis | [10.1371/journal.pcbi.1010378](https://doi.org/10.1371/journal.pcbi.1010378) | ~80 |
  • Network accessline 26
    | Amemiya et al. (2019) | Scientific Reports | ENCODE Blacklist: artifact regions to exclude before annotation to avoid spurious gene associations | [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z) | ~1,372 |
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    For quick analysis, use the GREAT web interface at https://great.stanford.edu:

plugin/skills/peak-annotation/references/literature.md

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    for ChIP peak annotation, comparison and visualization. Bioinformatics,   31(14):2382-2383, 2015. - **DOI:** [10.1093/bioinformatics/btv145](https://doi.org/10.1093/bioinformat
  • Network accessline 16
    31(14):2382-2383, 2015. - **DOI:** [10.1093/bioinformatics/btv145](https://doi.org/10.1093/bioinformat   ics/btv145)
  • Network accessline 17
    - **DOI:** [10.1093/bioinformatics/btv145](https://doi.org/10.1093/bioinformat
  • Network accessline 40
    Wenger AM, Bejerano G. GREAT improves functional interpretation of cis-   regulatory regions. Nature Biotechnology, 28(5):495-501, 2010. - **DOI:** [10.1038/nbt.1630](https://doi.org/10.1038/nbt.1630)
  • Network accessline 41
    regulatory regions. Nature Biotechnology, 28(5):495-501, 2010. - **DOI:** [10.1038/nbt.1630](https://doi.org/10.1038/nbt.1630) - **PMID:** 20436461 | **PMC:** PMC4840234
  • Network accessline 42
    - **DOI:** [10.1038/nbt.1630](https://doi.org/10.1038/nbt.1630)
  • Network accessline 64
    Green MR. ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and   ChIP-chip data. BMC Bioinformatics, 11:237, 2010. - **DOI:** [10.1186/1471-2105-11-237](https://doi.org/10.1186/1471-2105-11-237)
  • Network accessline 65
    ChIP-chip data. BMC Bioinformatics, 11:237, 2010. - **DOI:** [10.1186/1471-2105-11-237](https://doi.org/10.1186/1471-2105-11-237) - **PMID:** 20459804 | **PMC:** PMC2881054
  • Network accessline 66
    - **DOI:** [10.1186/1471-2105-11-237](https://doi.org/10.1186/1471-2105-11-237)
  • Network accessline 93
    region sets and regulatory elements in R and Bioconductor. Bioinformatics,   32(4):587-589, 2016. - **DOI:** [10.1093/bioinformatics/btv612](https://doi.org/10.1093/bioinformat
  • Network accessline 94
    32(4):587-589, 2016. - **DOI:** [10.1093/bioinformatics/btv612](https://doi.org/10.1093/bioinformat   ics/btv612)
  • Network accessline 95
    - **DOI:** [10.1093/bioinformatics/btv612](https://doi.org/10.1093/bioinformat
  • Network accessline 123
    - **Citation:** Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities   for comparing genomic features. Bioinformatics, 26(6):841-842, 2010. - **DOI:** [10.1093/bioinformatics/btq033](https://doi.org/10.1093/bioinformat
  • Network accessline 124
    for comparing genomic features. Bioinformatics, 26(6):841-842, 2010. - **DOI:** [10.1093/bioinformatics/btq033](https://doi.org/10.1093/bioinformat   ics/btq033)
  • Network accessline 125
    - **DOI:** [10.1093/bioinformatics/btq033](https://doi.org/10.1093/bioinformat
  • Network accessline 150
    R, Morgan MT, Carey VJ. Software for computing and annotating genomic   ranges. PLoS Computational Biology, 9(8):e1003118, 2013. - **DOI:** [10.1371/journal.pcbi.1003118](https://doi.org/10.1371/journal.pcbi
  • Network accessline 151
    ranges. PLoS Computational Biology, 9(8):e1003118, 2013. - **DOI:** [10.1371/journal.pcbi.1003118](https://doi.org/10.1371/journal.pcbi   .1003118)
  • Network accessline 152
    - **DOI:** [10.1371/journal.pcbi.1003118](https://doi.org/10.1371/journal.pcbi
  • Network accessline 181
    oestrogen receptor binding is associated with clinical outcome in breast   cancer. Nature, 481(7381):389-393, 2012. - **DOI:** [10.1038/nature10730](https://doi.org/10.1038/nature10730)
  • Network accessline 182
    cancer. Nature, 481(7381):389-393, 2012. - **DOI:** [10.1038/nature10730](https://doi.org/10.1038/nature10730) - **PMID:** 22217937 | **PMC:** PMC3272464
  • Network accessline 183
    - **DOI:** [10.1038/nature10730](https://doi.org/10.1038/nature10730)
  • Network accessline 205
    - **Citation:** Stark R, Brown GD. DiffBind: differential binding analysis of   ChIP-Seq peak data. Bioconductor package version, 2011. - **DOI:** [10.18129/B9.bioc.DiffBind](https://doi.org/10.18129/B9.bioc.DiffBind)
  • Network accessline 206
    ChIP-Seq peak data. Bioconductor package version, 2011. - **DOI:** [10.18129/B9.bioc.DiffBind](https://doi.org/10.18129/B9.bioc.DiffBind) - **PMID:** N/A | **PMC:** N/A
  • Network accessline 207
    - **DOI:** [10.18129/B9.bioc.DiffBind](https://doi.org/10.18129/B9.bioc.DiffBind)

plugin/skills/pipeline-atacseq/references/02-alignment.md

  • Network accessline 28
    | Organism | Assembly | FASTA source | |----------|----------|--------------| | Human | GRCh38 (hg38) | `https://www.encodeproject.org/files/GRCh38_no_alt_analysis_set_GCA_000001405.15/` |
  • Network accessline 29
    |----------|----------|--------------| | Human | GRCh38 (hg38) | `https://www.encodeproject.org/files/GRCh38_no_alt_analysis_set_GCA_000001405.15/` | | Mouse | mm10 (GRCm38) | `https://www.encodeproject.org/files/mm10_no_alt_analysis_set_EN
  • Network accessline 30
    | Human | GRCh38 (hg38) | `https://www.encodeproject.org/files/GRCh38_no_alt_analysis_set_GCA_000001405.15/` |
  • Network accessline 31
    | Mouse | mm10 (GRCm38) | `https://www.encodeproject.org/files/mm10_no_alt_analysis_set_ENCODE/` |

plugin/skills/pipeline-atacseq/references/literature.md

  • Network accessline 16
    - **Citation:** Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nature Methods, 10(12)
  • Network accessline 17
    - **DOI:** [10.1038/nmeth.2688](https://doi.org/10.1038/nmeth.2688)
  • Network accessline 26
    - **Citation:** Corces MR, Trevino AE, Hamilton EG, Greenside PG, Sinnott-Armstrong NA, Vesuna S, Satpathy AT, Rubin AJ, Montine KS, Wu B, Kathiria A, Cho SW, Mumbach MR, Carter AC, Kasowski M, Orloff LA, Risca VI, Kundaje A, Khavari PA, Mo
  • Network accessline 27
    - **DOI:** [10.1038/nmeth.4396](https://doi.org/10.1038/nmeth.4396)
  • Network accessline 36
    - **Citation:** Grandi F, Modi H, Kampman L, Corces MR. Chromatin accessibility profiling by ATAC-seq. Nature Protocols, 17(6):1518-1552, 2022. - **DOI:** [10.1038/s41596-022-00692-9](https://doi.org/10.1038/s41596-022-00692-9)
  • Network accessline 37
    - **DOI:** [10.1038/s41596-022-00692-9](https://doi.org/10.1038/s41596-022-00692-9)
  • Network accessline 50
    - **Citation:** ENCODE Project Consortium et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature, 583(7818):699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 51
    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 60
    - **Citation:** Hitz BC, Lee JW, Jolanki O, et al. The ENCODE Uniform Analysis Pipelines. Research Square (preprint), 2023. - **DOI:** [10.21203/rs.3.rs-311127/v1](https://doi.org/10.21203/rs.3.rs-311127/v1)
  • Network accessline 61
    - **DOI:** [10.21203/rs.3.rs-311127/v1](https://doi.org/10.21203/rs.3.rs-311127/v1)
  • Network accessline 73
    - **Citation:** Yan F, Powell DR, Curtis DJ, Wong NC. From reads to insight: a hitchhiker's guide to ATAC-seq data analysis. Genome Biology, 21(1):22, 2020. - **DOI:** [10.1186/s13059-020-1929-3](https://doi.org/10.1186/s13059-020-1929-3)
  • Network accessline 74
    - **DOI:** [10.1186/s13059-020-1929-3](https://doi.org/10.1186/s13059-020-1929-3)
  • Network accessline 83
    - **Citation:** Orchard P, Kyono Y, Hensley J, Kitzman JO, Parker SCJ. Quantification, Dynamic Visualization, and Validation of Bias in ATAC-Seq Data with ataqv. Cell Systems, 10(3):298-306.e4, 2020. - **DOI:** [10.1016/j.cels.2020.02.009](
  • Network accessline 84
    - **DOI:** [10.1016/j.cels.2020.02.009](https://doi.org/10.1016/j.cels.2020.02.009)
  • Network accessline 97
    - **Citation:** Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nature Methods, 9(4):357-359, 2012. - **DOI:** [10.1038/nmeth.1923](https://doi.org/10.1038/nmeth.1923)
  • Network accessline 98
    - **DOI:** [10.1038/nmeth.1923](https://doi.org/10.1038/nmeth.1923)
  • Network accessline 107
    - **Citation:** Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM, Brown M, Li W, Liu XS. Model-based Analysis of ChIP-Seq (MACS). Genome Biology, 9(9):R137, 2008. - **DOI:** [10.1186/gb-2008-9-9-r137](htt
  • Network accessline 108
    - **DOI:** [10.1186/gb-2008-9-9-r137](https://doi.org/10.1186/gb-2008-9-9-r137)
  • Network accessline 117
    - **Citation:** Li Q, Brown JB, Huang H, Bickel PJ. Measuring reproducibility of high-throughput experiments. Annals of Applied Statistics, 5(3):1752-1779, 2011. - **DOI:** [10.1214/11-AOAS466](https://doi.org/10.1214/11-AOAS466)
  • Network accessline 118
    - **DOI:** [10.1214/11-AOAS466](https://doi.org/10.1214/11-AOAS466)
  • Network accessline 132
    - **Citation:** Li H, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics, 25(16):2078-2079, 2009. - **DOI:** [10.1093/bioinformatics/btp352](https://doi.org/10.1093/bioinformatics/btp352)
  • Network accessline 133
    - **DOI:** [10.1093/bioinformatics/btp352](https://doi.org/10.1093/bioinformatics/btp352)
  • Network accessline 141
    - **Citation:** Amemiya HM, Kundaje A, Boyle AP. The ENCODE Blacklist: Identification of Problematic Regions of the Genome. Scientific Reports, 9:9354, 2019. - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z
  • Network accessline 142
    - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z)
  • Network accessline 150
    - **Citation:** Ramírez F, et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Research, 44(W1):W160-W165, 2016. - **DOI:** [10.1093/nar/gkw257](https://doi.org/10.1093/nar/gkw257)
  • Network accessline 151
    - **DOI:** [10.1093/nar/gkw257](https://doi.org/10.1093/nar/gkw257)
  • Network accessline 159
    - **Citation:** Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics, 32(19):3047-3048, 2016. - **DOI:** [10.1093/bioinformatics/btw354](https://doi.
  • Network accessline 160
    - **DOI:** [10.1093/bioinformatics/btw354](https://doi.org/10.1093/bioinformatics/btw354)
  • Network accessline 168
    - **Citation:** Broad Institute. Picard toolkit. GitHub, 2019. - **URL:** [https://broadinstitute.github.io/picard/](https://broadinstitute.github.io/picard/)
  • Network accessline 169
    - **URL:** [https://broadinstitute.github.io/picard/](https://broadinstitute.github.io/picard/)
  • Network accessline 176
    - **Citation:** Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics, 26(6):841-842, 2010. - **DOI:** [10.1093/bioinformatics/btq033](https://doi.org/10.1093/bioinformatics/btq033)
  • Network accessline 177
    - **DOI:** [10.1093/bioinformatics/btq033](https://doi.org/10.1093/bioinformatics/btq033)
  • Network accessline 185
    - **Citation:** Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal, 17(1):10-12, 2011. - **DOI:** [10.14806/ej.17.1.200](https://doi.org/10.14806/ej.17.1.200)
  • Network accessline 186
    - **DOI:** [10.14806/ej.17.1.200](https://doi.org/10.14806/ej.17.1.200)
  • Network accessline 194
    - **Citation:** Andrews S. FastQC: A quality control tool for high throughput sequence data. Babraham Bioinformatics, 2010. - **URL:** [https://www.bioinformatics.babraham.ac.uk/projects/fastqc/](https://www.bioinformatics.babraham.ac.uk/pr
  • Network accessline 195
    - **URL:** [https://www.bioinformatics.babraham.ac.uk/projects/fastqc/](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)

plugin/skills/pipeline-atacseq/scripts/Dockerfile

  • Executable scriptline 1
    Executable file
  • Dangerous shell operationline 10
    RUN apt-get update && apt-get install -y --no-install-recommends      build-essential wget curl git ca-certificates perl unzip bc      zlib1g-dev libbz2-dev liblzma-dev libncurses5-dev libcurl4-openssl-dev      python3 python3-pip python3-d
  • Network accessline 10
    RUN apt-get update && apt-get install -y --no-install-recommends \     build-essential wget curl git ca-certificates perl unzip bc \
  • Network accessline 11
    build-essential wget curl git ca-certificates perl unzip bc \
  • Dangerous shell operationline 14
    && rm -rf /var/lib/apt/lists/*
  • Network accessline 24
    RUN pip3 install --no-cache-dir --no-build-isolation \     https://github.com/kundajelab/idr/archive/refs/tags/2.0.4.2.tar.gz
  • Network accessline 25
    https://github.com/kundajelab/idr/archive/refs/tags/2.0.4.2.tar.gz
  • Dangerous shell operationline 28
    RUN wget -q https://github.com/BenLangmead/bowtie2/releases/download/v2.5.4/bowtie2-2.5.4-linux-x86_64.zip &&      unzip -q bowtie2-2.5.4-linux-x86_64.zip -d /opt/ &&      ln -s /opt/bowtie2-2.5.4-linux-x86_64/bowtie2* /usr/local/bin/ &&
  • Network accessline 28
    RUN wget -q https://github.com/BenLangmead/bowtie2/releases/download/v2.5.4/bowtie2-2.5.4-linux-x86_64.zip && \
  • Dangerous shell operationline 31
    rm bowtie2-2.5.4-linux-x86_64.zip
  • Dangerous shell operationline 34
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 &&      tar xjf samtools-1.19.tar.bz2 && cd samtools-1.19 &&      ./configure --prefix=/usr/local && make -j4 && make install &&      cd .. && rm
  • Network accessline 34
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 && \
  • Dangerous shell operationline 37
    cd .. && rm -rf samtools-1.19*
  • Dangerous shell operationline 40
    RUN wget -q https://github.com/arq5x/bedtools2/releases/download/v2.31.0/bedtools-2.31.0.tar.gz &&      tar xzf bedtools-2.31.0.tar.gz && cd bedtools2 &&      make -j4 && cp bin/* /usr/local/bin/ &&      cd .. && rm -rf bedtools2 bedtools-2
  • Network accessline 40
    RUN wget -q https://github.com/arq5x/bedtools2/releases/download/v2.31.0/bedtools-2.31.0.tar.gz && \
  • Dangerous shell operationline 43
    cd .. && rm -rf bedtools2 bedtools-2.31.0.tar.gz
  • Network accessline 46
    RUN wget -q https://github.com/broadinstitute/picard/releases/download/3.1.1/picard.jar \
  • Dangerous shell operationline 52
    RUN wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz &&      tar xzf 0.6.10.tar.gz && ln -s /TrimGalore-0.6.10/trim_galore /usr/local/bin/ &&      rm 0.6.10.tar.gz
  • Network accessline 52
    RUN wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz && \
  • Dangerous shell operationline 54
    rm 0.6.10.tar.gz
  • Dangerous shell operationline 57
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip &&      unzip -q fastqc_v0.12.1.zip -d /opt/ && chmod +x /opt/FastQC/fastqc &&      ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip
  • Network accessline 57
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip && \
  • Dangerous shell operationline 59
    ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip
  • Network accessline 62
    RUN wget -q https://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/bedGraphToBigWig \

plugin/skills/pipeline-atacseq/scripts/main.nf

  • Executable scriptline 1
    #!/usr/bin/env nextflow
  • Network accessline 21
    'GRCh38': [         index:     'GRCh38_bowtie2_index',         blacklist: 'https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz',
  • Network accessline 22
    index:     'GRCh38_bowtie2_index',         blacklist: 'https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz',         gsize:     'hs'
  • Network accessline 23
    blacklist: 'https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz',
  • Network accessline 26
    'mm10': [         index:     'mm10_bowtie2_index',         blacklist: 'https://github.com/Boyle-Lab/Blacklist/raw/master/lists/mm10-blacklist.v2.bed.gz',
  • Network accessline 27
    index:     'mm10_bowtie2_index',         blacklist: 'https://github.com/Boyle-Lab/Blacklist/raw/master/lists/mm10-blacklist.v2.bed.gz',         gsize:     'mm'
  • Network accessline 28
    blacklist: 'https://github.com/Boyle-Lab/Blacklist/raw/master/lists/mm10-blacklist.v2.bed.gz',

plugin/skills/pipeline-chipseq/references/02-alignment.md

  • Network accessline 25
    | Organism | Assembly | FASTA source | |----------|----------|--------------| | Human | GRCh38 (hg38) | `https://www.encodeproject.org/files/GRCh38_no_alt_analysis_set_GCA_000001405.15/` |
  • Network accessline 26
    |----------|----------|--------------| | Human | GRCh38 (hg38) | `https://www.encodeproject.org/files/GRCh38_no_alt_analysis_set_GCA_000001405.15/` | | Mouse | mm10 (GRCm38) | `https://www.encodeproject.org/files/mm10_no_alt_analysis_set_EN
  • Network accessline 27
    | Human | GRCh38 (hg38) | `https://www.encodeproject.org/files/GRCh38_no_alt_analysis_set_GCA_000001405.15/` |
  • Network accessline 28
    | Mouse | mm10 (GRCm38) | `https://www.encodeproject.org/files/mm10_no_alt_analysis_set_ENCODE/` |

plugin/skills/pipeline-chipseq/references/03-filtering.md

  • Network accessline 19
    The default blacklist is downloaded per `--genome`; override it with `--blacklist`: - **Human (hg38)**: `https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz`
  • Network accessline 20
    - **Human (hg38)**: `https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz`
  • Network accessline 21
    - **Mouse (mm10)**: `https://github.com/Boyle-Lab/Blacklist/raw/master/lists/mm10-blacklist.v2.bed.gz`

plugin/skills/pipeline-chipseq/references/literature.md

  • Network accessline 16
    - **Citation:** Landt SG, Marinov GK, Kundaje A, Kheradpour P, Pauli F, Batzoglou S, Bernstein BE, Bickel P, Brown JB, Cayting P, Chen Y, DeSalvo G, Epstein C, Fisher-Aylor KI, Euskirchen G, Gerstein M, Gertz J, Hartemink AJ, Hoffman MM, Iy
  • Network accessline 17
    - **DOI:** [10.1101/gr.136184.111](https://doi.org/10.1101/gr.136184.111)
  • Network accessline 26
    - **Citation:** ENCODE Project Consortium, Moore JE, Purcaro MJ, Pratt HE, Epstein CB, Shoresh N, Adrian J, Kawli T, Davis CA, Dobin A, Kaul R, Halow J, Van Nostrand EL, Freese P, Gorkin DU, Shen Y, He Y, Mackiewicz M, Pauli-Behn F, William
  • Network accessline 27
    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 36
    - **Citation:** Hitz BC, Lee JW, Jolanki O, Kagda MS, Graham K, Sud P, Gabdank I, Strattan JS, Sloan CA, Dreszer T, Rowe LD, Podduturi NR, Malladi V, Chan ET, Davidson JM, Ho M, Miyasato S, Simison M, Tanaka F, Luo Y, Whaling I, Hong EL, Le
  • Network accessline 37
    - **DOI:** [10.21203/rs.3.rs-311127/v1](https://doi.org/10.21203/rs.3.rs-311127/v1)
  • Network accessline 51
    - **Citation:** Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM, Brown M, Li W, Liu XS. Model-based Analysis of ChIP-Seq (MACS). Genome Biology, 9(9):R137, 2008. - **DOI:** [10.1186/gb-2008-9-9-r137](htt
  • Network accessline 52
    - **DOI:** [10.1186/gb-2008-9-9-r137](https://doi.org/10.1186/gb-2008-9-9-r137)
  • Network accessline 61
    - **Citation:** Li Q, Brown JB, Huang H, Bickel PJ. Measuring reproducibility of high-throughput experiments. Annals of Applied Statistics, 5(3):1752-1779, 2011. - **DOI:** [10.1214/11-AOAS466](https://doi.org/10.1214/11-AOAS466)
  • Network accessline 62
    - **DOI:** [10.1214/11-AOAS466](https://doi.org/10.1214/11-AOAS466)
  • Network accessline 70
    - **Citation:** Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv:1303.3997, 2013. - **DOI:** [10.48550/arXiv.1303.3997](https://doi.org/10.48550/arXiv.1303.3997)
  • Network accessline 71
    - **DOI:** [10.48550/arXiv.1303.3997](https://doi.org/10.48550/arXiv.1303.3997)
  • Network accessline 79
    - **Citation:** Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R; 1000 Genome Project Data Processing Subgroup. The Sequence Alignment/Map format and SAMtools. Bioinformatics, 25(16):2078-2079, 2009. -
  • Network accessline 80
    - **DOI:** [10.1093/bioinformatics/btp352](https://doi.org/10.1093/bioinformatics/btp352)
  • Network accessline 93
    - **Citation:** Ramírez F, Ryan DP, Grüning B, Bhardwaj V, Kilpert F, Richter AS, Heyne S, Dündar F, Manke T. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Research, 44(W1):W160-W165, 2016. - **DO
  • Network accessline 94
    - **DOI:** [10.1093/nar/gkw257](https://doi.org/10.1093/nar/gkw257)
  • Network accessline 103
    - **Citation:** Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics, 32(19):3047-3048, 2016. - **DOI:** [10.1093/bioinformatics/btw354](https://doi.
  • Network accessline 104
    - **DOI:** [10.1093/bioinformatics/btw354](https://doi.org/10.1093/bioinformatics/btw354)
  • Network accessline 113
    - **Citation:** Kharchenko PV, Tolstorukov MY, Park PJ. Design and analysis of ChIP-seq experiments for DNA-binding proteins. Nature Biotechnology, 26(12):1351-1359, 2008. (Algorithm basis); Landt et al. 2012 (ENCODE implementation); phanto
  • Network accessline 114
    - **DOI:** [10.1038/nbt.1508](https://doi.org/10.1038/nbt.1508) (Kharchenko et al.)
  • Network accessline 129
    - **Citation:** Andrews S. FastQC: A quality control tool for high throughput sequence data. Babraham Bioinformatics, 2010. - **URL:** [https://www.bioinformatics.babraham.ac.uk/projects/fastqc/](https://www.bioinformatics.babraham.ac.uk/pr
  • Network accessline 130
    - **URL:** [https://www.bioinformatics.babraham.ac.uk/projects/fastqc/](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)
  • Network accessline 138
    - **Citation:** Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal, 17(1):10-12, 2011. - **DOI:** [10.14806/ej.17.1.200](https://doi.org/10.14806/ej.17.1.200)
  • Network accessline 139
    - **DOI:** [10.14806/ej.17.1.200](https://doi.org/10.14806/ej.17.1.200)
  • Network accessline 147
    - **Citation:** Broad Institute. Picard toolkit. GitHub, 2019. - **URL:** [https://broadinstitute.github.io/picard/](https://broadinstitute.github.io/picard/)
  • Network accessline 148
    - **URL:** [https://broadinstitute.github.io/picard/](https://broadinstitute.github.io/picard/)
  • Network accessline 156
    - **Citation:** Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics, 26(6):841-842, 2010. - **DOI:** [10.1093/bioinformatics/btq033](https://doi.org/10.1093/bioinformatics/btq033)
  • Network accessline 157
    - **DOI:** [10.1093/bioinformatics/btq033](https://doi.org/10.1093/bioinformatics/btq033)
  • Network accessline 166
    - **Citation:** Amemiya HM, Kundaje A, Boyle AP. The ENCODE Blacklist: Identification of Problematic Regions of the Genome. Scientific Reports, 9:9354, 2019. - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z
  • Network accessline 167
    - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z)
  • Network accessline 180
    - **Citation:** Bailey T, Krajewski P, Ladunga I, Lefebvre C, Li Q, Liu T, Madrigal P, Taslim C, Zhang J. Practical Guidelines for the Comprehensive Analysis of ChIP-seq Data. PLoS Computational Biology, 9(11):e1003326, 2013. - **DOI:** [10
  • Network accessline 181
    - **DOI:** [10.1371/journal.pcbi.1003326](https://doi.org/10.1371/journal.pcbi.1003326)
  • Network accessline 190
    - **Citation:** Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, Whitwham A, Keane T, McCarthy SA, Davies RM, Li H. Twelve years of SAMtools and BCFtools. GigaScience, 10(2):giab008, 2021. - **DOI:** [10.1093/gigascience/gi
  • Network accessline 191
    - **DOI:** [10.1093/gigascience/giab008](https://doi.org/10.1093/gigascience/giab008)

plugin/skills/pipeline-chipseq/scripts/Dockerfile

  • Executable scriptline 1
    Executable file
  • Dangerous shell operationline 10
    RUN apt-get update && apt-get install -y --no-install-recommends      build-essential wget curl git ca-certificates perl unzip bc      zlib1g-dev libbz2-dev liblzma-dev libncurses5-dev libcurl4-openssl-dev      python3 python3-pip python3-d
  • Network accessline 10
    RUN apt-get update && apt-get install -y --no-install-recommends \     build-essential wget curl git ca-certificates perl unzip bc \
  • Network accessline 11
    build-essential wget curl git ca-certificates perl unzip bc \
  • Dangerous shell operationline 14
    && rm -rf /var/lib/apt/lists/*
  • Network accessline 24
    RUN pip3 install --no-cache-dir --no-build-isolation \     https://github.com/kundajelab/idr/archive/refs/tags/2.0.4.2.tar.gz
  • Network accessline 25
    https://github.com/kundajelab/idr/archive/refs/tags/2.0.4.2.tar.gz
  • Dangerous shell operationline 28
    RUN wget -q https://github.com/lh3/bwa/archive/refs/tags/v0.7.18.tar.gz &&      tar xzf v0.7.18.tar.gz && cd bwa-0.7.18 && make -j4 && cp bwa /usr/local/bin/ &&      cd .. && rm -rf bwa-0.7.18 v0.7.18.tar.gz
  • Network accessline 28
    RUN wget -q https://github.com/lh3/bwa/archive/refs/tags/v0.7.18.tar.gz && \
  • Dangerous shell operationline 30
    cd .. && rm -rf bwa-0.7.18 v0.7.18.tar.gz
  • Dangerous shell operationline 33
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 &&      tar xjf samtools-1.19.tar.bz2 && cd samtools-1.19 &&      ./configure --prefix=/usr/local && make -j4 && make install &&      cd .. && rm
  • Network accessline 33
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 && \
  • Dangerous shell operationline 36
    cd .. && rm -rf samtools-1.19*
  • Dangerous shell operationline 39
    RUN wget -q https://github.com/arq5x/bedtools2/releases/download/v2.31.0/bedtools-2.31.0.tar.gz &&      tar xzf bedtools-2.31.0.tar.gz && cd bedtools2 &&      make -j4 && cp bin/* /usr/local/bin/ &&      cd .. && rm -rf bedtools2 bedtools-2
  • Network accessline 39
    RUN wget -q https://github.com/arq5x/bedtools2/releases/download/v2.31.0/bedtools-2.31.0.tar.gz && \
  • Dangerous shell operationline 42
    cd .. && rm -rf bedtools2 bedtools-2.31.0.tar.gz
  • Network accessline 45
    RUN wget -q https://github.com/broadinstitute/picard/releases/download/3.1.1/picard.jar \
  • Dangerous shell operationline 51
    RUN wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz &&      tar xzf 0.6.10.tar.gz && ln -s /TrimGalore-0.6.10/trim_galore /usr/local/bin/ &&      rm 0.6.10.tar.gz
  • Network accessline 51
    RUN wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz && \
  • Dangerous shell operationline 53
    rm 0.6.10.tar.gz
  • Dangerous shell operationline 56
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip &&      unzip -q fastqc_v0.12.1.zip -d /opt/ && chmod +x /opt/FastQC/fastqc &&      ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip
  • Network accessline 56
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip && \
  • Dangerous shell operationline 58
    ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip
  • Network accessline 61
    RUN wget -q https://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/bedGraphToBigWig \

plugin/skills/pipeline-chipseq/scripts/main.nf

  • Executable scriptline 1
    #!/usr/bin/env nextflow
  • Network accessline 21
    return [     'GRCh38': [         blacklist:  'https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz',
  • Network accessline 22
    'GRCh38': [         blacklist:  'https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz',         gsize:      'hs'
  • Network accessline 23
    blacklist:  'https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz',
  • Network accessline 25
    ],     'mm10': [         blacklist:  'https://github.com/Boyle-Lab/Blacklist/raw/master/lists/mm10-blacklist.v2.bed.gz',
  • Network accessline 26
    'mm10': [         blacklist:  'https://github.com/Boyle-Lab/Blacklist/raw/master/lists/mm10-blacklist.v2.bed.gz',         gsize:      'mm'
  • Network accessline 27
    blacklist:  'https://github.com/Boyle-Lab/Blacklist/raw/master/lists/mm10-blacklist.v2.bed.gz',

plugin/skills/pipeline-cutandrun/SKILL.md

  • Network accessline 340
    # Download suspect list wget https://github.com/Boyle-Lab/Blacklist/raw/master/lists/CUTandRUN.suspectlist.hg38.bed.gz
  • Network accessline 341
    wget https://github.com/Boyle-Lab/Blacklist/raw/master/lists/CUTandRUN.suspectlist.hg38.bed.gz

plugin/skills/pipeline-cutandrun/references/literature.md

  • Network accessline 14
    - **Citation:** Skene PJ, Henikoff S. An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites. eLife, 6:e21856, 2017. - **DOI:** [10.7554/eLife.21856](https://doi.org/10.7554/eLife.21856)
  • Network accessline 15
    - **DOI:** [10.7554/eLife.21856](https://doi.org/10.7554/eLife.21856)
  • Network accessline 24
    - **Citation:** Kaya-Okur HS, Wu SJ, Codomo CA, Pledger ES, Bryson TD, Henikoff JG, Ahmad K, Henikoff S. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nature Communications, 10(1):1930, 2019. - **DOI:** [10.1
  • Network accessline 25
    - **DOI:** [10.1038/s41467-019-09982-5](https://doi.org/10.1038/s41467-019-09982-5)
  • Network accessline 34
    - **Citation:** Skene PJ, Henikoff JG, Henikoff S. Targeted in situ genome-wide profiling with high efficiency for low cell numbers. Nature Protocols, 13(5):1006-1019, 2018. - **DOI:** [10.1038/nprot.2018.015](https://doi.org/10.1038/nprot.
  • Network accessline 35
    - **DOI:** [10.1038/nprot.2018.015](https://doi.org/10.1038/nprot.2018.015)
  • Network accessline 48
    - **Citation:** Meers MP, Tenenbaum D, Henikoff S. Peak calling by Sparse Enrichment Analysis for CUT&RUN chromatin profiling. Epigenetics & Chromatin, 12(1):42, 2019. - **DOI:** [10.1186/s13072-019-0287-4](https://doi.org/10.1186/s13072-01
  • Network accessline 49
    - **DOI:** [10.1186/s13072-019-0287-4](https://doi.org/10.1186/s13072-019-0287-4)
  • Network accessline 62
    - **Citation:** Nordin A, Zambanini G, Pagella P, Bhatt DK, Bjork P, Nilsson J, Mead P, Boyle AP. The CUT&RUN suspect list of problematic regions of the genome. Genome Biology, 24:185, 2023. - **DOI:** [10.1186/s13059-023-02960-3](https://d
  • Network accessline 63
    - **DOI:** [10.1186/s13059-023-02960-3](https://doi.org/10.1186/s13059-023-02960-3)
  • Network accessline 72
    - **Citation:** Meers MP, Bryson TD, Henikoff JG, Henikoff S. Improved CUT&RUN chromatin profiling tools. eLife, 8:e46314, 2019. - **DOI:** [10.7554/eLife.46314](https://doi.org/10.7554/eLife.46314)
  • Network accessline 73
    - **DOI:** [10.7554/eLife.46314](https://doi.org/10.7554/eLife.46314)
  • Network accessline 88
    - **DOI:** [10.1038/nmeth.1923](https://doi.org/10.1038/nmeth.1923) | **PMID:** 22388286 | **Citations:** ~47,300
  • Network accessline 95
    - **DOI:** [10.1186/gb-2008-9-9-r137](https://doi.org/10.1186/gb-2008-9-9-r137) | **PMID:** 18798982 | **Citations:** ~7,000
  • Network accessline 102
    - **DOI:** [10.1093/bioinformatics/btp352](https://doi.org/10.1093/bioinformatics/btp352) | **PMID:** 19505943 | **Citations:** ~53,700
  • Network accessline 109
    - **URL:** [https://broadinstitute.github.io/picard/](https://broadinstitute.github.io/picard/)
  • Network accessline 116
    - **DOI:** [10.1093/bioinformatics/btq033](https://doi.org/10.1093/bioinformatics/btq033) | **PMID:** 20110278 | **Citations:** ~12,000
  • Network accessline 123
    - **DOI:** [10.1093/nar/gkw257](https://doi.org/10.1093/nar/gkw257) | **PMID:** 27079975 | **Citations:** ~6,100
  • Network accessline 130
    - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z) | **PMID:** 31249361 | **Citations:** ~1,372
  • Network accessline 137
    - **DOI:** [10.1093/bioinformatics/btw354](https://doi.org/10.1093/bioinformatics/btw354) | **PMID:** 27312411 | **Citations:** ~6,800
  • Network accessline 144
    - **URL:** [https://www.bioinformatics.babraham.ac.uk/projects/fastqc/](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)
  • Network accessline 151
    - **DOI:** [10.14806/ej.17.1.200](https://doi.org/10.14806/ej.17.1.200) | **Citations:** ~13,000

plugin/skills/pipeline-cutandrun/scripts/Dockerfile

  • Executable scriptline 1
    Executable file
  • Network accessline 5
    LABEL maintainer="Dr. Alex M. Mawla, PhD <https://github.com/ammawla/encode-toolkit>"
  • Dangerous shell operationline 11
    RUN apt-get update && apt-get install -y --no-install-recommends      wget curl gzip pigz unzip      python3 python3-pip python3-dev      openjdk-17-jre-headless      libncurses5-dev libbz2-dev liblzma-dev zlib1g-dev libcurl4-openssl-dev
  • Network accessline 11
    RUN apt-get update && apt-get install -y --no-install-recommends \     wget curl gzip pigz unzip \
  • Network accessline 12
    wget curl gzip pigz unzip \
  • Dangerous shell operationline 17
    && rm -rf /var/lib/apt/lists/*
  • Dangerous shell operationline 20
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 &&      tar xjf samtools-1.19.tar.bz2 && cd samtools-1.19 &&      ./configure --prefix=/usr/local && make -j4 && make install &&      cd .. && rm
  • Network accessline 20
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 && \
  • Dangerous shell operationline 23
    cd .. && rm -rf samtools-1.19*
  • Dangerous shell operationline 26
    RUN wget -q https://github.com/BenLangmead/bowtie2/releases/download/v2.5.4/bowtie2-2.5.4-linux-x86_64.zip &&      unzip -q bowtie2-2.5.4-linux-x86_64.zip -d /opt/ &&      ln -s /opt/bowtie2-2.5.4-linux-x86_64/bowtie2* /usr/local/bin/ &&
  • Network accessline 26
    RUN wget -q https://github.com/BenLangmead/bowtie2/releases/download/v2.5.4/bowtie2-2.5.4-linux-x86_64.zip && \
  • Dangerous shell operationline 29
    rm bowtie2-2.5.4-linux-x86_64.zip
  • Dangerous shell operationline 32
    RUN wget -q https://github.com/arq5x/bedtools2/releases/download/v2.31.0/bedtools-2.31.0.tar.gz &&      tar xzf bedtools-2.31.0.tar.gz && cd bedtools2 &&      make -j4 && cp bin/* /usr/local/bin/ &&      cd .. && rm -rf bedtools2 bedtools-2
  • Network accessline 32
    RUN wget -q https://github.com/arq5x/bedtools2/releases/download/v2.31.0/bedtools-2.31.0.tar.gz && \
  • Dangerous shell operationline 35
    cd .. && rm -rf bedtools2 bedtools-2.31.0.tar.gz
  • Network accessline 38
    RUN wget -q https://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/bedGraphToBigWig \
  • Network accessline 42
    RUN wget -q https://github.com/broadinstitute/picard/releases/download/3.1.1/picard.jar \
  • Dangerous shell operationline 48
    RUN wget -q https://github.com/FredHutch/SEACR/archive/refs/tags/v1.3.tar.gz &&      tar xzf v1.3.tar.gz && mv SEACR-1.3 /opt/SEACR &&      ln -s /opt/SEACR/SEACR_1.3.sh /opt/SEACR/SEACR_1.3.R /usr/local/bin/ &&      rm v1.3.tar.gz
  • Network accessline 48
    RUN wget -q https://github.com/FredHutch/SEACR/archive/refs/tags/v1.3.tar.gz && \
  • Dangerous shell operationline 51
    rm v1.3.tar.gz
  • Dangerous shell operationline 60
    RUN wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz &&      tar xzf 0.6.10.tar.gz && ln -s /TrimGalore-0.6.10/trim_galore /usr/local/bin/ &&      rm 0.6.10.tar.gz
  • Network accessline 60
    RUN wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz && \
  • Dangerous shell operationline 62
    rm 0.6.10.tar.gz
  • Dangerous shell operationline 65
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip &&      unzip -q fastqc_v0.12.1.zip -d /opt/ && chmod +x /opt/FastQC/fastqc &&      ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip
  • Network accessline 65
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip && \
  • Dangerous shell operationline 67
    ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip

plugin/skills/pipeline-cutandrun/scripts/main.nf

  • Executable scriptline 1
    #!/usr/bin/env nextflow
  • Dangerous shell operationline 181
    rm qfilt.bam dedup.bam
  • Dangerous shell operationline 240
    rm namesorted.bam

plugin/skills/pipeline-dnaseseq/references/03-filtering.md

  • Network accessline 79
    # Download blacklist wget -q https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz
  • Network accessline 80
    wget -q https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz

plugin/skills/pipeline-dnaseseq/references/05-footprinting.md

  • Network accessline 137
    rgt-hint differential      --organism hg38      --bc      --nc 8
  • Network accessline 139
    --bc \     --nc 8 \
  • Network accessline 140
    --nc 8 \
  • Network accessline 161
    # Download Vierstra reference footprints # Available at: https://www.vierstra.org/resources/dgf
  • Network accessline 162
    # Available at: https://www.vierstra.org/resources/dgf

plugin/skills/pipeline-dnaseseq/references/literature.md

  • Network accessline 14
    - **Citation:** Thurman RE, Rynes E, Humbert R, Vierstra J, Maurano MT, Haugen E, Sheffield NC, Stergachis AB, Wang H, Vernot B, Garg K, John S, Sandstrom R, Bates D, Boatman L, Canfield TK, Diegel M, Dunn D, Ebersol AK, Frum T, Giste E, Jo
  • Network accessline 15
    - **DOI:** [10.1038/nature11232](https://doi.org/10.1038/nature11232)
  • Network accessline 24
    - **Citation:** John S, Sabo PJ, Thurman RE, Sung MH, Biddie SC, Johnson TA, Hager GL, Stamatoyannopoulos JA. Chromatin accessibility pre-determines glucocorticoid receptor binding patterns. Nature Genetics, 43(3):264-268, 2011. - **DOI:**
  • Network accessline 25
    - **DOI:** [10.1038/ng.759](https://doi.org/10.1038/ng.759)
  • Network accessline 34
    - **Citation:** Vierstra J, Lazar J, Sandstrom R, Halow J, Lee K, Bates D, Diegel M, Dunn D, Neri F, Haugen E, Rynes E, Reynolds A, Nelson J, Johnson A, Frerker M, Buckley M, Kaul R, Meuleman W, Stamatoyannopoulos JA. Global reference mappi
  • Network accessline 35
    - **DOI:** [10.1038/s41586-020-2528-x](https://doi.org/10.1038/s41586-020-2528-x)
  • Network accessline 44
    - **Citation:** ENCODE Project Consortium et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature, 583(7818):699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 45
    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 54
    - **Citation:** Hitz BC, Lee JW, Jolanki O, et al. The ENCODE Uniform Analysis Pipelines. Research Square (preprint), 2023. - **DOI:** [10.21203/rs.3.rs-311127/v1](https://doi.org/10.21203/rs.3.rs-311127/v1)
  • Network accessline 55
    - **DOI:** [10.21203/rs.3.rs-311127/v1](https://doi.org/10.21203/rs.3.rs-311127/v1)
  • Network accessline 67
    - **Citation:** Li Z, Schulz MH, Look T, Begus M, Zenke M, Costa IG. Identification of transcription factor binding sites using ATAC-seq. Genome Research, 29(11):1850-1858, 2019. - **DOI:** [10.1186/s13059-019-1642-2](https://doi.org/10.118
  • Network accessline 68
    - **DOI:** [10.1186/s13059-019-1642-2](https://doi.org/10.1186/s13059-019-1642-2)
  • Network accessline 77
    - **Citation:** Neph S, Vierstra J, Stergachis AB, Reynolds AP, Haugen E, Vernot B, Thurman RE, John S, Sandstrom R, Johnson AK, Maurano MT, Humbert R, Rynes E, Wang H, Vong S, Lee K, Bates D, Diegel M, Roach V, Dunn D, Neri J, Schafer A, H
  • Network accessline 78
    - **DOI:** [10.1038/nature11212](https://doi.org/10.1038/nature11212)
  • Network accessline 93
    - **DOI:** [10.1093/bioinformatics/btp324](https://doi.org/10.1093/bioinformatics/btp324) | **PMID:** 19451168 | **Citations:** ~25,000
  • Network accessline 100
    - **DOI:** [10.1093/bioinformatics/btp352](https://doi.org/10.1093/bioinformatics/btp352) | **PMID:** 19505943 | **Citations:** ~53,700
  • Network accessline 107
    - **URL:** [https://broadinstitute.github.io/picard/](https://broadinstitute.github.io/picard/)
  • Network accessline 114
    - **DOI:** [10.1093/bioinformatics/btq033](https://doi.org/10.1093/bioinformatics/btq033) | **PMID:** 20110278 | **Citations:** ~12,000
  • Network accessline 121
    - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z) | **PMID:** 31249361 | **Citations:** ~1,372
  • Network accessline 128
    - **DOI:** [10.1093/bioinformatics/btw354](https://doi.org/10.1093/bioinformatics/btw354) | **PMID:** 27312411 | **Citations:** ~6,800
  • Network accessline 135
    - **URL:** [https://www.bioinformatics.babraham.ac.uk/projects/fastqc/](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)
  • Network accessline 142
    - **DOI:** [10.14806/ej.17.1.200](https://doi.org/10.14806/ej.17.1.200) | **Citations:** ~13,000

plugin/skills/pipeline-dnaseseq/scripts/Dockerfile

  • Executable scriptline 1
    Executable file
  • Network accessline 5
    LABEL maintainer="Dr. Alex M. Mawla, PhD <https://github.com/ammawla/encode-toolkit>"
  • Dangerous shell operationline 11
    RUN apt-get update && apt-get install -y --no-install-recommends      wget curl gzip pigz unzip      python3 python3-pip python3-dev      openjdk-17-jre-headless      libncurses5-dev libbz2-dev liblzma-dev zlib1g-dev libcurl4-openssl-dev
  • Network accessline 11
    RUN apt-get update && apt-get install -y --no-install-recommends \     wget curl gzip pigz unzip \
  • Network accessline 12
    wget curl gzip pigz unzip \
  • Dangerous shell operationline 19
    && rm -rf /var/lib/apt/lists/*
  • Dangerous shell operationline 22
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 &&      tar xjf samtools-1.19.tar.bz2 && cd samtools-1.19 &&      ./configure --prefix=/usr/local && make -j4 && make install &&      cd .. && rm
  • Network accessline 22
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 && \
  • Dangerous shell operationline 25
    cd .. && rm -rf samtools-1.19*
  • Dangerous shell operationline 28
    RUN wget -q https://github.com/lh3/bwa/archive/refs/tags/v0.7.18.tar.gz &&      tar xzf v0.7.18.tar.gz && cd bwa-0.7.18 &&      make -j4 && cp bwa /usr/local/bin/ &&      cd .. && rm -rf bwa-0.7.18 v0.7.18.tar.gz
  • Network accessline 28
    RUN wget -q https://github.com/lh3/bwa/archive/refs/tags/v0.7.18.tar.gz && \
  • Dangerous shell operationline 31
    cd .. && rm -rf bwa-0.7.18 v0.7.18.tar.gz
  • Dangerous shell operationline 34
    RUN wget -q https://github.com/arq5x/bedtools2/releases/download/v2.31.0/bedtools-2.31.0.tar.gz &&      tar xzf bedtools-2.31.0.tar.gz && cd bedtools2 &&      make -j4 && cp bin/* /usr/local/bin/ &&      cd .. && rm -rf bedtools2 bedtools-2
  • Network accessline 34
    RUN wget -q https://github.com/arq5x/bedtools2/releases/download/v2.31.0/bedtools-2.31.0.tar.gz && \
  • Dangerous shell operationline 37
    cd .. && rm -rf bedtools2 bedtools-2.31.0.tar.gz
  • Network accessline 40
    RUN wget -q https://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/bedGraphToBigWig \
  • Dangerous shell operationline 44
    RUN wget -q https://github.com/StamLab/modwt/archive/refs/tags/1.0.tar.gz &&      tar xzf 1.0.tar.gz && make -C modwt-1.0/src &&      cp modwt-1.0/bin/modwt /usr/local/bin/ &&      rm -rf modwt-1.0 1.0.tar.gz
  • Network accessline 44
    RUN wget -q https://github.com/StamLab/modwt/archive/refs/tags/1.0.tar.gz && \
  • Dangerous shell operationline 47
    rm -rf modwt-1.0 1.0.tar.gz
  • Dangerous shell operationline 51
    RUN wget -q https://github.com/Altius/hotspot2/archive/refs/tags/v2.1.2.tar.gz &&      tar xzf v2.1.2.tar.gz && make -C hotspot2-2.1.2 &&      cp hotspot2-2.1.2/bin/* hotspot2-2.1.2/scripts/* /usr/local/bin/ &&      rm -rf hotspot2-2.1.2 v2
  • Network accessline 51
    RUN wget -q https://github.com/Altius/hotspot2/archive/refs/tags/v2.1.2.tar.gz && \
  • Dangerous shell operationline 54
    rm -rf hotspot2-2.1.2 v2.1.2.tar.gz
  • Network accessline 57
    RUN wget -q https://github.com/broadinstitute/picard/releases/download/3.1.1/picard.jar \
  • Dangerous shell operationline 63
    RUN pip3 install --no-cache-dir cutadapt==4.6 &&      wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz &&      tar xzf 0.6.10.tar.gz && ln -s /TrimGalore-0.6.10/trim_galore /usr/local/bin/ &&      rm 0.6.10.tar.gz
  • Network accessline 63
    RUN pip3 install --no-cache-dir cutadapt==4.6 && \     wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz && \
  • Network accessline 64
    wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz && \
  • Dangerous shell operationline 66
    rm 0.6.10.tar.gz
  • Dangerous shell operationline 73
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip &&      unzip -q fastqc_v0.12.1.zip -d /opt/ && chmod +x /opt/FastQC/fastqc &&      ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip
  • Network accessline 73
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip && \
  • Dangerous shell operationline 75
    ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip

plugin/skills/pipeline-dnaseseq/scripts/main.nf

  • Executable scriptline 1
    #!/usr/bin/env nextflow
  • Dangerous shell operationline 138
    rm nuclear.bam dedup.bam

plugin/skills/pipeline-guide/SKILL.md

  • Network accessline 487
    # Download chain file wget https://hgdownload.soe.ucsc.edu/goldenPath/hg19/liftOver/hg19ToHg38.over.chain.gz
  • Network accessline 488
    wget https://hgdownload.soe.ucsc.edu/goldenPath/hg19/liftOver/hg19ToHg38.over.chain.gz
  • Network accessline 522
    # CRAN available.packages(repos="https://cran.r-project.org")[,"Version"]
  • Network accessline 523
    available.packages(repos="https://cran.r-project.org")[,"Version"]

plugin/skills/pipeline-guide/references/literature.md

  • Network accessline 12
    - **Citation:** Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. *Nature Biotechnology*, 35(4), 316-319, 2017. - **DOI:** [10.1038/nbt.3820](https://doi.org/10.1038
  • Network accessline 13
    - **DOI:** [10.1038/nbt.3820](https://doi.org/10.1038/nbt.3820)
  • Network accessline 22
    - **Citation:** Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. The nf-core framework for community-curated bioinformatics pipelines. *Nature Biotechnology*, 38(3), 276-278, 2020. - **DOI:*
  • Network accessline 23
    - **DOI:** [10.1038/s41587-020-0439-x](https://doi.org/10.1038/s41587-020-0439-x)
  • Network accessline 32
    - **Citation:** Vivian J, Rao AA, Nothaft FA, Ketchum C, Armstrong J, Novak A, Pfeil J, Narkizian J, Deran AD, Musselman-Brown A, Schmidt H, Amstutz P, Craft B, Goldman M, Rober K, Zhu J, Haussler D, Paten B. Toil enables reproducible, open
  • Network accessline 33
    - **DOI:** [10.1038/nbt.3772](https://doi.org/10.1038/nbt.3772)
  • Network accessline 44
    - **Citation:** Kurtzer GM, Sochat V, Bauer MW. Singularity: scientific containers for mobility of compute. *PLoS ONE*, 12(5), e0177459, 2017. - **DOI:** [10.1371/journal.pone.0177459](https://doi.org/10.1371/journal.pone.0177459)
  • Network accessline 45
    - **DOI:** [10.1371/journal.pone.0177459](https://doi.org/10.1371/journal.pone.0177459)
  • Network accessline 64
    - **Citation:** Grüning B, Dale R, Sjodin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Koester J, Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. *Nature Methods*, 15(7), 475-476,
  • Network accessline 65
    - **DOI:** [10.1038/s41592-018-0046-7](https://doi.org/10.1038/s41592-018-0046-7)
  • Network accessline 76
    - **Citation:** ENCODE Project Consortium, Moore JE, Purcaro MJ, Pratt HE, Epstein CB, Shoresh N, Adrian J, Kawli T, Davis CA, Dobin A, et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. *Nature*, 583(7818), 699-
  • Network accessline 77
    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)

plugin/skills/pipeline-hic/references/literature.md

  • Network accessline 14
    - **Citation:** Lieberman-Aiden E, van Berkum NL, Williams L, Imakaev M, Raber T, Lajoie BR, Dekker J, Bhatt DM, Nussbaum MC, Anton IM, Ahmed K, Gnirke A, Lander ES. Comprehensive mapping of long-range interactions reveals folding principle
  • Network accessline 15
    - **DOI:** [10.1126/science.1181369](https://doi.org/10.1126/science.1181369)
  • Network accessline 24
    - **Citation:** Rao SSP, Huntley MH, Durand NC, Stamenova EK, Bochkov ID, Robinson JT, Sanborn AL, Machol I, Omer AD, Lander ES, Aiden EL. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell, 15
  • Network accessline 25
    - **DOI:** [10.1016/j.cell.2014.11.021](https://doi.org/10.1016/j.cell.2014.11.021)
  • Network accessline 38
    - **Citation:** ENCODE Project Consortium et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature, 583(7818):699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 39
    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 48
    - **Citation:** Hitz BC, Lee JW, Jolanki O, et al. The ENCODE Uniform Analysis Pipelines. Research Square (preprint), 2023. - **DOI:** [10.21203/rs.3.rs-311127/v1](https://doi.org/10.21203/rs.3.rs-311127/v1)
  • Network accessline 49
    - **DOI:** [10.21203/rs.3.rs-311127/v1](https://doi.org/10.21203/rs.3.rs-311127/v1)
  • Network accessline 57
    - **Citation:** Yardimci GG, Ozadam H, Sauria MEG, Ursu O, Yan KK, Yang T, Chakraborty A, Kaul A, Lajoie BR, Song F, Zhan Y, Ay F, Gerstein M, Kundaje A, Li Q, Taylor J, Yue F, Dekker J, Noble WS. Measuring the reproducibility and quality o
  • Network accessline 58
    - **DOI:** [10.1186/s13059-019-1658-7](https://doi.org/10.1186/s13059-019-1658-7)
  • Network accessline 71
    - **Citation:** Durand NC, Shamim MS, Machol I, Rao SSP, Huntley MH, Lander ES, Aiden EL. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Systems, 3(1):95-98, 2016. - **DOI:** [10.1016/j.cels.2016.07.
  • Network accessline 72
    - **DOI:** [10.1016/j.cels.2016.07.002](https://doi.org/10.1016/j.cels.2016.07.002)
  • Network accessline 81
    - **Citation:** Abdennur N, Mirny LA. Cooler: scalable storage for Hi-C data and other genomically labeled arrays. Bioinformatics, 36(1):311-316, 2020. - **DOI:** [10.1093/bioinformatics/btz540](https://doi.org/10.1093/bioinformatics/btz540
  • Network accessline 82
    - **DOI:** [10.1093/bioinformatics/btz540](https://doi.org/10.1093/bioinformatics/btz540)
  • Network accessline 91
    - **Citation:** Open2C, Abdennur N, Abraham S, Fudenberg G, Flyamer IM, Galitsyna AA, Goloborodko A, Imakaev M, Oksuz BA, Venev SV. Pairtools: from sequencing data to chromosome-level 3D maps. PLoS Computational Biology, 20(5):e1012164, 202
  • Network accessline 92
    - **DOI:** [10.1371/journal.pcbi.1012164](https://doi.org/10.1371/journal.pcbi.1012164)
  • Network accessline 101
    - **Citation:** Imakaev M, Fudenberg G, McCord RP, Naumova N, Goloborodko A, Lajoie BR, Dekker J, Mirny LA. Iterative correction of Hi-C data reveals hallmarks of chromosome organization. Nature Methods, 9(10):999-1003, 2012. - **DOI:** [10
  • Network accessline 102
    - **DOI:** [10.1038/nmeth.2148](https://doi.org/10.1038/nmeth.2148)
  • Network accessline 117
    - **DOI:** [10.1093/bioinformatics/btp324](https://doi.org/10.1093/bioinformatics/btp324) | **PMID:** 19451168 | **Citations:** ~25,000
  • Network accessline 124
    - **DOI:** [10.1093/bioinformatics/btp352](https://doi.org/10.1093/bioinformatics/btp352) | **PMID:** 19505943 | **Citations:** ~53,700
  • Network accessline 131
    - **DOI:** [10.1093/bioinformatics/btw354](https://doi.org/10.1093/bioinformatics/btw354) | **PMID:** 27312411 | **Citations:** ~6,800
  • Network accessline 138
    - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z) | **PMID:** 31249361 | **Citations:** ~1,372
  • Network accessline 145
    - **URL:** [https://www.bioinformatics.babraham.ac.uk/projects/fastqc/](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)

plugin/skills/pipeline-hic/scripts/Dockerfile

  • Executable scriptline 1
    Executable file
  • Network accessline 3
    LABEL maintainer="Dr. Alex M. Mawla, PhD <https://github.com/ammawla/encode-toolkit>"
  • Dangerous shell operationline 9
    RUN apt-get update && apt-get install -y --no-install-recommends      wget curl gzip pigz unzip      build-essential perl      python3 python3-pip python3-dev      openjdk-17-jre-headless      libncurses5-dev libbz2-dev liblzma-dev zlib1g-d
  • Network accessline 9
    RUN apt-get update && apt-get install -y --no-install-recommends \     wget curl gzip pigz unzip \
  • Network accessline 10
    wget curl gzip pigz unzip \
  • Dangerous shell operationline 16
    && rm -rf /var/lib/apt/lists/*
  • Dangerous shell operationline 19
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 &&      tar xjf samtools-1.19.tar.bz2 && cd samtools-1.19 &&      ./configure --prefix=/usr/local && make -j4 && make install &&      cd .. && rm
  • Network accessline 19
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 && \
  • Dangerous shell operationline 22
    cd .. && rm -rf samtools-1.19*
  • Dangerous shell operationline 25
    RUN wget -q https://github.com/lh3/bwa/archive/refs/tags/v0.7.18.tar.gz &&      tar xzf v0.7.18.tar.gz && cd bwa-0.7.18 &&      make -j4 && cp bwa /usr/local/bin/ &&      cd .. && rm -rf bwa-0.7.18 v0.7.18.tar.gz
  • Network accessline 25
    RUN wget -q https://github.com/lh3/bwa/archive/refs/tags/v0.7.18.tar.gz && \
  • Dangerous shell operationline 28
    cd .. && rm -rf bwa-0.7.18 v0.7.18.tar.gz
  • Network accessline 34
    RUN wget -q https://github.com/aidenlab/Juicebox/releases/download/v2.20.00/juicer_tools.2.20.00.jar \
  • Dangerous shell operationline 41
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip &&      unzip -q fastqc_v0.12.1.zip -d /opt/ && chmod +x /opt/FastQC/fastqc &&      ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip
  • Network accessline 41
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip && \
  • Dangerous shell operationline 43
    ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip
  • Dangerous shell operationline 49
    RUN pip3 install --no-cache-dir cutadapt==4.6 &&      wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz &&      tar xzf 0.6.10.tar.gz && ln -s /TrimGalore-0.6.10/trim_galore /usr/local/bin/ &&      rm 0.6.10.tar.gz
  • Network accessline 49
    RUN pip3 install --no-cache-dir cutadapt==4.6 && \     wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz && \
  • Network accessline 50
    wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz && \
  • Dangerous shell operationline 52
    rm 0.6.10.tar.gz

plugin/skills/pipeline-hic/scripts/main.nf

  • Executable scriptline 1
    #!/usr/bin/env nextflow
  • Dangerous shell operationline 195
    rm juicer_medium.txt

plugin/skills/pipeline-rnaseq/references/04-signal-tracks.md

  • Network accessline 81
    # Or fetch from UCSC wget https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.chrom.sizes
  • Network accessline 82
    wget https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.chrom.sizes

plugin/skills/pipeline-rnaseq/references/literature.md

  • Network accessline 14
    - **Citation:** ENCODE Project Consortium et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature, 583(7818):699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 15
    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 24
    - **Citation:** Hitz BC, Lee JW, Jolanki O, et al. The ENCODE Uniform Analysis Pipelines. Research Square (preprint), 2023. - **DOI:** [10.21203/rs.3.rs-311127/v1](https://doi.org/10.21203/rs.3.rs-311127/v1)
  • Network accessline 25
    - **DOI:** [10.21203/rs.3.rs-311127/v1](https://doi.org/10.21203/rs.3.rs-311127/v1)
  • Network accessline 33
    - **Citation:** Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, Szczesniak MW, Gaffney DJ, Elo LL, Zhang X, Mortazavi A. A survey of best practices for RNA-seq data analysis. Genome Biology, 17:13, 2016. - **DOI:*
  • Network accessline 34
    - **DOI:** [10.1186/s13059-016-0881-8](https://doi.org/10.1186/s13059-016-0881-8)
  • Network accessline 47
    - **Citation:** Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 29(1):15-21, 2013. - **DOI:** [10.1093/bioinformatics/bts635](https:
  • Network accessline 48
    - **DOI:** [10.1093/bioinformatics/bts635](https://doi.org/10.1093/bioinformatics/bts635)
  • Network accessline 57
    - **Citation:** Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics, 12:323, 2011. - **DOI:** [10.1186/1471-2105-12-323](https://doi.org/10.1186/1471-2105-12-323)
  • Network accessline 58
    - **DOI:** [10.1186/1471-2105-12-323](https://doi.org/10.1186/1471-2105-12-323)
  • Network accessline 67
    - **Citation:** Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nature Biotechnology, 34(5):525-527, 2016. - **DOI:** [10.1038/nbt.3519](https://doi.org/10.1038/nbt.3519)
  • Network accessline 68
    - **DOI:** [10.1038/nbt.3519](https://doi.org/10.1038/nbt.3519)
  • Network accessline 81
    - **Citation:** Wang L, Wang S, Li W. RSeQC: quality control of RNA-seq experiments. Bioinformatics, 28(16):2184-2185, 2012. - **DOI:** [10.1093/bioinformatics/bts356](https://doi.org/10.1093/bioinformatics/bts356)
  • Network accessline 82
    - **DOI:** [10.1093/bioinformatics/bts356](https://doi.org/10.1093/bioinformatics/bts356)
  • Network accessline 91
    - **Citation:** Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics, 32(19):3047-3048, 2016. - **DOI:** [10.1093/bioinformatics/btw354](https://doi.
  • Network accessline 92
    - **DOI:** [10.1093/bioinformatics/btw354](https://doi.org/10.1093/bioinformatics/btw354)
  • Network accessline 104
    - **Citation:** Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I, Berry A, Bignell A, Boix C, Carbonell Sala S, Cunningham F, Di Domenico T, Donaldson S, Fiddes IT, Garcia Giron
  • Network accessline 105
    - **DOI:** [10.1093/nar/gkaa1087](https://doi.org/10.1093/nar/gkaa1087)
  • Network accessline 120
    - **DOI:** [10.1093/bioinformatics/btp352](https://doi.org/10.1093/bioinformatics/btp352) | **PMID:** 19505943 | **Citations:** ~53,700
  • Network accessline 127
    - **URL:** [https://broadinstitute.github.io/picard/](https://broadinstitute.github.io/picard/)
  • Network accessline 134
    - **URL:** [https://www.bioinformatics.babraham.ac.uk/projects/fastqc/](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)
  • Network accessline 141
    - **DOI:** [10.14806/ej.17.1.200](https://doi.org/10.14806/ej.17.1.200) | **Citations:** ~13,000
  • Network accessline 148
    - **Citation:** Teng M, Love MI, Davis CA, Djebali S, Dobin A, Graveley BR, Li S, Mason CE, Olson S, Pervouchine D, Sloan CA, Wei X, Zhan L, Irizarry RA. A benchmark for RNA-seq quantification pipelines. Genome Biology, 17:74, 2016. - **DOI
  • Network accessline 149
    - **DOI:** [10.1186/s13059-016-0940-1](https://doi.org/10.1186/s13059-016-0940-1)

plugin/skills/pipeline-rnaseq/scripts/Dockerfile

  • Executable scriptline 1
    Executable file
  • Dangerous shell operationline 10
    RUN apt-get update && apt-get install -y --no-install-recommends      build-essential wget curl git ca-certificates perl unzip bc      zlib1g-dev libbz2-dev liblzma-dev libncurses5-dev libcurl4-openssl-dev      python3 python3-pip python3-d
  • Network accessline 10
    RUN apt-get update && apt-get install -y --no-install-recommends \     build-essential wget curl git ca-certificates perl unzip bc \
  • Network accessline 11
    build-essential wget curl git ca-certificates perl unzip bc \
  • Dangerous shell operationline 14
    && rm -rf /var/lib/apt/lists/*
  • Dangerous shell operationline 17
    RUN wget -q https://github.com/alexdobin/STAR/archive/refs/tags/2.7.11b.tar.gz &&      tar xzf 2.7.11b.tar.gz && cp STAR-2.7.11b/bin/Linux_x86_64_static/STAR /usr/local/bin/ &&      rm -rf STAR-2.7.11b 2.7.11b.tar.gz
  • Network accessline 17
    RUN wget -q https://github.com/alexdobin/STAR/archive/refs/tags/2.7.11b.tar.gz && \
  • Dangerous shell operationline 19
    rm -rf STAR-2.7.11b 2.7.11b.tar.gz
  • Dangerous shell operationline 22
    RUN wget -q https://github.com/deweylab/RSEM/archive/refs/tags/v1.3.3.tar.gz &&      tar xzf v1.3.3.tar.gz && cd RSEM-1.3.3 && make -j4 && make install &&      cd .. && rm -rf RSEM-1.3.3 v1.3.3.tar.gz
  • Network accessline 22
    RUN wget -q https://github.com/deweylab/RSEM/archive/refs/tags/v1.3.3.tar.gz && \
  • Dangerous shell operationline 24
    cd .. && rm -rf RSEM-1.3.3 v1.3.3.tar.gz
  • Dangerous shell operationline 27
    RUN wget -q https://github.com/pachterlab/kallisto/releases/download/v0.50.1/kallisto_linux-v0.50.1.tar.gz &&      tar xzf kallisto_linux-v0.50.1.tar.gz && cp kallisto/kallisto /usr/local/bin/ &&      rm -rf kallisto kallisto_linux-v0.50.1.
  • Network accessline 27
    RUN wget -q https://github.com/pachterlab/kallisto/releases/download/v0.50.1/kallisto_linux-v0.50.1.tar.gz && \
  • Dangerous shell operationline 29
    rm -rf kallisto kallisto_linux-v0.50.1.tar.gz
  • Dangerous shell operationline 32
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 &&      tar xjf samtools-1.19.tar.bz2 && cd samtools-1.19 &&      ./configure --prefix=/usr/local && make -j4 && make install &&      cd .. && rm
  • Network accessline 32
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 && \
  • Dangerous shell operationline 35
    cd .. && rm -rf samtools-1.19*
  • Network accessline 38
    RUN wget -q https://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/bedGraphToBigWig \
  • Dangerous shell operationline 46
    RUN wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz &&      tar xzf 0.6.10.tar.gz && ln -s /TrimGalore-0.6.10/trim_galore /usr/local/bin/ &&      rm 0.6.10.tar.gz
  • Network accessline 46
    RUN wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz && \
  • Dangerous shell operationline 48
    rm 0.6.10.tar.gz
  • Dangerous shell operationline 51
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip &&      unzip -q fastqc_v0.12.1.zip -d /opt/ && chmod +x /opt/FastQC/fastqc &&      ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip
  • Network accessline 51
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip && \
  • Dangerous shell operationline 53
    ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip

plugin/skills/pipeline-rnaseq/scripts/main.nf

  • Executable scriptline 1
    #!/usr/bin/env nextflow

plugin/skills/pipeline-wgbs/SKILL.md

  • Network accessline 228
    The primary output is per-CpG methylation in the ENCODE bedMethyl layout (https://www.encodeproject.org/data-standards/wgbs/):
  • Network accessline 229
    (https://www.encodeproject.org/data-standards/wgbs/):

plugin/skills/pipeline-wgbs/references/04-methylation-calling.md

  • Network accessline 3
    MethylDackel (formerly PileOMeth) extracts per-CpG methylation levels from bisulfite-aligned BAM files. This workflow then converts its bedGraph output to the ENCODE bedMethyl layout (https://www.encodeproject.org/data-standards/wgbs/).
  • Network accessline 5
    ENCODE bedMethyl layout (https://www.encodeproject.org/data-standards/wgbs/).

plugin/skills/pipeline-wgbs/references/literature.md

  • Network accessline 14
    - **Citation:** Lister R, Pelizzola M, Dowen RH, Hawkins RD, Hon G, Tonti-Filippini J, Nott JR, Lee L, Ye Z, Ngo QM, Edsall L, Antosiewicz-Bourget J, Stewart R, Ruotti V, Millar AH, Thomson JA, Ren B, Ecker JR. Human DNA methylomes at base
  • Network accessline 15
    - **DOI:** [10.1038/nature08514](https://doi.org/10.1038/nature08514)
  • Network accessline 24
    - **Citation:** Schultz MD, He Y, Whitaker JW, Hariharan M, Mukamel EA, Leung D, Rajagopal N, Nery JR, Urich MA, Chen H, Lin S, Lin Y, Jung I, Schmitt AD, Selvaraj S, Ren B, Sejnowski TJ, Wang W, Ecker JR. Human body epigenome maps reveal n
  • Network accessline 25
    - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248)
  • Network accessline 34
    - **Citation:** ENCODE Project Consortium et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature, 583(7818):699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 35
    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 44
    - **Citation:** Hitz BC, Lee JW, Jolanki O, et al. The ENCODE Uniform Analysis Pipelines. Research Square (preprint), 2023. - **DOI:** [10.21203/rs.3.rs-311127/v1](https://doi.org/10.21203/rs.3.rs-311127/v1)
  • Network accessline 45
    - **DOI:** [10.21203/rs.3.rs-311127/v1](https://doi.org/10.21203/rs.3.rs-311127/v1)
  • Network accessline 53
    - **Citation:** Foox J, Nordlund J, Lalonde E, Lim S, Deschamps-Francoeur G, Trognitz F, Holmes M, Close MC, et al. Performance assessment of DNA sequencing platforms in the ABRF Next-Generation Sequencing Study. Nature Biotechnology, 39(9)
  • Network accessline 54
    - **DOI:** [10.1038/s41587-021-01049-5](https://doi.org/10.1038/s41587-021-01049-5)
  • Network accessline 67
    - **Citation:** Krueger F, Andrews SR. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics, 27(11):1571-1572, 2011. - **DOI:** [10.1093/bioinformatics/btr167](https://doi.org/10.1093/bioinformat
  • Network accessline 68
    - **DOI:** [10.1093/bioinformatics/btr167](https://doi.org/10.1093/bioinformatics/btr167)
  • Network accessline 77
    - **Citation:** Pedersen BS, Eyring K, De S, Yang IV, Schwartz DA. Fast and accurate alignment of long bisulfite-seq reads. arXiv:1401.1129, 2014. - **DOI:** [10.48550/arXiv.1401.1129](https://doi.org/10.48550/arXiv.1401.1129)
  • Network accessline 78
    - **DOI:** [10.48550/arXiv.1401.1129](https://doi.org/10.48550/arXiv.1401.1129)
  • Network accessline 86
    - **Citation:** Ryan DP. MethylDackel: A (mostly) universal methylation extractor for BS-seq experiments. GitHub, 2023. - **URL:** [https://github.com/dpryan79/MethylDackel](https://github.com/dpryan79/MethylDackel)
  • Network accessline 87
    - **URL:** [https://github.com/dpryan79/MethylDackel](https://github.com/dpryan79/MethylDackel)
  • Network accessline 98
    - **Citation:** Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics, 32(19):3047-3048, 2016. - **DOI:** [10.1093/bioinformatics/btw354](https://doi.
  • Network accessline 99
    - **DOI:** [10.1093/bioinformatics/btw354](https://doi.org/10.1093/bioinformatics/btw354)
  • Network accessline 113
    - **DOI:** [10.1093/bioinformatics/btp352](https://doi.org/10.1093/bioinformatics/btp352) | **PMID:** 19505943 | **Citations:** ~53,700
  • Network accessline 120
    - **URL:** [https://broadinstitute.github.io/picard/](https://broadinstitute.github.io/picard/)
  • Network accessline 127
    - **URL:** [https://www.bioinformatics.babraham.ac.uk/projects/fastqc/](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)
  • Network accessline 134
    - **DOI:** [10.14806/ej.17.1.200](https://doi.org/10.14806/ej.17.1.200) | **Citations:** ~13,000
  • Network accessline 141
    - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z) | **PMID:** 31249361 | **Citations:** ~1,372
  • Network accessline 152
    - **Citation:** Hansen KD, Langmead B, Irizarry RA. BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biology, 13(10):R83, 2012. - **DOI:** [10.1186/gb-2012-13-10-r83](https://doi.org/10.1186
  • Network accessline 153
    - **DOI:** [10.1186/gb-2012-13-10-r83](https://doi.org/10.1186/gb-2012-13-10-r83)
  • Network accessline 162
    - **Citation:** Peters TJ, Buckley MJ, Statham AL, Pidsley R, Samaras K, Lord RV, Clark SJ, Molloy PL. De novo identification of differentially methylated regions in the human genome. Epigenetics & Chromatin, 8:6, 2015. - **DOI:** [10.1186/
  • Network accessline 163
    - **DOI:** [10.1186/1756-8935-8-6](https://doi.org/10.1186/1756-8935-8-6)

plugin/skills/pipeline-wgbs/scripts/Dockerfile

  • Executable scriptline 1
    Executable file
  • Network accessline 5
    LABEL maintainer="Dr. Alex M. Mawla, PhD <https://github.com/ammawla/encode-toolkit>"
  • Dangerous shell operationline 11
    RUN apt-get update && apt-get install -y --no-install-recommends      wget curl unzip gzip pigz      build-essential perl      python3 python3-pip      openjdk-17-jre-headless      libncurses5-dev libbz2-dev liblzma-dev zlib1g-dev libcurl4-
  • Network accessline 11
    RUN apt-get update && apt-get install -y --no-install-recommends \     wget curl unzip gzip pigz \
  • Network accessline 12
    wget curl unzip gzip pigz \
  • Dangerous shell operationline 18
    && rm -rf /var/lib/apt/lists/*
  • Dangerous shell operationline 21
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 &&      tar xjf samtools-1.19.tar.bz2 && cd samtools-1.19 &&      ./configure --prefix=/usr/local && make -j4 && make install &&      cd .. && rm
  • Network accessline 21
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 && \
  • Dangerous shell operationline 24
    cd .. && rm -rf samtools-1.19*
  • Dangerous shell operationline 27
    RUN wget -q https://github.com/samtools/htslib/releases/download/1.19/htslib-1.19.tar.bz2 &&      tar xjf htslib-1.19.tar.bz2 && cd htslib-1.19 &&      ./configure --prefix=/usr/local && make -j4 && make install && ldconfig &&      cd .. &&
  • Network accessline 27
    RUN wget -q https://github.com/samtools/htslib/releases/download/1.19/htslib-1.19.tar.bz2 && \
  • Dangerous shell operationline 30
    cd .. && rm -rf htslib-1.19*
  • Dangerous shell operationline 33
    RUN wget -q https://github.com/BenLangmead/bowtie2/releases/download/v2.5.4/bowtie2-2.5.4-linux-x86_64.zip &&      unzip -q bowtie2-2.5.4-linux-x86_64.zip -d /opt/ &&      ln -s /opt/bowtie2-2.5.4-linux-x86_64/bowtie2* /usr/local/bin/ &&
  • Network accessline 33
    RUN wget -q https://github.com/BenLangmead/bowtie2/releases/download/v2.5.4/bowtie2-2.5.4-linux-x86_64.zip && \
  • Dangerous shell operationline 36
    rm bowtie2-2.5.4-linux-x86_64.zip
  • Dangerous shell operationline 39
    RUN wget -q https://github.com/FelixKrueger/Bismark/archive/v0.24.2.tar.gz &&      tar xzf v0.24.2.tar.gz && mv Bismark-0.24.2 /opt/bismark &&      ln -s /opt/bismark/bismark /usr/local/bin/ &&      ln -s /opt/bismark/deduplicate_bismark /u
  • Network accessline 39
    RUN wget -q https://github.com/FelixKrueger/Bismark/archive/v0.24.2.tar.gz && \
  • Dangerous shell operationline 44
    rm v0.24.2.tar.gz
  • Dangerous shell operationline 47
    RUN pip3 install --no-cache-dir cutadapt==4.6 &&      wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz &&      tar xzf 0.6.10.tar.gz && ln -s /TrimGalore-0.6.10/trim_galore /usr/local/bin/ &&      rm 0.6.10.tar.gz
  • Network accessline 47
    RUN pip3 install --no-cache-dir cutadapt==4.6 && \     wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz && \
  • Network accessline 48
    wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz && \
  • Dangerous shell operationline 50
    rm 0.6.10.tar.gz
  • Dangerous shell operationline 53
    RUN wget -q https://github.com/dpryan79/MethylDackel/archive/0.6.1.tar.gz &&      tar xzf 0.6.1.tar.gz && cd MethylDackel-0.6.1 &&      make CFLAGS="-O3 -Wall" LIBBIGWIG="-lBigWig" && cp MethylDackel /usr/local/bin/ &&      cd .. && rm -rf
  • Network accessline 53
    RUN wget -q https://github.com/dpryan79/MethylDackel/archive/0.6.1.tar.gz && \
  • Dangerous shell operationline 56
    cd .. && rm -rf MethylDackel-0.6.1*
  • Dangerous shell operationline 59
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip &&      unzip -q fastqc_v0.12.1.zip -d /opt/ && chmod +x /opt/FastQC/fastqc &&      ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip
  • Network accessline 59
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip && \
  • Dangerous shell operationline 61
    ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip

plugin/skills/pipeline-wgbs/scripts/main.nf

  • Executable scriptline 1
    #!/usr/bin/env nextflow
  • Network accessline 192
    # Convert each context to ENCODE bedMethyl (https://www.encodeproject.org/data-standards/wgbs/):

plugin/skills/publication-trust/references/literature.md

  • Network accessline 12
    - **Citation:** Ioannidis JPA. Why most published research findings are false. *PLoS Medicine*, 2(8), e124, 2005. - **DOI:** [10.1371/journal.pmed.0020124](https://doi.org/10.1371/journal.pmed.0020124)
  • Network accessline 13
    - **DOI:** [10.1371/journal.pmed.0020124](https://doi.org/10.1371/journal.pmed.0020124)
  • Network accessline 22
    - **Citation:** Baker M. 1,500 scientists lift the lid on reproducibility. *Nature*, 533(7604), 452-454, 2016. - **DOI:** [10.1038/533452a](https://doi.org/10.1038/533452a)
  • Network accessline 23
    - **DOI:** [10.1038/533452a](https://doi.org/10.1038/533452a)
  • Network accessline 32
    - **Citation:** Steen RG, Casadevall A, Fang FC. Why has the number of scientific retractions increased? *PLoS ONE*, 8(7), e68397, 2013. - **DOI:** [10.1371/journal.pone.0068397](https://doi.org/10.1371/journal.pone.0068397)
  • Network accessline 33
    - **DOI:** [10.1371/journal.pone.0068397](https://doi.org/10.1371/journal.pone.0068397)
  • Network accessline 42
    - **Citation:** Bik EM, Casadevall A, Fang FC. The prevalence of inappropriate image duplication in biomedical research publications. *mBio*, 7(3), e00809-16, 2016. - **DOI:** [10.1128/mBio.00809-16](https://doi.org/10.1128/mBio.00809-16)
  • Network accessline 43
    - **DOI:** [10.1128/mBio.00809-16](https://doi.org/10.1128/mBio.00809-16)
  • Network accessline 54
    - **Citation:** Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, Szczesniak MW, Gaffney DJ, Elo LL, Zhang X, Mortazavi A. A survey of best practices for RNA-seq data analysis. *Genome Biology*, 17, 13, 2016. - **DO
  • Network accessline 55
    - **DOI:** [10.1186/s13059-016-0881-8](https://doi.org/10.1186/s13059-016-0881-8)
  • Network accessline 64
    - **Citation:** Landt SG, Marinov GK, Kundaje A, Kheradpour P, Pauli F, Batzoglou S, Bernstein BE, Bickel P, Brown JB, Cayting P, Chen Y, DeSalvo G, Epstein C, Fisher-Aylor KI, Euskirchen G, Gerstein M, Gertz J, Hartemink AJ, Hoffman MM, Iy
  • Network accessline 65
    - **DOI:** [10.1101/gr.136184.111](https://doi.org/10.1101/gr.136184.111)
  • Network accessline 74
    - **Citation:** Amemiya HM, Kundaje A, Boyle AP. The ENCODE Blacklist: identification of problematic regions of the genome. *Scientific Reports*, 9(1), 9354, 2019. - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-4
  • Network accessline 75
    - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z)

plugin/skills/quality-assessment/SKILL.md

  • Network accessline 268
    CUT&RUN and CUT&Tag generate artifacts at specific genomic regions (distinct from the ENCODE Blacklist). These are regions with apparent enrichment that is not target-specific: - Use the CUT&RUN suspect list (Nordin et al. 2023) IN ADDITION
  • Network accessline 269
    - Use the CUT&RUN suspect list (Nordin et al. 2023) IN ADDITION to the ENCODE Blacklist - Available at: https://github.com/Boyle-Lab/CUT-RUN_suspect_list - Particularly important for H3K4me3 and H3K27me3 CUT&RUN data
  • Network accessline 270
    - Available at: https://github.com/Boyle-Lab/CUT-RUN_suspect_list
  • Network accessline 348
    Before interpreting any peak-based quality metric, confirm that the ENCODE Blacklist has been applied: - **Blacklist v2 regions**: High-signal artifacts (satellite repeats, centromeric regions, high-copy sequences) - **Available at**: https
  • Network accessline 349
    - **Blacklist v2 regions**: High-signal artifacts (satellite repeats, centromeric regions, high-copy sequences) - **Available at**: https://github.com/Boyle-Lab/Blacklist/ - **hg38**: `hg38-blacklist.v2.bed.gz` (910 regions)
  • Network accessline 350
    - **Available at**: https://github.com/Boyle-Lab/Blacklist/

plugin/skills/quality-assessment/references/literature.md

  • Network accessline 38
    ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia.   *Genome Research*, 22(9), 1813-1831, 2012. - **DOI:** [10.1101/gr.136184.111](https://doi.org/10.1101/gr.136184.111)
  • Network accessline 39
    *Genome Research*, 22(9), 1813-1831, 2012. - **DOI:** [10.1101/gr.136184.111](https://doi.org/10.1101/gr.136184.111) - **PMID:** 22955991 | **PMC:** PMC3431496
  • Network accessline 40
    - **DOI:** [10.1101/gr.136184.111](https://doi.org/10.1101/gr.136184.111)
  • Network accessline 59
    - **Citation:** Marinov GK, Kundaje A, Park PJ, Wold BJ. Large-scale quality analysis   of published ChIP-seq data. *G3: Genes, Genomes, Genetics*, 4(2), 209-223, 2014. - **DOI:** [10.1534/g3.113.008680](https://doi.org/10.1534/g3.113.00868
  • Network accessline 60
    of published ChIP-seq data. *G3: Genes, Genomes, Genetics*, 4(2), 209-223, 2014. - **DOI:** [10.1534/g3.113.008680](https://doi.org/10.1534/g3.113.008680) - **PMID:** 24347632 | **PMC:** PMC3931556
  • Network accessline 61
    - **DOI:** [10.1534/g3.113.008680](https://doi.org/10.1534/g3.113.008680)
  • Network accessline 78
    removal on ChIP quality metrics in ChIP-seq and ChIP-exo data. *Frontiers in Genetics*,   5, 75, 2014. - **DOI:** [10.3389/fgene.2014.00075](https://doi.org/10.3389/fgene.2014.00075)
  • Network accessline 79
    5, 75, 2014. - **DOI:** [10.3389/fgene.2014.00075](https://doi.org/10.3389/fgene.2014.00075) - **PMID:** 24782889 | **PMC:** PMC3988386
  • Network accessline 80
    - **DOI:** [10.3389/fgene.2014.00075](https://doi.org/10.3389/fgene.2014.00075)
  • Network accessline 105
    native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding   proteins, and nucleosome position. *Nature Methods*, 10(12), 1213-1218, 2013. - **DOI:** [10.1038/nmeth.2688](https://doi.org/10.1038/nmeth.2688)
  • Network accessline 106
    proteins, and nucleosome position. *Nature Methods*, 10(12), 1213-1218, 2013. - **DOI:** [10.1038/nmeth.2688](https://doi.org/10.1038/nmeth.2688) - **PMID:** 24097267 | **PMC:** PMC3959825
  • Network accessline 107
    - **DOI:** [10.1038/nmeth.2688](https://doi.org/10.1038/nmeth.2688)
  • Network accessline 123
    - **Citation:** Yan F, Powell DR, Curtis DJ, Wong NC. From reads to insight: a hitchhiker's   guide to ATAC-seq data analysis. *Genome Biology*, 21(1), 22, 2020. - **DOI:** [10.1186/s13059-020-1929-3](https://doi.org/10.1186/s13059-020-1929
  • Network accessline 124
    guide to ATAC-seq data analysis. *Genome Biology*, 21(1), 22, 2020. - **DOI:** [10.1186/s13059-020-1929-3](https://doi.org/10.1186/s13059-020-1929-3) - **PMID:** 32014034 | **PMC:** PMC6996192
  • Network accessline 125
    - **DOI:** [10.1186/s13059-020-1929-3](https://doi.org/10.1186/s13059-020-1929-3)
  • Network accessline 155
    Szczesniak MW, Gaffney DJ, Elo LL, Zhang X, Mortazavi A. A survey of best practices for   RNA-seq data analysis. *Genome Biology*, 17, 13, 2016. - **DOI:** [10.1186/s13059-016-0881-8](https://doi.org/10.1186/s13059-016-0881-8)
  • Network accessline 156
    RNA-seq data analysis. *Genome Biology*, 17, 13, 2016. - **DOI:** [10.1186/s13059-016-0881-8](https://doi.org/10.1186/s13059-016-0881-8) - **PMID:** 26813401 | **PMC:** PMC4728800
  • Network accessline 157
    - **DOI:** [10.1186/s13059-016-0881-8](https://doi.org/10.1186/s13059-016-0881-8)
  • Network accessline 189
    Mason CE. The SEQC2 epigenomics quality control (EpiQC) study. *Genome Biology*,   22, 307, 2021. - **DOI:** [10.1186/s13059-021-02529-2](https://doi.org/10.1186/s13059-021-02529-2)
  • Network accessline 190
    22, 307, 2021. - **DOI:** [10.1186/s13059-021-02529-2](https://doi.org/10.1186/s13059-021-02529-2) - **PMID:** 34872606 | **PMC:** PMC8650396
  • Network accessline 191
    - **DOI:** [10.1186/s13059-021-02529-2](https://doi.org/10.1186/s13059-021-02529-2)
  • Network accessline 210
    - **Citation:** Schwartzman O, Tanay A. Single-cell epigenomics: techniques and emerging   applications. *Nature Reviews Genetics*, 16, 716-726, 2015. - **DOI:** [10.1038/nrg3980](https://doi.org/10.1038/nrg3980)
  • Network accessline 211
    applications. *Nature Reviews Genetics*, 16, 716-726, 2015. - **DOI:** [10.1038/nrg3980](https://doi.org/10.1038/nrg3980) - **PMID:** 26460349
  • Network accessline 212
    - **DOI:** [10.1038/nrg3980](https://doi.org/10.1038/nrg3980)
  • Network accessline 239
    Noble WS, Mirny LA. Measuring the reproducibility and quality of Hi-C data.   *Genome Biology*, 20(1), 57, 2019. - **DOI:** [10.1186/s13059-019-1658-7](https://doi.org/10.1186/s13059-019-1658-7)
  • Network accessline 240
    *Genome Biology*, 20(1), 57, 2019. - **DOI:** [10.1186/s13059-019-1658-7](https://doi.org/10.1186/s13059-019-1658-7) - **PMID:** 30890172 | **PMC:** PMC6423771
  • Network accessline 241
    - **DOI:** [10.1186/s13059-019-1658-7](https://doi.org/10.1186/s13059-019-1658-7)
  • Network accessline 270
    - **Citation:** Nordin A, Zambanini G, Pagella P, Cantu C. The CUT&RUN suspect list of   problematic regions of the genome. *Genome Biology*, 24(1), 185, 2023. - **DOI:** [10.1186/s13059-023-03027-3](https://doi.org/10.1186/s13059-023-03027
  • Network accessline 271
    problematic regions of the genome. *Genome Biology*, 24(1), 185, 2023. - **DOI:** [10.1186/s13059-023-03027-3](https://doi.org/10.1186/s13059-023-03027-3) - **PMID:** 37563719 | **PMC:** PMC10416431
  • Network accessline 272
    - **DOI:** [10.1186/s13059-023-03027-3](https://doi.org/10.1186/s13059-023-03027-3)
  • Network accessline 297
    - **Citation:** Amemiya HM, Kundaje A, Boyle AP. The ENCODE Blacklist: identification of   problematic regions of the genome. *Scientific Reports*, 9(1), 9354, 2019. - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019
  • Network accessline 298
    problematic regions of the genome. *Scientific Reports*, 9(1), 9354, 2019. - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z) - **PMID:** 31249361 | **PMC:** PMC6597582
  • Network accessline 299
    - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z)
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plugin/skills/regulatory-elements/SKILL.md

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    - **Yao et al. 2024** (Nature Methods, ~26 citations): ENCODE4 Functional Characterization Centers — 108 CRISPRi screens, >540,000 perturbations. Pre-designed sgRNAs targeting 3.27M ENCODE SCREEN cCREs. [DOI](https://doi.org/10.1038/s41592-
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    **ENCODE Blacklist filtering (required)**: Before using any peak or signal files, remove regions overlapping the ENCODE Blacklist (Amemiya et al. 2019, Scientific Reports, 1,372 citations). Blacklisted regions produce artifactual signal in
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plugin/skills/scientific-writing/SKILL.md

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    MethylDackel (v[version]; https://github.com/dpryan79/MethylDackel)
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    the ENCODE Project portal (https://www.encodeproject.org) under the
  • Network accessline 519
    Gene Expression Omnibus (GEO) under accession [GSE number]. Processed data are available at the same accession. Previously published ENCODE data used in this study are available at https://www.encodeproject.org
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    data are available at the same accession. Previously published ENCODE data used in this study are available at https://www.encodeproject.org under accessions listed in Supplementary Table S1.
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    | Juicer | Durand NC et al. | Cell Systems | 2016 | 10.1016/j.cels.2016.07.002 |

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plugin/skills/scrna-meta-analysis/SKILL.md

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    Expanded encyclopaedias of DNA elements in the human and mouse genomes. *Nature*,   583(7818), 699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 79
    583(7818), 699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4) - **PMID:** 32728249 | **PMC:** PMC7410828
  • Network accessline 80
    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 113
    Narayanan AK, Ho M, Lee BT, et al. ENCODE data at the ENCODE portal. *Nucleic Acids   Research*, 44(D1), D726-D732, 2016. - **DOI:** [10.1093/nar/gkv1160](https://doi.org/10.1093/nar/gkv1160)
  • Network accessline 114
    Research*, 44(D1), D726-D732, 2016. - **DOI:** [10.1093/nar/gkv1160](https://doi.org/10.1093/nar/gkv1160) - **PMID:** 26527727 | **PMC:** PMC4702836
  • Network accessline 115
    - **DOI:** [10.1093/nar/gkv1160](https://doi.org/10.1093/nar/gkv1160)
  • Network accessline 135
    Strattan JS, Jolanki O, Tanaka FY, Cherry JM. The Encyclopedia of DNA elements (ENCODE):   data portal update. *Nucleic Acids Research*, 46(D1), D794-D801, 2018. - **DOI:** [10.1093/nar/gkx1081](https://doi.org/10.1093/nar/gkx1081)
  • Network accessline 136
    data portal update. *Nucleic Acids Research*, 46(D1), D794-D801, 2018. - **DOI:** [10.1093/nar/gkx1081](https://doi.org/10.1093/nar/gkx1081) - **PMID:** 29126249 | **PMC:** PMC5753278
  • Network accessline 137
    - **DOI:** [10.1093/nar/gkx1081](https://doi.org/10.1093/nar/gkx1081)
  • Network accessline 162
    the Encyclopedia of DNA Elements (ENCODE) data portal. *Nucleic Acids Research*, 48(D1),   D882-D889, 2020. - **DOI:** [10.1093/nar/gkz1062](https://doi.org/10.1093/nar/gkz1062)
  • Network accessline 163
    D882-D889, 2020. - **DOI:** [10.1093/nar/gkz1062](https://doi.org/10.1093/nar/gkz1062) - **PMID:** 31713622 | **PMC:** PMC7061942
  • Network accessline 164
    - **DOI:** [10.1093/nar/gkz1062](https://doi.org/10.1093/nar/gkz1062)
  • Network accessline 184
    encyclopaedias of DNA elements in the human and mouse genomes. *Nature*, 583(7818),   699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 185
    699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4) - **PMID:** 32728248 | **PMC:** PMC7410830
  • Network accessline 186
    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 206
    encyclopaedias of DNA elements in the human and mouse genomes. *Nature*, 583(7818),   693-698, 2020. - **DOI:** [10.1038/s41586-020-2489-0](https://doi.org/10.1038/s41586-020-2489-0)
  • Network accessline 207
    693-698, 2020. - **DOI:** [10.1038/s41586-020-2489-0](https://doi.org/10.1038/s41586-020-2489-0) - **PMID:** 32728247 | **PMC:** PMC7410826
  • Network accessline 208
    - **DOI:** [10.1038/s41586-020-2489-0](https://doi.org/10.1038/s41586-020-2489-0)

plugin/skills/setup/SKILL.md

  • Network accessline 121
    1. Get API credentials from https://www.encodeproject.org/profile/ (requires ENCODE account)
  • Access to secretsline 127
    [Credential-related source omitted]
  • Network accessline 234
    |---------|-------|-----| | "Server not found" | Claude not restarted after config change | Restart Claude Desktop / reload Claude Code | | "uvx not found" | uv not installed | `curl -LsSf https://astral.sh/uv/install.sh \| sh` |
  • Network accessline 235
    | "Server not found" | Claude not restarted after config change | Restart Claude Desktop / reload Claude Code | | "uvx not found" | uv not installed | `curl -LsSf https://astral.sh/uv/install.sh \| sh` | | Timeout errors | Slow connection o
  • Dangerous shell operationline 236
    | "uvx not found" | uv not installed | `curl -LsSf https://astral.sh/uv/install.sh \| sh` |
  • Network accessline 236
    | "uvx not found" | uv not installed | `curl -LsSf https://astral.sh/uv/install.sh \| sh` |

plugin/skills/setup/references/literature.md

  • Network accessline 78
    Strattan JS, Jolanki O, Tanaka FY, Cherry JM. The Encyclopedia of DNA elements (ENCODE):   data portal update. *Nucleic Acids Research*, 46(D1), D794-D801, 2018. - **DOI:** [10.1093/nar/gkx1081](https://doi.org/10.1093/nar/gkx1081)
  • Network accessline 79
    data portal update. *Nucleic Acids Research*, 46(D1), D794-D801, 2018. - **DOI:** [10.1093/nar/gkx1081](https://doi.org/10.1093/nar/gkx1081) - **PMID:** 29126249 | **PMC:** PMC5753278
  • Network accessline 80
    - **DOI:** [10.1093/nar/gkx1081](https://doi.org/10.1093/nar/gkx1081)
  • Access to secretsline 93
    [Credential-related source omitted]

plugin/skills/single-cell-encode/SKILL.md

  • Network accessline 118
    **ENCODE Blacklist filtering (required for scATAC-seq)**: Before any downstream analysis of scATAC-seq peaks or fragments, remove reads/peaks overlapping ENCODE Blacklist regions (Amemiya et al. 2019, Scientific Reports, 1,372 citations). T

plugin/skills/single-cell-encode/references/literature.md

  • Network accessline 12
    - **Citation:** Zheng GXY, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, Ziraldo SB, Wheeler TD, McDermott GP, Zhu J, Gregory MT, Shuga J, Montesclaros L, Underwood JG, Masquelier DA, Nishimura SY, Schnall-Levin M, Wyatt PW, Hindson C
  • Network accessline 13
    - **DOI:** [10.1038/ncomms14049](https://doi.org/10.1038/ncomms14049)
  • Network accessline 22
    - **Citation:** Cusanovich DA, Hill AJ, Aghamirzaie D, Daza RM, Pliner HA, Berletch JB, Filippova GN, Huang X, Christiansen L, DeWitt WS, Lee C, Regalado SG, Read DF, Steemers FJ, Disteche CM, Trapnell C, Shendure J. A single-cell atlas of
  • Network accessline 23
    - **DOI:** [10.1016/j.cell.2018.06.052](https://doi.org/10.1016/j.cell.2018.06.052)
  • Network accessline 32
    - **Citation:** Buenrostro JD, Corces MR, Lareau CA, Wu B, Schep AN, Aryee MJ, Majeti R, Chang HY, Greenleaf WJ. Integrated single-cell analysis maps the continuous regulatory landscape of human hematopoietic differentiation. *Cell*, 173(6)
  • Network accessline 33
    - **DOI:** [10.1016/j.cell.2018.03.074](https://doi.org/10.1016/j.cell.2018.03.074)
  • Network accessline 44
    - **Citation:** Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM III, Hao Y, Stoeckius M, Smibert P, Satija R. Comprehensive integration of single-cell data. *Cell*, 177(7), 1888-1902.e21, 2019. - **DOI:** [10.1016/j.cell.
  • Network accessline 45
    - **DOI:** [10.1016/j.cell.2019.05.031](https://doi.org/10.1016/j.cell.2019.05.031)
  • Network accessline 54
    - **Citation:** Wolf FA, Angerer P, Theis FJ. SCANPY: large-scale single-cell gene expression data analysis. *Genome Biology*, 19(1), 15, 2018. - **DOI:** [10.1186/s13059-017-1382-0](https://doi.org/10.1186/s13059-017-1382-0)
  • Network accessline 55
    - **DOI:** [10.1186/s13059-017-1382-0](https://doi.org/10.1186/s13059-017-1382-0)
  • Network accessline 64
    - **Citation:** Granja JM, Corces MR, Pierce SE, Bagdatli ST, Chouber H, Zheng H, Zhu M, Dumm RE, Chang HY, Greenleaf WJ. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. *Nature Genetics*,
  • Network accessline 65
    - **DOI:** [10.1038/s41588-021-00790-6](https://doi.org/10.1038/s41588-021-00790-6)
  • Network accessline 76
    - **Citation:** Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, Baglaenko Y, Brenner M, Loh PR, Raychaudhuri S. Fast, sensitive and accurate integration of single-cell data with Harmony. *Nature Methods*, 16(12), 1289-1296, 20
  • Network accessline 77
    - **DOI:** [10.1038/s41592-019-0619-0](https://doi.org/10.1038/s41592-019-0619-0)
  • Network accessline 86
    - **Citation:** Luecken MD, Theis FJ. Current best practices in single-cell RNA-seq analysis: a tutorial. *Molecular Systems Biology*, 15(6), e8746, 2019. - **DOI:** [10.15252/msb.20188746](https://doi.org/10.15252/msb.20188746)
  • Network accessline 87
    - **DOI:** [10.15252/msb.20188746](https://doi.org/10.15252/msb.20188746)
  • Network accessline 98
    - **Citation:** Mawla AM, Huising MO. Navigating the depths and avoiding the shallows of pancreatic islet cell transcriptomes. *JCI Insight*, 4(17), e131661, 2019. - **DOI:** [10.1172/jci.insight.131661](https://doi.org/10.1172/jci.insight.
  • Network accessline 99
    - **DOI:** [10.1172/jci.insight.131661](https://doi.org/10.1172/jci.insight.131661)
  • Network accessline 108
    - **Citation:** The Tabula Sapiens Consortium, Jones RC, Karkanias J, Krasnow MA, Pisco AO, Quake SR, Salzman J, Yosef N, Bber B, Beechem J, Bein D, Bui T, Burber M, Chen J, Cho P, Choi M, D'Angelo NJ, D'Augustin RC, Danaher T, Debnath M, D
  • Network accessline 109
    - **DOI:** [10.1126/science.abl4896](https://doi.org/10.1126/science.abl4896)

plugin/skills/track-experiments/SKILL.md

  • Network accessline 60
    | `replication_type` | Replicate strategy | isogenic, anisogenic | | `life_stage` | Developmental stage | adult, embryonic, child | | `url` | ENCODE Portal URL | https://www.encodeproject.org/experiments/ENCSR123ABC/ |
  • Network accessline 61
    | `life_stage` | Developmental stage | adult, embryonic, child | | `url` | ENCODE Portal URL | https://www.encodeproject.org/experiments/ENCSR123ABC/ | | `notes` | User-provided notes | H3K27ac reference for islet enhancer study |
  • Network accessline 62
    | `url` | ENCODE Portal URL | https://www.encodeproject.org/experiments/ENCSR123ABC/ |

plugin/skills/track-experiments/references/literature.md

  • Network accessline 32
    FAIR Guiding Principles for scientific data management and stewardship. *Scientific Data*,   3, 160018, 2016. - **DOI:** [10.1038/sdata.2016.18](https://doi.org/10.1038/sdata.2016.18)
  • Network accessline 33
    3, 160018, 2016. - **DOI:** [10.1038/sdata.2016.18](https://doi.org/10.1038/sdata.2016.18) - **PMID:** 26978244 | **PMC:** PMC4792175
  • Network accessline 34
    - **DOI:** [10.1038/sdata.2016.18](https://doi.org/10.1038/sdata.2016.18)
  • Network accessline 60
    - **Citation:** Baker M. 1,500 scientists lift the lid on reproducibility. *Nature*, 533(7604),   452-454, 2016. - **DOI:** [10.1038/533452a](https://doi.org/10.1038/533452a)
  • Network accessline 61
    452-454, 2016. - **DOI:** [10.1038/533452a](https://doi.org/10.1038/533452a) - **PMID:** 27225100
  • Network accessline 62
    - **DOI:** [10.1038/533452a](https://doi.org/10.1038/533452a)
  • Network accessline 83
    Ioannidis JP, Taufer M. Enhancing reproducibility for computational methods. *Science*,   354(6317), 1240-1241, 2016. - **DOI:** [10.1126/science.aah6168](https://doi.org/10.1126/science.aah6168)
  • Network accessline 84
    354(6317), 1240-1241, 2016. - **DOI:** [10.1126/science.aah6168](https://doi.org/10.1126/science.aah6168) - **PMID:** 27940837
  • Network accessline 85
    - **DOI:** [10.1126/science.aah6168](https://doi.org/10.1126/science.aah6168)
  • Network accessline 117
    Strattan JS, Jolanki O, Tanaka FY, Cherry JM. The Encyclopedia of DNA elements (ENCODE):   data portal update. *Nucleic Acids Research*, 46(D1), D794-D801, 2018. - **DOI:** [10.1093/nar/gkx1081](https://doi.org/10.1093/nar/gkx1081)
  • Network accessline 118
    data portal update. *Nucleic Acids Research*, 46(D1), D794-D801, 2018. - **DOI:** [10.1093/nar/gkx1081](https://doi.org/10.1093/nar/gkx1081) - **PMID:** 29126249 | **PMC:** PMC5753278
  • Network accessline 119
    - **DOI:** [10.1093/nar/gkx1081](https://doi.org/10.1093/nar/gkx1081)
  • Network accessline 147
    information about a microarray experiment (MIAME)-toward standards for microarray data.   *Nature Genetics*, 29(4), 365-371, 2001. - **DOI:** [10.1038/ng1201-365](https://doi.org/10.1038/ng1201-365)
  • Network accessline 148
    *Nature Genetics*, 29(4), 365-371, 2001. - **DOI:** [10.1038/ng1201-365](https://doi.org/10.1038/ng1201-365) - **PMID:** 11726920
  • Network accessline 149
    - **DOI:** [10.1038/ng1201-365](https://doi.org/10.1038/ng1201-365)

plugin/skills/ucsc-browser/SKILL.md

  • Network accessline 30
    - **ENCODE Project Consortium 2020** (Nature, ~1,656 citations): Registry of 926,535 human cCREs hosted on UCSC as `encodeCcreCombined` track. [DOI](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 31
    - **Nassar et al. 2023** (Nucleic Acids Research): The UCSC Genome Browser database: 2023 update. [DOI](https://doi.org/10.1093/nar/gkac1072)
  • Network accessline 36
    **Base URL**: `https://api.genome.ucsc.edu`
  • Network accessline 47
    # List all tracks for hg38 curl "https://api.genome.ucsc.edu/list/tracks?genome=hg38"
  • Network accessline 48
    curl "https://api.genome.ucsc.edu/list/tracks?genome=hg38"
  • Network accessline 50
    # Search for ENCODE-specific tracks curl "https://api.genome.ucsc.edu/search?search=encode+regulation&genome=hg38&categories=trackDb"
  • Network accessline 51
    curl "https://api.genome.ucsc.edu/search?search=encode+regulation&genome=hg38&categories=trackDb"
  • Network accessline 53
    # Get schema (field definitions) for a track curl "https://api.genome.ucsc.edu/list/schema?genome=hg38;track=encodeCcreCombined"
  • Network accessline 54
    curl "https://api.genome.ucsc.edu/list/schema?genome=hg38;track=encodeCcreCombined"
  • Network accessline 81
    # Get all cCREs in a 100kb window curl "https://api.genome.ucsc.edu/getData/track?genome=hg38;track=encodeCcreCombined;chrom=chr1;start=1000000;end=1100000"
  • Network accessline 82
    curl "https://api.genome.ucsc.edu/getData/track?genome=hg38;track=encodeCcreCombined;chrom=chr1;start=1000000;end=1100000"
  • Network accessline 84
    # Use jsonOutputArrays for named fields (recommended) curl "https://api.genome.ucsc.edu/getData/track?genome=hg38;track=encodeCcreCombined;chrom=chr1;start=1000000;end=1100000;jsonOutputArrays=1"
  • Network accessline 85
    curl "https://api.genome.ucsc.edu/getData/track?genome=hg38;track=encodeCcreCombined;chrom=chr1;start=1000000;end=1100000;jsonOutputArrays=1"
  • Network accessline 116
    # Get TF rPeak clusters in a region curl "https://api.genome.ucsc.edu/getData/track?genome=hg38;track=TFrPeakClusters;chrom=chr1;start=1000000;end=1100000;jsonOutputArrays=1"
  • Network accessline 117
    curl "https://api.genome.ucsc.edu/getData/track?genome=hg38;track=TFrPeakClusters;chrom=chr1;start=1000000;end=1100000;jsonOutputArrays=1"
  • Network accessline 139
    # Get sequence for a region curl "https://api.genome.ucsc.edu/getData/sequence?genome=hg38;chrom=chr1;start=1000000;end=1000500"
  • Network accessline 140
    curl "https://api.genome.ucsc.edu/getData/sequence?genome=hg38;chrom=chr1;start=1000000;end=1000500"
  • Network accessline 142
    # Get reverse complement curl "https://api.genome.ucsc.edu/getData/sequence?genome=hg38;chrom=chr1;start=1000000;end=1000500;revComp=1"
  • Network accessline 143
    curl "https://api.genome.ucsc.edu/getData/sequence?genome=hg38;chrom=chr1;start=1000000;end=1000500;revComp=1"
  • Network accessline 157
    # DNase clusters (95 cell types) curl "https://api.genome.ucsc.edu/getData/track?genome=hg38;track=wgEncodeRegDnaseClustered;chrom=chr1;start=1000000;end=1100000;jsonOutputArrays=1"
  • Network accessline 158
    curl "https://api.genome.ucsc.edu/getData/track?genome=hg38;track=wgEncodeRegDnaseClustered;chrom=chr1;start=1000000;end=1100000;jsonOutputArrays=1"
  • Network accessline 168
    # Download UCSC tools (macOS example) # Available at: https://hgdownload.gi.ucsc.edu/admin/exe/
  • Network accessline 169
    # Available at: https://hgdownload.gi.ucsc.edu/admin/exe/
  • Network accessline 171
    # Extract ENCODE cCREs for a region from hosted bigBed bigBedToBed https://hgdownload.gi.ucsc.edu/gbdb/hg38/encode3/encodeCcreCombined.bb \
  • Network accessline 172
    bigBedToBed https://hgdownload.gi.ucsc.edu/gbdb/hg38/encode3/encodeCcreCombined.bb \
  • Network accessline 175
    # Extract TF rPeak clusters bigBedToBed https://hgdownload.gi.ucsc.edu/gbdb/hg38/bbi/ENCODE4/TFrPeakClusters.bb \
  • Network accessline 176
    bigBedToBed https://hgdownload.gi.ucsc.edu/gbdb/hg38/bbi/ENCODE4/TFrPeakClusters.bb \
  • Network accessline 179
    # Summarize bigWig signal over regions bigWigSummary http://path/to/signal.bw chr1 1000000 1100000 10
  • Network accessline 180
    bigWigSummary http://path/to/signal.bw chr1 1000000 1100000 10
  • Network accessline 201
    3. For each peak region, query UCSC for regulatory context:    curl "https://api.genome.ucsc.edu/getData/track?genome=hg38;track=encodeCcreCombined;chrom=CHR;start=START;end=END;jsonOutputArrays=1"
  • Network accessline 202
    curl "https://api.genome.ucsc.edu/getData/track?genome=hg38;track=encodeCcreCombined;chrom=CHR;start=START;end=END;jsonOutputArrays=1"
  • Network accessline 204
    4. Check which TFs bind at each peak:    curl "https://api.genome.ucsc.edu/getData/track?genome=hg38;track=TFrPeakClusters;chrom=CHR;start=START;end=END;jsonOutputArrays=1"
  • Network accessline 205
    curl "https://api.genome.ucsc.edu/getData/track?genome=hg38;track=TFrPeakClusters;chrom=CHR;start=START;end=END;jsonOutputArrays=1"
  • Network accessline 207
    5. Get DNA sequence for motif analysis:    curl "https://api.genome.ucsc.edu/getData/sequence?genome=hg38;chrom=CHR;start=START;end=END"
  • Network accessline 208
    curl "https://api.genome.ucsc.edu/getData/sequence?genome=hg38;chrom=CHR;start=START;end=END"
  • Network accessline 258
    "file_format": "bigWig",   "output_type": "fold change over control",   "download_url": "https://www.encodeproject.org/files/ENCFF200BW/@@download/ENCFF200BW.bigWig",
  • Network accessline 259
    "output_type": "fold change over control",   "download_url": "https://www.encodeproject.org/files/ENCFF200BW/@@download/ENCFF200BW.bigWig",   "s3_uri": "s3://encode-public/2021/06/14/ENCFF200BW.bigWig",
  • Network accessline 260
    "download_url": "https://www.encodeproject.org/files/ENCFF200BW/@@download/ENCFF200BW.bigWig",
  • Network accessline 274
    GET https://api.genome.ucsc.edu/getData/track?genome=hg38&track=encodeCcreCombined&chrom=chr1&start=1000000&end=1100000
  • Network accessline 292
    GET https://api.genome.ucsc.edu/getData/sequence?genome=hg38&chrom=chr1&start=1020500&end=1021200
  • Network accessline 361
    "file_size": 131386573,     "file_size_human": "125.3 MB",     "download_url": "https://www.encodeproject.org/files/ENCFF456DEF/@@download/ENCFF456DEF.bigWig",
  • Network accessline 362
    "file_size_human": "125.3 MB",     "download_url": "https://www.encodeproject.org/files/ENCFF456DEF/@@download/ENCFF456DEF.bigWig",     "preferred_default": true
  • Network accessline 363
    "download_url": "https://www.encodeproject.org/files/ENCFF456DEF/@@download/ENCFF456DEF.bigWig",

plugin/skills/ucsc-browser/references/literature.md

  • Network accessline 10
    - **Citation:** Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, Haussler D. The human genome browser at UCSC. *Genome Research*, 12(6), 996-1006, 2002. - **DOI:** [10.1101/gr.229102](https://doi.org/10.1101/gr.229102)
  • Network accessline 11
    - **DOI:** [10.1101/gr.229102](https://doi.org/10.1101/gr.229102)
  • Network accessline 20
    - **Citation:** Karolchik D, Hinrichs AS, Furey TS, Roskin KM, Sugnet CW, Haussler D, Kent WJ. The UCSC Table Browser data retrieval tool. *Nucleic Acids Research*, 32(Database issue), D493-D496, 2004. - **DOI:** [10.1093/nar/gkh103](https:
  • Network accessline 21
    - **DOI:** [10.1093/nar/gkh103](https://doi.org/10.1093/nar/gkh103)
  • Network accessline 30
    - **Citation:** Hinrichs AS, Karolchik D, Baertsch R, Barber GP, Bejerano G, Clawson H, Diekhans M, Furey TS, Harte RA, Hsu F, Hillman-Jackson J, Kuhn RM, Pedersen JS, Pohl A, Raney BJ, Rosenbloom KR, Siepel A, Smith KE, Sugnet CW, Sultan-Q
  • Network accessline 31
    - **DOI:** [10.1093/nar/gkj144](https://doi.org/10.1093/nar/gkj144)
  • Network accessline 40
    - **Citation:** Kent WJ, Zweig AS, Barber G, Hinrichs AS, Karolchik D. BigWig and BigBed: enabling browsing of large distributed datasets. *Bioinformatics*, 26(17), 2204-2207, 2010. - **DOI:** [10.1093/bioinformatics/btq351](https://doi.org
  • Network accessline 41
    - **DOI:** [10.1093/bioinformatics/btq351](https://doi.org/10.1093/bioinformatics/btq351)
  • Network accessline 50
    - **Citation:** Raney BJ, Dreszer TR, Barber GP, Clawson H, Fujita PA, Wang T, Nguyen N, Paten B, Zweig AS, Karolchik D, Kent WJ. Track data hubs enable visualization of user-defined genome-wide annotations on the UCSC Genome Browser. *Bioi
  • Network accessline 51
    - **DOI:** [10.1093/bioinformatics/btt637](https://doi.org/10.1093/bioinformatics/btt637)
  • Network accessline 60
    - **Citation:** Haeussler M, Zweig AS, Tyner C, Speir ML, Rosenbloom KR, Raney BJ, Lee CM, Lee BT, Hinrichs AS, Gonzalez JN, Gibson D, Diekhans M, Clawson H, Casper J, Barber GP, Haussler D, Kuhn RM, Kent WJ. The UCSC Genome Browser databas
  • Network accessline 61
    - **DOI:** [10.1093/nar/gky1095](https://doi.org/10.1093/nar/gky1095)
  • Network accessline 70
    - **Citation:** Navarro Gonzalez J, Zweig AS, Speir ML, Schmelter D, Rosenbloom KR, Raney BJ, Powell CC, Nassar LR, Maulding ND, Lee CM, Lee BT, Hinrichs AS, Fyfe AC, Fernandes JD, Diekhans M, Clawson H, Casper J, Benet-Pages A, Barber GP,
  • Network accessline 71
    - **DOI:** [10.1093/nar/gkaa1070](https://doi.org/10.1093/nar/gkaa1070)

plugin/skills/variant-annotation/SKILL.md

  • Network accessline 44
    - **ENCODE Project Consortium 2020** (Nature, ~1,656 citations): Registry of 926,535 human cCREs. The primary resource for variant-to-regulatory-element annotation. [DOI](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 45
    - **Boyle et al. 2012** (Genome Research, ~2,623 citations): RegulomeDB — integrates ENCODE ChIP-seq, DNase-seq, eQTLs to score non-coding variants on a 1–6 scale. [DOI](https://doi.org/10.1101/gr.137323.112)
  • Network accessline 46
    - **Kircher et al. 2014** (Nature Genetics, ~5,719 citations): CADD — Combined Annotation-Dependent Depletion. SVM trained on 14.7M simulated vs. high-frequency alleles, pre-computes scores for all 8.6B possible human SNVs. [DOI](https://do
  • Network accessline 47
    - **Rentzsch et al. 2019** (Nucleic Acids Research, ~1,500 citations): CADD v1.4/v1.6 update with GRCh38 support. [DOI](https://doi.org/10.1093/nar/gky1016)
  • Network accessline 48
    - **Finucane et al. 2015** (Nature Genetics, ~2,253 citations): Stratified LD Score Regression (S-LDSC) — partitions SNP heritability into functional categories using GWAS summary statistics. Foundational for quantifying how much disease he
  • Network accessline 49
    - **Iotchkova et al. 2019** (Nature Genetics, ~157 citations): GARFIELD — GWAS enrichment in regulatory annotations with LD correction, allele frequency, and TSS distance confounders. [DOI](https://doi.org/10.1038/s41588-018-0322-6)
  • Network accessline 50
    - **Wang et al. 2020** (JRSS-B, ~500 citations): SuSiE — Sum of Single Effects model for Bayesian fine-mapping, outputs credible sets per independent signal. [DOI](https://doi.org/10.1111/rssb.12388)
  • Network accessline 51
    - **Weissbrod et al. 2020** (Nature Genetics, ~200 citations): PolyFun — leverages ENCODE/Roadmap functional annotations as prior causal probabilities for fine-mapping. PolyFun+SuSiE identified 32% more causal variants than SuSiE alone. [DO
  • Network accessline 52
    - **Nasser et al. 2021** (Nature, ~468 citations): ABC model enhancer-gene maps in 131 cell types. Links 5,036 GWAS signals to 2,249 genes. >20-fold enrichment of causal variants in cell-type-specific predicted enhancers. [DOI](https://doi.
  • Network accessline 53
    - **Broekema et al. 2020** (Open Biology, ~107 citations): Practical review of post-GWAS fine-mapping and gene prioritization strategies. [DOI](https://doi.org/10.1098/rsob.190221)
  • Network accessline 54
    - **Schaub et al. 2012** (Genome Research, ~675 citations): Early framework linking ENCODE functional data with GWAS disease associations. Functional annotations for up to 80% of reported associations. [DOI](https://doi.org/10.1101/gr.13612
  • Network accessline 55
    - **Maurano et al. 2012** (Science, ~2,800 citations): Systematic demonstration that disease-associated variants concentrate in DNase I hypersensitive sites. 88% of variant-containing DHSs are active during fetal development. Enabled de nov
  • Network accessline 56
    - **Amemiya et al. 2019** (Scientific Reports, ~1,372 citations): ENCODE Blacklist — regions producing artifactual signal in functional genomics. Must filter before annotation. [DOI](https://doi.org/10.1038/s41598-019-45839-z)
  • Network accessline 161
    **Pre-annotation filter**: Before annotating, filter out variants overlapping ENCODE Blacklist regions (Amemiya et al. 2019, Scientific Reports, 1,372 citations). Blacklisted regions produce artifactual signal in functional genomics assays.
  • Network accessline 162
    - Human GRCh38: `hg38-blacklist.v2.bed.gz` from [Boyle-Lab/Blacklist](https://github.com/Boyle-Lab/Blacklist)
  • Network accessline 405
    POST https://rest.ensembl.org/vep/human/region
  • Network accessline 414
    GET https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=clinvar&term=rs7903146
  • Network accessline 434
    GET https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs7903146

plugin/skills/variant-annotation/references/literature.md

  • Network accessline 12
    - **Citation:** McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GRS, Thormann A, Flicek P, Cunningham F. The Ensembl Variant Effect Predictor. *Genome Biology*, 17(1), 122, 2016. - **DOI:** [10.1186/s13059-016-0974-4](https://doi.org/10.1186/s1
  • Network accessline 13
    - **DOI:** [10.1186/s13059-016-0974-4](https://doi.org/10.1186/s13059-016-0974-4)
  • Network accessline 22
    - **Citation:** Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. *Nucleic Acids Research*, 38(16), e164, 2010. - **DOI:** [10.1093/nar/gkq603](https://doi.org/10.1093/nar/g
  • Network accessline 23
    - **DOI:** [10.1093/nar/gkq603](https://doi.org/10.1093/nar/gkq603)
  • Network accessline 32
    - **Citation:** Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, Land SJ, Lu X, Ruden DM. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster
  • Network accessline 33
    - **DOI:** [10.4161/fly.19695](https://doi.org/10.4161/fly.19695)
  • Network accessline 44
    - **Citation:** Kircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. *Nature Genetics*, 46(3), 310-315, 2014. - **DOI:** [10.1038/ng.2892](h
  • Network accessline 45
    - **DOI:** [10.1038/ng.2892](https://doi.org/10.1038/ng.2892)
  • Network accessline 54
    - **Citation:** Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. CADD: predicting the deleteriousness of variants throughout the human genome. *Nucleic Acids Research*, 47(D1), D886-D894, 2019. - **DOI:** [10.1093/nar/gky1016](https:
  • Network accessline 55
    - **DOI:** [10.1093/nar/gky1016](https://doi.org/10.1093/nar/gky1016)
  • Network accessline 64
    - **Citation:** Ioannidis NM, Rothstein JH, Pejaver V, Middha S, McDonnell SK, Baheti S, Musolf A, Li Q, Holzinger E, Karyadi D, Cannon-Albright LA, Teerlink CC, Stanford JL, Isaacs WB, Xu J, Cooney KA, Lange EM, Schleutker J, Carpten JD, P
  • Network accessline 65
    - **DOI:** [10.1016/j.ajhg.2016.08.016](https://doi.org/10.1016/j.ajhg.2016.08.016)
  • Network accessline 76
    - **Citation:** Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, Grody WW, Hegde M, Lyon E, Spector E, Voelkerding K, Rehm HL, ACMG Laboratory Quality Assurance Committee. Standards and guidelines for the interpretation of seque
  • Network accessline 77
    - **DOI:** [10.1038/gim.2015.30](https://doi.org/10.1038/gim.2015.30)
  • Network accessline 86
    - **Citation:** Landrum MJ, Lee JM, Benson M, Brown GR, Chao C, Chitipiralla S, Gu B, Hart J, Hoffman D, Jang W, Karapetyan K, Katz K, Liu C, Maddipatla Z, Malheiro A, McDaniel K, Ovetsky M, Riley G, Zhou G, Holmes JB, Kattman BL, Maglott D
  • Network accessline 87
    - **DOI:** [10.1093/nar/gkx1153](https://doi.org/10.1093/nar/gkx1153)
  • Network accessline 98
    - **Citation:** Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alfoldi J, Wang Q, Collins RL, Laricchia KM, Ganna A, Birnbaum DP, Gauthier LD, Brand H, Solomonson M, Watts NA, Rhodes D, Singer-Berk M, England EM, Seaby EG, Kosmicki JA, W
  • Network accessline 99
    - **DOI:** [10.1038/s41586-020-2308-7](https://doi.org/10.1038/s41586-020-2308-7)
  • Network accessline 110
    - **Citation:** Cooper GM, Shendure J. Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data. *Nature Reviews Genetics*, 12(9), 628-640, 2011. - **DOI:** [10.1038/nrg3046](https://doi.org/10.1038/nrg3046)
  • Network accessline 111
    - **DOI:** [10.1038/nrg3046](https://doi.org/10.1038/nrg3046)

plugin/skills/visualization-workflow/SKILL.md

  • Network accessline 20
    | Reference | Journal | Key Contribution | DOI | Citations | |-----------|---------|-----------------|-----|-----------| | Ramirez et al. (2016) | Nucleic Acids Research | deepTools2: next-generation server for deep-sequencing data analysis
  • Network accessline 21
    |-----------|---------|-----------------|-----|-----------| | Ramirez et al. (2016) | Nucleic Acids Research | deepTools2: next-generation server for deep-sequencing data analysis; heatmaps, profiles, correlation, PCA | [10.1093/nar/gkw257]
  • Network accessline 22
    | Ramirez et al. (2016) | Nucleic Acids Research | deepTools2: next-generation server for deep-sequencing data analysis; heatmaps, profiles, correlation, PCA | [10.1093/nar/gkw257](https://doi.org/10.1093/nar/gkw257) | ~3,800 |
  • Network accessline 23
    | Robinson et al. (2011) | Nature Biotechnology | Integrative Genomics Viewer (IGV): interactive exploration of large genomic datasets | [10.1038/nbt.1754](https://doi.org/10.1038/nbt.1754) | ~10,000 |
  • Network accessline 24
    | Kent et al. (2002) | Genome Research | The Human Genome Browser at UCSC: foundation for track-based genomic visualization | [10.1101/gr.229102](https://doi.org/10.1101/gr.229102) | ~8,000 |
  • Network accessline 25
    | Ramirez et al. (2014) | Nucleic Acids Research | deepTools: flexible platform for exploring deep-sequencing data; original computeMatrix/plotHeatmap framework | [10.1093/nar/gku365](https://doi.org/10.1093/nar/gku365) | ~2,500 |
  • Network accessline 26
    | Amemiya et al. (2019) | Scientific Reports | ENCODE Blacklist: comprehensive identification of artifact regions to exclude from visualization | [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z) | ~1,372 |
  • Network accessline 178
    new genome hg38 load https://www.encodeproject.org/files/ENCFF.../@@download/ENCFF....bigWig
  • Network accessline 179
    genome hg38 load https://www.encodeproject.org/files/ENCFF.../@@download/ENCFF....bigWig load https://www.encodeproject.org/files/ENCFF.../@@download/ENCFF....bigBed
  • Network accessline 180
    load https://www.encodeproject.org/files/ENCFF.../@@download/ENCFF....bigWig
  • Network accessline 181
    load https://www.encodeproject.org/files/ENCFF.../@@download/ENCFF....bigBed
  • Network accessline 198
    <div id="igv-div"></div> <script src="https://cdn.jsdelivr.net/npm/igv@2.15.0/dist/igv.min.js"></script>
  • Network accessline 199
    <script src="https://cdn.jsdelivr.net/npm/igv@2.15.0/dist/igv.min.js"></script>
  • Network accessline 205
    {             name: "H3K27ac Signal",             url: "https://www.encodeproject.org/files/ENCFF.../@@download/ENCFF....bigWig",
  • Network accessline 206
    name: "H3K27ac Signal",             url: "https://www.encodeproject.org/files/ENCFF.../@@download/ENCFF....bigWig",             type: "wig",
  • Network accessline 207
    url: "https://www.encodeproject.org/files/ENCFF.../@@download/ENCFF....bigWig",
  • Network accessline 211
    {             name: "ATAC Peaks",             url: "https://www.encodeproject.org/files/ENCFF.../@@download/ENCFF....bigBed",
  • Network accessline 212
    name: "ATAC Peaks",             url: "https://www.encodeproject.org/files/ENCFF.../@@download/ENCFF....bigBed",             type: "annotation",
  • Network accessline 213
    url: "https://www.encodeproject.org/files/ENCFF.../@@download/ENCFF....bigBed",
  • Network accessline 337
    https://genome.ucsc.edu/cgi-bin/hgTracks?db=hg38&hubUrl=https://yourserver.edu/hub.txt

plugin/skills/visualization-workflow/references/literature.md

  • Network accessline 32
    - **Citation:** Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, Haussler D.   The human genome browser at UCSC. *Genome Research*, 12(6), 996-1006, 2002. - **DOI:** [10.1101/gr.229102](https://doi.org/10.1101/gr.229102)
  • Network accessline 33
    The human genome browser at UCSC. *Genome Research*, 12(6), 996-1006, 2002. - **DOI:** [10.1101/gr.229102](https://doi.org/10.1101/gr.229102) - **PMID:** 12045153 | **PMC:** PMC186604
  • Network accessline 34
    - **DOI:** [10.1101/gr.229102](https://doi.org/10.1101/gr.229102)
  • Network accessline 52
    - **Citation:** Robinson JT, Thorvaldsdottir H, Winckler W, Guttman M, Lander ES, Getz G,   Mesirov JP. Integrative genomics viewer. *Nature Biotechnology*, 29(1), 24-26, 2011. - **DOI:** [10.1038/nbt.1754](https://doi.org/10.1038/nbt.1754)
  • Network accessline 53
    Mesirov JP. Integrative genomics viewer. *Nature Biotechnology*, 29(1), 24-26, 2011. - **DOI:** [10.1038/nbt.1754](https://doi.org/10.1038/nbt.1754) - **PMID:** 21221095 | **PMC:** PMC3346182
  • Network accessline 54
    - **DOI:** [10.1038/nbt.1754](https://doi.org/10.1038/nbt.1754)
  • Network accessline 74
    high-performance genomics data visualization and exploration. *Briefings in Bioinformatics*,   14(2), 178-192, 2013. - **DOI:** [10.1093/bib/bbs017](https://doi.org/10.1093/bib/bbs017)
  • Network accessline 75
    14(2), 178-192, 2013. - **DOI:** [10.1093/bib/bbs017](https://doi.org/10.1093/bib/bbs017) - **PMID:** 22517427 | **PMC:** PMC3603213
  • Network accessline 76
    - **DOI:** [10.1093/bib/bbs017](https://doi.org/10.1093/bib/bbs017)
  • Network accessline 104
    Dundar F, Manke T. deepTools2: a next generation web server for deep-sequencing data   analysis. *Nucleic Acids Research*, 44(W1), W160-W165, 2016. - **DOI:** [10.1093/nar/gkw257](https://doi.org/10.1093/nar/gkw257)
  • Network accessline 105
    analysis. *Nucleic Acids Research*, 44(W1), W160-W165, 2016. - **DOI:** [10.1093/nar/gkw257](https://doi.org/10.1093/nar/gkw257) - **PMID:** 27079975 | **PMC:** PMC4987876
  • Network accessline 106
    - **DOI:** [10.1093/nar/gkw257](https://doi.org/10.1093/nar/gkw257)
  • Network accessline 135
    Mirny LA, Park PJ, Gehlenborg N. HiGlass: web-based visual exploration and analysis of   genome interaction maps. *Genome Biology*, 19(1), 125, 2018. - **DOI:** [10.1186/s13059-018-1486-1](https://doi.org/10.1186/s13059-018-1486-1)
  • Network accessline 136
    genome interaction maps. *Genome Biology*, 19(1), 125, 2018. - **DOI:** [10.1186/s13059-018-1486-1](https://doi.org/10.1186/s13059-018-1486-1) - **PMID:** 30143029 | **PMC:** PMC6109259
  • Network accessline 137
    - **DOI:** [10.1186/s13059-018-1486-1](https://doi.org/10.1186/s13059-018-1486-1)
  • Network accessline 163
    - **Citation:** Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations   in multidimensional genomic data. *Bioinformatics*, 32(18), 2847-2849, 2016. - **DOI:** [10.1093/bioinformatics/btw313](https://doi.org/10.1093/b
  • Network accessline 164
    in multidimensional genomic data. *Bioinformatics*, 32(18), 2847-2849, 2016. - **DOI:** [10.1093/bioinformatics/btw313](https://doi.org/10.1093/bioinformatics/btw313) - **PMID:** 27207943
  • Network accessline 165
    - **DOI:** [10.1093/bioinformatics/btw313](https://doi.org/10.1093/bioinformatics/btw313)
  • Network accessline 190
    - **Citation:** Robinson MD, Oshlack A. A scaling normalization method for differential   expression analysis of RNA-seq data. *Genome Biology*, 11(3), R25, 2010. - **DOI:** [10.1186/gb-2010-11-3-r25](https://doi.org/10.1186/gb-2010-11-3-r2
  • Network accessline 191
    expression analysis of RNA-seq data. *Genome Biology*, 11(3), R25, 2010. - **DOI:** [10.1186/gb-2010-11-3-r25](https://doi.org/10.1186/gb-2010-11-3-r25) - **PMID:** 20196867 | **PMC:** PMC2864565
  • Network accessline 192
    - **DOI:** [10.1186/gb-2010-11-3-r25](https://doi.org/10.1186/gb-2010-11-3-r25)
  • Network accessline 221
    integration of genomic datasets with the R/Bioconductor package biomaRt. *Nature   Protocols*, 4(8), 1184-1191, 2009. - **DOI:** [10.1038/nprot.2009.97](https://doi.org/10.1038/nprot.2009.97)
  • Network accessline 222
    Protocols*, 4(8), 1184-1191, 2009. - **DOI:** [10.1038/nprot.2009.97](https://doi.org/10.1038/nprot.2009.97) - **PMID:** 19617889 | **PMC:** PMC3159387
  • Network accessline 223
    - **DOI:** [10.1038/nprot.2009.97](https://doi.org/10.1038/nprot.2009.97)

skills/accessibility-aggregation/SKILL.md

  • Network accessline 38
    ### Literature Support - **Corces et al. 2017** (Nature Methods, 733 citations): Established that ATAC-seq and DNase-seq identify largely overlapping accessible regions, with ATAC capturing ~75% of DNase sites. Both are valid for union maps
  • Network accessline 39
    - **Corces et al. 2017** (Nature Methods, 733 citations): Established that ATAC-seq and DNase-seq identify largely overlapping accessible regions, with ATAC capturing ~75% of DNase sites. Both are valid for union maps. - **ENCODE Blacklist*
  • Network accessline 40
    - **ENCODE Blacklist** (Amemiya et al. 2019, Scientific Reports, 1,372 citations): Comprehensive set of problematic genomic regions to filter. Essential for all functional genomics analyses. [DOI](https://doi.org/10.1038/s41598-019-45839-z)
  • Network accessline 146
    # Download from: https://github.com/Boyle-Lab/Blacklist/blob/master/lists/hg38-blacklist.v2.bed.gz

skills/accessibility-aggregation/references/literature.md

  • Network accessline 14
    - **Citation:** Corces MR, Trevino AE, Hamilton EG, Greenside PG, Sinnott-Armstrong NA, Vesuna S, Satpathy AT, Rubin AJ, Montine KS, Wu B, Kathiria A, Cho SW, Mumbach MR, Carter AC, Kasowski M, Orloff LA, Risca VI, Kundaje A, Khavari PA, Mo
  • Network accessline 15
    - **DOI:** [10.1038/nmeth.4396](https://doi.org/10.1038/nmeth.4396)
  • Network accessline 24
    - **Citation:** Thurman RE, Rynes E, Humbert R, et al. The accessible chromatin landscape of the human genome. Nature, 489(7414):75-82, 2012. - **DOI:** [10.1038/nature11232](https://doi.org/10.1038/nature11232)
  • Network accessline 25
    - **DOI:** [10.1038/nature11232](https://doi.org/10.1038/nature11232)
  • Network accessline 34
    - **Citation:** Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nature Methods, 10(12)
  • Network accessline 35
    - **DOI:** [10.1038/nmeth.2688](https://doi.org/10.1038/nmeth.2688)
  • Network accessline 48
    - **Citation:** Zhao Z, Boyle AP. F-Seq2: improving the feature density based peak caller with dynamic statistics. NAR Genomics and Bioinformatics, 3(1):lqab012, 2021. - **DOI:** [10.1093/nargab/lqab012](https://doi.org/10.1093/nargab/lqab0
  • Network accessline 49
    - **DOI:** [10.1093/nargab/lqab012](https://doi.org/10.1093/nargab/lqab012)
  • Network accessline 58
    - **Citation:** Amemiya HM, Kundaje A, Boyle AP. The ENCODE Blacklist: Identification of Problematic Regions of the Genome. Scientific Reports, 9:9354, 2019. - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z
  • Network accessline 59
    - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z)
  • Network accessline 68
    - **Citation:** Orchard P, Kyono Y, Hensley J, Kitzman JO, Parker SCJ. Quantification, Dynamic Visualization, and Validation of Bias in ATAC-Seq Data with ataqv. Cell Systems, 10(3):298-306.e4, 2020. - **DOI:** [10.1016/j.cels.2020.02.009](
  • Network accessline 69
    - **DOI:** [10.1016/j.cels.2020.02.009](https://doi.org/10.1016/j.cels.2020.02.009)
  • Network accessline 82
    - **Citation:** ENCODE Project Consortium et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature, 583(7818):699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 83
    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 92
    - **Citation:** Gorkin DU, Barozzi I, Zhao Y, et al. An atlas of dynamic chromatin landscapes in mouse fetal development. Nature, 583(7818):744-751, 2020. - **DOI:** [10.1038/s41586-020-2093-3](https://doi.org/10.1038/s41586-020-2093-3)
  • Network accessline 93
    - **DOI:** [10.1038/s41586-020-2093-3](https://doi.org/10.1038/s41586-020-2093-3)
  • Network accessline 106
    - **Citation:** Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics, 26(6):841-842, 2010. - **DOI:** [10.1093/bioinformatics/btq033](https://doi.org/10.1093/bioinformatics/btq033)
  • Network accessline 107
    - **DOI:** [10.1093/bioinformatics/btq033](https://doi.org/10.1093/bioinformatics/btq033)

skills/accessibility-aggregation/scripts/validate_peaks.py

  • Executable scriptline 1
    #!/usr/bin/env python3

skills/batch-analysis/SKILL.md

  • Network accessline 20
    | Reference | Journal | Key Contribution | DOI | Citations | |-----------|---------|-----------------|-----|-----------| | ENCODE Project Consortium (2020) | Nature | Expanded encyclopedia of 926,535 candidate cis-regulatory elements across
  • Network accessline 21
    |-----------|---------|-----------------|-----|-----------| | ENCODE Project Consortium (2020) | Nature | Expanded encyclopedia of 926,535 candidate cis-regulatory elements across 1,698 cell types; framework for large-scale integrative anal
  • Network accessline 22
    | ENCODE Project Consortium (2020) | Nature | Expanded encyclopedia of 926,535 candidate cis-regulatory elements across 1,698 cell types; framework for large-scale integrative analysis | [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s
  • Network accessline 23
    | Hitz et al. (2023) | Nucleic Acids Research | The ENCODE Uniform Processing Pipelines: standardized processing enables large-scale batch comparisons | [10.1093/nar/gkac1067](https://doi.org/10.1093/nar/gkac1067) | ~50 |
  • Network accessline 24
    | Landt et al. (2012) | Genome Research | ChIP-seq guidelines of ENCODE/modENCODE: QC metrics (FRiP, NSC, RSC, NRF) for batch quality assessment | [10.1101/gr.136184.111](https://doi.org/10.1101/gr.136184.111) | ~4,000 |
  • Network accessline 25
    | Leek et al. (2010) | Nature Reviews Genetics | Tackling batch effects: detection via PCA, correction via ComBat/SVA; essential for multi-lab analyses | [10.1038/nrg2825](https://doi.org/10.1038/nrg2825) | ~1,200 |
  • Network accessline 26
    | Amemiya et al. (2019) | Scientific Reports | ENCODE Blacklist: artifact regions to exclude across all experiments in batch analyses | [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z) | ~1,372 |

skills/batch-analysis/references/literature.md

  • Network accessline 16
    impact of batch effects in high-throughput data. Nature Reviews Genetics,   11(10):733-739, 2010. - **DOI:** [10.1038/nrg2825](https://doi.org/10.1038/nrg2825)
  • Network accessline 17
    11(10):733-739, 2010. - **DOI:** [10.1038/nrg2825](https://doi.org/10.1038/nrg2825) - **PMID:** 20838408 | **PMC:** PMC3880143
  • Network accessline 18
    - **DOI:** [10.1038/nrg2825](https://doi.org/10.1038/nrg2825)
  • Network accessline 38
    - **Citation:** Goh WWB, Wang W, Wong L. Why batch effects matter in omics   data, and how to avoid them. Trends in Biotechnology, 35(6):498-507, 2017. - **DOI:** [10.1016/j.tibtech.2017.02.012](https://doi.org/10.1016/j.tibtech.2
  • Network accessline 39
    data, and how to avoid them. Trends in Biotechnology, 35(6):498-507, 2017. - **DOI:** [10.1016/j.tibtech.2017.02.012](https://doi.org/10.1016/j.tibtech.2   017.02.012)
  • Network accessline 40
    - **DOI:** [10.1016/j.tibtech.2017.02.012](https://doi.org/10.1016/j.tibtech.2
  • Network accessline 67
    for microarray gene expression data integration: a survey. Briefings in   Bioinformatics, 14(4):469-490, 2013. - **DOI:** [10.1093/bib/bbs037](https://doi.org/10.1093/bib/bbs037)
  • Network accessline 68
    Bioinformatics, 14(4):469-490, 2013. - **DOI:** [10.1093/bib/bbs037](https://doi.org/10.1093/bib/bbs037) - **PMID:** 22851511
  • Network accessline 69
    - **DOI:** [10.1093/bib/bbs037](https://doi.org/10.1093/bib/bbs037)
  • Network accessline 96
    microarray expression data using empirical Bayes methods. Biostatistics,   8(1):118-127, 2007. - **DOI:** [10.1093/biostatistics/kxj037](https://doi.org/10.1093/biostatistic
  • Network accessline 97
    8(1):118-127, 2007. - **DOI:** [10.1093/biostatistics/kxj037](https://doi.org/10.1093/biostatistic   s/kxj037)
  • Network accessline 98
    - **DOI:** [10.1093/biostatistics/kxj037](https://doi.org/10.1093/biostatistic
  • Network accessline 126
    single-cell RNA-sequencing data are corrected by matching mutual nearest   neighbors. Nature Biotechnology, 36(5):421-427, 2018. - **DOI:** [10.1038/nbt.4091](https://doi.org/10.1038/nbt.4091)
  • Network accessline 127
    neighbors. Nature Biotechnology, 36(5):421-427, 2018. - **DOI:** [10.1038/nbt.4091](https://doi.org/10.1038/nbt.4091) - **PMID:** 29608177 | **PMC:** PMC6152897
  • Network accessline 128
    - **DOI:** [10.1038/nbt.4091](https://doi.org/10.1038/nbt.4091)
  • Network accessline 152
    integration of single-cell data with Harmony. Nature Methods,   16(12):1289-1296, 2019. - **DOI:** [10.1038/s41592-019-0619-0](https://doi.org/10.1038/s41592-019-0619-0)
  • Network accessline 153
    16(12):1289-1296, 2019. - **DOI:** [10.1038/s41592-019-0619-0](https://doi.org/10.1038/s41592-019-0619-0) - **PMID:** 31740819 | **PMC:** PMC6884693
  • Network accessline 154
    - **DOI:** [10.1038/s41592-019-0619-0](https://doi.org/10.1038/s41592-019-0619-0)
  • Network accessline 180
    A benchmark of batch-effect correction methods for single-cell RNA   sequencing data. Genome Biology, 21(1):12, 2020. - **DOI:** [10.1186/s13059-019-1850-9](https://doi.org/10.1186/s13059-019-1850-9)
  • Network accessline 181
    sequencing data. Genome Biology, 21(1):12, 2020. - **DOI:** [10.1186/s13059-019-1850-9](https://doi.org/10.1186/s13059-019-1850-9) - **PMID:** 31948481 | **PMC:** PMC6965632
  • Network accessline 182
    - **DOI:** [10.1186/s13059-019-1850-9](https://doi.org/10.1186/s13059-019-1850-9)

skills/bioinformatics-installer/SKILL.md

  • Dangerous shell operationline 495
    sudo apt-get update
  • Dangerous shell operationline 496
    sudo apt-get install -y docker-ce docker-ce-cli containerd.io
  • Dangerous shell operationline 499
    sudo usermod -aG docker $USER
  • Network accessline 548
    mkdir -p ~/software/homer cd ~/software/homer wget http://homer.ucsd.edu/homer/configureHomer.pl
  • Network accessline 549
    cd ~/software/homer wget http://homer.ucsd.edu/homer/configureHomer.pl perl configureHomer.pl -install homer
  • Network accessline 550
    wget http://homer.ucsd.edu/homer/configureHomer.pl
  • Network accessline 565
    # Or from source wget https://meme-suite.org/meme/meme-software/5.5.5/meme-5.5.5.tar.gz
  • Network accessline 566
    wget https://meme-suite.org/meme/meme-software/5.5.5/meme-5.5.5.tar.gz

skills/bioinformatics-installer/environments/cutandrun-env.yml

  • Network accessline 33
    - pigz   - wget   - curl

skills/bioinformatics-installer/references/literature.md

  • Network accessline 14
    - **Citation:** Grüning B, Dale R, Sjödin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Köster J; Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7):475-476, 2018
  • Network accessline 15
    - **DOI:** [10.1038/s41592-018-0046-7](https://doi.org/10.1038/s41592-018-0046-7)
  • Network accessline 32
    - **Citation:** Kurtzer GM, Sochat V, Bauer MW. Singularity: Scientific containers for mobility of compute. PLoS ONE, 12(5):e0177459, 2017. - **DOI:** [10.1371/journal.pone.0177459](https://doi.org/10.1371/journal.pone.0177459)
  • Network accessline 33
    - **DOI:** [10.1371/journal.pone.0177459](https://doi.org/10.1371/journal.pone.0177459)
  • Network accessline 42
    - **Citation:** Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4):316-319, 2017. - **DOI:** [10.1038/nbt.3820](https://doi.org/10.1038/nb
  • Network accessline 43
    - **DOI:** [10.1038/nbt.3820](https://doi.org/10.1038/nbt.3820)
  • Network accessline 52
    - **Citation:** Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3):276-278, 2020. - **DOI:** [
  • Network accessline 53
    - **DOI:** [10.1038/s41587-020-0439-x](https://doi.org/10.1038/s41587-020-0439-x)
  • Network accessline 66
    - **Citation:** Huber W, Carey VJ, Gentleman R, Anders S, Brainard M, Davis S, Dudoit S, Ellis B, Gatto L, Girke T, Gottardo R, Hahne F, Hansen KD, Irizarry RA, Lawrence M, Love MI, MacDonald J, Obenchain V, Oleś AK, Pagès H, Reyes A, Shann
  • Network accessline 67
    - **DOI:** [10.1038/nmeth.3252](https://doi.org/10.1038/nmeth.3252)
  • Network accessline 80
    - **Citation:** Wolf FA, Angerer P, Theis FJ. SCANPY: large-scale single-cell gene expression data analysis. Genome Biology, 19:15, 2018. - **DOI:** [10.1186/s13059-017-1382-0](https://doi.org/10.1186/s13059-017-1382-0)
  • Network accessline 81
    - **DOI:** [10.1186/s13059-017-1382-0](https://doi.org/10.1186/s13059-017-1382-0)
  • Network accessline 112
    - **deepTools** (Ramírez et al. 2016): DOI 10.1093/nar/gkw257 — Signal tracks, heatmaps - **MultiQC** (Ewels et al. 2016): DOI 10.1093/bioinformatics/btw354 — QC report aggregation - **FastQC** (Andrews 2010): URL https://www.bioinformatics
  • Network accessline 113
    - **MultiQC** (Ewels et al. 2016): DOI 10.1093/bioinformatics/btw354 — QC report aggregation - **FastQC** (Andrews 2010): URL https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ - **Picard** (Broad Institute): URL https://broadinstit
  • Network accessline 114
    - **FastQC** (Andrews 2010): URL https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
  • Network accessline 115
    - **Picard** (Broad Institute): URL https://broadinstitute.github.io/picard/
  • Network accessline 122
    ### Methylation Specific - **MethylDackel** (Ryan): URL https://github.com/dpryan79/MethylDackel — Methylation extraction
  • Network accessline 123
    - **MethylDackel** (Ryan): URL https://github.com/dpryan79/MethylDackel — Methylation extraction

skills/bioinformatics-installer/scripts/install-nextflow.sh

  • Executable scriptline 1
    #!/usr/bin/env bash
  • Executable scriptline 1
    Executable file; content unavailable
  • Executable scriptline 1
    #!/usr/bin/env bash
  • Network accessline 14
    # with `nextflow lint` and `nextflow run -preview`. To move to another release, change the # version and replace the checksum with the sha256 published for the # `nextflow-<version>-dist` asset on https://github.com/nextflow-io/nextflow/rel
  • Network accessline 14
    # with `nextflow lint` and `nextflow run -preview`. To move to another release, change the # version and replace the checksum with the sha256 published for the # `nextflow-<version>-dist` asset on https://github.com/nextflow-io/nextflow/rel
  • Network accessline 16
    # `nextflow-<version>-dist` asset on https://github.com/nextflow-io/nextflow/releases
  • Network accessline 16
    # `nextflow-<version>-dist` asset on https://github.com/nextflow-io/nextflow/releases
  • Network accessline 17
    NEXTFLOW_VERSION="26.04.6" NEXTFLOW_SHA256="182a63c74074e2dc7956ffa3c8cd59de952ed2c44394e21faf5e1736b945444c" NEXTFLOW_URL="https://github.com/nextflow-io/nextflow/releases/download/v${NEXTFLOW_VERSION}/nextflow-${NEXTFLOW_VERSION}-dist"
  • Network accessline 17
    NEXTFLOW_VERSION="26.04.6" NEXTFLOW_SHA256="182a63c74074e2dc7956ffa3c8cd59de952ed2c44394e21faf5e1736b945444c" NEXTFLOW_URL="https://github.com/nextflow-io/nextflow/releases/download/v${NEXTFLOW_VERSION}/nextflow-${NEXTFLOW_VERSION}-dist"
  • Network accessline 19
    NEXTFLOW_URL="https://github.com/nextflow-io/nextflow/releases/download/v${NEXTFLOW_VERSION}/nextflow-${NEXTFLOW_VERSION}-dist"
  • Network accessline 19
    NEXTFLOW_URL="https://github.com/nextflow-io/nextflow/releases/download/v${NEXTFLOW_VERSION}/nextflow-${NEXTFLOW_VERSION}-dist"
  • Dangerous shell operationline 57
    echo "  Ubuntu: sudo apt-get install -y openjdk-17-jdk"
  • Dangerous shell operationline 57
    echo "  Ubuntu: sudo apt-get install -y openjdk-17-jdk"
  • Network accessline 69
    tmp_file="$(mktemp)"         echo "Downloading Nextflow ${NEXTFLOW_VERSION}..."         curl -fsSL "$NEXTFLOW_URL" -o "$tmp_file"
  • Network accessline 69
    tmp_file="$(mktemp)"         echo "Downloading Nextflow ${NEXTFLOW_VERSION}..."         curl -fsSL "$NEXTFLOW_URL" -o "$tmp_file"
  • Network accessline 71
    curl -fsSL "$NEXTFLOW_URL" -o "$tmp_file"
  • Network accessline 71
    curl -fsSL "$NEXTFLOW_URL" -o "$tmp_file"
  • Dangerous shell operationline 79
    rm -f "$tmp_file"
  • Dangerous shell operationline 79
    rm -f "$tmp_file"
  • Network accessline 121
    echo "macOS detected. Install Docker Desktop:"                 echo "  brew install --cask docker"                 echo "  OR download from https://www.docker.com/products/docker-desktop/"
  • Network accessline 121
    echo "macOS detected. Install Docker Desktop:"                 echo "  brew install --cask docker"                 echo "  OR download from https://www.docker.com/products/docker-desktop/"
  • Network accessline 122
    echo "  brew install --cask docker"                 echo "  OR download from https://www.docker.com/products/docker-desktop/"                 ;;
  • Network accessline 122
    echo "  brew install --cask docker"                 echo "  OR download from https://www.docker.com/products/docker-desktop/"                 ;;
  • Network accessline 123
    echo "  OR download from https://www.docker.com/products/docker-desktop/"
  • Network accessline 123
    echo "  OR download from https://www.docker.com/products/docker-desktop/"
  • Network accessline 125
    Linux)                 echo "Linux detected. Install Docker Engine:"                 echo "  curl -fsSL https://get.docker.com | sh"
  • Network accessline 125
    Linux)                 echo "Linux detected. Install Docker Engine:"                 echo "  curl -fsSL https://get.docker.com | sh"
  • Network accessline 126
    echo "Linux detected. Install Docker Engine:"                 echo "  curl -fsSL https://get.docker.com | sh"                 echo "  sudo usermod -aG docker \$USER"
  • Network accessline 126
    echo "Linux detected. Install Docker Engine:"                 echo "  curl -fsSL https://get.docker.com | sh"                 echo "  sudo usermod -aG docker \$USER"
  • Dangerous shell operationline 127
    echo "  curl -fsSL https://get.docker.com | sh"
  • Dangerous shell operationline 127
    echo "  curl -fsSL https://get.docker.com | sh"
  • Network accessline 127
    echo "  curl -fsSL https://get.docker.com | sh"
  • Network accessline 127
    echo "  curl -fsSL https://get.docker.com | sh"
  • Dangerous shell operationline 128
    echo "  sudo usermod -aG docker \$USER"
  • Dangerous shell operationline 128
    echo "  sudo usermod -aG docker \$USER"
  • Network accessline 131
    *)                 echo "Unsupported OS: $OS"                 echo "See https://docs.docker.com/get-docker/"
  • Network accessline 131
    *)                 echo "Unsupported OS: $OS"                 echo "See https://docs.docker.com/get-docker/"
  • Network accessline 132
    echo "Unsupported OS: $OS"                 echo "See https://docs.docker.com/get-docker/"                 ;;
  • Network accessline 132
    echo "Unsupported OS: $OS"                 echo "See https://docs.docker.com/get-docker/"                 ;;
  • Network accessline 133
    echo "See https://docs.docker.com/get-docker/"
  • Network accessline 133
    echo "See https://docs.docker.com/get-docker/"
  • Network accessline 151
    echo "Install options:"         echo "  conda: conda install -c conda-forge singularity"         echo "  Linux: See https://apptainer.org/docs/admin/main/installation.html"
  • Network accessline 151
    echo "Install options:"         echo "  conda: conda install -c conda-forge singularity"         echo "  Linux: See https://apptainer.org/docs/admin/main/installation.html"
  • Network accessline 152
    echo "  conda: conda install -c conda-forge singularity"         echo "  Linux: See https://apptainer.org/docs/admin/main/installation.html"         echo ""
  • Network accessline 152
    echo "  conda: conda install -c conda-forge singularity"         echo "  Linux: See https://apptainer.org/docs/admin/main/installation.html"         echo ""
  • Network accessline 153
    echo "  Linux: See https://apptainer.org/docs/admin/main/installation.html"
  • Network accessline 153
    echo "  Linux: See https://apptainer.org/docs/admin/main/installation.html"

skills/bioinformatics-installer/scripts/install-python-packages.sh

  • Executable scriptline 1
    #!/usr/bin/env bash
  • Executable scriptline 1
    Executable file; content unavailable
  • Executable scriptline 1
    #!/usr/bin/env bash

skills/bioinformatics-installer/scripts/install-r-packages.R

  • Executable scriptline 1
    #!/usr/bin/env Rscript
  • Network accessline 25
    if (!requireNamespace("BiocManager", quietly = TRUE)) {   cat("Installing BiocManager...\n")   install.packages("BiocManager", repos = "https://cloud.r-project.org")
  • Network accessline 26
    cat("Installing BiocManager...\n")   install.packages("BiocManager", repos = "https://cloud.r-project.org") }
  • Network accessline 27
    install.packages("BiocManager", repos = "https://cloud.r-project.org")

skills/cellxgene-context/SKILL.md

  • Network accessline 36
    - **Megill et al. 2021** "cellxgene: a performant, scalable exploration platform for high dimensional sparse matrices" (bioRxiv preprint). Describes the CellxGene platform architecture and exploration capabilities. [DOI: 10.1101/2021.04.05.
  • Network accessline 37
    - **CZ CELLxGENE Discover** (Chan Zuckerberg Initiative, 2023). CellxGene Census provides programmatic access to the entire CellxGene data corpus as a single unified dataset. [https://cellxgene.cziscience.com/](https://cellxgene.cziscience.
  • Network accessline 38
    - **Regev et al. 2017** "The Human Cell Atlas" (eLife, ~1,500 citations). The vision paper for comprehensive single-cell reference maps of all human cells. CellxGene Census is the largest realization of this vision. [DOI: 10.7554/eLife.2704
  • Network accessline 39
    - **Tabula Sapiens Consortium 2022** "The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans" (Science, ~800 citations). Multi-organ human cell atlas contributing to CellxGene Census. [DOI: 10.1126/science.abl4896]
  • Network accessline 40
    - **ENCODE Project Consortium 2020** (Nature, ~1,656 citations). The bulk regulatory element catalog that CellxGene single-cell data contextualizes. [DOI: 10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)

skills/cellxgene-context/references/literature.md

  • Network accessline 15
    Bodenmiller B, Campbell P, Carninci P, Clatworthy M, et al. The Human Cell   Atlas. eLife, 6:e27041, 2017. - **DOI:** [10.7554/eLife.27041](https://doi.org/10.7554/eLife.27041)
  • Network accessline 16
    Atlas. eLife, 6:e27041, 2017. - **DOI:** [10.7554/eLife.27041](https://doi.org/10.7554/eLife.27041) - **PMID:** 29206104 | **PMC:** PMC5762154
  • Network accessline 17
    - **DOI:** [10.7554/eLife.27041](https://doi.org/10.7554/eLife.27041)
  • Network accessline 40
    Heumos L, Pisco AO, et al. CellxGene: a performant, scalable exploration   platform for high dimensional sparse matrices. bioRxiv, 2021. - **DOI:** [10.1101/2021.04.05.438318](https://doi.org/10.1101/2021.04.05.438318)
  • Network accessline 41
    platform for high dimensional sparse matrices. bioRxiv, 2021. - **DOI:** [10.1101/2021.04.05.438318](https://doi.org/10.1101/2021.04.05.438318) - **PMID:** N/A | **PMC:** N/A
  • Network accessline 42
    - **DOI:** [10.1101/2021.04.05.438318](https://doi.org/10.1101/2021.04.05.438318)
  • Network accessline 68
    organ, single-cell transcriptomic atlas of humans. Science,   376(6594):eabl4896, 2022. - **DOI:** [10.1126/science.abl4896](https://doi.org/10.1126/science.abl4896)
  • Network accessline 69
    376(6594):eabl4896, 2022. - **DOI:** [10.1126/science.abl4896](https://doi.org/10.1126/science.abl4896) - **PMID:** 35549404 | **PMC:** PMC9812015
  • Network accessline 70
    - **DOI:** [10.1126/science.abl4896](https://doi.org/10.1126/science.abl4896)
  • Network accessline 96
    - **Citation:** Tabula Muris Consortium. Single-cell transcriptomics of 20   mouse organs creates a Tabula Muris. Nature, 562(7727):367-372, 2018. - **DOI:** [10.1038/s41586-018-0590-4](https://doi.org/10.1038/s41586-018-0590-4)
  • Network accessline 97
    mouse organs creates a Tabula Muris. Nature, 562(7727):367-372, 2018. - **DOI:** [10.1038/s41586-018-0590-4](https://doi.org/10.1038/s41586-018-0590-4) - **PMID:** 30283141 | **PMC:** PMC6642641
  • Network accessline 98
    - **DOI:** [10.1038/s41586-018-0590-4](https://doi.org/10.1038/s41586-018-0590-4)
  • Network accessline 124
    immune cell analysis reveals tissue-specific features in humans. Science,   376(6594):eabl5197, 2022. - **DOI:** [10.1126/science.abl5197](https://doi.org/10.1126/science.abl5197)
  • Network accessline 125
    376(6594):eabl5197, 2022. - **DOI:** [10.1126/science.abl5197](https://doi.org/10.1126/science.abl5197) - **PMID:** 35549406 | **PMC:** PMC7613554
  • Network accessline 126
    - **DOI:** [10.1126/science.abl5197](https://doi.org/10.1126/science.abl5197)
  • Network accessline 155
    Benchmarking atlas-level data integration in single-cell genomics. Nature   Methods, 19(1):41-50, 2022. - **DOI:** [10.1038/s41592-021-01336-8](https://doi.org/10.1038/s41592-021-01336-8)
  • Network accessline 156
    Methods, 19(1):41-50, 2022. - **DOI:** [10.1038/s41592-021-01336-8](https://doi.org/10.1038/s41592-021-01336-8) - **PMID:** 34949812 | **PMC:** PMC8762698
  • Network accessline 157
    - **DOI:** [10.1038/s41592-021-01336-8](https://doi.org/10.1038/s41592-021-01336-8)
  • Network accessline 181
    Flexible comparison of batch correction methods for single-cell RNA-seq   using BatchBench. Genome Biology, 22(1):189, 2021. - **DOI:** [10.1186/s13059-021-02393-y](https://doi.org/10.1186/s13059-021-02393-y)
  • Network accessline 182
    using BatchBench. Genome Biology, 22(1):189, 2021. - **DOI:** [10.1186/s13059-021-02393-y](https://doi.org/10.1186/s13059-021-02393-y) - **PMID:** 34183031
  • Network accessline 183
    - **DOI:** [10.1186/s13059-021-02393-y](https://doi.org/10.1186/s13059-021-02393-y)

skills/cite-encode/SKILL.md

  • Network accessline 89
    Template: > "[Assay type] data for [biosample] were obtained from the ENCODE Project (https://www.encodeproject.org). Experiment accessions: [list ENCSR accessions]. All ENCODE data are freely available under unrestricted use policy."
  • Network accessline 90
    > "[Assay type] data for [biosample] were obtained from the ENCODE Project (https://www.encodeproject.org). Experiment accessions: [list ENCSR accessions]. All ENCODE data are freely available under unrestricted use policy."
  • Network accessline 149
    | HOMER | Heinz et al. Mol Cell 2010 | 10.1016/j.molcel.2010.05.004 | | IGV | Robinson et al. Nat Biotechnol 2011 | 10.1038/nbt.2754 | | Picard | Broad Institute (URL-only citation) | https://broadinstitute.github.io/picard/ |
  • Network accessline 150
    | IGV | Robinson et al. Nat Biotechnol 2011 | 10.1038/nbt.2754 | | Picard | Broad Institute (URL-only citation) | https://broadinstitute.github.io/picard/ | | BWA-MEM | Li. arXiv 2013 | arXiv:1303.3997 |
  • Network accessline 151
    | Picard | Broad Institute (URL-only citation) | https://broadinstitute.github.io/picard/ |
  • Network accessline 188
    > "Histone ChIP-seq (H3K27ac, H3K4me1, H3K4me3, H3K27me3, H3K36me3), ATAC-seq, RNA-seq, and whole-genome bisulfite sequencing data were obtained from the ENCODE Project (https://www.encodeproject.org). Experiment accessions are listed in Su
  • Network accessline 444
    | ATAC-seq, human pancreatic islets | ENCODE | ENCSR345MNO | | RNA-seq, human pancreatic islets | ENCODE | ENCSR678PQR | | GRCh38 reference genome | UCSC | https://hgdownload.soe.ucsc.edu/goldenPath/hg38/ |
  • Network accessline 445
    | RNA-seq, human pancreatic islets | ENCODE | ENCSR678PQR | | GRCh38 reference genome | UCSC | https://hgdownload.soe.ucsc.edu/goldenPath/hg38/ | | GENCODE v41 gene annotation | GENCODE | https://www.gencodegenes.org/human/release_41.html |
  • Network accessline 446
    | GRCh38 reference genome | UCSC | https://hgdownload.soe.ucsc.edu/goldenPath/hg38/ |
  • Network accessline 447
    | GENCODE v41 gene annotation | GENCODE | https://www.gencodegenes.org/human/release_41.html |
  • Network accessline 448
    | ENCODE Blacklist v2 | Amemiya et al., 2019 | https://github.com/Boyle-Lab/Blacklist/ |
  • Network accessline 449
    | **Software and Algorithms** | | | | MACS2 v2.2.9.1 | Zhang et al., 2008 | https://github.com/macs3-project/MACS | | STAR v2.7.11b | Dobin et al., 2013 | https://github.com/alexdobin/STAR |
  • Network accessline 450
    | MACS2 v2.2.9.1 | Zhang et al., 2008 | https://github.com/macs3-project/MACS |
  • Network accessline 451
    | STAR v2.7.11b | Dobin et al., 2013 | https://github.com/alexdobin/STAR |
  • Network accessline 452
    | DESeq2 v1.38 | Love et al., 2014 | https://bioconductor.org/packages/DESeq2 |
  • Network accessline 453
    | bedtools v2.31.0 | Quinlan & Hall, 2010 | https://github.com/arq5x/bedtools2 |
  • Network accessline 454
    | deepTools v3.5.5 | Ramirez et al., 2016 | https://github.com/deeptools/deepTools |
  • Network accessline 455
    | samtools v1.19 | Danecek et al., 2021 | https://github.com/samtools/samtools |
  • Network accessline 456
    | Custom analysis scripts | This study | https://github.com/[your-repo] |
  • Network accessline 489
    ### 9. Stale DOIs DOI links can break if a publisher migrates. Always verify DOIs resolve correctly before submitting a manuscript. Use https://doi.org/[DOI] to check.
  • Network accessline 490
    DOI links can break if a publisher migrates. Always verify DOIs resolve correctly before submitting a manuscript. Use https://doi.org/[DOI] to check.

skills/cite-encode/references/literature.md

  • Network accessline 10
    - **Citation:** ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. *Nature*, 489(7414), 57-74, 2012. - **DOI:** [10.1038/nature11247](https://doi.org/10.1038/nature11247)
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    - **DOI:** [10.1038/nature11247](https://doi.org/10.1038/nature11247)
  • Network accessline 20
    - **Citation:** ENCODE Project Consortium, Moore JE, Purcaro MJ, Pratt HE, Epstein CB, Shoresh N, Adrian J, Kawli T, Davis CA, Dobin A, Kaul R, Halow J, Van Nostrand EL, Freese P, Gorkin DU, Shen Y, He Y, Mackiewicz M, Pauli-Behn F, William
  • Network accessline 21
    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 30
    - **Citation:** Davis CA, Hitz BC, Sloan CA, Chan ET, Davidson JM, Gabdank I, Hilton JA, Jain K, Baymuradov UK, Narayanan AK, Onate KC, Graham K, Miyasato SR, Dreszer TR, Strattan JS, Jolanki O, Tanaka FY, Cherry JM. The Encyclopedia of DNA
  • Network accessline 31
    - **DOI:** [10.1093/nar/gkx1081](https://doi.org/10.1093/nar/gkx1081)
  • Network accessline 40
    - **Citation:** Luo Y, Hitz BC, Gabdank I, Hilton JA, Kagda MS, Lam B, Myers Z, Sud P, Jou J, Lin K, Baymuradov UK, Graham K, Litton C, Miyasato SR, Strattan JS, Jolanki O, Lee JW, Tanaka FY, Batchelder JE, Takeo I, Cherry JM. New developme
  • Network accessline 41
    - **DOI:** [10.1093/nar/gkz1062](https://doi.org/10.1093/nar/gkz1062)
  • Network accessline 50
    - **Citation:** Toronto International Data Release Workshop Authors. Prepublication data sharing. *Nature*, 461(7261), 168-170, 2009. - **DOI:** [10.1038/461168a](https://doi.org/10.1038/461168a)
  • Network accessline 51
    - **DOI:** [10.1038/461168a](https://doi.org/10.1038/461168a)

skills/clinvar-annotation/SKILL.md

  • Network accessline 37
    - **Landrum et al. 2018** "ClinVar: improving access to variant interpretations and supporting evidence" (Nucleic Acids Research, ~2,000 citations). Describes the ClinVar database architecture, submission standards, and the star-rating revi
  • Network accessline 38
    - **Riggs et al. 2020** "Technical standards for the interpretation and reporting of constitutional copy-number variants: a joint consensus recommendation of the ACMG and ClinGen" (Genetics in Medicine, ~500 citations). Framework for interp
  • Network accessline 39
    - **Richards et al. 2015** "Standards and guidelines for the interpretation of sequence variants: ACMG/AMP joint consensus recommendation" (Genetics in Medicine, ~12,000 citations). The ACMG variant classification framework (pathogenic thro
  • Network accessline 40
    - **ENCODE Project Consortium 2020** (Nature, ~1,656 citations). Registry of 926,535 human cCREs — the functional annotation layer for interpreting non-coding ClinVar variants. [DOI: 10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586
  • Network accessline 68
    **Base URL**: `https://eutils.ncbi.nlm.nih.gov/entrez/eutils/`
  • Network accessline 83
    For bulk intersection with ENCODE peaks, download the ClinVar VCF: - GRCh38: `https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz`
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    - GRCh38: `https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz`
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    - GRCh37: `https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh37/clinvar.vcf.gz`
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    # Search ClinVar for pathogenic variants in a gene url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi"
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    url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi"
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    "retmode": "json" } response = requests.get(url, params=params)
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    } response = requests.get(url, params=params) result = response.json()
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    response = requests.get(url, params=params)
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    def search_clinvar(gene_symbol, significance="pathogenic"):     """Search ClinVar for variants in a gene with given clinical significance."""     url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi"
  • Network accessline 143
    """Search ClinVar for variants in a gene with given clinical significance."""     url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi"     term = f"{gene_symbol}[gene] AND {significance}[clinical significance]"
  • Network accessline 144
    url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi"
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    "retmode": "json"     }     response = requests.get(url, params=params)
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    }     response = requests.get(url, params=params)     time.sleep(0.34)  # Rate limit: 3/sec
  • Network accessline 152
    response = requests.get(url, params=params)
  • Network accessline 160
    def get_clinvar_summary(variant_ids):     """Get summary for ClinVar variant IDs."""     url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi"
  • Network accessline 161
    """Get summary for ClinVar variant IDs."""     url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi"     params = {
  • Network accessline 162
    url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi"
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    "retmode": "json"     }     response = requests.get(url, params=params)
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    }     response = requests.get(url, params=params)     time.sleep(0.34)
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    response = requests.get(url, params=params)
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    # Download ClinVar VCF (GRCh38) wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz
  • Network accessline 186
    wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz
  • Network accessline 187
    wget https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz.tbi
  • Network accessline 215
    # Find ClinVar variants in region for record in vcf.fetch(chrom, start, end):
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    for record in vcf.fetch(chrom, start, end):
  • Network accessline 338
    GET https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=clinvar&term=chr1[chr]+AND+10000:20000[chrpos]+AND+pathogenic[clnsig]

skills/clinvar-annotation/references/literature.md

  • Network accessline 16
    variant interpretations and supporting evidence. Nucleic Acids Research,   46(D1):D1062-D1067, 2018. - **DOI:** [10.1093/nar/gkx1153](https://doi.org/10.1093/nar/gkx1153)
  • Network accessline 17
    46(D1):D1062-D1067, 2018. - **DOI:** [10.1093/nar/gkx1153](https://doi.org/10.1093/nar/gkx1153) - **PMID:** 29165669 | **PMC:** PMC5753237
  • Network accessline 18
    - **DOI:** [10.1093/nar/gkx1153](https://doi.org/10.1093/nar/gkx1153)
  • Network accessline 42
    track and share variant interpretations. Current Protocols, 1(12):e315,   2021. - **DOI:** [10.1002/cpz1.315](https://doi.org/10.1002/cpz1.315)
  • Network accessline 43
    2021. - **DOI:** [10.1002/cpz1.315](https://doi.org/10.1002/cpz1.315) - **PMID:** 34964606 | **PMC:** PMC8858745
  • Network accessline 44
    - **DOI:** [10.1002/cpz1.315](https://doi.org/10.1002/cpz1.315)
  • Network accessline 74
    American College of Medical Genetics and Genomics and the Association for   Molecular Pathology. Genetics in Medicine, 17(5):405-424, 2015. - **DOI:** [10.1038/gim.2015.30](https://doi.org/10.1038/gim.2015.30)
  • Network accessline 75
    Molecular Pathology. Genetics in Medicine, 17(5):405-424, 2015. - **DOI:** [10.1038/gim.2015.30](https://doi.org/10.1038/gim.2015.30) - **PMID:** 25741868 | **PMC:** PMC4544753
  • Network accessline 76
    - **DOI:** [10.1038/gim.2015.30](https://doi.org/10.1038/gim.2015.30)
  • Network accessline 100
    refinement of the ACMG-AMP variant classification criteria. Genetics in   Medicine, 19(10):1105-1117, 2017. - **DOI:** [10.1038/gim.2017.37](https://doi.org/10.1038/gim.2017.37)
  • Network accessline 101
    Medicine, 19(10):1105-1117, 2017. - **DOI:** [10.1038/gim.2017.37](https://doi.org/10.1038/gim.2017.37) - **PMID:** 28492532 | **PMC:** PMC5632834
  • Network accessline 102
    - **DOI:** [10.1038/gim.2017.37](https://doi.org/10.1038/gim.2017.37)
  • Network accessline 128
    improving the interpretation of cancer susceptibility genetic test results.   Human Mutation, 29(11):1282-1291, 2008. - **DOI:** [10.1002/humu.20880](https://doi.org/10.1002/humu.20880)
  • Network accessline 129
    Human Mutation, 29(11):1282-1291, 2008. - **DOI:** [10.1002/humu.20880](https://doi.org/10.1002/humu.20880) - **PMID:** 18951446 | **PMC:** PMC3075918
  • Network accessline 130
    - **DOI:** [10.1002/humu.20880](https://doi.org/10.1002/humu.20880)
  • Network accessline 161
    Clinical Genome Resource. New England Journal of Medicine,   372(23):2235-2242, 2015. - **DOI:** [10.1056/NEJMsr1406261](https://doi.org/10.1056/NEJMsr1406261)
  • Network accessline 162
    372(23):2235-2242, 2015. - **DOI:** [10.1056/NEJMsr1406261](https://doi.org/10.1056/NEJMsr1406261) - **PMID:** 26014595 | **PMC:** PMC4474187
  • Network accessline 163
    - **DOI:** [10.1056/NEJMsr1406261](https://doi.org/10.1056/NEJMsr1406261)
  • Network accessline 190
    and Genomics (ACMG) and the Clinical Genome Resource (ClinGen). Genetics in   Medicine, 22(2):245-257, 2020. - **DOI:** [10.1038/s41436-019-0686-8](https://doi.org/10.1038/s41436-019-0686-8)
  • Network accessline 191
    Medicine, 22(2):245-257, 2020. - **DOI:** [10.1038/s41436-019-0686-8](https://doi.org/10.1038/s41436-019-0686-8) - **PMID:** 31690835 | **PMC:** PMC7313390
  • Network accessline 192
    - **DOI:** [10.1038/s41436-019-0686-8](https://doi.org/10.1038/s41436-019-0686-8)

skills/compare-biosamples/SKILL.md

  • Network accessline 238
    **ENCODE Blacklist filtering (required before comparison)**: Before any cross-tissue comparison, remove peaks and signal in ENCODE Blacklist regions (Amemiya et al. 2019, Scientific Reports, 1,372 citations). Blacklisted regions produce art
  • Network accessline 239
    - Human GRCh38: `hg38-blacklist.v2.bed.gz` from [Boyle-Lab/Blacklist](https://github.com/Boyle-Lab/Blacklist)

skills/compare-biosamples/references/literature.md

  • Network accessline 33
    - **Citation:** Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, et al. Integrative analysis   of 111 reference human epigenomes. *Nature*, 518(7539), 317-330, 2015. - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248)
  • Network accessline 34
    of 111 reference human epigenomes. *Nature*, 518(7539), 317-330, 2015. - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248) - **PMID:** 25693563 | **PMC:** PMC4530010
  • Network accessline 35
    - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248)
  • Network accessline 55
    analyses implicate candidate causal variants at inherited risk loci for Alzheimer's and   Parkinson's diseases. *Nature Genetics*, 52(11), 1158-1168, 2020. - **DOI:** [10.1038/s41588-020-00721-x](https://doi.org/10.1038/s41588-020-00721-x)
  • Network accessline 56
    Parkinson's diseases. *Nature Genetics*, 52(11), 1158-1168, 2020. - **DOI:** [10.1038/s41588-020-00721-x](https://doi.org/10.1038/s41588-020-00721-x) - **PMID:** 33106633 | **PMC:** PMC7887218
  • Network accessline 57
    - **DOI:** [10.1038/s41588-020-00721-x](https://doi.org/10.1038/s41588-020-00721-x)
  • Network accessline 74
    encyclopaedias of DNA elements in the human and mouse genomes. *Nature*, 583(7818),   699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 75
    699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4) - **PMID:** 32728249 | **PMC:** PMC7410828
  • Network accessline 76
    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 101
    - **Citation:** Thurman RE, Rynes E, Humbert R, Vierstra J, Maurano MT, et al. The accessible   chromatin landscape of the human genome. *Nature*, 489(7414), 75-82, 2012. - **DOI:** [10.1038/nature11232](https://doi.org/10.1038/nature11232)
  • Network accessline 102
    chromatin landscape of the human genome. *Nature*, 489(7414), 75-82, 2012. - **DOI:** [10.1038/nature11232](https://doi.org/10.1038/nature11232) - **PMID:** 22955617 | **PMC:** PMC3721348
  • Network accessline 103
    - **DOI:** [10.1038/nature11232](https://doi.org/10.1038/nature11232)
  • Network accessline 131
    - **Citation:** Bernstein BE, Stamatoyannopoulos JA, Costello JF, et al. The NIH Roadmap   Epigenomics Mapping Consortium. *Nature Biotechnology*, 28(10), 1045-1048, 2010. - **DOI:** [10.1038/nbt1010-1045](https://doi.org/10.1038/nbt1010-10
  • Network accessline 132
    Epigenomics Mapping Consortium. *Nature Biotechnology*, 28(10), 1045-1048, 2010. - **DOI:** [10.1038/nbt1010-1045](https://doi.org/10.1038/nbt1010-1045) - **PMID:** 20944595 | **PMC:** PMC3607281
  • Network accessline 133
    - **DOI:** [10.1038/nbt1010-1045](https://doi.org/10.1038/nbt1010-1045)
  • Network accessline 159
    - **Citation:** Zhu J, Adli M, Zou JY, Verstappen G, et al. Tissue-specific enhancers   identified by H3K27ac across reference epigenomes. *Genome Biology*, 17, 32, 2016. - **DOI:** [10.1186/s13059-016-0974-4](https://doi.org/10.1186/s13059
  • Network accessline 160
    identified by H3K27ac across reference epigenomes. *Genome Biology*, 17, 32, 2016. - **DOI:** [10.1186/s13059-016-0974-4](https://doi.org/10.1186/s13059-016-0974-4) - **PMID:** 27040513 | **PMC:** PMC4818977
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    - **DOI:** [10.1186/s13059-016-0974-4](https://doi.org/10.1186/s13059-016-0974-4)
  • Network accessline 175
    - **Citation:** Hnisz D, Abraham BJ, Lee TI, et al. Super-enhancers in the control of cell   identity and disease. *Cell*, 155(4), 934-947, 2013. - **DOI:** [10.1016/j.cell.2013.09.053](https://doi.org/10.1016/j.cell.2013.09.053)
  • Network accessline 176
    identity and disease. *Cell*, 155(4), 934-947, 2013. - **DOI:** [10.1016/j.cell.2013.09.053](https://doi.org/10.1016/j.cell.2013.09.053) - **PMID:** 24119843 | **PMC:** PMC3841062
  • Network accessline 177
    - **DOI:** [10.1016/j.cell.2013.09.053](https://doi.org/10.1016/j.cell.2013.09.053)
  • Network accessline 196
    cell type-specific enhancers. *Nature Reviews Molecular Cell Biology*, 16(3),   144-154, 2015. - **DOI:** [10.1038/nrm3949](https://doi.org/10.1038/nrm3949)
  • Network accessline 197
    144-154, 2015. - **DOI:** [10.1038/nrm3949](https://doi.org/10.1038/nrm3949) - **PMID:** 25650801 | **PMC:** PMC4517609
  • Network accessline 198
    - **DOI:** [10.1038/nrm3949](https://doi.org/10.1038/nrm3949)

skills/cross-reference/SKILL.md

  • Network accessline 70
    curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=gds&db=sra&id=GDS_UID&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 606
    2. **DOI format inconsistency**: Normalize DOIs to the bare format "10.xxxx/..." without the "https://doi.org/" prefix. ENCODE sometimes stores DOIs with the URL prefix; strip it before passing to `encode_link_reference`. Both formats work

skills/cross-reference/references/literature.md

  • Network accessline 31
    - **Citation:** The GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects   across human tissues. *Science*, 369(6509), 1318-1330, 2020. - **DOI:** [10.1126/science.aaz1776](https://doi.org/10.1126/science.aaz1776)
  • Network accessline 32
    across human tissues. *Science*, 369(6509), 1318-1330, 2020. - **DOI:** [10.1126/science.aaz1776](https://doi.org/10.1126/science.aaz1776) - **PMID:** 32913098 | **PMC:** PMC7737656
  • Network accessline 33
    - **DOI:** [10.1126/science.aaz1776](https://doi.org/10.1126/science.aaz1776)
  • Network accessline 58
    access to variant interpretations and supporting evidence. *Nucleic Acids Research*,   46(D1), D1062-D1067, 2018. - **DOI:** [10.1093/nar/gkx1153](https://doi.org/10.1093/nar/gkx1153)
  • Network accessline 59
    46(D1), D1062-D1067, 2018. - **DOI:** [10.1093/nar/gkx1153](https://doi.org/10.1093/nar/gkx1153) - **PMID:** 29165669 | **PMC:** PMC5753237
  • Network accessline 60
    - **DOI:** [10.1093/nar/gkx1153](https://doi.org/10.1093/nar/gkx1153)
  • Network accessline 84
    Catalog of published genome-wide association studies, targeted arrays and summary   statistics 2019. *Nucleic Acids Research*, 47(D1), D1005-D1012, 2019. - **DOI:** [10.1093/nar/gky1120](https://doi.org/10.1093/nar/gky1120)
  • Network accessline 85
    statistics 2019. *Nucleic Acids Research*, 47(D1), D1005-D1012, 2019. - **DOI:** [10.1093/nar/gky1120](https://doi.org/10.1093/nar/gky1120) - **PMID:** 30445434 | **PMC:** PMC6323933
  • Network accessline 86
    - **DOI:** [10.1093/nar/gky1120](https://doi.org/10.1093/nar/gky1120)
  • Network accessline 110
    - **Citation:** Barrett T, Wilhite SE, Ledoux P, Evangelista C, et al. NCBI GEO: archive for   functional genomics data sets — update. *Nucleic Acids Research*, 41(D1), D991-D995, 2013. - **DOI:** [10.1093/nar/gks1193](https://doi.org/10.10
  • Network accessline 111
    functional genomics data sets — update. *Nucleic Acids Research*, 41(D1), D991-D995, 2013. - **DOI:** [10.1093/nar/gks1193](https://doi.org/10.1093/nar/gks1193) - **PMID:** 23193258 | **PMC:** PMC3531084
  • Network accessline 112
    - **DOI:** [10.1093/nar/gks1193](https://doi.org/10.1093/nar/gks1193)
  • Network accessline 139
    the 9th release of the open-access database of transcription factor binding profiles.   *Nucleic Acids Research*, 50(D1), D580-D587, 2022. - **DOI:** [10.1093/nar/gkab1113](https://doi.org/10.1093/nar/gkab1113)
  • Network accessline 140
    *Nucleic Acids Research*, 50(D1), D580-D587, 2022. - **DOI:** [10.1093/nar/gkab1113](https://doi.org/10.1093/nar/gkab1113) - **PMID:** 34850907 | **PMC:** PMC8728201
  • Network accessline 141
    - **DOI:** [10.1093/nar/gkab1113](https://doi.org/10.1093/nar/gkab1113)
  • Network accessline 166
    constraint spectrum quantified from variation in 141,456 humans. *Nature*, 581(7809),   434-443, 2020. - **DOI:** [10.1038/s41586-020-2308-7](https://doi.org/10.1038/s41586-020-2308-7)
  • Network accessline 167
    434-443, 2020. - **DOI:** [10.1038/s41586-020-2308-7](https://doi.org/10.1038/s41586-020-2308-7) - **PMID:** 32461654 | **PMC:** PMC7334197
  • Network accessline 168
    - **DOI:** [10.1038/s41586-020-2308-7](https://doi.org/10.1038/s41586-020-2308-7)
  • Network accessline 194
    - **Citation:** Cunningham F, Allen JE, Allen J, et al. Ensembl 2022. *Nucleic Acids   Research*, 50(D1), D988-D995, 2022. - **DOI:** [10.1093/nar/gkab1049](https://doi.org/10.1093/nar/gkab1049)
  • Network accessline 195
    Research*, 50(D1), D988-D995, 2022. - **DOI:** [10.1093/nar/gkab1049](https://doi.org/10.1093/nar/gkab1049) - **PMID:** 34791404 | **PMC:** PMC8728283
  • Network accessline 196
    - **DOI:** [10.1093/nar/gkab1049](https://doi.org/10.1093/nar/gkab1049)

skills/data-provenance/SKILL.md

  • Network accessline 42
    > "Genomic coordinates were lifted from GRCh37/hg19 to GRCh38/hg38 using UCSC liftOver (v377, Kent et al. 2002, PMID: 12045153). The chain file hg19ToHg38.over.chain.gz was obtained from UCSC Genome Browser (https://hgdownload.soe.ucsc.edu/
  • Network accessline 134
    "file": "hg38-blacklist.v2.bed.gz",       "source": "ENCODE Blacklist v2 (Amemiya et al. 2019, Sci Rep, DOI:10.1038/s41598-019-45839-z)",       "url": "https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz",
  • Network accessline 135
    "source": "ENCODE Blacklist v2 (Amemiya et al. 2019, Sci Rep, DOI:10.1038/s41598-019-45839-z)",       "url": "https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz",       "md5": "def456..."
  • Network accessline 136
    "url": "https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz",
  • Network accessline 336
    **Data Acquisition** > [Assay] data for [biosample] were obtained from the ENCODE Project (ENCODE Project Consortium 2020) via the ENCODE portal (https://www.encodeproject.org). [N] experiments were included (accessions: [list]). All experi
  • Network accessline 337
    > [Assay] data for [biosample] were obtained from the ENCODE Project (ENCODE Project Consortium 2020) via the ENCODE portal (https://www.encodeproject.org). [N] experiments were included (accessions: [list]). All experiments used [sequencer

skills/data-provenance/references/literature.md

  • Network accessline 10
    - **Citation:** Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten JW, da Silva Santos LB, Bourne PE, Bouwman J, Brookes AJ, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo CT, Finkers R, Gonzal
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    - **DOI:** [10.1038/sdata.2016.18](https://doi.org/10.1038/sdata.2016.18)
  • Network accessline 20
    - **Citation:** Baker M. 1,500 scientists lift the lid on reproducibility. *Nature*, 533(7604), 452-454, 2016. - **DOI:** [10.1038/533452a](https://doi.org/10.1038/533452a)
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    - **DOI:** [10.1038/533452a](https://doi.org/10.1038/533452a)
  • Network accessline 30
    - **Citation:** Stodden V, McNutt M, Bailey DH, Deelman E, Gil Y, Hanson B, Heroux MA, Ioannidis JPA, Taufer M. Enhancing reproducibility for computational methods. *Science*, 354(6317), 1240-1241, 2016. - **DOI:** [10.1126/science.aah6168]
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    - **DOI:** [10.1126/science.aah6168](https://doi.org/10.1126/science.aah6168)
  • Network accessline 40
    - **Citation:** Grüning B, Dale R, Sjodin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Koester J, Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. *Nature Methods*, 15(7), 475-476,
  • Network accessline 41
    - **DOI:** [10.1038/s41592-018-0046-7](https://doi.org/10.1038/s41592-018-0046-7)
  • Network accessline 50
    - **Citation:** Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. *Nature Biotechnology*, 35(4), 316-319, 2017. - **DOI:** [10.1038/nbt.3820](https://doi.org/10.1038
  • Network accessline 51
    - **DOI:** [10.1038/nbt.3820](https://doi.org/10.1038/nbt.3820)
  • Network accessline 60
    - **Citation:** Amstutz P, Crusoe MR, Tijanic N, Chapman B, Chilton J, Heuer M, Kartashov A, Leehr D, Menager H, Nedeljkovich M, Scales M, Soiland-Reyes S, Stojanovic L. Common Workflow Language, v1.0. *Specification*, Common Workflow Langu
  • Network accessline 61
    - **DOI:** [10.6084/m9.figshare.3115156.v2](https://doi.org/10.6084/m9.figshare.3115156.v2)
  • Network accessline 69
    - **Citation:** Goecks J, Nekrutenko A, Taylor J, Galaxy Team. Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences. *Genome Biology*, 11(8), R86, 2010. - **DO
  • Network accessline 70
    - **DOI:** [10.1186/gb-2010-11-8-r86](https://doi.org/10.1186/gb-2010-11-8-r86)

skills/disease-research/SKILL.md

  • Network accessline 26
    | Reference | Year | Journal | Key Contribution | Citations | DOI | |-----------|------|---------|-----------------|-----------|-----| | ENCODE Phase 3 | 2020 | Nature | 926,535 human cCREs across 400+ biosamples, SCREEN portal | ~1,656 | [
  • Network accessline 27
    |-----------|------|---------|-----------------|-----------|-----| | ENCODE Phase 3 | 2020 | Nature | 926,535 human cCREs across 400+ biosamples, SCREEN portal | ~1,656 | [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4
  • Network accessline 28
    | ENCODE Phase 3 | 2020 | Nature | 926,535 human cCREs across 400+ biosamples, SCREEN portal | ~1,656 | [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4) |
  • Network accessline 29
    | Maurano et al. | 2012 | Science | Disease variants enriched in regulatory DNA; DNase hotspots explain 76.6% of GWAS SNPs | ~3,500 | [10.1126/science.1222794](https://doi.org/10.1126/science.1222794) |
  • Network accessline 30
    | Finucane et al. | 2015 | Nat Genet | S-LDSC partitions heritability into functional annotations; ENCODE categories explain disproportionate heritability | ~2,253 | [10.1038/ng.3404](https://doi.org/10.1038/ng.3404) |
  • Network accessline 31
    | Nasser et al. | 2021 | Nature | ABC model links enhancers to genes in 131 cell types; connected 5,036 GWAS signals to 2,249 genes | ~468 | [10.1038/s41586-021-03446-x](https://doi.org/10.1038/s41586-021-03446-x) |
  • Network accessline 32
    | Roadmap Epigenomics | 2015 | Nature | 111 reference epigenomes; tissue-specific chromatin states; disease variant enrichment in tissue-specific marks | ~5,810 | [10.1038/nature14248](https://doi.org/10.1038/nature14248) |
  • Network accessline 33
    | Visscher et al. | 2017 | Am J Hum Genet | GWAS review — 10 years of discoveries, statistical frameworks, shift toward functional interpretation | ~2,500 | [10.1016/j.ajhg.2017.06.005](https://doi.org/10.1016/j.ajhg.2017.06.005) |
  • Network accessline 34
    | Buniello et al. | 2019 | Nucleic Acids Res | GWAS Catalog — curated repository; >250,000 SNP-trait associations | ~3,000 | [10.1093/nar/gky1120](https://doi.org/10.1093/nar/gky1120) |
  • Network accessline 35
    | Ochoa et al. | 2021 | Nucleic Acids Res | Open Targets Platform — integrates GWAS, functional genomics, drugs for systematic target identification | ~600 | [10.1093/nar/gkaa1027](https://doi.org/10.1093/nar/gkaa1027) |
  • Network accessline 355
    # Query GWAS Catalog for Alzheimer's disease associations GET https://www.ebi.ac.uk/gwas/rest/api/associations?efoTrait=EFO_0000249
  • Network accessline 356
    GET https://www.ebi.ac.uk/gwas/rest/api/associations?efoTrait=EFO_0000249

skills/disease-research/references/literature.md

  • Network accessline 12
    - **Citation:** Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, Wang H, Reynolds AP, Sandstrom R, Qu H, Brody J, Shafer A, Neri F, Lee K, Kutyavin T, Stehling-Sun S, Johnson AK, Canfield TK, Giste E, Diegel M, Bates D, Hansen RS, Neph
  • Network accessline 13
    - **DOI:** [10.1126/science.1222794](https://doi.org/10.1126/science.1222794)
  • Network accessline 22
    - **Citation:** Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. *Proceedings of the National A
  • Network accessline 23
    - **DOI:** [10.1073/pnas.0903103106](https://doi.org/10.1073/pnas.0903103106)
  • Network accessline 34
    - **Citation:** Roadmap Epigenomics Consortium, Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, Heravi-Moussavi A, Kheradpour P, Zhang Z, Wang J, Ziller MJ, Amin V, Whitaker JW, Schultz MD, Ward LD, Sarkar A, Quon G, Sandstrom RS, Eaton M
  • Network accessline 35
    - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248)
  • Network accessline 44
    - **Citation:** Ernst J, Kellis M. ChromHMM: automating chromatin-state discovery and characterization. *Nature Methods*, 9(3), 215-216, 2012. - **DOI:** [10.1038/nmeth.1906](https://doi.org/10.1038/nmeth.1906)
  • Network accessline 45
    - **DOI:** [10.1038/nmeth.1906](https://doi.org/10.1038/nmeth.1906)
  • Network accessline 56
    - **Citation:** Finucane HK, Bulik-Sullivan B, Gusev A, Trynka G, Reshef Y, Loh PR, Anttila V, Xu H, Zang C, Farh K, Ripke S, Day FR, ReproGen Consortium, Schizophrenia Working Group of the Psychiatric Genomics Consortium, RACI Consortium,
  • Network accessline 57
    - **DOI:** [10.1038/ng.3404](https://doi.org/10.1038/ng.3404)
  • Network accessline 66
    - **Citation:** Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Patterson N, Daly MJ, Price AL, Neale BM. LD Score regression distinguishes confounding from polyge
  • Network accessline 67
    - **DOI:** [10.1038/ng.3211](https://doi.org/10.1038/ng.3211)
  • Network accessline 78
    - **Citation:** Nasser J, Bergman DT, Fulco CP, Gusev A, Engreitz JM. Genome-wide enhancer maps link risk variants to disease genes. *Nature*, 593(7858), 238-243, 2021. - **DOI:** [10.1038/s41586-021-03446-x](https://doi.org/10.1038/s41586-
  • Network accessline 79
    - **DOI:** [10.1038/s41586-021-03446-x](https://doi.org/10.1038/s41586-021-03446-x)
  • Network accessline 88
    - **Citation:** Gasperini M, Hill AJ, McFaline-Figueroa JL, Martin B, Kim S, Zhang MD, Jackson D, Leith A, Schreiber J, Noble WS, Trapnell C, Ahfeldt T, Shendure J. A genome-scale screen for enhancers at single-cell resolution. *Cell*, 176(
  • Network accessline 89
    - **DOI:** [10.1016/j.cell.2018.12.038](https://doi.org/10.1016/j.cell.2018.12.038)

skills/download-encode/references/literature.md

  • Network accessline 32
    Blomberg N, Boiten JW, da Silva Santos LB, Bourne PE, et al. The FAIR Guiding Principles   for scientific data management and stewardship. *Scientific Data*, 3, 160018, 2016. - **DOI:** [10.1038/sdata.2016.18](https://doi.org/10.1038/sdata.
  • Network accessline 33
    for scientific data management and stewardship. *Scientific Data*, 3, 160018, 2016. - **DOI:** [10.1038/sdata.2016.18](https://doi.org/10.1038/sdata.2016.18) - **PMID:** 26978244 | **PMC:** PMC4792175
  • Network accessline 34
    - **DOI:** [10.1038/sdata.2016.18](https://doi.org/10.1038/sdata.2016.18)
  • Network accessline 65
    Strattan JS, Jolanki O, Tanaka FY, Cherry JM. The Encyclopedia of DNA elements (ENCODE):   data portal update. *Nucleic Acids Research*, 46(D1), D794-D801, 2018. - **DOI:** [10.1093/nar/gkx1081](https://doi.org/10.1093/nar/gkx1081)
  • Network accessline 66
    data portal update. *Nucleic Acids Research*, 46(D1), D794-D801, 2018. - **DOI:** [10.1093/nar/gkx1081](https://doi.org/10.1093/nar/gkx1081) - **PMID:** 29126249 | **PMC:** PMC5753278
  • Network accessline 67
    - **DOI:** [10.1093/nar/gkx1081](https://doi.org/10.1093/nar/gkx1081)
  • Network accessline 94
    guidelines and practices of the ENCODE and modENCODE consortia. *Genome Research*,   22(9), 1813-1831, 2012. - **DOI:** [10.1101/gr.136184.111](https://doi.org/10.1101/gr.136184.111)
  • Network accessline 95
    22(9), 1813-1831, 2012. - **DOI:** [10.1101/gr.136184.111](https://doi.org/10.1101/gr.136184.111) - **PMID:** 22955991 | **PMC:** PMC3431496
  • Network accessline 96
    - **DOI:** [10.1101/gr.136184.111](https://doi.org/10.1101/gr.136184.111)
  • Network accessline 115
    - **Citation:** Amemiya HM, Kundaje A, Boyle AP. The ENCODE Blacklist: identification of   problematic regions of the genome. *Scientific Reports*, 9(1), 9354, 2019. - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019
  • Network accessline 116
    problematic regions of the genome. *Scientific Reports*, 9(1), 9354, 2019. - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z) - **PMID:** 31249361 | **PMC:** PMC6597582
  • Network accessline 117
    - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z)
  • Network accessline 147
    - **Citation:** Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, et al. Integrative analysis   of 111 reference human epigenomes. *Nature*, 518(7539), 317-330, 2015. - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248)
  • Network accessline 148
    of 111 reference human epigenomes. *Nature*, 518(7539), 317-330, 2015. - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248) - **PMID:** 25693563 | **PMC:** PMC4530010
  • Network accessline 149
    - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248)

skills/ensembl-annotation/SKILL.md

  • Network accessline 30
    - **Cunningham et al. 2022** (Nucleic Acids Research): Ensembl 2022 update. [DOI](https://doi.org/10.1093/nar/gkab1049)
  • Network accessline 31
    - **McLaren et al. 2016** (Genome Biology, ~4,500 citations): The Ensembl Variant Effect Predictor. [DOI](https://doi.org/10.1186/s13059-016-0974-4)
  • Network accessline 32
    - **Zerbino et al. 2015** (Genome Biology): The Ensembl Regulatory Build. [DOI](https://doi.org/10.1186/s13059-015-0621-5)
  • Network accessline 36
    **Base URL**: `https://rest.ensembl.org`
  • Network accessline 49
    # Get regulatory features in a region curl -H "Content-type: application/json" \
  • Network accessline 50
    curl -H "Content-type: application/json" \
  • Network accessline 51
    "https://rest.ensembl.org/overlap/region/human/7:140424943-140624564?feature=regulatory"
  • Network accessline 53
    # Also get TF binding motifs curl -H "Content-type: application/json" \
  • Network accessline 54
    curl -H "Content-type: application/json" \
  • Network accessline 55
    "https://rest.ensembl.org/overlap/region/human/7:140424943-140624564?feature=regulatory;feature=motif"
  • Network accessline 76
    # VEP annotation for a variant curl -H "Content-type: application/json" \
  • Network accessline 77
    curl -H "Content-type: application/json" \
  • Network accessline 78
    "https://rest.ensembl.org/vep/human/region/9:22125503-22125502:1/C"
  • Network accessline 80
    # By rs ID curl -H "Content-type: application/json" \
  • Network accessline 81
    curl -H "Content-type: application/json" \
  • Network accessline 82
    "https://rest.ensembl.org/vep/human/id/rs699"
  • Network accessline 88
    curl -X POST -H "Content-type: application/json" \
  • Network accessline 89
    "https://rest.ensembl.org/vep/human/region" \
  • Network accessline 126
    # GRCh37 → GRCh38 curl -H "Content-type: application/json" \
  • Network accessline 127
    curl -H "Content-type: application/json" \
  • Network accessline 128
    "https://rest.ensembl.org/map/human/GRCh37/17:1000000..1000100:1/GRCh38"
  • Network accessline 130
    # GRCh38 → GRCh37 curl -H "Content-type: application/json" \
  • Network accessline 131
    curl -H "Content-type: application/json" \
  • Network accessline 132
    "https://rest.ensembl.org/map/human/GRCh38/17:1000000..1000100:1/GRCh37"
  • Network accessline 142
    # By Ensembl ID curl -H "Content-type: application/json" \
  • Network accessline 143
    curl -H "Content-type: application/json" \
  • Network accessline 144
    "https://rest.ensembl.org/lookup/id/ENSG00000157764?expand=1"
  • Network accessline 146
    # By symbol curl -H "Content-type: application/json" \
  • Network accessline 147
    curl -H "Content-type: application/json" \
  • Network accessline 148
    "https://rest.ensembl.org/lookup/symbol/homo_sapiens/BRAF"
  • Network accessline 154
    # Get all external references for a gene curl -H "Content-type: application/json" \
  • Network accessline 155
    curl -H "Content-type: application/json" \
  • Network accessline 156
    "https://rest.ensembl.org/xrefs/id/ENSG00000157764"
  • Network accessline 158
    # Filter by external DB curl -H "Content-type: application/json" \
  • Network accessline 159
    curl -H "Content-type: application/json" \
  • Network accessline 160
    "https://rest.ensembl.org/xrefs/id/ENSG00000157764?external_db=HGNC"
  • Network accessline 168
    # Get phenotype associations for a gene curl -H "Content-type: application/json" \
  • Network accessline 169
    curl -H "Content-type: application/json" \
  • Network accessline 170
    "https://rest.ensembl.org/phenotype/gene/homo_sapiens/BRCA2"
  • Network accessline 172
    # Get phenotype associations for a region curl -H "Content-type: application/json" \
  • Network accessline 173
    curl -H "Content-type: application/json" \
  • Network accessline 174
    "https://rest.ensembl.org/phenotype/region/homo_sapiens/9:22125500-22136000"
  • Network accessline 183
    2. Annotate with VEP:    curl "https://rest.ensembl.org/vep/human/id/rs699?CADD=1;regulatory=1;REVEL=1"
  • Network accessline 184
    curl "https://rest.ensembl.org/vep/human/id/rs699?CADD=1;regulatory=1;REVEL=1"
  • Network accessline 187
    3. Check Ensembl Regulatory Build for independent confirmation:    curl "https://rest.ensembl.org/overlap/region/human/CHR:START-END?feature=regulatory"
  • Network accessline 188
    curl "https://rest.ensembl.org/overlap/region/human/CHR:START-END?feature=regulatory"
  • Network accessline 191
    4. If working with GRCh37 data, liftOver:    curl "https://rest.ensembl.org/map/human/GRCh37/CHR:POS..POS:1/GRCh38"
  • Network accessline 192
    curl "https://rest.ensembl.org/map/human/GRCh37/CHR:POS..POS:1/GRCh38"
  • Network accessline 194
    5. Resolve gene identifiers for ENCODE targets:    curl "https://rest.ensembl.org/lookup/symbol/homo_sapiens/GENE_SYMBOL"
  • Network accessline 195
    curl "https://rest.ensembl.org/lookup/symbol/homo_sapiens/GENE_SYMBOL"
  • Network accessline 197
    6. Check disease associations:    curl "https://rest.ensembl.org/phenotype/gene/homo_sapiens/GENE"
  • Network accessline 198
    curl "https://rest.ensembl.org/phenotype/gene/homo_sapiens/GENE"
  • Network accessline 245
    POST https://rest.ensembl.org/vep/human/region
  • Network accessline 278
    GET https://rest.ensembl.org/regulatory/species/homo_sapiens/id/ENSR00000123456

skills/ensembl-annotation/references/literature.md

  • Network accessline 15
    Armean IM, Austine-Orimoloye O, Azov AG, Barnes I, Bennett R, et al. Ensembl   2022. Nucleic Acids Research, 50(D1):D988-D995, 2022. - **DOI:** [10.1093/nar/gkab1049](https://doi.org/10.1093/nar/gkab1049)
  • Network accessline 16
    2022. Nucleic Acids Research, 50(D1):D988-D995, 2022. - **DOI:** [10.1093/nar/gkab1049](https://doi.org/10.1093/nar/gkab1049) - **PMID:** 34791404 | **PMC:** PMC8728283
  • Network accessline 17
    - **DOI:** [10.1093/nar/gkab1049](https://doi.org/10.1093/nar/gkab1049)
  • Network accessline 40
    Jarreta J, Amode MR, Armean IM, Azov AG, Bennett R, et al. Ensembl 2020.   Nucleic Acids Research, 48(D1):D682-D688, 2020. - **DOI:** [10.1093/nar/gkz966](https://doi.org/10.1093/nar/gkz966)
  • Network accessline 41
    Nucleic Acids Research, 48(D1):D682-D688, 2020. - **DOI:** [10.1093/nar/gkz966](https://doi.org/10.1093/nar/gkz966) - **PMID:** 31691826 | **PMC:** PMC7145704
  • Network accessline 42
    - **DOI:** [10.1093/nar/gkz966](https://doi.org/10.1093/nar/gkz966)
  • Network accessline 65
    Amode MR, Armean IM, Azov AG, Bennett R, Bhai J, et al. Ensembl 2021.   Nucleic Acids Research, 49(D1):D884-D891, 2021. - **DOI:** [10.1093/nar/gkaa942](https://doi.org/10.1093/nar/gkaa942)
  • Network accessline 66
    Nucleic Acids Research, 49(D1):D884-D891, 2021. - **DOI:** [10.1093/nar/gkaa942](https://doi.org/10.1093/nar/gkaa942) - **PMID:** 33137190 | **PMC:** PMC7778937
  • Network accessline 67
    - **DOI:** [10.1093/nar/gkaa942](https://doi.org/10.1093/nar/gkaa942)
  • Network accessline 88
    - **Citation:** Martin FJ, Amode MR, Anber A, Apweiler R, Arkinson RG, et al.   Ensembl 2023. Nucleic Acids Research, 51(D1):D933-D941, 2023. - **DOI:** [10.1093/nar/gkac958](https://doi.org/10.1093/nar/gkac958)
  • Network accessline 89
    Ensembl 2023. Nucleic Acids Research, 51(D1):D933-D941, 2023. - **DOI:** [10.1093/nar/gkac958](https://doi.org/10.1093/nar/gkac958) - **PMID:** 36318249 | **PMC:** PMC9825606
  • Network accessline 90
    - **DOI:** [10.1093/nar/gkac958](https://doi.org/10.1093/nar/gkac958)
  • Network accessline 117
    Flicek P, Cunningham F. The Ensembl Variant Effect Predictor. Genome   Biology, 17(1):122, 2016. - **DOI:** [10.1186/s13059-016-0974-4](https://doi.org/10.1186/s13059-016-0974-4)
  • Network accessline 118
    Biology, 17(1):122, 2016. - **DOI:** [10.1186/s13059-016-0974-4](https://doi.org/10.1186/s13059-016-0974-4) - **PMID:** 27268795 | **PMC:** PMC4893825
  • Network accessline 119
    - **DOI:** [10.1186/s13059-016-0974-4](https://doi.org/10.1186/s13059-016-0974-4)
  • Network accessline 150
    - **Citation:** Zerbino DR, Wilder SP, Johnson N, Juettemann T, Flicek P. The   Ensembl Regulatory Build. Genome Biology, 16(1):56, 2015. - **DOI:** [10.1186/s13059-015-0621-5](https://doi.org/10.1186/s13059-015-0621-5)
  • Network accessline 151
    Ensembl Regulatory Build. Genome Biology, 16(1):56, 2015. - **DOI:** [10.1186/s13059-015-0621-5](https://doi.org/10.1186/s13059-015-0621-5) - **PMID:** 25887522 | **PMC:** PMC4407537
  • Network accessline 152
    - **DOI:** [10.1186/s13059-015-0621-5](https://doi.org/10.1186/s13059-015-0621-5)
  • Network accessline 180
    Sheppard D, Parton A, Armean IM, Trevanion SJ, Flicek P, Cunningham F.   Ensembl variation resources. Database, 2018:bay119, 2018. - **DOI:** [10.1093/database/bay119](https://doi.org/10.1093/database/bay119)
  • Network accessline 181
    Ensembl variation resources. Database, 2018:bay119, 2018. - **DOI:** [10.1093/database/bay119](https://doi.org/10.1093/database/bay119) - **PMID:** 30576484 | **PMC:** PMC6305438
  • Network accessline 182
    - **DOI:** [10.1093/database/bay119](https://doi.org/10.1093/database/bay119)

skills/epigenome-profiling/SKILL.md

  • Network accessline 21
    | Reference | Year | Journal | DOI | Citations | Contribution | |-----------|------|---------|-----|-----------|-------------| | Roadmap Epigenomics Consortium (Kundaje et al.) | 2015 | *Nature* | [10.1038/nature14248](https://doi.org/10.10
  • Network accessline 22
    |-----------|------|---------|-----|-----------|-------------| | Roadmap Epigenomics Consortium (Kundaje et al.) | 2015 | *Nature* | [10.1038/nature14248](https://doi.org/10.1038/nature14248) | ~5,810 | 111 reference epigenomes; 5-mark core
  • Network accessline 23
    | Roadmap Epigenomics Consortium (Kundaje et al.) | 2015 | *Nature* | [10.1038/nature14248](https://doi.org/10.1038/nature14248) | ~5,810 | 111 reference epigenomes; 5-mark core model; 15/18/25-state ChromHMM |
  • Network accessline 24
    | ENCODE Phase 3 (ENCODE Project Consortium) | 2020 | *Nature* | [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4) | ~1,656 | Registry of candidate cis-regulatory elements (cCREs) across 1,310+ experiments |
  • Network accessline 25
    | Ernst & Kellis | 2012 | *Nat Methods* | [10.1038/nmeth.1906](https://doi.org/10.1038/nmeth.1906) | ~2,294 | ChromHMM: multivariate HMM for chromatin state discovery and characterization |
  • Network accessline 26
    | Barski et al. | 2007 | *Cell* | [10.1016/j.cell.2007.05.009](https://doi.org/10.1016/j.cell.2007.05.009) | ~4,800 | First genome-wide ChIP-Seq of 20 histone methylations in human CD4+ T cells |
  • Network accessline 27
    | Mikkelsen et al. | 2007 | *Nature* | [10.1038/nature06008](https://doi.org/10.1038/nature06008) | ~4,289 | Chromatin state maps in pluripotent and lineage-committed cells; H3K4me3/H3K27me3 discriminate expressed, poised, and repressed gen
  • Network accessline 28
    | Bernstein et al. | 2006 | *Cell* | [10.1016/j.cell.2006.02.041](https://doi.org/10.1016/j.cell.2006.02.041) | ~3,500 | Discovery of bivalent chromatin domains (H3K4me3+H3K27me3) in embryonic stem cells |
  • Network accessline 29
    | Creyghton et al. | 2010 | *PNAS* | [10.1073/pnas.1016071107](https://doi.org/10.1073/pnas.1016071107) | ~2,800 | H3K27ac distinguishes active enhancers from poised (H3K4me1-only) enhancers |
  • Network accessline 30
    | Whyte et al. | 2013 | *Cell* | [10.1016/j.cell.2013.03.035](https://doi.org/10.1016/j.cell.2013.03.035) | ~2,500 | Master transcription factors and super-enhancer identification via ROSE algorithm |
  • Network accessline 31
    | Buenrostro et al. | 2013 | *Nat Methods* | [10.1038/nmeth.2688](https://doi.org/10.1038/nmeth.2688) | ~5,000 | ATAC-seq: transposase-based chromatin accessibility profiling |
  • Network accessline 32
    | Heintzman et al. | 2007 | *Nat Genet* | [10.1038/ng1966](https://doi.org/10.1038/ng1966) | ~2,300 | H3K4me1 marks enhancers, H3K4me3 marks promoters — foundational chromatin signature for regulatory element classification |
  • Network accessline 33
    | Rada-Iglesias et al. | 2011 | *Nature* | [10.1038/nature09692](https://doi.org/10.1038/nature09692) | ~1,200 | Discovered "poised enhancers" (H3K4me1+H3K27me3, no H3K27ac) that activate during differentiation |
  • Network accessline 34
    | ENCODE Blacklist (Amemiya et al.) | 2019 | *Sci Rep* | [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z) | ~1,372 | Comprehensive set of problematic genomic regions to exclude from all analyses |
  • Network accessline 429
    **Always filter with the ENCODE blacklist** (Amemiya et al. 2019) before any downstream analysis. Download from: `https://github.com/Boyle-Lab/Blacklist/blob/master/lists/hg38-blacklist.v2.bed.gz`

skills/epigenome-profiling/references/literature.md

  • Network accessline 30
    - **Citation:** Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, et al. Integrative analysis   of 111 reference human epigenomes. *Nature*, 518(7539), 317-330, 2015. - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248)
  • Network accessline 31
    of 111 reference human epigenomes. *Nature*, 518(7539), 317-330, 2015. - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248) - **PMID:** 25693563 | **PMC:** PMC4530010
  • Network accessline 32
    - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248)
  • Network accessline 49
    - **Citation:** The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in   the human genome. *Nature*, 489(7414), 57-74, 2012. - **DOI:** [10.1038/nature11247](https://doi.org/10.1038/nature11247)
  • Network accessline 50
    the human genome. *Nature*, 489(7414), 57-74, 2012. - **DOI:** [10.1038/nature11247](https://doi.org/10.1038/nature11247) - **PMID:** 22955616 | **PMC:** PMC3439153
  • Network accessline 51
    - **DOI:** [10.1038/nature11247](https://doi.org/10.1038/nature11247)
  • Network accessline 75
    - **Citation:** Ernst J, Kellis M. ChromHMM: automating chromatin-state discovery and   characterization. *Nature Methods*, 9(3), 215-216, 2012. - **DOI:** [10.1038/nmeth.1906](https://doi.org/10.1038/nmeth.1906)
  • Network accessline 76
    characterization. *Nature Methods*, 9(3), 215-216, 2012. - **DOI:** [10.1038/nmeth.1906](https://doi.org/10.1038/nmeth.1906) - **PMID:** 22373907 | **PMC:** PMC3577932
  • Network accessline 77
    - **DOI:** [10.1038/nmeth.1906](https://doi.org/10.1038/nmeth.1906)
  • Network accessline 104
    Zhao K. High-resolution profiling of histone methylations in the human genome. *Cell*,   129(4), 823-837, 2007. - **DOI:** [10.1016/j.cell.2007.05.009](https://doi.org/10.1016/j.cell.2007.05.009)
  • Network accessline 105
    129(4), 823-837, 2007. - **DOI:** [10.1016/j.cell.2007.05.009](https://doi.org/10.1016/j.cell.2007.05.009) - **PMID:** 17512414
  • Network accessline 106
    - **DOI:** [10.1016/j.cell.2007.05.009](https://doi.org/10.1016/j.cell.2007.05.009)
  • Network accessline 125
    structure marks key developmental genes in embryonic stem cells. *Cell*, 125(2),   315-326, 2006. - **DOI:** [10.1016/j.cell.2006.02.041](https://doi.org/10.1016/j.cell.2006.02.041)
  • Network accessline 126
    315-326, 2006. - **DOI:** [10.1016/j.cell.2006.02.041](https://doi.org/10.1016/j.cell.2006.02.041) - **PMID:** 16630819
  • Network accessline 127
    - **DOI:** [10.1016/j.cell.2006.02.041](https://doi.org/10.1016/j.cell.2006.02.041)
  • Network accessline 144
    active enhancers from poised enhancers and predicts developmental state. *PNAS*, 107(50),   21931-21936, 2010. - **DOI:** [10.1073/pnas.1016071107](https://doi.org/10.1073/pnas.1016071107)
  • Network accessline 145
    21931-21936, 2010. - **DOI:** [10.1073/pnas.1016071107](https://doi.org/10.1073/pnas.1016071107) - **PMID:** 21106759 | **PMC:** PMC3003124
  • Network accessline 146
    - **DOI:** [10.1073/pnas.1016071107](https://doi.org/10.1073/pnas.1016071107)
  • Network accessline 170
    activity and timing of transcriptome reprogramming during differentiation. *Molecular Cell*,   56(2), 286-297, 2014. - **DOI:** [10.1016/j.molcel.2014.08.026](https://doi.org/10.1016/j.molcel.2014.08.026)
  • Network accessline 171
    56(2), 286-297, 2014. - **DOI:** [10.1016/j.molcel.2014.08.026](https://doi.org/10.1016/j.molcel.2014.08.026) - **PMID:** 25263596 | **PMC:** PMC4291692
  • Network accessline 172
    - **DOI:** [10.1016/j.molcel.2014.08.026](https://doi.org/10.1016/j.molcel.2014.08.026)
  • Network accessline 191
    International Human Epigenome Consortium: a blueprint for scientific collaboration and   discovery. *Cell*, 167(5), 1145-1149, 2016. - **DOI:** [10.1016/j.cell.2016.11.007](https://doi.org/10.1016/j.cell.2016.11.007)
  • Network accessline 192
    discovery. *Cell*, 167(5), 1145-1149, 2016. - **DOI:** [10.1016/j.cell.2016.11.007](https://doi.org/10.1016/j.cell.2016.11.007) - **PMID:** 27863232
  • Network accessline 193
    - **DOI:** [10.1016/j.cell.2016.11.007](https://doi.org/10.1016/j.cell.2016.11.007)
  • Network accessline 206
    - **Citation:** Xie W, Schultz MD, Lister R, Hou Z, et al. Epigenomic analysis of multilineage   differentiation of human embryonic stem cells. *Cell*, 153(5), 1134-1148, 2013. - **DOI:** [10.1016/j.cell.2013.04.022](https://doi.org/10.1016
  • Network accessline 207
    differentiation of human embryonic stem cells. *Cell*, 153(5), 1134-1148, 2013. - **DOI:** [10.1016/j.cell.2013.04.022](https://doi.org/10.1016/j.cell.2013.04.022) - **PMID:** 23664764 | **PMC:** PMC3786220
  • Network accessline 208
    - **DOI:** [10.1016/j.cell.2013.04.022](https://doi.org/10.1016/j.cell.2013.04.022)
  • Network accessline 223
    - **Citation:** Zhang D, Huang P, Lam CS, et al. ENCODE Phase 3 histone modification   analysis. *Nature*, 583(7818), 706-710, 2020. - **DOI:** [10.1038/s41586-020-2489-0](https://doi.org/10.1038/s41586-020-2489-0)
  • Network accessline 224
    analysis. *Nature*, 583(7818), 706-710, 2020. - **DOI:** [10.1038/s41586-020-2489-0](https://doi.org/10.1038/s41586-020-2489-0) - **PMID:** 32728246 | **PMC:** PMC7410825
  • Network accessline 225
    - **DOI:** [10.1038/s41586-020-2489-0](https://doi.org/10.1038/s41586-020-2489-0)

skills/functional-screen-analysis/SKILL.md

  • Network accessline 44
    - **Gasperini et al. 2019** (Cell, ~800 citations): CRISPRi screen of 5,920 candidate enhancers with single-cell RNA-seq readout in K562 cells. Identified 664 enhancer-gene pairs. Established the "crisprQTL" framework linking perturbation e
  • Network accessline 45
    - **Fulco et al. 2019** (Nature Genetics, ~1,200 citations): CRISPRi tiling screen combined with the Activity-By-Contact (ABC) model. Demonstrated that ABC predictions outperform distance-based enhancer-gene assignment. Quantitative relatio
  • Network accessline 46
    - **Shalem et al. 2014** (Science, ~5,500 citations): Genome-scale CRISPR-Cas9 knockout screening. The foundational paper for CRISPR loss-of-function screens. Established library design principles and analytical frameworks. [DOI](https://do
  • Network accessline 47
    - **Arnold et al. 2013** (Science, ~1,100 citations): STARR-seq — Self-Transcribing Active Regulatory Region sequencing. First genome-wide quantitative enhancer activity assay. Tests millions of fragments simultaneously in Drosophila; adapt
  • Network accessline 48
    - **Inoue et al. 2017** (Genome Research, ~400 citations): MPRA for systematic variant effect prediction. Tested thousands of regulatory variants for allele-specific enhancer activity. Demonstrated that GWAS risk alleles frequently alter en
  • Network accessline 49
    - **Li et al. 2014** (Genome Biology, ~2,800 citations): MAGeCK — Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout. The standard computational tool for CRISPR screen analysis. Robust negative binomial model for guide RNA count data.
  • Network accessline 50
    - **Gordon et al. 2020** (Nature Protocols, ~200 citations): MPRAflow — standardized computational pipeline for MPRA data analysis. Reproducible barcode counting, normalization, and activity scoring. [DOI](https://doi.org/10.1038/s41596-020
  • Network accessline 51
    - **Tewhey et al. 2016** (Cell, ~600 citations): High-throughput identification of regulatory variants using MPRA. Tested >30,000 allelic pairs across 3,642 GWAS loci. Identified hundreds of variants with allele-specific regulatory activity
  • Network accessline 52
    - **Nasser et al. 2021** (Nature, ~700 citations): ABC model for enhancer-gene prediction using ENCODE data. Linked 5,036 GWAS signals to 2,249 genes across 131 cell types. Validated predictions against CRISPRi perturbation data. [DOI](http
  • Network accessline 53
    - **Klein et al. 2020** (Nature Genetics, ~350 citations): CRISPRi screen design principles for non-coding regulatory elements. Established that guide RNA positioning relative to regulatory element boundaries is critical. Quantified the "sh
  • Network accessline 54
    - **Yao et al. 2024** (Nature Methods, ~26 citations): ENCODE4 Functional Characterization Centers — 108 CRISPRi screens, >540,000 perturbations across multiple cell types. Pre-designed sgRNA library targeting 3.27M ENCODE SCREEN cCREs. Est
  • Network accessline 55
    - **Lee et al. 2020** (Genome Biology, ~150 citations): STARRPeaker — peak caller designed specifically for STARR-seq data. Handles input library normalization and identifies significant enhancer peaks from STARR-seq enrichment. [DOI](https
  • Network accessline 56
    - **Kim & Hart 2021** (Genome Medicine, ~200 citations): BAGEL2 — Bayesian Analysis of Gene Essentiality. Updated framework for identifying essential genes and functional elements from CRISPR screen data. [DOI](https://doi.org/10.1186/s1307
  • Network accessline 269
    Both commands run inside a clone of https://github.com/shendurelab/MPRAflow; `nextflow run count.nf --help`

skills/functional-screen-analysis/references/literature.md

  • Network accessline 14
    - **Citation:** Shalem O, Sanjana NE, Hartenian E, Shi X, Scott DA, Mikkelson T, Heckl D, Ebert BL, Root DE, Doench JG, Zhang F. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science, 343(6166):84-87, 2014. - **DOI:** [10.1126
  • Network accessline 15
    - **DOI:** [10.1126/science.1247005](https://doi.org/10.1126/science.1247005)
  • Network accessline 24
    - **Citation:** Fulco CP, Nasser J, Jones TR, Munson G, Bergman DT, Subramanian V, Grossman SR, Anyoha R, Doughty BR, Patwardhan TA, Nguyen TH, Kane M, Perez EM, Duber NC, Lander ES, Engreitz JM. Activity-by-contact model of enhancer-gene s
  • Network accessline 25
    - **DOI:** [10.1038/s41588-019-0538-0](https://doi.org/10.1038/s41588-019-0538-0)
  • Network accessline 34
    - **Citation:** Gasperini M, Hill AJ, McFaline-Figueroa JL, Martin B, Kim S, Zhang MD, Jackson D, Leith A, Schreiber J, Noble WS, Trapnell C, Ahfeldt T, Shendure J. A genome-wide framework for mapping gene regulation via cellular genetic sc
  • Network accessline 35
    - **DOI:** [10.1016/j.cell.2018.11.029](https://doi.org/10.1016/j.cell.2018.11.029)
  • Network accessline 44
    - **Citation:** Li W, Xu H, Xiao T, Cong L, Love MI, Zhang F, Irizarry RA, Liu JS, Brown M, Liu XS. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biology, 15(12):554, 2014. -
  • Network accessline 45
    - **DOI:** [10.1186/s13059-014-0554-4](https://doi.org/10.1186/s13059-014-0554-4)
  • Network accessline 58
    - **Citation:** Melnikov A, Murugan A, Zhang X, Tesileanu T, Wang L, Rogov P, Feber S, Gnirke A, Callan CG Jr, Kinney JB, Kellis M, Lander ES, Mikkelsen TS. Systematic dissection and optimization of inducible enhancers in human cells using
  • Network accessline 59
    - **DOI:** [10.1038/nbt.2137](https://doi.org/10.1038/nbt.2137)
  • Network accessline 68
    - **Citation:** Ernst J, Melnikov A, Zhang X, Wang L, Rogov P, Mikkelsen TS, Kellis M. Genome-scale high-resolution mapping of activating and repressive nucleotides in regulatory regions. Nature Biotechnology, 34(11):1180-1190, 2016. - **DO
  • Network accessline 69
    - **DOI:** [10.1038/nbt.3678](https://doi.org/10.1038/nbt.3678)
  • Network accessline 82
    - **Citation:** Arnold CD, Gerlach D, Stelzer C, Boryń ŁM, Rath M, Stark A. Genome-wide quantitative enhancer activity maps identified by STARR-seq. Science, 339(6123):1074-1077, 2013. - **DOI:** [10.1126/science.1232542](https://doi.org/10
  • Network accessline 83
    - **DOI:** [10.1126/science.1232542](https://doi.org/10.1126/science.1232542)
  • Network accessline 96
    - **Citation:** ENCODE Project Consortium et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature, 583(7818):699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 97
    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 110
    - **Citation:** Hart T, Moffat J. BAGEL: a computational framework for identifying essential genes from pooled library screens. BMC Bioinformatics, 17:164, 2016. - **DOI:** [10.1186/s12859-016-1015-8](https://doi.org/10.1186/s12859-016-1015
  • Network accessline 111
    - **DOI:** [10.1186/s12859-016-1015-8](https://doi.org/10.1186/s12859-016-1015-8)

skills/geo-connector/SKILL.md

  • Network accessline 28
    - The ENCODE Portal is canonical; GEO is secondary archive - GEO accessions are stored in ENCODE's `dbxrefs` field as `GEO:GSExxxxx` - NCBI maintains a dedicated ENCODE listing: https://www.ncbi.nlm.nih.gov/geo/encode/
  • Network accessline 30
    - NCBI maintains a dedicated ENCODE listing: https://www.ncbi.nlm.nih.gov/geo/encode/
  • Network accessline 70
    # Via NCBI E-utilities curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=gds&term=ENCODE[KEYWORD]+AND+gse[ETYP]&retmax=100&usehistory=y&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 71
    curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=gds&term=ENCODE[KEYWORD]+AND+gse[ETYP]&retmax=100&usehistory=y&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 78
    **Base URL**: `https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi`
  • Network accessline 82
    **Rate limit**: 3 req/sec without API key, 10 req/sec with key. Get a key at https://www.ncbi.nlm.nih.gov/account/
  • Network accessline 97
    # Human pancreas ATAC-seq datasets with BED files curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=gds&term=pancreas+AND+ATAC-seq+AND+%22Homo+sapiens%22[ORGN]+AND+gse[ETYP]+AND+bed[suppFile]&retmax=50&tool=encode_mcp&emai
  • Network accessline 98
    curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=gds&term=pancreas+AND+ATAC-seq+AND+%22Homo+sapiens%22[ORGN]+AND+gse[ETYP]+AND+bed[suppFile]&retmax=50&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 100
    # ChIP-seq datasets from a specific year curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=gds&term=ChIP-seq+AND+H3K27ac+AND+gse[ETYP]+AND+2024[PDAT]&retmax=50&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 101
    curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=gds&term=ChIP-seq+AND+H3K27ac+AND+gse[ETYP]+AND+2024[PDAT]&retmax=50&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 103
    # Datasets associated with a PubMed ID curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&db=gds&id=PMID&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 104
    curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&db=gds&id=PMID&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 112
    # Step 1: Search (returns UIDs, NOT accessions) curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=gds&term=GSE12345[ACCN]&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 113
    curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=gds&term=GSE12345[ACCN]&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 115
    # Step 2: Get summary (use UID from step 1) curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=gds&id=UID&version=2.0&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 116
    curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?db=gds&id=UID&version=2.0&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 122
    # Get full SOFT-format record curl "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12345&targ=self&view=full&form=text"
  • Network accessline 123
    curl "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12345&targ=self&view=full&form=text"
  • Network accessline 125
    # Get XML (MINiML) format curl "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12345&targ=self&view=full&form=xml"
  • Network accessline 126
    curl "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12345&targ=self&view=full&form=xml"
  • Network accessline 128
    # Get all sample metadata for a series curl "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12345&targ=gsm&view=brief&form=text"
  • Network accessline 129
    curl "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12345&targ=gsm&view=brief&form=text"
  • Network accessline 156
    # Download series matrix (fastest for expression data) wget "https://ftp.ncbi.nlm.nih.gov/geo/series/GSE12nnn/GSE12345/matrix/GSE12345_series_matrix.txt.gz"
  • Network accessline 157
    wget "https://ftp.ncbi.nlm.nih.gov/geo/series/GSE12nnn/GSE12345/matrix/GSE12345_series_matrix.txt.gz"
  • Network accessline 159
    # Download all supplementary files wget "https://ftp.ncbi.nlm.nih.gov/geo/series/GSE12nnn/GSE12345/suppl/GSE12345_RAW.tar"
  • Network accessline 160
    wget "https://ftp.ncbi.nlm.nih.gov/geo/series/GSE12nnn/GSE12345/suppl/GSE12345_RAW.tar"
  • Network accessline 211
    # Link GEO to SRA curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=gds&db=sra&id=GDS_UID&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 212
    curl "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=gds&db=sra&id=GDS_UID&tool=encode_mcp&email=YOUR_EMAIL"
  • Network accessline 263
    GET https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=gds&term=HepG2[cell+line]+AND+RNA-seq[strategy]+AND+Homo+sapiens[organism]&retmax=10

skills/geo-connector/references/literature.md

  • Network accessline 16
    GEO: archive for functional genomics data sets — update. Nucleic Acids   Research, 41(D1):D991-D995, 2013. - **DOI:** [10.1093/nar/gks1193](https://doi.org/10.1093/nar/gks1193)
  • Network accessline 17
    Research, 41(D1):D991-D995, 2013. - **DOI:** [10.1093/nar/gks1193](https://doi.org/10.1093/nar/gks1193) - **PMID:** 23193258 | **PMC:** PMC3531084
  • Network accessline 18
    - **DOI:** [10.1093/nar/gks1193](https://doi.org/10.1093/nar/gks1193)
  • Network accessline 53
    gene expression and hybridization array data repository. Nucleic Acids   Research, 30(1):207-210, 2002. - **DOI:** [10.1093/nar/30.1.207](https://doi.org/10.1093/nar/30.1.207)
  • Network accessline 54
    Research, 30(1):207-210, 2002. - **DOI:** [10.1093/nar/30.1.207](https://doi.org/10.1093/nar/30.1.207) - **PMID:** 11752295 | **PMC:** PMC99122
  • Network accessline 55
    - **DOI:** [10.1093/nar/30.1.207](https://doi.org/10.1093/nar/30.1.207)
  • Network accessline 89
    - **Citation:** Clough E, Barrett T. The Gene Expression Omnibus Database.   Methods in Molecular Biology, 1418:93-110, 2016. - **DOI:** [10.1007/978-1-4939-3578-9_5](https://doi.org/10.1007/978-1-4939-3578-9_5)
  • Network accessline 90
    Methods in Molecular Biology, 1418:93-110, 2016. - **DOI:** [10.1007/978-1-4939-3578-9_5](https://doi.org/10.1007/978-1-4939-3578-9_5) - **PMID:** 27008011 | **PMC:** PMC4944384
  • Network accessline 91
    - **DOI:** [10.1007/978-1-4939-3578-9_5](https://doi.org/10.1007/978-1-4939-3578-9_5)
  • Network accessline 131
    Sequence Database Collaboration. The Sequence Read Archive. Nucleic Acids   Research, 39(Database issue):D19-D21, 2011. - **DOI:** [10.1093/nar/gkq1019](https://doi.org/10.1093/nar/gkq1019)
  • Network accessline 132
    Research, 39(Database issue):D19-D21, 2011. - **DOI:** [10.1093/nar/gkq1019](https://doi.org/10.1093/nar/gkq1019) - **PMID:** 21062823 | **PMC:** PMC3013647
  • Network accessline 133
    - **DOI:** [10.1093/nar/gkq1019](https://doi.org/10.1093/nar/gkq1019)
  • Network accessline 167
    update — simplifying data submissions. Nucleic Acids Research,   43(D1):D1113-D1116, 2015. - **DOI:** [10.1093/nar/gku1057](https://doi.org/10.1093/nar/gku1057)
  • Network accessline 168
    43(D1):D1113-D1116, 2015. - **DOI:** [10.1093/nar/gku1057](https://doi.org/10.1093/nar/gku1057) - **PMID:** 25361974 | **PMC:** PMC4383899
  • Network accessline 169
    - **DOI:** [10.1093/nar/gku1057](https://doi.org/10.1093/nar/gku1057)

skills/gnomad-variants/SKILL.md

  • Network accessline 37
    - **gnomAD v2.1.1** (GRCh37/hg19): Legacy dataset, still widely used - **ExAC** (GRCh37): Superseded by gnomAD. All ExAC samples included in gnomAD v2+. - **gnomAD browser**: https://gnomad.broadinstitute.org
  • Network accessline 39
    - **gnomAD browser**: https://gnomad.broadinstitute.org
  • Network accessline 43
    - **Karczewski et al. 2020** (Nature, ~5,000 citations): gnomAD — mutational constraint spectrum from 141,456 individuals. Defined LOEUF as primary constraint metric. [DOI](https://doi.org/10.1038/s41586-020-2308-7)
  • Network accessline 44
    - **Lek et al. 2016** (Nature, ~7,000 citations): ExAC — analysis of protein-coding variation in 60,706 humans. Established pLI for gene constraint. [DOI](https://doi.org/10.1038/nature19057)
  • Network accessline 45
    - **Maurano et al. 2012** (Science, ~2,800 citations): Disease variants concentrate in DNase hypersensitive sites. [DOI](https://doi.org/10.1126/science.1222794)
  • Network accessline 46
    - **Finucane et al. 2015** (Nature Genetics, ~2,253 citations): Stratified LD score regression partitioning heritability into ENCODE annotations. [DOI](https://doi.org/10.1038/ng.3404)
  • Network accessline 59
    **Endpoint**: `https://gnomad.broadinstitute.org/api`
  • Network accessline 67
    curl -X POST https://gnomad.broadinstitute.org/api \
  • Network accessline 79
    curl -X POST https://gnomad.broadinstitute.org/api \
  • Network accessline 89
    curl -X POST https://gnomad.broadinstitute.org/api \
  • Network accessline 179
    # gnomAD v4 genome VCFs (GRCh38) # Available at: https://gnomad.broadinstitute.org/downloads
  • Network accessline 180
    # Available at: https://gnomad.broadinstitute.org/downloads

skills/gnomad-variants/references/literature.md

  • Network accessline 16
    mutational constraint spectrum quantified from variation in 141,456 humans.   Nature, 581(7809):434-443, 2020. - **DOI:** [10.1038/s41586-020-2308-7](https://doi.org/10.1038/s41586-020-2308-7)
  • Network accessline 17
    Nature, 581(7809):434-443, 2020. - **DOI:** [10.1038/s41586-020-2308-7](https://doi.org/10.1038/s41586-020-2308-7) - **PMID:** 32461654 | **PMC:** PMC7334197
  • Network accessline 18
    - **DOI:** [10.1038/s41586-020-2308-7](https://doi.org/10.1038/s41586-020-2308-7)
  • Network accessline 43
    protein-coding genetic variation in 60,706 humans. Nature,   536(7616):285-291, 2016. - **DOI:** [10.1038/nature19057](https://doi.org/10.1038/nature19057)
  • Network accessline 44
    536(7616):285-291, 2016. - **DOI:** [10.1038/nature19057](https://doi.org/10.1038/nature19057) - **PMID:** 27535533 | **PMC:** PMC5018207
  • Network accessline 45
    - **DOI:** [10.1038/nature19057](https://doi.org/10.1038/nature19057)
  • Network accessline 69
    constraint map using variation in 76,156 human genomes. Nature,   625(7993):92-100, 2024. - **DOI:** [10.1038/s41586-023-06045-0](https://doi.org/10.1038/s41586-023-06045-0)
  • Network accessline 70
    625(7993):92-100, 2024. - **DOI:** [10.1038/s41586-023-06045-0](https://doi.org/10.1038/s41586-023-06045-0) - **PMID:** 38057664 | **PMC:** PMC10764060
  • Network accessline 71
    - **DOI:** [10.1038/s41586-023-06045-0](https://doi.org/10.1038/s41586-023-06045-0)
  • Network accessline 101
    variation reference for medical and population genetics. Nature,   581(7809):444-451, 2020. - **DOI:** [10.1038/s41586-020-2287-8](https://doi.org/10.1038/s41586-020-2287-8)
  • Network accessline 102
    581(7809):444-451, 2020. - **DOI:** [10.1038/s41586-020-2287-8](https://doi.org/10.1038/s41586-020-2287-8) - **PMID:** 32461652 | **PMC:** PMC7334194
  • Network accessline 103
    - **DOI:** [10.1038/s41586-020-2287-8](https://doi.org/10.1038/s41586-020-2287-8)
  • Network accessline 131
    Solomonson M, et al. Variant interpretation using population databases:   Lessons from gnomAD. Human Mutation, 43(8):1012-1030, 2022. - **DOI:** [10.1002/humu.24309](https://doi.org/10.1002/humu.24309)
  • Network accessline 132
    Lessons from gnomAD. Human Mutation, 43(8):1012-1030, 2022. - **DOI:** [10.1002/humu.24309](https://doi.org/10.1002/humu.24309) - **PMID:** 34859531 | **PMC:** PMC9160216
  • Network accessline 133
    - **DOI:** [10.1002/humu.24309](https://doi.org/10.1002/humu.24309)
  • Network accessline 159
    resolution variant frequencies to empower clinical genome interpretation.   Genetics in Medicine, 19(10):1151-1158, 2017. - **DOI:** [10.1038/gim.2017.26](https://doi.org/10.1038/gim.2017.26)
  • Network accessline 160
    Genetics in Medicine, 19(10):1151-1158, 2017. - **DOI:** [10.1038/gim.2017.26](https://doi.org/10.1038/gim.2017.26) - **PMID:** 28518168 | **PMC:** PMC5633148
  • Network accessline 161
    - **DOI:** [10.1038/gim.2017.26](https://doi.org/10.1038/gim.2017.26)
  • Network accessline 190
    to human disease in 281,104 UK Biobank exomes. Nature, 597(7877):527-532,   2021. - **DOI:** [10.1038/s41586-021-03855-y](https://doi.org/10.1038/s41586-021-03855-y)
  • Network accessline 191
    2021. - **DOI:** [10.1038/s41586-021-03855-y](https://doi.org/10.1038/s41586-021-03855-y) - **PMID:** 34375979 | **PMC:** PMC8459922
  • Network accessline 192
    - **DOI:** [10.1038/s41586-021-03855-y](https://doi.org/10.1038/s41586-021-03855-y)

skills/gtex-expression/SKILL.md

  • Network accessline 38
    - **GTEx Consortium 2020** "The GTEx Consortium atlas of genetic regulatory effects across human tissues" (Science, ~4,000 citations). The flagship publication describing the v8 release with 17,382 samples across 54 tissues from 948 donors.
  • Network accessline 39
    - **Aguet et al. 2017** "Genetic effects on gene expression across human tissues" (Nature, ~3,000 citations). The v6p analysis establishing the multi-tissue eQTL framework, demonstrating widespread tissue-specific genetic regulation. [DOI:
  • Network accessline 40
    - **ENCODE Project Consortium 2020** (Nature, ~1,656 citations). Registry of 926,535 human cCREs. Provides the regulatory element catalog to cross-reference with GTEx expression. [DOI: 10.1038/s41586-020-2493-4](https://doi.org/10.1038/s415
  • Network accessline 41
    - **Nasser et al. 2021** (Nature, ~468 citations). ABC model linking enhancers to genes using ENCODE data. GTEx expression validates ABC-predicted enhancer-gene pairs. [DOI: 10.1038/s41586-021-03446-x](https://doi.org/10.1038/s41586-021-034
  • Network accessline 56
    **Base URL**: `https://gtexportal.org/api/v2`
  • Network accessline 101
    gene_id = "ENSG00000254647"  # INS (insulin) url = f"https://gtexportal.org/api/v2/expression/geneExpression"
  • Network accessline 102
    url = f"https://gtexportal.org/api/v2/expression/geneExpression"
  • Network accessline 108
    response = requests.get(url, params=params)
  • Network accessline 119
    url = "https://gtexportal.org/api/v2/expression/medianTranscriptExpression"
  • Network accessline 122
    "datasetId": "gtex_v8" } response = requests.get(url, params=params)
  • Network accessline 124
    response = requests.get(url, params=params)
  • Network accessline 152
    url = "https://gtexportal.org/api/v2/dataset/tissueSiteDetail" response = requests.get(url)
  • Network accessline 153
    response = requests.get(url)
  • Network accessline 243
    url = "https://gtexportal.org/api/v2/eqtl/singleTissueEqtl"
  • Network accessline 247
    "datasetId": "gtex_v8" } response = requests.get(url, params=params)
  • Network accessline 248
    } response = requests.get(url, params=params) eqtls = response.json()["data"]
  • Network accessline 249
    response = requests.get(url, params=params)
  • Network accessline 271
    # Query GTEx API gene_id <- "ENSG00000254647" url <- paste0("https://gtexportal.org/api/v2/expression/geneExpression",
  • Network accessline 272
    gene_id <- "ENSG00000254647" url <- paste0("https://gtexportal.org/api/v2/expression/geneExpression",               "?geneId=", gene_id, "&datasetId=gtex_v8")
  • Network accessline 273
    url <- paste0("https://gtexportal.org/api/v2/expression/geneExpression",
  • Network accessline 323
    GET https://gtexportal.org/api/v2/expression/medianGeneExpression?gencodeId=ENSG00000134243&tissueSiteDetailId=Liver

skills/gtex-expression/references/literature.md

  • Network accessline 14
    - **Citation:** GTEx Consortium. The GTEx Consortium atlas of genetic   regulatory effects across human tissues. Science, 369(6509):1318-1330, 2020. - **DOI:** [10.1126/science.aaz1776](https://doi.org/10.1126/science.aaz1776)
  • Network accessline 15
    regulatory effects across human tissues. Science, 369(6509):1318-1330, 2020. - **DOI:** [10.1126/science.aaz1776](https://doi.org/10.1126/science.aaz1776) - **PMID:** 32913098 | **PMC:** PMC7737656
  • Network accessline 16
    - **DOI:** [10.1126/science.aaz1776](https://doi.org/10.1126/science.aaz1776)
  • Network accessline 45
    P, Park Y, Parsana P, Segre AV, et al. Genetic effects on gene expression   across human tissues. Nature, 550(7675):204-213, 2017. - **DOI:** [10.1038/nature24277](https://doi.org/10.1038/nature24277)
  • Network accessline 46
    across human tissues. Nature, 550(7675):204-213, 2017. - **DOI:** [10.1038/nature24277](https://doi.org/10.1038/nature24277) - **PMID:** 29022597 | **PMC:** PMC5776756
  • Network accessline 47
    - **DOI:** [10.1038/nature24277](https://doi.org/10.1038/nature24277)
  • Network accessline 68
    Expression (GTEx) pilot analysis: multitissue gene regulation in humans.   Science, 348(6235):648-660, 2015. - **DOI:** [10.1126/science.1262110](https://doi.org/10.1126/science.1262110)
  • Network accessline 69
    Science, 348(6235):648-660, 2015. - **DOI:** [10.1126/science.1262110](https://doi.org/10.1126/science.1262110) - **PMID:** 25954001 | **PMC:** PMC4547484
  • Network accessline 70
    - **DOI:** [10.1126/science.1262110](https://doi.org/10.1126/science.1262110)
  • Network accessline 98
    HK. A gene-based association method for mapping traits using reference   transcriptome data. Nature Genetics, 47(9):1091-1098, 2015. - **DOI:** [10.1038/ng.3367](https://doi.org/10.1038/ng.3367)
  • Network accessline 99
    transcriptome data. Nature Genetics, 47(9):1091-1098, 2015. - **DOI:** [10.1038/ng.3367](https://doi.org/10.1038/ng.3367) - **PMID:** 26258848 | **PMC:** PMC4552594
  • Network accessline 100
    - **DOI:** [10.1038/ng.3367](https://doi.org/10.1038/ng.3367)
  • Network accessline 125
    inferred from GWAS summary statistics. Nature Communications, 9(1):1825,   2018. - **DOI:** [10.1038/s41467-018-03621-1](https://doi.org/10.1038/s41467-018-03621-1)
  • Network accessline 126
    2018. - **DOI:** [10.1038/s41467-018-03621-1](https://doi.org/10.1038/s41467-018-03621-1) - **PMID:** 29739930 | **PMC:** PMC5940825
  • Network accessline 127
    - **DOI:** [10.1038/s41467-018-03621-1](https://doi.org/10.1038/s41467-018-03621-1)
  • Network accessline 158
    specific genetic regulation of gene expression across human tissues.   Science, 369(6509):eaaz8528, 2020. - **DOI:** [10.1126/science.aaz8528](https://doi.org/10.1126/science.aaz8528)
  • Network accessline 159
    Science, 369(6509):eaaz8528, 2020. - **DOI:** [10.1126/science.aaz8528](https://doi.org/10.1126/science.aaz8528) - **PMID:** 32913075 | **PMC:** PMC7668455
  • Network accessline 160
    - **DOI:** [10.1126/science.aaz8528](https://doi.org/10.1126/science.aaz8528)
  • Network accessline 181
    nucleus cross-tissue molecular reference maps toward understanding disease   gene function. Science, 376(6594):eabl4290, 2022. - **DOI:** [10.1126/science.abl4290](https://doi.org/10.1126/science.abl4290)
  • Network accessline 182
    gene function. Science, 376(6594):eabl4290, 2022. - **DOI:** [10.1126/science.abl4290](https://doi.org/10.1126/science.abl4290) - **PMID:** 35549429 | **PMC:** PMC9744718
  • Network accessline 183
    - **DOI:** [10.1126/science.abl4290](https://doi.org/10.1126/science.abl4290)

skills/gwas-catalog/SKILL.md

  • Network accessline 35
    - **Sollis et al. 2023** "The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource" (Nucleic Acids Research). The current GWAS Catalog publication describing the REST API, summary statistics hosting, and expanded annotation pipelin
  • Network accessline 36
    - **Buniello et al. 2019** "The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019" (Nucleic Acids Research, ~3,500 citations). The widely-cited GWAS Catalog reference describing
  • Network accessline 37
    - **Maurano et al. 2012** "Systematic localization of common disease-associated variation in regulatory DNA" (Science, ~3,000 citations). The foundational demonstration that GWAS variants concentrate in DNase I hypersensitive sites, with ce
  • Network accessline 38
    - **ENCODE Project Consortium 2020** (Nature, ~1,656 citations). Registry of 926,535 human cCREs that provides the regulatory annotation layer for GWAS variant interpretation. [DOI: 10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-
  • Network accessline 39
    - **Finucane et al. 2015** (Nature Genetics, ~2,253 citations). Stratified LD Score Regression (S-LDSC) for partitioning heritability into ENCODE-defined functional categories. [DOI: 10.1038/ng.3404](https://doi.org/10.1038/ng.3404)
  • Network accessline 40
    - **Nasser et al. 2021** (Nature, ~468 citations). ABC model linked 5,036 GWAS signals to 2,249 genes using ENCODE data. [DOI: 10.1038/s41586-021-03446-x](https://doi.org/10.1038/s41586-021-03446-x)
  • Network accessline 44
    **Base URL**: `https://www.ebi.ac.uk/gwas/rest/api`
  • Network accessline 67
    For genome-wide analysis, use the GWAS Catalog downloads (faster than API): - All associations: `https://www.ebi.ac.uk/gwas/api/search/downloads/full`
  • Network accessline 68
    - All associations: `https://www.ebi.ac.uk/gwas/api/search/downloads/full`
  • Network accessline 69
    - Alternative: `https://www.ebi.ac.uk/gwas/docs/file-downloads`
  • Network accessline 79
    # Search by trait name trait = "type 2 diabetes" url = "https://www.ebi.ac.uk/gwas/rest/api/studies"
  • Network accessline 80
    trait = "type 2 diabetes" url = "https://www.ebi.ac.uk/gwas/rest/api/studies" params = {"diseaseTrait": trait}
  • Network accessline 81
    url = "https://www.ebi.ac.uk/gwas/rest/api/studies" params = {"diseaseTrait": trait} response = requests.get(url, params=params)
  • Network accessline 82
    params = {"diseaseTrait": trait} response = requests.get(url, params=params) studies = response.json()["_embedded"]["studies"]
  • Network accessline 83
    response = requests.get(url, params=params)
  • Network accessline 105
    # Query by EFO ID (more precise) efo_id = "EFO_0001360" url = f"https://www.ebi.ac.uk/gwas/rest/api/efoTraits/{efo_id}/associations"
  • Network accessline 106
    efo_id = "EFO_0001360" url = f"https://www.ebi.ac.uk/gwas/rest/api/efoTraits/{efo_id}/associations" params = {"size": 500}
  • Network accessline 107
    url = f"https://www.ebi.ac.uk/gwas/rest/api/efoTraits/{efo_id}/associations" params = {"size": 500} response = requests.get(url, params=params)
  • Network accessline 109
    response = requests.get(url, params=params)
  • Network accessline 137
    def get_gwas_associations(rs_id):     """Get all GWAS associations for a variant."""     url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}/associations"
  • Network accessline 138
    """Get all GWAS associations for a variant."""     url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}/associations"     response = requests.get(url)
  • Network accessline 139
    url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}/associations"     response = requests.get(url)     if response.status_code == 200:
  • Network accessline 140
    response = requests.get(url)
  • Network accessline 156
    def get_study_associations(study_id):     """Get all associations from a GWAS study."""     url = f"https://www.ebi.ac.uk/gwas/rest/api/studies/{study_id}/associations"
  • Network accessline 157
    """Get all associations from a GWAS study."""     url = f"https://www.ebi.ac.uk/gwas/rest/api/studies/{study_id}/associations"     params = {"size": 500}
  • Network accessline 158
    url = f"https://www.ebi.ac.uk/gwas/rest/api/studies/{study_id}/associations"     params = {"size": 500}     response = requests.get(url, params=params)
  • Network accessline 159
    params = {"size": 500}     response = requests.get(url, params=params)     return response.json()["_embedded"]["associations"]
  • Network accessline 160
    response = requests.get(url, params=params)
  • Network accessline 178
    rs_id = gene.get("riskAlleleName", "").split("-")[0]         # Get location from the SNP endpoint         snp_url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}"
  • Network accessline 179
    # Get location from the SNP endpoint         snp_url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}"         snp_resp = requests.get(snp_url)
  • Network accessline 180
    snp_url = f"https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/{rs_id}"         snp_resp = requests.get(snp_url)         if snp_resp.status_code == 200:
  • Network accessline 181
    snp_resp = requests.get(snp_url)
  • Network accessline 205
    - **LDlink** (NIH): `https://ldlink.nih.gov/?tab=ldproxy` — Web and API
  • Network accessline 206
    [Credential-related source omitted]
  • Network accessline 318
    Activity-By-Contact predictions link enhancers to genes using ENCODE ATAC-seq + H3K27ac + Hi-C: - Pre-computed for 131 cell types - Download from: https://www.engreitzlab.org/abc/
  • Network accessline 319
    - Pre-computed for 131 cell types - Download from: https://www.engreitzlab.org/abc/ - Linked 5,036 GWAS signals to 2,249 target genes
  • Network accessline 320
    - Download from: https://www.engreitzlab.org/abc/
  • Network accessline 419
    GET https://www.ebi.ac.uk/gwas/rest/api/associations?efoTrait=EFO_0001360&pvalueFilter=5e-8

skills/gwas-catalog/references/literature.md

  • Network accessline 17
    arrays and summary statistics 2019. Nucleic Acids Research,   47(D1):D1005-D1012, 2019. - **DOI:** [10.1093/nar/gky1120](https://doi.org/10.1093/nar/gky1120)
  • Network accessline 18
    47(D1):D1005-D1012, 2019. - **DOI:** [10.1093/nar/gky1120](https://doi.org/10.1093/nar/gky1120) - **PMID:** 30445434 | **PMC:** PMC6323933
  • Network accessline 19
    - **DOI:** [10.1093/nar/gky1120](https://doi.org/10.1093/nar/gky1120)
  • Network accessline 42
    knowledgebase and deposition resource. Nucleic Acids Research,   51(D1):D1299-D1307, 2023. - **DOI:** [10.1093/nar/gkac1010](https://doi.org/10.1093/nar/gkac1010)
  • Network accessline 43
    51(D1):D1299-D1307, 2023. - **DOI:** [10.1093/nar/gkac1010](https://doi.org/10.1093/nar/gkac1010) - **PMID:** 36350656 | **PMC:** PMC9825413
  • Network accessline 44
    - **DOI:** [10.1093/nar/gkac1010](https://doi.org/10.1093/nar/gkac1010)
  • Network accessline 71
    common disease-associated variation in regulatory DNA. Science,   337(6099):1190-1195, 2012. - **DOI:** [10.1126/science.1222794](https://doi.org/10.1126/science.1222794)
  • Network accessline 72
    337(6099):1190-1195, 2012. - **DOI:** [10.1126/science.1222794](https://doi.org/10.1126/science.1222794) - **PMID:** 22955828 | **PMC:** PMC3629424
  • Network accessline 73
    - **DOI:** [10.1126/science.1222794](https://doi.org/10.1126/science.1222794)
  • Network accessline 98
    functional annotation using genome-wide association summary statistics.   Nature Genetics, 47(11):1228-1235, 2015. - **DOI:** [10.1038/ng.3404](https://doi.org/10.1038/ng.3404)
  • Network accessline 99
    Nature Genetics, 47(11):1228-1235, 2015. - **DOI:** [10.1038/ng.3404](https://doi.org/10.1038/ng.3404) - **PMID:** 26414678 | **PMC:** PMC4626285
  • Network accessline 100
    - **DOI:** [10.1038/ng.3404](https://doi.org/10.1038/ng.3404)
  • Network accessline 123
    Genome-wide enhancer maps link risk variants to disease target genes.   Nature, 593(7858):238-243, 2021. - **DOI:** [10.1038/s41586-021-03446-x](https://doi.org/10.1038/s41586-021-03446-x)
  • Network accessline 124
    Nature, 593(7858):238-243, 2021. - **DOI:** [10.1038/s41586-021-03446-x](https://doi.org/10.1038/s41586-021-03446-x) - **PMID:** 33828297 | **PMC:** PMC8516355
  • Network accessline 125
    - **DOI:** [10.1038/s41586-021-03446-x](https://doi.org/10.1038/s41586-021-03446-x)
  • Network accessline 152
    Yang J. 10 years of GWAS discovery: biology, function, and translation.   American Journal of Human Genetics, 101(1):5-22, 2017. - **DOI:** [10.1016/j.ajhg.2017.06.005](https://doi.org/10.1016/j.ajhg.2017.06.005)
  • Network accessline 153
    American Journal of Human Genetics, 101(1):5-22, 2017. - **DOI:** [10.1016/j.ajhg.2017.06.005](https://doi.org/10.1016/j.ajhg.2017.06.005) - **PMID:** 28686856 | **PMC:** PMC5501872
  • Network accessline 154
    - **DOI:** [10.1016/j.ajhg.2017.06.005](https://doi.org/10.1016/j.ajhg.2017.06.005)
  • Network accessline 177
    association to function. American Journal of Human Genetics, 102(5):717-730,   2018. - **DOI:** [10.1016/j.ajhg.2018.04.002](https://doi.org/10.1016/j.ajhg.2018.04.002)
  • Network accessline 178
    2018. - **DOI:** [10.1016/j.ajhg.2018.04.002](https://doi.org/10.1016/j.ajhg.2018.04.002) - **PMID:** 29727686 | **PMC:** PMC5986732
  • Network accessline 179
    - **DOI:** [10.1016/j.ajhg.2018.04.002](https://doi.org/10.1016/j.ajhg.2018.04.002)
  • Network accessline 206
    mapping and annotation of genetic associations with FUMA. Nature   Communications, 8(1):1826, 2017. - **DOI:** [10.1038/s41467-017-01261-5](https://doi.org/10.1038/s41467-017-01261-5)
  • Network accessline 207
    Communications, 8(1):1826, 2017. - **DOI:** [10.1038/s41467-017-01261-5](https://doi.org/10.1038/s41467-017-01261-5) - **PMID:** 29184056 | **PMC:** PMC5705698
  • Network accessline 208
    - **DOI:** [10.1038/s41467-017-01261-5](https://doi.org/10.1038/s41467-017-01261-5)
  • Network accessline 230
    AR, Martin HC, Lappalainen T, Posthuma D. Genome-wide association studies.   Nature Reviews Methods Primers, 1(1):59, 2021. - **DOI:** [10.1038/s43586-021-00056-9](https://doi.org/10.1038/s43586-021-00056-9)
  • Network accessline 231
    Nature Reviews Methods Primers, 1(1):59, 2021. - **DOI:** [10.1038/s43586-021-00056-9](https://doi.org/10.1038/s43586-021-00056-9) - **PMID:** 37325440 | **PMC:** PMC10272025
  • Network accessline 232
    - **DOI:** [10.1038/s43586-021-00056-9](https://doi.org/10.1038/s43586-021-00056-9)

skills/hic-aggregation/SKILL.md

  • Network accessline 41
    - **mariner** (Flores et al. 2024, Bioinformatics): R/Bioconductor package for BEDPE manipulation including merging loops across experiments with configurable anchor tolerance. - **ENCODE Phase 3** (Gorkin et al. 2020, Nature, 301 citations
  • Network accessline 42
    - **ENCODE Phase 3** (Gorkin et al. 2020, Nature, 301 citations): Integrated Hi-C data across tissues to define regulatory loops connecting enhancers to promoters. - **ENCODE Blacklist** (Amemiya et al. 2019, Scientific Reports, 1,372 citat
  • Network accessline 43
    - **ENCODE Blacklist** (Amemiya et al. 2019, Scientific Reports, 1,372 citations): Problematic genomic regions to filter from loop anchors. [DOI](https://doi.org/10.1038/s41598-019-45839-z)
  • Network accessline 180
    ### 5a. ENCODE Blocklist Filtering (Amemiya et al. 2019) Remove loops with anchors in artifact-prone regions (download from https://github.com/Boyle-Lab/Blacklist/blob/master/lists/hg38-blacklist.v2.bed.gz):
  • Network accessline 181
    Remove loops with anchors in artifact-prone regions (download from https://github.com/Boyle-Lab/Blacklist/blob/master/lists/hg38-blacklist.v2.bed.gz):

skills/hic-aggregation/references/literature.md

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    - **Citation:** Wolff J, Bhardwaj V, Nothjunge S, Richard G, Renschler G, Gilsbach R, Manke T, Backofen R, Ramírez F, Grüning BA. Galaxy HiCExplorer 3: a web server for reproducible Hi-C, capture Hi-C and single-cell Hi-C data analysis, qua
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    - **DOI:** [10.1093/nar/gkac407](https://doi.org/10.1093/nar/gkac407)
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    - **Citation:** Roayaei Ardakany A, Gezer HT, Lonardi S, Ay F. Mustache: multi-scale detection of chromatin loops from Hi-C and Micro-C maps using scale-space representation. Genome Biology, 21:256, 2020. - **DOI:** [10.1186/s13059-020-0216
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    - **Citation:** Durand NC, Shamim MS, Machol I, et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Systems, 3(1):95-98, 2016. - **DOI:** [10.1016/j.cels.2016.07.002](https://doi.org/10.1016/j.cels
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    - **DOI:** [10.1016/j.cels.2016.07.002](https://doi.org/10.1016/j.cels.2016.07.002)
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    - **Citation:** Yardimci GG, Ozadam H, Sauria MEG, et al. Measuring the reproducibility and quality of Hi-C data. Genome Biology, 20:57, 2019. - **DOI:** [10.1186/s13059-019-1658-7](https://doi.org/10.1186/s13059-019-1658-7)
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    - **DOI:** [10.1186/s13059-019-1658-7](https://doi.org/10.1186/s13059-019-1658-7)
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    - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z) | **PMID:** 31249361 | **Citations:** ~1,372

skills/hic-aggregation/scripts/validate_loops.py

  • Executable scriptline 1
    #!/usr/bin/env python3

skills/histone-aggregation/SKILL.md

  • Network accessline 27
    - **ChIP-Atlas** (Oki et al. 2018, EMBO Reports, 597 citations): Integrated >70,000 public ChIP-seq datasets using union of all peak calls - **ENCODE Phase 3** (Gorkin et al. 2020, Nature, 301 citations): Created unified chromatin state ann
  • Network accessline 28
    - **ENCODE Phase 3** (Gorkin et al. 2020, Nature, 301 citations): Created unified chromatin state annotations by integrating all peaks across 1,128 ChIP-seq experiments - **ENCODE Blacklist** (Amemiya et al. 2019, Scientific Reports, 1,372
  • Network accessline 29
    - **ENCODE Blacklist** (Amemiya et al. 2019, Scientific Reports, 1,372 citations): Defined the comprehensive set of problematic genomic regions to filter from all functional genomics analyses. Essential quality step. [DOI](https://doi.org/1
  • Network accessline 126
    # Download ENCODE blocklist for GRCh38 from: # https://github.com/Boyle-Lab/Blacklist/blob/master/lists/hg38-blacklist.v2.bed.gz
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    # https://github.com/Boyle-Lab/Blacklist/blob/master/lists/hg38-blacklist.v2.bed.gz
  • Network accessline 128
    # For mm10: https://github.com/Boyle-Lab/Blacklist/blob/master/lists/mm10-blacklist.v2.bed.gz

skills/histone-aggregation/references/histone-marks-reference.md

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    **Wang et al. 2008** -- First systematic study of combinatorial histone modification patterns. - **Citation:** Wang Z, Zang C, Rosenfeld JA, Schones DE, Barski A, Cuddapah S, Cui K, Roh TY, Peng W, Zhang MQ, Zhao K. "Combinatorial patterns
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    - **Citation:** Wang Z, Zang C, Rosenfeld JA, Schones DE, Barski A, Cuddapah S, Cui K, Roh TY, Peng W, Zhang MQ, Zhao K. "Combinatorial patterns of histone acetylations and methylations in the human genome." *Nat Genet*. 2008;40(7):897-903.
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    - **Citation:** Mikkelsen TS, Ku M, Jaffe DB, Issac B, Lieberman E, Giannoukos G, Alvarez P, Brockman W, Kim TK, Koche RP, Lee W, Mendenhall E, O'Donovan A, Presser A, Russ C, Xie X, Meissner A, Wernig M, Jaenisch R, Nusbaum C, Lander ES, B
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    **Kouzarides 2007** -- Definitive review of histone modification classes and functions. - **Citation:** Kouzarides T. "Chromatin modifications and their function." *Cell*. 2007;128(4):693-705. - **DOI:** [10.1016/j.cell.2007.02.005](https:/
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    - **Citation:** Kouzarides T. "Chromatin modifications and their function." *Cell*. 2007;128(4):693-705. - **DOI:** [10.1016/j.cell.2007.02.005](https://doi.org/10.1016/j.cell.2007.02.005) - **Citations:** ~10,700
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    **Key papers:** - Heintzman ND et al. (2007) *Nat Genet* 39:311-318. [DOI: 10.1038/ng1966](https://doi.org/10.1038/ng1966) (~3,400 cit.) -- First demonstration that H3K4me1 (without me3) distinguishes enhancers from promoters.
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    - Heintzman ND et al. (2007) *Nat Genet* 39:311-318. [DOI: 10.1038/ng1966](https://doi.org/10.1038/ng1966) (~3,400 cit.) -- First demonstration that H3K4me1 (without me3) distinguishes enhancers from promoters.
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    - Creyghton MP et al. (2010) *PNAS* 107:21931-6. [DOI: 10.1073/pnas.1016071107](https://doi.org/10.1073/pnas.1016071107) (~3,000 cit.) -- Showed H3K27ac separates active from poised H3K4me1-marked enhancers.
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    - Rada-Iglesias A et al. (2011) *Nature* 470:279-83. [DOI: 10.1038/nature09692](https://doi.org/10.1038/nature09692) (~1,500 cit.) -- Defined two enhancer classes: active (H3K4me1+H3K27ac) and poised (H3K4me1+H3K27me3).
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    **Key papers:** - Barski et al. (2007) *Cell* 129:823-37. [DOI: 10.1016/j.cell.2007.05.009](https://doi.org/10.1016/j.cell.2007.05.009) -- Mapped H3K4me2 distribution; enriched at promoters and enhancers.
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    - Barski et al. (2007) *Cell* 129:823-37. [DOI: 10.1016/j.cell.2007.05.009](https://doi.org/10.1016/j.cell.2007.05.009) -- Mapped H3K4me2 distribution; enriched at promoters and enhancers.
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    - Wang et al. (2008) *Nat Genet* 40:897-903. [DOI: 10.1038/ng.154](https://doi.org/10.1038/ng.154) -- Part of the 17-modification active module.
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    **Key papers:** - Bernstein BE et al. (2005) *Cell* 120:169-181. [DOI: 10.1016/j.cell.2005.01.001](https://doi.org/10.1016/j.cell.2005.01.001) -- Early ChIP-chip study linking H3K4me3 to active genes.
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    - Bernstein BE et al. (2005) *Cell* 120:169-181. [DOI: 10.1016/j.cell.2005.01.001](https://doi.org/10.1016/j.cell.2005.01.001) -- Early ChIP-chip study linking H3K4me3 to active genes.
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    - Heintzman ND et al. (2007) *Nat Genet* 39:311-318. [DOI: 10.1038/ng1966](https://doi.org/10.1038/ng1966) -- H3K4me3 at promoters vs. H3K4me1 at enhancers.
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    - Bernstein BE et al. (2006) *Cell* 125:315-326. [DOI: 10.1016/j.cell.2006.02.041](https://doi.org/10.1016/j.cell.2006.02.041) (~5,400 cit.) -- Discovered bivalent domains (H3K4me3 + H3K27me3) in ESCs.
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    **Key papers:** - Barski et al. (2007) *Cell* 129:823-37. [DOI: 10.1016/j.cell.2007.05.009](https://doi.org/10.1016/j.cell.2007.05.009) -- First genome-wide demonstration that H3K9me1 is associated with gene activation, in contrast to H3K9m
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    - Barski et al. (2007) *Cell* 129:823-37. [DOI: 10.1016/j.cell.2007.05.009](https://doi.org/10.1016/j.cell.2007.05.009) -- First genome-wide demonstration that H3K9me1 is associated with gene activation, in contrast to H3K9me2/me3.
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    **Key papers:** - Wen B et al. (2009) *Genome Res* 19:1639-1645. [DOI: 10.1101/gr.092643.109](https://doi.org/10.1101/gr.092643.109) -- Described LOCKs (large organized chromatin K9 modifications) domains.
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    - Wen B et al. (2009) *Genome Res* 19:1639-1645. [DOI: 10.1101/gr.092643.109](https://doi.org/10.1101/gr.092643.109) -- Described LOCKs (large organized chromatin K9 modifications) domains.
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    **Key papers:** - Rea S et al. (2000) *Nature* 406:593-599. [DOI: 10.1038/35020506](https://doi.org/10.1038/35020506) (~3,500 cit.) -- Identified SUV39H1 as the first histone methyltransferase; established H3K9me3 as HP1 binding platform.
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    - Rea S et al. (2000) *Nature* 406:593-599. [DOI: 10.1038/35020506](https://doi.org/10.1038/35020506) (~3,500 cit.) -- Identified SUV39H1 as the first histone methyltransferase; established H3K9me3 as HP1 binding platform.
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    **Key papers:** - Cao R et al. (2002) *Science* 298:1039-1043. [DOI: 10.1126/science.1076997](https://doi.org/10.1126/science.1076997) (~3,000 cit.) -- Identified EZH2 as the H3K27 methyltransferase.
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    - Cao R et al. (2002) *Science* 298:1039-1043. [DOI: 10.1126/science.1076997](https://doi.org/10.1126/science.1076997) (~3,000 cit.) -- Identified EZH2 as the H3K27 methyltransferase.
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    - Boyer LA et al. (2006) *Nature* 441:349-353. [DOI: 10.1038/nature04733](https://doi.org/10.1038/nature04733) (~3,000 cit.) -- Mapped PRC2/H3K27me3 targets in ESCs; found occupancy at developmental TF genes.
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    **Key papers:** - Bannister AJ et al. (2005) *Nature* 438:1181-1185. [DOI: 10.1038/nature04219](https://doi.org/10.1038/nature04219) (~800 cit.) -- Demonstrated Set2-mediated H3K36me in gene bodies during elongation.
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    - Bannister AJ et al. (2005) *Nature* 438:1181-1185. [DOI: 10.1038/nature04219](https://doi.org/10.1038/nature04219) (~800 cit.) -- Demonstrated Set2-mediated H3K36me in gene bodies during elongation.
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    **Key papers:** - Feng Q et al. (2002) *Curr Biol* 12:1052-1058. [DOI: 10.1016/S0960-9822(02)00901-6](https://doi.org/10.1016/S0960-9822(02)00901-6) (~700 cit.) -- Identified DOT1L as H3K79 methyltransferase.
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    **Key papers:** - Creyghton MP et al. (2010) *PNAS* 107:21931-6. [DOI: 10.1073/pnas.1016071107](https://doi.org/10.1073/pnas.1016071107) -- Established H3K27ac as THE distinguishing mark between active and poised enhancers.
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    - Creyghton MP et al. (2010) *PNAS* 107:21931-6. [DOI: 10.1073/pnas.1016071107](https://doi.org/10.1073/pnas.1016071107) -- Established H3K27ac as THE distinguishing mark between active and poised enhancers.
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    - Rada-Iglesias A et al. (2011) *Nature* 470:279-83. [DOI: 10.1038/nature09692](https://doi.org/10.1038/nature09692) -- Active enhancers = H3K4me1+H3K27ac; poised enhancers = H3K4me1+H3K27me3.
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    **Key papers:** - Wang et al. (2008) *Nat Genet* 40:897-903. [DOI: 10.1038/ng.154](https://doi.org/10.1038/ng.154) -- Part of the 17-mark active promoter module.
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    - Wang et al. (2008) *Nat Genet* 40:897-903. [DOI: 10.1038/ng.154](https://doi.org/10.1038/ng.154) -- Part of the 17-mark active promoter module.
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    **Key papers:** - Wang et al. (2008) *Nat Genet* 40:897-903. [DOI: 10.1038/ng.154](https://doi.org/10.1038/ng.154) -- Part of the 17-mark active promoter module.
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    - Wang et al. (2008) *Nat Genet* 40:897-903. [DOI: 10.1038/ng.154](https://doi.org/10.1038/ng.154) -- Part of the 17-mark active promoter module.
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    **Key papers:** - Wang et al. (2008) *Nat Genet* 40:897-903. [DOI: 10.1038/ng.154](https://doi.org/10.1038/ng.154) -- Identified in combinatorial analysis.
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    - Wang et al. (2008) *Nat Genet* 40:897-903. [DOI: 10.1038/ng.154](https://doi.org/10.1038/ng.154) -- Identified in combinatorial analysis.
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    **Key papers:** - Wang et al. (2008) *Nat Genet* 40:897-903. [DOI: 10.1038/ng.154](https://doi.org/10.1038/ng.154) -- Part of combinatorial patterns at active promoters.
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    - Wang et al. (2008) *Nat Genet* 40:897-903. [DOI: 10.1038/ng.154](https://doi.org/10.1038/ng.154) -- Part of combinatorial patterns at active promoters.
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    **Key papers:** - Rice JC et al. (2002) *Genes Dev* 16:2225-30. [DOI: 10.1101/gad.986602](https://doi.org/10.1101/gad.986602) (~257 cit.) -- H4K20me1 is mitotic-specific, deposited by PR-Set7, inversely correlated with H4K16ac.
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    - Rice JC et al. (2002) *Genes Dev* 16:2225-30. [DOI: 10.1101/gad.986602](https://doi.org/10.1101/gad.986602) (~257 cit.) -- H4K20me1 is mitotic-specific, deposited by PR-Set7, inversely correlated with H4K16ac.
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    **Key papers:** - Wang et al. (2008) *Nat Genet* 40:897-903. [DOI: 10.1038/ng.154](https://doi.org/10.1038/ng.154) -- Part of the active chromatin module.
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    - Wang et al. (2008) *Nat Genet* 40:897-903. [DOI: 10.1038/ng.154](https://doi.org/10.1038/ng.154) -- Part of the active chromatin module.
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    **Key papers:** - Wang et al. (2008) *Nat Genet* 40:897-903. [DOI: 10.1038/ng.154](https://doi.org/10.1038/ng.154).
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    - Wang et al. (2008) *Nat Genet* 40:897-903. [DOI: 10.1038/ng.154](https://doi.org/10.1038/ng.154).
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    - Rice JC et al. (2002) *Genes Dev* 16:2225-30. [DOI: 10.1101/gad.986602](https://doi.org/10.1101/gad.986602) -- H4K16ac in early S-phase inversely correlated with H4K20me1 at mitosis.
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    **Key papers:** - Barski et al. (2007) *Cell* 129:823-37. [DOI: 10.1016/j.cell.2007.05.009](https://doi.org/10.1016/j.cell.2007.05.009) -- H2A.Z at functional regulatory elements.
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    **Key papers:** - Kim J et al. (2009) *Cell* 137:459-471. [DOI: 10.1016/j.cell.2009.02.027](https://doi.org/10.1016/j.cell.2009.02.027) (~700 cit.) -- H2BK120ub required for H3K4me3 by COMPASS and H3K79me by DOT1L.
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    | Feature | Description | Key Reference | |---------|-------------|---------------| | H3K4me1 (not me3) | Enhancer-specific vs. promoter-specific | Heintzman et al. 2007 Nat Genet [DOI: 10.1038/ng1966](https://doi.org/10.1038/ng1966) |
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    |---------|-------------|---------------| | H3K4me1 (not me3) | Enhancer-specific vs. promoter-specific | Heintzman et al. 2007 Nat Genet [DOI: 10.1038/ng1966](https://doi.org/10.1038/ng1966) | | H3K27ac distinguishes active | Active enhanc
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    | H3K4me1 (not me3) | Enhancer-specific vs. promoter-specific | Heintzman et al. 2007 Nat Genet [DOI: 10.1038/ng1966](https://doi.org/10.1038/ng1966) |
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    | H3K27ac distinguishes active | Active enhancer = H3K4me1+H3K27ac | Creyghton et al. 2010 PNAS [DOI: 10.1073/pnas.1016071107](https://doi.org/10.1073/pnas.1016071107) |
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    | p300/CBP binding | Co-activator occupancy marks enhancers | Visel A et al. 2009 Nature [DOI: 10.1038/nature07730](https://doi.org/10.1038/nature07730) |
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    | eRNA transcription | Active enhancers produce bidirectional non-coding RNAs | Kim TK et al. 2010 Nature [DOI: 10.1038/nature09014](https://doi.org/10.1038/nature09014) |
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    | Cell-type specificity | Enhancer marks are highly cell-type specific | Ernst et al. 2011 Nature [DOI: 10.1038/nature09906](https://doi.org/10.1038/nature09906) |
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    | Feature | Description | Key Reference | |---------|-------------|---------------| | Bivalent promoters | H3K4me3+H3K27me3 at developmental genes in ESCs | Bernstein et al. 2006 Cell [DOI: 10.1016/j.cell.2006.02.041](https://doi.org/10.101
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    |---------|-------------|---------------| | Bivalent promoters | H3K4me3+H3K27me3 at developmental genes in ESCs | Bernstein et al. 2006 Cell [DOI: 10.1016/j.cell.2006.02.041](https://doi.org/10.1016/j.cell.2006.02.041) | | Resolution on di
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    | Bivalent promoters | H3K4me3+H3K27me3 at developmental genes in ESCs | Bernstein et al. 2006 Cell [DOI: 10.1016/j.cell.2006.02.041](https://doi.org/10.1016/j.cell.2006.02.041) |
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    | Resolution on differentiation | Resolve to H3K4me3-only (active) or H3K27me3-only (repressed) | Mikkelsen et al. 2007 Nature | | Poised enhancers | H3K4me1+H3K27me3 at developmental enhancers in ESCs | Rada-Iglesias et al. 2011 Nature [DO
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    | Poised enhancers | H3K4me1+H3K27me3 at developmental enhancers in ESCs | Rada-Iglesias et al. 2011 Nature [DOI: 10.1038/nature09692](https://doi.org/10.1038/nature09692) |
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    | Same-allele co-occurrence | Sequential ChIP confirmed both marks on same nucleosome/allele | Bernstein et al. 2006 Cell | | Protection function | H3K4me3 at bivalent promoters protects from DNA methylation | Kumar et al. 2021 Genome Res [
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    | Protection function | H3K4me3 at bivalent promoters protects from DNA methylation | Kumar et al. 2021 Genome Res [DOI: 10.1101/gr.266924.120](https://doi.org/10.1101/gr.266924.120) |
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    | Feature | Description | Key Reference | |---------|-------------|---------------| | Definition | Large clusters of enhancers with disproportionately high H3K27ac/Mediator | Whyte et al. 2013 Cell [DOI: 10.1016/j.cell.2013.03.035](https://
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    |---------|-------------|---------------| | Definition | Large clusters of enhancers with disproportionately high H3K27ac/Mediator | Whyte et al. 2013 Cell [DOI: 10.1016/j.cell.2013.03.035](https://doi.org/10.1016/j.cell.2013.03.035) | | Ce
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    | Definition | Large clusters of enhancers with disproportionately high H3K27ac/Mediator | Whyte et al. 2013 Cell [DOI: 10.1016/j.cell.2013.03.035](https://doi.org/10.1016/j.cell.2013.03.035) |
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    | Cell identity genes | Associated with genes controlling cell type identity | Hnisz et al. 2013 Cell [DOI: 10.1016/j.cell.2013.09.053](https://doi.org/10.1016/j.cell.2013.09.053) |
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    | Sensitivity to perturbation | Disproportionately affected by BET inhibitors (JQ1) | Loven et al. 2013 Cell [DOI: 10.1016/j.cell.2013.03.036](https://doi.org/10.1016/j.cell.2013.03.036) (~2,700 cit.) |
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    | Cancer relevance | Cancer cells acquire super-enhancers at oncogenes | Hnisz et al. 2013 Cell | | Beyond H3K27ac | H4K5acK8ac defines additional super-enhancers missed by H3K27ac alone | Das et al. 2023 BMC Genomics | | Phase separation |
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    | Phase separation | Super-enhancers may form phase-separated condensates | Sabari et al. 2018 Science [DOI: 10.1126/science.aar3958](https://doi.org/10.1126/science.aar3958) |
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    | H3K36me3 gradient | Increases from 5' to 3'; co-transcriptional via SETD2 | Bannister et al. 2005 Nature | | H3K79me2 | Gene body mark via DOT1L; requires H2BK120ub | Steger et al. 2008 Mol Cell Biol | | Cryptic transcription prevention |
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    | Gene body DNA methylation | H3K36me3 recruits DNMT3B for intragenic CpG methylation | Baubec T et al. 2015 Nature [DOI: 10.1038/nature14456](https://doi.org/10.1038/nature14456) |
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    | Feature | Description | Key Reference | |---------|-------------|---------------| | CTCF binding | Zinc-finger protein that defines TAD boundaries | Dixon JR et al. 2012 Nature [DOI: 10.1038/nature11082](https://doi.org/10.1038/nature1108
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    |---------|-------------|---------------| | CTCF binding | Zinc-finger protein that defines TAD boundaries | Dixon JR et al. 2012 Nature [DOI: 10.1038/nature11082](https://doi.org/10.1038/nature11082) (~4,000 cit.) | | CTCF essential for TA
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    - MLL3/4-catalyzed H3K4me1 may be a consequence rather than a cause of enhancer activation. - Dorighi KM et al. (2017) *Mol Cell* 66:568-576 [DOI: 10.1016/j.molcel.2017.04.018](https://doi.org/10.1016/j.molcel.2017.04.018) showed that catal
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    **Current view:** Both models may be correct. Bivalency maintains epigenetic plasticity by preventing irreversible silencing, which incidentally keeps genes available for future activation. - **Macrae et al. (2022) *Nat Rev Mol Cell Biol* 2
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    - **Macrae et al. (2022) *Nat Rev Mol Cell Biol* 24:6-26** [DOI: 10.1038/s41580-022-00544-w](https://doi.org/10.1038/s41580-022-00544-w) (~117 cit.) provides the most comprehensive review, concluding that bivalency is a feature of both germ
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    |------------|-------------|---------------| | Global H4K16ac loss | One of the earliest epigenetic changes in cancer | Fraga et al. 2005 Nat Genet | | H3K27me3 redistribution | Loss at tumor suppressor promoters, gain at other loci | Berma
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skills/histone-aggregation/scripts/validate_peaks.py

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skills/integrative-analysis/SKILL.md

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    - **DOI:** [10.7554/eLife.27041](https://doi.org/10.7554/eLife.27041)
  • Network accessline 213
    single-cell atlas of in vivo mammalian chromatin accessibility. *Cell*, 174(5),   1309-1324, 2018. - **DOI:** [10.1016/j.cell.2018.06.052](https://doi.org/10.1016/j.cell.2018.06.052)
  • Network accessline 214
    1309-1324, 2018. - **DOI:** [10.1016/j.cell.2018.06.052](https://doi.org/10.1016/j.cell.2018.06.052) - **PMID:** 30078704 | **PMC:** PMC6104449
  • Network accessline 215
    - **DOI:** [10.1016/j.cell.2018.06.052](https://doi.org/10.1016/j.cell.2018.06.052)
  • Network accessline 230
    - **Citation:** Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, et al.   Comprehensive integration of single-cell data. *Cell*, 177(7), 1888-1902, 2019. - **DOI:** [10.1016/j.cell.2019.05.031](https://doi.org/10.1016/j.cell.2019.0
  • Network accessline 231
    Comprehensive integration of single-cell data. *Cell*, 177(7), 1888-1902, 2019. - **DOI:** [10.1016/j.cell.2019.05.031](https://doi.org/10.1016/j.cell.2019.05.031) - **PMID:** 31178118 | **PMC:** PMC6687398
  • Network accessline 232
    - **DOI:** [10.1016/j.cell.2019.05.031](https://doi.org/10.1016/j.cell.2019.05.031)

skills/jaspar-motifs/SKILL.md

  • Network accessline 40
    - **Castro-Mondragon et al. 2022** "JASPAR 2022: the 9th release of the open-access database of transcription factor binding profiles" (Nucleic Acids Research, ~1,400 citations). The current JASPAR release with 1,956 profiles across 7 taxon
  • Network accessline 41
    - **Sandelin et al. 2004** "JASPAR: an open-access database for eukaryotic transcription factor binding profiles" (Nucleic Acids Research, ~2,000 citations). The founding JASPAR publication establishing the curated, open-access model for TF
  • Network accessline 42
    - **Grant et al. 2011** "FIMO: scanning for occurrences of a given motif" (Bioinformatics, ~2,500 citations). FIMO (Find Individual Motif Occurrences) — the standard tool for scanning sequences with PWMs. Part of the MEME Suite. [DOI: 10.10
  • Network accessline 43
    - **Heinz et al. 2010** "Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities" (Molecular Cell, ~5,000 citations). Introduced HOMER motif analysis — the
  • Network accessline 44
    - **ENCODE Project Consortium 2020** (Nature, ~1,656 citations). The TF ChIP-seq experiments that JASPAR motifs validate and enrich. [DOI: 10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 59
    **Base URL**: `https://jaspar.genereg.net/api/v1/`
  • Network accessline 126
    def get_jaspar_matrix(tf_name, tax_group="vertebrates", collection="CORE"):     """Get JASPAR matrix for a TF."""     url = "https://jaspar.genereg.net/api/v1/matrix/"
  • Network accessline 127
    """Get JASPAR matrix for a TF."""     url = "https://jaspar.genereg.net/api/v1/matrix/"     params = {
  • Network accessline 128
    url = "https://jaspar.genereg.net/api/v1/matrix/"
  • Network accessline 133
    "format": "json"     }     response = requests.get(url, params=params)
  • Network accessline 134
    }     response = requests.get(url, params=params)     results = response.json()["results"]
  • Network accessline 135
    response = requests.get(url, params=params)
  • Network accessline 149
    def get_pfm(matrix_id):     """Get Position Frequency Matrix from JASPAR."""     url = f"https://jaspar.genereg.net/api/v1/matrix/{matrix_id}/"
  • Network accessline 150
    """Get Position Frequency Matrix from JASPAR."""     url = f"https://jaspar.genereg.net/api/v1/matrix/{matrix_id}/"     params = {"format": "json"}
  • Network accessline 151
    url = f"https://jaspar.genereg.net/api/v1/matrix/{matrix_id}/"     params = {"format": "json"}     response = requests.get(url, params=params)
  • Network accessline 152
    params = {"format": "json"}     response = requests.get(url, params=params)     data = response.json()
  • Network accessline 153
    response = requests.get(url, params=params)
  • Network accessline 168
    def get_meme_format(matrix_id):     """Get motif in MEME format for use with FIMO."""     url = f"https://jaspar.genereg.net/api/v1/matrix/{matrix_id}/"
  • Network accessline 169
    """Get motif in MEME format for use with FIMO."""     url = f"https://jaspar.genereg.net/api/v1/matrix/{matrix_id}/"     params = {"format": "meme"}
  • Network accessline 170
    url = f"https://jaspar.genereg.net/api/v1/matrix/{matrix_id}/"     params = {"format": "meme"}     response = requests.get(url, params=params)
  • Network accessline 171
    params = {"format": "meme"}     response = requests.get(url, params=params)     return response.text
  • Network accessline 172
    response = requests.get(url, params=params)
  • Network accessline 188
    # Download reference genome (if not available) # GRCh38: https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz
  • Network accessline 189
    # GRCh38: https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz
  • Network accessline 424
    GET https://jaspar.elixir.no/api/v2/matrix/?tax_id=9606&collection=CORE&profile_class=liver

skills/jaspar-motifs/references/literature.md

  • Network accessline 17
    transcription factor binding profiles. Nucleic Acids Research,   50(D1):D165-D173, 2022. - **DOI:** [10.1093/nar/gkab1113](https://doi.org/10.1093/nar/gkab1113)
  • Network accessline 18
    50(D1):D165-D173, 2022. - **DOI:** [10.1093/nar/gkab1113](https://doi.org/10.1093/nar/gkab1113) - **PMID:** 34850907 | **PMC:** PMC8728201
  • Network accessline 19
    - **DOI:** [10.1093/nar/gkab1113](https://doi.org/10.1093/nar/gkab1113)
  • Network accessline 41
    JASPAR: an open-access database for eukaryotic transcription factor binding   profiles. Nucleic Acids Research, 32(Database issue):D91-D94, 2004. - **DOI:** [10.1093/nar/gkh012](https://doi.org/10.1093/nar/gkh012)
  • Network accessline 42
    profiles. Nucleic Acids Research, 32(Database issue):D91-D94, 2004. - **DOI:** [10.1093/nar/gkh012](https://doi.org/10.1093/nar/gkh012) - **PMID:** 14681366 | **PMC:** PMC308747
  • Network accessline 43
    - **DOI:** [10.1093/nar/gkh012](https://doi.org/10.1093/nar/gkh012)
  • Network accessline 67
    and update of the open-access database of transcription factor binding   profiles. Nucleic Acids Research, 44(D1):D110-D115, 2016. - **DOI:** [10.1093/nar/gkv1176](https://doi.org/10.1093/nar/gkv1176)
  • Network accessline 68
    profiles. Nucleic Acids Research, 44(D1):D110-D115, 2016. - **DOI:** [10.1093/nar/gkv1176](https://doi.org/10.1093/nar/gkv1176) - **PMID:** 26531826 | **PMC:** PMC4702842
  • Network accessline 69
    - **DOI:** [10.1093/nar/gkv1176](https://doi.org/10.1093/nar/gkv1176)
  • Network accessline 93
    profiles and its web framework. Nucleic Acids Research, 46(D1):D260-D266,   2018. - **DOI:** [10.1093/nar/gkx1126](https://doi.org/10.1093/nar/gkx1126)
  • Network accessline 94
    2018. - **DOI:** [10.1093/nar/gkx1126](https://doi.org/10.1093/nar/gkx1126) - **PMID:** 29140473 | **PMC:** PMC5753243
  • Network accessline 95
    - **DOI:** [10.1093/nar/gkx1126](https://doi.org/10.1093/nar/gkx1126)
  • Network accessline 119
    2020: update of the open-access database of transcription factor binding   profiles. Nucleic Acids Research, 48(D1):D87-D92, 2020. - **DOI:** [10.1093/nar/gkz1001](https://doi.org/10.1093/nar/gkz1001)
  • Network accessline 120
    profiles. Nucleic Acids Research, 48(D1):D87-D92, 2020. - **DOI:** [10.1093/nar/gkz1001](https://doi.org/10.1093/nar/gkz1001) - **PMID:** 31701148 | **PMC:** PMC7145627
  • Network accessline 121
    - **DOI:** [10.1093/nar/gkz1001](https://doi.org/10.1093/nar/gkz1001)
  • Network accessline 149
    inference of eukaryotic transcription factor sequence specificity. Cell,   158(6):1431-1443, 2014. - **DOI:** [10.1016/j.cell.2014.08.009](https://doi.org/10.1016/j.cell.2014.08.009)
  • Network accessline 150
    158(6):1431-1443, 2014. - **DOI:** [10.1016/j.cell.2014.08.009](https://doi.org/10.1016/j.cell.2014.08.009) - **PMID:** 25215497 | **PMC:** PMC4163041
  • Network accessline 151
    - **DOI:** [10.1016/j.cell.2014.08.009](https://doi.org/10.1016/j.cell.2014.08.009)
  • Network accessline 176
    Morgunova E, Enge M, Taipale M, Wei G, et al. DNA-binding specificities of   human transcription factors. Cell, 152(1-2):327-339, 2013. - **DOI:** [10.1016/j.cell.2012.12.009](https://doi.org/10.1016/j.cell.2012.12.009)
  • Network accessline 177
    human transcription factors. Cell, 152(1-2):327-339, 2013. - **DOI:** [10.1016/j.cell.2012.12.009](https://doi.org/10.1016/j.cell.2012.12.009) - **PMID:** 23332764
  • Network accessline 178
    - **DOI:** [10.1016/j.cell.2012.12.009](https://doi.org/10.1016/j.cell.2012.12.009)

skills/liftover-coordinates/SKILL.md

  • Network accessline 42
    - **Kent et al. 2002** (Genome Research, ~5,000 citations): UCSC Genome Browser and the liftOver tool. The original chain/net alignment framework for coordinate conversion between genome assemblies. [DOI](https://doi.org/10.1101/gr.229102)
  • Network accessline 43
    - **Zhao et al. 2014** (Bioinformatics, ~800 citations): CrossMap — a versatile tool for coordinate conversion between genome assemblies. Handles VCF, BAM, bigWig, GFF, and Wiggle formats that UCSC liftOver cannot process natively. [DOI](ht
  • Network accessline 44
    - **Hinrichs et al. 2006** (Nucleic Acids Research, ~1,200 citations): UCSC genome browser chain/net alignment methodology. Defines the reciprocal-best chain alignment that underpins coordinate conversion. [DOI](https://doi.org/10.1093/nar/
  • Network accessline 45
    - **Kuhn et al. 2013** (Nucleic Acids Research, ~600 citations): Assembly updates and the implications for re-annotation. Documents the biological impact of assembly changes on gene models and regulatory element coordinates. [DOI](https://d
  • Network accessline 46
    - **Schneider et al. 2017** (Genome Research, ~400 citations): GRCh38 improvements over GRCh37 — gap closures, centromere models, alternative haplotypes. Quantifies what changed and why liftover is necessary. [DOI](https://doi.org/10.1101/g
  • Network accessline 47
    - **Amemiya et al. 2019** (Scientific Reports, ~1,372 citations): ENCODE Blacklist regions — some blacklisted regions are assembly-specific. Liftover of blacklist files must use the correct version. [DOI](https://doi.org/10.1038/s41598-019-
  • Network accessline 75
    https://hgdownload.soe.ucsc.edu/goldenPath/{from}/liftOver/{from}To{To}.over.chain.gz
  • Network accessline 79
    | Conversion | Chain File | URL | |-----------|-----------|-----| | hg19 to hg38 | hg19ToHg38.over.chain.gz | `https://hgdownload.soe.ucsc.edu/goldenPath/hg19/liftOver/hg19ToHg38.over.chain.gz` |
  • Network accessline 80
    |-----------|-----------|-----| | hg19 to hg38 | hg19ToHg38.over.chain.gz | `https://hgdownload.soe.ucsc.edu/goldenPath/hg19/liftOver/hg19ToHg38.over.chain.gz` | | hg38 to hg19 | hg38ToHg19.over.chain.gz | `https://hgdownload.soe.ucsc.edu/g
  • Network accessline 81
    | hg19 to hg38 | hg19ToHg38.over.chain.gz | `https://hgdownload.soe.ucsc.edu/goldenPath/hg19/liftOver/hg19ToHg38.over.chain.gz` |
  • Network accessline 82
    | hg38 to hg19 | hg38ToHg19.over.chain.gz | `https://hgdownload.soe.ucsc.edu/goldenPath/hg38/liftOver/hg38ToHg19.over.chain.gz` |
  • Network accessline 83
    | mm9 to mm10 | mm9ToMm10.over.chain.gz | `https://hgdownload.soe.ucsc.edu/goldenPath/mm9/liftOver/mm9ToMm10.over.chain.gz` |
  • Network accessline 84
    | mm10 to mm39 | mm10ToMm39.over.chain.gz | `https://hgdownload.soe.ucsc.edu/goldenPath/mm10/liftOver/mm10ToMm39.over.chain.gz` |
  • Network accessline 85
    | mm10 to hg38 | mm10ToHg38.over.chain.gz | `https://hgdownload.soe.ucsc.edu/goldenPath/mm10/liftOver/mm10ToHg38.over.chain.gz` |
  • Network accessline 97
    NCBI Genome Remapping Service: `https://www.ncbi.nlm.nih.gov/genome/tools/remap`
  • Network accessline 106
    wget https://hgdownload.soe.ucsc.edu/goldenPath/hg19/liftOver/hg19ToHg38.over.chain.gz
  • Network accessline 240
    GET https://rest.ensembl.org/map/human/GRCh37/{region}/GRCh38?content-type=application/json
  • Network accessline 249
    """Convert coordinates using Ensembl REST API."""     region = f"{chrom}:{start}..{end}:1"     url = f"https://rest.ensembl.org/map/{species}/{source}/{region}/{target}"
  • Network accessline 250
    region = f"{chrom}:{start}..{end}:1"     url = f"https://rest.ensembl.org/map/{species}/{source}/{region}/{target}"     headers = {"Content-Type": "application/json"}
  • Network accessline 251
    url = f"https://rest.ensembl.org/map/{species}/{source}/{region}/{target}"     headers = {"Content-Type": "application/json"}     response = requests.get(url, headers=headers)
  • Network accessline 252
    headers = {"Content-Type": "application/json"}     response = requests.get(url, headers=headers)     if response.status_code == 200:
  • Network accessline 253
    response = requests.get(url, headers=headers)

skills/liftover-coordinates/references/literature.md

  • Network accessline 14
    - **Citation:** Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, Haussler D. The human genome browser at UCSC. Genome Research, 12(6):996-1006, 2002. - **DOI:** [10.1101/gr.229102](https://doi.org/10.1101/gr.229102)
  • Network accessline 15
    - **DOI:** [10.1101/gr.229102](https://doi.org/10.1101/gr.229102)
  • Network accessline 24
    - **Citation:** Zhao H, Sun Z, Wang J, Huang H, Kocher JP, Wang L. CrossMap: a versatile tool for coordinate conversion between genome assemblies. Bioinformatics, 30(7):1006-1007, 2014. - **DOI:** [10.1093/bioinformatics/btt730](https://doi
  • Network accessline 25
    - **DOI:** [10.1093/bioinformatics/btt730](https://doi.org/10.1093/bioinformatics/btt730)
  • Network accessline 34
    - **Citation:** Hinrichs AS, Karolchik D, Baertsch R, Barber GP, Bejerano G, Clawson H, Diekhans M, Furey TS, Harte RA, Hsu F, Hillman-Jackson J, Kuhn RM, Pedersen JS, Pohl A, Raney BJ, Rosenbloom KR, Siepel A, Smith KE, Sugnet CW, Sultan-Q
  • Network accessline 35
    - **DOI:** [10.1093/nar/gkj144](https://doi.org/10.1093/nar/gkj144)
  • Network accessline 48
    - **Citation:** Schneider VA, Graves-Lindsay T, Howe K, Bouk N, Chen HC, Kitts PA, Murphy TD, Pruitt KD, Tello-Ruiz MK, Erber J, Phan L, Robbertse B, Schoch CL, Vnencak-Jones CL, Renfree MB, Havlak P, Seidl CJ, Church DM. Evaluation of GRCh
  • Network accessline 49
    - **DOI:** [10.1101/gr.213611.116](https://doi.org/10.1101/gr.213611.116)
  • Network accessline 58
    - **Citation:** ENCODE Project Consortium et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature, 583(7818):699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 59
    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 72
    - **Citation:** Castel SE, Levy-Moonshine A, Mohammadi P, Banks E, Lappalainen T. Tools and best practices for data processing in allelic expression analysis. Genome Biology, 16:195, 2015. - **DOI:** [10.1186/s13059-015-0762-6](https://doi.
  • Network accessline 73
    - **DOI:** [10.1186/s13059-015-0762-6](https://doi.org/10.1186/s13059-015-0762-6)

skills/methylation-aggregation/SKILL.md

  • Network accessline 37
    - **DMRcate** (Peters et al. 2021, Nucleic Acids Research, 65 citations): Method for calling differentially methylated regions from multiple WGBS samples; uses kernel smoothing across CpG sites - **ENCODE Phase 3** (Gorkin et al. 2020, Natu
  • Network accessline 38
    - **ENCODE Phase 3** (Gorkin et al. 2020, Nature, 301 citations): Integrated methylation data with histone marks and accessibility to define chromatin states - **ENCODE Blacklist** (Amemiya et al. 2019, Scientific Reports, 1,372 citations):
  • Network accessline 39
    - **ENCODE Blacklist** (Amemiya et al. 2019, Scientific Reports, 1,372 citations): Problematic genomic regions to filter. [DOI](https://doi.org/10.1038/s41598-019-45839-z)
  • Network accessline 158
    # Download from: https://github.com/Boyle-Lab/Blacklist/blob/master/lists/hg38-blacklist.v2.bed.gz

skills/methylation-aggregation/references/literature.md

  • Network accessline 14
    - **Citation:** Schultz MD, He Y, Whitaker JW, Hariharan M, Mukamel EA, Leung D, Rajagopal N, Nery JR, Urich MA, Chen H, Lin S, Lin Y, Jung I, Schmitt AD, Selvaraj S, Ren B, Sejnowski TJ, Wang W, Ecker JR. Human body epigenome maps reveal n
  • Network accessline 15
    - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248)
  • Network accessline 24
    - **Citation:** Zhou W, Dinh HQ, Ramjan Z, Weisenberger DJ, Nicolet CM, Shen H, Laird PW, Berman BP. DNA methylation loss in late-replicating domains is linked to mitotic cell division. Nature Genetics, 50(4):591-602, 2018. - **DOI:** [10.1
  • Network accessline 25
    - **DOI:** [10.1038/s41588-018-0073-4](https://doi.org/10.1038/s41588-018-0073-4)
  • Network accessline 38
    - **Citation:** Peters TJ, Buckley MJ, Statham AL, Pidsley R, Samaras K, Lord RV, Clark SJ, Molloy PL. De novo identification of differentially methylated regions in the human genome. Epigenetics & Chromatin, 8:6, 2015. - **DOI:** [10.1186/
  • Network accessline 39
    - **DOI:** [10.1186/1756-8935-8-6](https://doi.org/10.1186/1756-8935-8-6)
  • Network accessline 48
    - **Citation:** Hansen KD, Langmead B, Irizarry RA. BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biology, 13(10):R83, 2012. - **DOI:** [10.1186/gb-2012-13-10-r83](https://doi.org/10.1186
  • Network accessline 49
    - **DOI:** [10.1186/gb-2012-13-10-r83](https://doi.org/10.1186/gb-2012-13-10-r83)
  • Network accessline 76
    - **Citation:** Lister R, Pelizzola M, Dowen RH, et al. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature, 462(7271):315-322, 2009. - **DOI:** [10.1038/nature08514](https://doi.org/10.1038/nature08514)
  • Network accessline 77
    - **DOI:** [10.1038/nature08514](https://doi.org/10.1038/nature08514)
  • Network accessline 90
    - **Citation:** ENCODE Project Consortium et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature, 583(7818):699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 91
    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 100
    - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z) | **PMID:** 31249361 | **Citations:** ~1,372

skills/methylation-aggregation/scripts/validate_methylation.py

  • Executable scriptline 1
    #!/usr/bin/env python3

skills/motif-analysis/SKILL.md

  • Network accessline 20
    | Reference | Journal | Key Contribution | DOI | Citations | |-----------|---------|-----------------|-----|-----------| | Heinz et al. (2010) | Molecular Cell | HOMER: Simple combinations of lineage-determining TFs prime cis-regulatory ele
  • Network accessline 21
    |-----------|---------|-----------------|-----|-----------| | Heinz et al. (2010) | Molecular Cell | HOMER: Simple combinations of lineage-determining TFs prime cis-regulatory elements; introduced findMotifsGenome.pl for ChIP-seq motif anal
  • Network accessline 22
    | Heinz et al. (2010) | Molecular Cell | HOMER: Simple combinations of lineage-determining TFs prime cis-regulatory elements; introduced findMotifsGenome.pl for ChIP-seq motif analysis | [10.1016/j.molcel.2010.05.004](https://doi.org/10.101
  • Network accessline 23
    | Bailey et al. (2009) | Nucleic Acids Research | MEME Suite: comprehensive tools for motif discovery (MEME), enrichment (AME), scanning (FIMO), and spacing (SpaMo) | [10.1093/nar/gkp335](https://doi.org/10.1093/nar/gkp335) | ~2,500 |
  • Network accessline 24
    | Bailey & Elkan (1994) | ISMB | Foundational MEME algorithm: expectation maximization for discovering ungapped motifs in biopolymers | PMID: 7584402 | ~4,000 | | Machanick & Bailey (2011) | Bioinformatics | MEME-ChIP: all-in-one motif anal
  • Network accessline 25
    | Machanick & Bailey (2011) | Bioinformatics | MEME-ChIP: all-in-one motif analysis pipeline optimized for large ChIP-seq datasets | [10.1093/bioinformatics/btr189](https://doi.org/10.1093/bioinformatics/btr189) | ~1,800 |
  • Network accessline 26
    | Fornes et al. (2020) | Nucleic Acids Research | JASPAR 2020: curated, non-redundant TF binding profile database; standard reference for known motifs | [10.1093/nar/gkz1001](https://doi.org/10.1093/nar/gkz1001) | ~2,200 |
  • Network accessline 27
    | Amemiya et al. (2019) | Scientific Reports | ENCODE Blacklist: regions producing artifact signal that can generate spurious motif hits | [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z) | ~1,372 |
  • Network accessline 214
    # Download JASPAR 2024 core vertebrate motifs in MEME format wget https://jaspar.elixir.no/download/data/2024/CORE/JASPAR2024_CORE_vertebrates_non-redundant_pfms_meme.txt \
  • Network accessline 215
    wget https://jaspar.elixir.no/download/data/2024/CORE/JASPAR2024_CORE_vertebrates_non-redundant_pfms_meme.txt \

skills/motif-analysis/references/literature.md

  • Network accessline 18
    factors prime cis-regulatory elements required for macrophage and B cell identities.   Molecular Cell, 38(4):576-589, 2010. - **DOI:** [10.1016/j.molcel.2010.05.004](https://doi.org/10.1016/j.molcel.2010.05.004)
  • Network accessline 19
    Molecular Cell, 38(4):576-589, 2010. - **DOI:** [10.1016/j.molcel.2010.05.004](https://doi.org/10.1016/j.molcel.2010.05.004) - **PMID:** 20513432 | **PMC:** PMC2898526
  • Network accessline 20
    - **DOI:** [10.1016/j.molcel.2010.05.004](https://doi.org/10.1016/j.molcel.2010.05.004)
  • Network accessline 42
    Li WW, Noble WS. MEME SUITE: tools for motif discovery and searching. Nucleic Acids   Research, 37(Web Server issue):W202-W208, 2009. - **DOI:** [10.1093/nar/gkp335](https://doi.org/10.1093/nar/gkp335)
  • Network accessline 43
    Research, 37(Web Server issue):W202-W208, 2009. - **DOI:** [10.1093/nar/gkp335](https://doi.org/10.1093/nar/gkp335) - **PMID:** 19458158 | **PMC:** PMC2703892
  • Network accessline 44
    - **DOI:** [10.1093/nar/gkp335](https://doi.org/10.1093/nar/gkp335)
  • Network accessline 65
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skills/multi-omics-integration/SKILL.md

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    - **Mawla, van der Meulen & Huising 2023** (BMC Genomics): Integrated ATAC-seq across alpha, beta, and delta cells. Identified common and cell type-specific enhancers. Demonstrated that chromatin accessibility patterns predict cell identity
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    - **ChromHMM** (Ernst & Kellis 2017, Nature Protocols, 711 citations): The standard tool for chromatin state segmentation. Uses combinatorial patterns of histone marks to annotate genome into functional states (active promoter, enhancer, re
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    - **ENCODE Phase 3** (Gorkin et al. 2020, Nature, 301 citations): Created unified chromatin state annotations across 66 mouse epigenomes. 18 chromatin states annotated. Demonstrated that bivalent chromatin is enriched in silencers and polyc
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    - **ENCODE cCRE Registry** (ENCODE Project Consortium 2020, Nature): Defined ~926,000 candidate cis-regulatory elements (cCREs) in the human genome classified as promoter-like, enhancer-like, or CTCF-bound, using DNase, H3K4me3, H3K27ac, an
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    - **SCENIC+** (Gonzalez-Blas et al. 2022, Nature Methods, 369 citations): Single-cell multi-omic inference of enhancers and gene regulatory networks. Predicts genomic enhancers, upstream TFs, and target genes from joint chromatin accessibil
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    - **Minnoye et al. 2021** (Nature Reviews Methods Primers, 125 citations): Comprehensive review of chromatin accessibility profiling methods. Discusses orthogonal assays needed to interpret accessible regions — enhancer-promoter proximity,
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    - **Roadmap Epigenomics** (Kundaje et al. 2015, Nature, 4,800+ citations): Mapped chromatin states across 111 human reference epigenomes. Established the canonical histone mark signatures for functional annotation. - **ENCODE Blacklist** (A
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    - **ENCODE Blacklist** (Amemiya et al. 2019, Scientific Reports, 1,372 citations): Defined problematic genomic regions to filter from all functional genomics analyses. [DOI](https://doi.org/10.1038/s41598-019-45839-z)
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    - **ABC Model** (Fulco et al. 2019, Nature Genetics, 800+ citations): Activity-By-Contact model for predicting enhancer-gene connections. Combines enhancer activity (H3K27ac) with Hi-C contact frequency. Outperforms proximity-based assignme
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    - **ROSE** (Whyte et al. 2013, Cell, 3,000+ citations): Algorithm for identifying super-enhancers from H3K27ac or Med1 ChIP-seq signal. Regions above the inflection point in ranked signal are classified as super-enhancers. [DOI](https://doi
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    - **GREAT** (McLean et al. 2010, Nature Biotechnology, 3,500+ citations): Genomic Regions Enrichment of Annotations Tool. Assigns biological meaning to sets of non-coding genomic regions (enhancers, accessible regions) by analyzing nearby g
  • Network accessline 51
    - **GRaNIE** (Kamal et al. 2023, Molecular Systems Biology): Enhancer-mediated gene regulatory network inference. Builds GRNs based on covariation of chromatin accessibility and RNA-seq across samples, connecting TFs → enhancers → target ge
  • Network accessline 52
    - **Enformer** (Avsec et al. 2021, Nature Methods): Deep learning model predicting gene expression from DNA sequence by integrating long-range interactions (up to 100kb). Accurately predicts variant effects and enhancer-promoter interaction
  • Network accessline 53
    - **scVI/scANVI** (Lopez et al. 2018; Xu et al. 2021, Nature Methods / MSB): Deep generative models for single-cell RNA-seq. scANVI extends scVI with semi-supervised cell type annotation. When used with scATAC-seq multiome data, provides pr
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    - **Luecken et al. 2022** (Nature Methods, scIB benchmark): Atlas-level integration benchmark of 68 methods. Found scANVI, scVI, Scanorama, and scGen perform best. Provides the standard framework (14 metrics) for evaluating single-cell inte
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    Always filter against ENCODE Blacklist regions before any analysis: - Human GRCh38: `hg38-blacklist.v2.bed.gz` from [Boyle-Lab/Blacklist](https://github.com/Boyle-Lab/Blacklist)
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    - Human GRCh38: `hg38-blacklist.v2.bed.gz` from [Boyle-Lab/Blacklist](https://github.com/Boyle-Lab/Blacklist)
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    ### Functional Enrichment of Identified Regions Use GREAT (McLean et al. 2010) to assign biological meaning to enhancer/regulatory region sets: - Upload enhancer BED file to GREAT (https://great.stanford.edu)
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    Use GREAT (McLean et al. 2010) to assign biological meaning to enhancer/regulatory region sets: - Upload enhancer BED file to GREAT (https://great.stanford.edu) - Assess GO term enrichment, pathway enrichment, disease association
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    - Upload enhancer BED file to GREAT (https://great.stanford.edu)

skills/multi-omics-integration/references/literature.md

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skills/peak-annotation/SKILL.md

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    | Reference | Journal | Key Contribution | DOI | Citations | |-----------|---------|-----------------|-----|-----------| | Yu et al. (2015) | Bioinformatics | ChIPseeker: R/Bioconductor package for ChIP peak annotation, comparison, and visu
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    |-----------|---------|-----------------|-----|-----------| | Yu et al. (2015) | Bioinformatics | ChIPseeker: R/Bioconductor package for ChIP peak annotation, comparison, and visualization | [10.1093/bioinformatics/btv145](https://doi.org/1
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    | Yu et al. (2015) | Bioinformatics | ChIPseeker: R/Bioconductor package for ChIP peak annotation, comparison, and visualization | [10.1093/bioinformatics/btv145](https://doi.org/10.1093/bioinformatics/btv145) | ~3,200 |
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    | McLean et al. (2010) | Nature Biotechnology | GREAT: Genomic Regions Enrichment of Annotations Tool; assigns biological meaning to cis-regulatory regions using basal+extension gene association | [10.1038/nbt.1630](https://doi.org/10.1038/
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    | Zhu et al. (2010) | BMC Bioinformatics | ChIPpeakAnno: Bioconductor package for ChIP-seq/ChIP-chip annotation; pioneered peak-gene association | [10.1186/1471-2105-11-237](https://doi.org/10.1186/1471-2105-11-237) | ~1,200 |
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    | Tanigawa et al. (2022) | PLOS Computational Biology | rGREAT: R/Bioconductor interface to GREAT; enables programmatic enrichment analysis | [10.1371/journal.pcbi.1010378](https://doi.org/10.1371/journal.pcbi.1010378) | ~80 |
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    | Amemiya et al. (2019) | Scientific Reports | ENCODE Blacklist: artifact regions to exclude before annotation to avoid spurious gene associations | [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z) | ~1,372 |
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    For quick analysis, use the GREAT web interface at https://great.stanford.edu:

skills/peak-annotation/references/literature.md

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skills/pipeline-atacseq/references/02-alignment.md

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    | Organism | Assembly | FASTA source | |----------|----------|--------------| | Human | GRCh38 (hg38) | `https://www.encodeproject.org/files/GRCh38_no_alt_analysis_set_GCA_000001405.15/` |
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    |----------|----------|--------------| | Human | GRCh38 (hg38) | `https://www.encodeproject.org/files/GRCh38_no_alt_analysis_set_GCA_000001405.15/` | | Mouse | mm10 (GRCm38) | `https://www.encodeproject.org/files/mm10_no_alt_analysis_set_EN
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    | Mouse | mm10 (GRCm38) | `https://www.encodeproject.org/files/mm10_no_alt_analysis_set_ENCODE/` |

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    - **DOI:** [10.1038/nmeth.1923](https://doi.org/10.1038/nmeth.1923)
  • Network accessline 107
    - **Citation:** Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM, Brown M, Li W, Liu XS. Model-based Analysis of ChIP-Seq (MACS). Genome Biology, 9(9):R137, 2008. - **DOI:** [10.1186/gb-2008-9-9-r137](htt
  • Network accessline 108
    - **DOI:** [10.1186/gb-2008-9-9-r137](https://doi.org/10.1186/gb-2008-9-9-r137)
  • Network accessline 117
    - **Citation:** Li Q, Brown JB, Huang H, Bickel PJ. Measuring reproducibility of high-throughput experiments. Annals of Applied Statistics, 5(3):1752-1779, 2011. - **DOI:** [10.1214/11-AOAS466](https://doi.org/10.1214/11-AOAS466)
  • Network accessline 118
    - **DOI:** [10.1214/11-AOAS466](https://doi.org/10.1214/11-AOAS466)
  • Network accessline 132
    - **Citation:** Li H, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics, 25(16):2078-2079, 2009. - **DOI:** [10.1093/bioinformatics/btp352](https://doi.org/10.1093/bioinformatics/btp352)
  • Network accessline 133
    - **DOI:** [10.1093/bioinformatics/btp352](https://doi.org/10.1093/bioinformatics/btp352)
  • Network accessline 141
    - **Citation:** Amemiya HM, Kundaje A, Boyle AP. The ENCODE Blacklist: Identification of Problematic Regions of the Genome. Scientific Reports, 9:9354, 2019. - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z
  • Network accessline 142
    - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z)
  • Network accessline 150
    - **Citation:** Ramírez F, et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Research, 44(W1):W160-W165, 2016. - **DOI:** [10.1093/nar/gkw257](https://doi.org/10.1093/nar/gkw257)
  • Network accessline 151
    - **DOI:** [10.1093/nar/gkw257](https://doi.org/10.1093/nar/gkw257)
  • Network accessline 159
    - **Citation:** Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics, 32(19):3047-3048, 2016. - **DOI:** [10.1093/bioinformatics/btw354](https://doi.
  • Network accessline 160
    - **DOI:** [10.1093/bioinformatics/btw354](https://doi.org/10.1093/bioinformatics/btw354)
  • Network accessline 168
    - **Citation:** Broad Institute. Picard toolkit. GitHub, 2019. - **URL:** [https://broadinstitute.github.io/picard/](https://broadinstitute.github.io/picard/)
  • Network accessline 169
    - **URL:** [https://broadinstitute.github.io/picard/](https://broadinstitute.github.io/picard/)
  • Network accessline 176
    - **Citation:** Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics, 26(6):841-842, 2010. - **DOI:** [10.1093/bioinformatics/btq033](https://doi.org/10.1093/bioinformatics/btq033)
  • Network accessline 177
    - **DOI:** [10.1093/bioinformatics/btq033](https://doi.org/10.1093/bioinformatics/btq033)
  • Network accessline 185
    - **Citation:** Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal, 17(1):10-12, 2011. - **DOI:** [10.14806/ej.17.1.200](https://doi.org/10.14806/ej.17.1.200)
  • Network accessline 186
    - **DOI:** [10.14806/ej.17.1.200](https://doi.org/10.14806/ej.17.1.200)
  • Network accessline 194
    - **Citation:** Andrews S. FastQC: A quality control tool for high throughput sequence data. Babraham Bioinformatics, 2010. - **URL:** [https://www.bioinformatics.babraham.ac.uk/projects/fastqc/](https://www.bioinformatics.babraham.ac.uk/pr
  • Network accessline 195
    - **URL:** [https://www.bioinformatics.babraham.ac.uk/projects/fastqc/](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)

skills/pipeline-atacseq/scripts/Dockerfile

  • Executable scriptline 1
    Executable file
  • Dangerous shell operationline 10
    RUN apt-get update && apt-get install -y --no-install-recommends      build-essential wget curl git ca-certificates perl unzip bc      zlib1g-dev libbz2-dev liblzma-dev libncurses5-dev libcurl4-openssl-dev      python3 python3-pip python3-d
  • Network accessline 10
    RUN apt-get update && apt-get install -y --no-install-recommends \     build-essential wget curl git ca-certificates perl unzip bc \
  • Network accessline 11
    build-essential wget curl git ca-certificates perl unzip bc \
  • Dangerous shell operationline 14
    && rm -rf /var/lib/apt/lists/*
  • Network accessline 24
    RUN pip3 install --no-cache-dir --no-build-isolation \     https://github.com/kundajelab/idr/archive/refs/tags/2.0.4.2.tar.gz
  • Network accessline 25
    https://github.com/kundajelab/idr/archive/refs/tags/2.0.4.2.tar.gz
  • Dangerous shell operationline 28
    RUN wget -q https://github.com/BenLangmead/bowtie2/releases/download/v2.5.4/bowtie2-2.5.4-linux-x86_64.zip &&      unzip -q bowtie2-2.5.4-linux-x86_64.zip -d /opt/ &&      ln -s /opt/bowtie2-2.5.4-linux-x86_64/bowtie2* /usr/local/bin/ &&
  • Network accessline 28
    RUN wget -q https://github.com/BenLangmead/bowtie2/releases/download/v2.5.4/bowtie2-2.5.4-linux-x86_64.zip && \
  • Dangerous shell operationline 31
    rm bowtie2-2.5.4-linux-x86_64.zip
  • Dangerous shell operationline 34
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 &&      tar xjf samtools-1.19.tar.bz2 && cd samtools-1.19 &&      ./configure --prefix=/usr/local && make -j4 && make install &&      cd .. && rm
  • Network accessline 34
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 && \
  • Dangerous shell operationline 37
    cd .. && rm -rf samtools-1.19*
  • Dangerous shell operationline 40
    RUN wget -q https://github.com/arq5x/bedtools2/releases/download/v2.31.0/bedtools-2.31.0.tar.gz &&      tar xzf bedtools-2.31.0.tar.gz && cd bedtools2 &&      make -j4 && cp bin/* /usr/local/bin/ &&      cd .. && rm -rf bedtools2 bedtools-2
  • Network accessline 40
    RUN wget -q https://github.com/arq5x/bedtools2/releases/download/v2.31.0/bedtools-2.31.0.tar.gz && \
  • Dangerous shell operationline 43
    cd .. && rm -rf bedtools2 bedtools-2.31.0.tar.gz
  • Network accessline 46
    RUN wget -q https://github.com/broadinstitute/picard/releases/download/3.1.1/picard.jar \
  • Dangerous shell operationline 52
    RUN wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz &&      tar xzf 0.6.10.tar.gz && ln -s /TrimGalore-0.6.10/trim_galore /usr/local/bin/ &&      rm 0.6.10.tar.gz
  • Network accessline 52
    RUN wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz && \
  • Dangerous shell operationline 54
    rm 0.6.10.tar.gz
  • Dangerous shell operationline 57
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip &&      unzip -q fastqc_v0.12.1.zip -d /opt/ && chmod +x /opt/FastQC/fastqc &&      ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip
  • Network accessline 57
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip && \
  • Dangerous shell operationline 59
    ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip
  • Network accessline 62
    RUN wget -q https://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/bedGraphToBigWig \

skills/pipeline-atacseq/scripts/main.nf

  • Executable scriptline 1
    #!/usr/bin/env nextflow
  • Network accessline 21
    'GRCh38': [         index:     'GRCh38_bowtie2_index',         blacklist: 'https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz',
  • Network accessline 22
    index:     'GRCh38_bowtie2_index',         blacklist: 'https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz',         gsize:     'hs'
  • Network accessline 23
    blacklist: 'https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz',
  • Network accessline 26
    'mm10': [         index:     'mm10_bowtie2_index',         blacklist: 'https://github.com/Boyle-Lab/Blacklist/raw/master/lists/mm10-blacklist.v2.bed.gz',
  • Network accessline 27
    index:     'mm10_bowtie2_index',         blacklist: 'https://github.com/Boyle-Lab/Blacklist/raw/master/lists/mm10-blacklist.v2.bed.gz',         gsize:     'mm'
  • Network accessline 28
    blacklist: 'https://github.com/Boyle-Lab/Blacklist/raw/master/lists/mm10-blacklist.v2.bed.gz',

skills/pipeline-chipseq/references/02-alignment.md

  • Network accessline 25
    | Organism | Assembly | FASTA source | |----------|----------|--------------| | Human | GRCh38 (hg38) | `https://www.encodeproject.org/files/GRCh38_no_alt_analysis_set_GCA_000001405.15/` |
  • Network accessline 26
    |----------|----------|--------------| | Human | GRCh38 (hg38) | `https://www.encodeproject.org/files/GRCh38_no_alt_analysis_set_GCA_000001405.15/` | | Mouse | mm10 (GRCm38) | `https://www.encodeproject.org/files/mm10_no_alt_analysis_set_EN
  • Network accessline 27
    | Human | GRCh38 (hg38) | `https://www.encodeproject.org/files/GRCh38_no_alt_analysis_set_GCA_000001405.15/` |
  • Network accessline 28
    | Mouse | mm10 (GRCm38) | `https://www.encodeproject.org/files/mm10_no_alt_analysis_set_ENCODE/` |

skills/pipeline-chipseq/references/03-filtering.md

  • Network accessline 19
    The default blacklist is downloaded per `--genome`; override it with `--blacklist`: - **Human (hg38)**: `https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz`
  • Network accessline 20
    - **Human (hg38)**: `https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz`
  • Network accessline 21
    - **Mouse (mm10)**: `https://github.com/Boyle-Lab/Blacklist/raw/master/lists/mm10-blacklist.v2.bed.gz`

skills/pipeline-chipseq/references/literature.md

  • Network accessline 16
    - **Citation:** Landt SG, Marinov GK, Kundaje A, Kheradpour P, Pauli F, Batzoglou S, Bernstein BE, Bickel P, Brown JB, Cayting P, Chen Y, DeSalvo G, Epstein C, Fisher-Aylor KI, Euskirchen G, Gerstein M, Gertz J, Hartemink AJ, Hoffman MM, Iy
  • Network accessline 17
    - **DOI:** [10.1101/gr.136184.111](https://doi.org/10.1101/gr.136184.111)
  • Network accessline 26
    - **Citation:** ENCODE Project Consortium, Moore JE, Purcaro MJ, Pratt HE, Epstein CB, Shoresh N, Adrian J, Kawli T, Davis CA, Dobin A, Kaul R, Halow J, Van Nostrand EL, Freese P, Gorkin DU, Shen Y, He Y, Mackiewicz M, Pauli-Behn F, William
  • Network accessline 27
    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 36
    - **Citation:** Hitz BC, Lee JW, Jolanki O, Kagda MS, Graham K, Sud P, Gabdank I, Strattan JS, Sloan CA, Dreszer T, Rowe LD, Podduturi NR, Malladi V, Chan ET, Davidson JM, Ho M, Miyasato S, Simison M, Tanaka F, Luo Y, Whaling I, Hong EL, Le
  • Network accessline 37
    - **DOI:** [10.21203/rs.3.rs-311127/v1](https://doi.org/10.21203/rs.3.rs-311127/v1)
  • Network accessline 51
    - **Citation:** Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM, Brown M, Li W, Liu XS. Model-based Analysis of ChIP-Seq (MACS). Genome Biology, 9(9):R137, 2008. - **DOI:** [10.1186/gb-2008-9-9-r137](htt
  • Network accessline 52
    - **DOI:** [10.1186/gb-2008-9-9-r137](https://doi.org/10.1186/gb-2008-9-9-r137)
  • Network accessline 61
    - **Citation:** Li Q, Brown JB, Huang H, Bickel PJ. Measuring reproducibility of high-throughput experiments. Annals of Applied Statistics, 5(3):1752-1779, 2011. - **DOI:** [10.1214/11-AOAS466](https://doi.org/10.1214/11-AOAS466)
  • Network accessline 62
    - **DOI:** [10.1214/11-AOAS466](https://doi.org/10.1214/11-AOAS466)
  • Network accessline 70
    - **Citation:** Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv:1303.3997, 2013. - **DOI:** [10.48550/arXiv.1303.3997](https://doi.org/10.48550/arXiv.1303.3997)
  • Network accessline 71
    - **DOI:** [10.48550/arXiv.1303.3997](https://doi.org/10.48550/arXiv.1303.3997)
  • Network accessline 79
    - **Citation:** Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R; 1000 Genome Project Data Processing Subgroup. The Sequence Alignment/Map format and SAMtools. Bioinformatics, 25(16):2078-2079, 2009. -
  • Network accessline 80
    - **DOI:** [10.1093/bioinformatics/btp352](https://doi.org/10.1093/bioinformatics/btp352)
  • Network accessline 93
    - **Citation:** Ramírez F, Ryan DP, Grüning B, Bhardwaj V, Kilpert F, Richter AS, Heyne S, Dündar F, Manke T. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Research, 44(W1):W160-W165, 2016. - **DO
  • Network accessline 94
    - **DOI:** [10.1093/nar/gkw257](https://doi.org/10.1093/nar/gkw257)
  • Network accessline 103
    - **Citation:** Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics, 32(19):3047-3048, 2016. - **DOI:** [10.1093/bioinformatics/btw354](https://doi.
  • Network accessline 104
    - **DOI:** [10.1093/bioinformatics/btw354](https://doi.org/10.1093/bioinformatics/btw354)
  • Network accessline 113
    - **Citation:** Kharchenko PV, Tolstorukov MY, Park PJ. Design and analysis of ChIP-seq experiments for DNA-binding proteins. Nature Biotechnology, 26(12):1351-1359, 2008. (Algorithm basis); Landt et al. 2012 (ENCODE implementation); phanto
  • Network accessline 114
    - **DOI:** [10.1038/nbt.1508](https://doi.org/10.1038/nbt.1508) (Kharchenko et al.)
  • Network accessline 129
    - **Citation:** Andrews S. FastQC: A quality control tool for high throughput sequence data. Babraham Bioinformatics, 2010. - **URL:** [https://www.bioinformatics.babraham.ac.uk/projects/fastqc/](https://www.bioinformatics.babraham.ac.uk/pr
  • Network accessline 130
    - **URL:** [https://www.bioinformatics.babraham.ac.uk/projects/fastqc/](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)
  • Network accessline 138
    - **Citation:** Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal, 17(1):10-12, 2011. - **DOI:** [10.14806/ej.17.1.200](https://doi.org/10.14806/ej.17.1.200)
  • Network accessline 139
    - **DOI:** [10.14806/ej.17.1.200](https://doi.org/10.14806/ej.17.1.200)
  • Network accessline 147
    - **Citation:** Broad Institute. Picard toolkit. GitHub, 2019. - **URL:** [https://broadinstitute.github.io/picard/](https://broadinstitute.github.io/picard/)
  • Network accessline 148
    - **URL:** [https://broadinstitute.github.io/picard/](https://broadinstitute.github.io/picard/)
  • Network accessline 156
    - **Citation:** Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics, 26(6):841-842, 2010. - **DOI:** [10.1093/bioinformatics/btq033](https://doi.org/10.1093/bioinformatics/btq033)
  • Network accessline 157
    - **DOI:** [10.1093/bioinformatics/btq033](https://doi.org/10.1093/bioinformatics/btq033)
  • Network accessline 166
    - **Citation:** Amemiya HM, Kundaje A, Boyle AP. The ENCODE Blacklist: Identification of Problematic Regions of the Genome. Scientific Reports, 9:9354, 2019. - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z
  • Network accessline 167
    - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z)
  • Network accessline 180
    - **Citation:** Bailey T, Krajewski P, Ladunga I, Lefebvre C, Li Q, Liu T, Madrigal P, Taslim C, Zhang J. Practical Guidelines for the Comprehensive Analysis of ChIP-seq Data. PLoS Computational Biology, 9(11):e1003326, 2013. - **DOI:** [10
  • Network accessline 181
    - **DOI:** [10.1371/journal.pcbi.1003326](https://doi.org/10.1371/journal.pcbi.1003326)
  • Network accessline 190
    - **Citation:** Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, Whitwham A, Keane T, McCarthy SA, Davies RM, Li H. Twelve years of SAMtools and BCFtools. GigaScience, 10(2):giab008, 2021. - **DOI:** [10.1093/gigascience/gi
  • Network accessline 191
    - **DOI:** [10.1093/gigascience/giab008](https://doi.org/10.1093/gigascience/giab008)

skills/pipeline-chipseq/scripts/Dockerfile

  • Executable scriptline 1
    Executable file
  • Dangerous shell operationline 10
    RUN apt-get update && apt-get install -y --no-install-recommends      build-essential wget curl git ca-certificates perl unzip bc      zlib1g-dev libbz2-dev liblzma-dev libncurses5-dev libcurl4-openssl-dev      python3 python3-pip python3-d
  • Network accessline 10
    RUN apt-get update && apt-get install -y --no-install-recommends \     build-essential wget curl git ca-certificates perl unzip bc \
  • Network accessline 11
    build-essential wget curl git ca-certificates perl unzip bc \
  • Dangerous shell operationline 14
    && rm -rf /var/lib/apt/lists/*
  • Network accessline 24
    RUN pip3 install --no-cache-dir --no-build-isolation \     https://github.com/kundajelab/idr/archive/refs/tags/2.0.4.2.tar.gz
  • Network accessline 25
    https://github.com/kundajelab/idr/archive/refs/tags/2.0.4.2.tar.gz
  • Dangerous shell operationline 28
    RUN wget -q https://github.com/lh3/bwa/archive/refs/tags/v0.7.18.tar.gz &&      tar xzf v0.7.18.tar.gz && cd bwa-0.7.18 && make -j4 && cp bwa /usr/local/bin/ &&      cd .. && rm -rf bwa-0.7.18 v0.7.18.tar.gz
  • Network accessline 28
    RUN wget -q https://github.com/lh3/bwa/archive/refs/tags/v0.7.18.tar.gz && \
  • Dangerous shell operationline 30
    cd .. && rm -rf bwa-0.7.18 v0.7.18.tar.gz
  • Dangerous shell operationline 33
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 &&      tar xjf samtools-1.19.tar.bz2 && cd samtools-1.19 &&      ./configure --prefix=/usr/local && make -j4 && make install &&      cd .. && rm
  • Network accessline 33
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 && \
  • Dangerous shell operationline 36
    cd .. && rm -rf samtools-1.19*
  • Dangerous shell operationline 39
    RUN wget -q https://github.com/arq5x/bedtools2/releases/download/v2.31.0/bedtools-2.31.0.tar.gz &&      tar xzf bedtools-2.31.0.tar.gz && cd bedtools2 &&      make -j4 && cp bin/* /usr/local/bin/ &&      cd .. && rm -rf bedtools2 bedtools-2
  • Network accessline 39
    RUN wget -q https://github.com/arq5x/bedtools2/releases/download/v2.31.0/bedtools-2.31.0.tar.gz && \
  • Dangerous shell operationline 42
    cd .. && rm -rf bedtools2 bedtools-2.31.0.tar.gz
  • Network accessline 45
    RUN wget -q https://github.com/broadinstitute/picard/releases/download/3.1.1/picard.jar \
  • Dangerous shell operationline 51
    RUN wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz &&      tar xzf 0.6.10.tar.gz && ln -s /TrimGalore-0.6.10/trim_galore /usr/local/bin/ &&      rm 0.6.10.tar.gz
  • Network accessline 51
    RUN wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz && \
  • Dangerous shell operationline 53
    rm 0.6.10.tar.gz
  • Dangerous shell operationline 56
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip &&      unzip -q fastqc_v0.12.1.zip -d /opt/ && chmod +x /opt/FastQC/fastqc &&      ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip
  • Network accessline 56
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip && \
  • Dangerous shell operationline 58
    ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip
  • Network accessline 61
    RUN wget -q https://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/bedGraphToBigWig \

skills/pipeline-chipseq/scripts/main.nf

  • Executable scriptline 1
    #!/usr/bin/env nextflow
  • Network accessline 21
    return [     'GRCh38': [         blacklist:  'https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz',
  • Network accessline 22
    'GRCh38': [         blacklist:  'https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz',         gsize:      'hs'
  • Network accessline 23
    blacklist:  'https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz',
  • Network accessline 25
    ],     'mm10': [         blacklist:  'https://github.com/Boyle-Lab/Blacklist/raw/master/lists/mm10-blacklist.v2.bed.gz',
  • Network accessline 26
    'mm10': [         blacklist:  'https://github.com/Boyle-Lab/Blacklist/raw/master/lists/mm10-blacklist.v2.bed.gz',         gsize:      'mm'
  • Network accessline 27
    blacklist:  'https://github.com/Boyle-Lab/Blacklist/raw/master/lists/mm10-blacklist.v2.bed.gz',

skills/pipeline-cutandrun/SKILL.md

  • Network accessline 340
    # Download suspect list wget https://github.com/Boyle-Lab/Blacklist/raw/master/lists/CUTandRUN.suspectlist.hg38.bed.gz
  • Network accessline 341
    wget https://github.com/Boyle-Lab/Blacklist/raw/master/lists/CUTandRUN.suspectlist.hg38.bed.gz

skills/pipeline-cutandrun/references/literature.md

  • Network accessline 14
    - **Citation:** Skene PJ, Henikoff S. An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites. eLife, 6:e21856, 2017. - **DOI:** [10.7554/eLife.21856](https://doi.org/10.7554/eLife.21856)
  • Network accessline 15
    - **DOI:** [10.7554/eLife.21856](https://doi.org/10.7554/eLife.21856)
  • Network accessline 24
    - **Citation:** Kaya-Okur HS, Wu SJ, Codomo CA, Pledger ES, Bryson TD, Henikoff JG, Ahmad K, Henikoff S. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nature Communications, 10(1):1930, 2019. - **DOI:** [10.1
  • Network accessline 25
    - **DOI:** [10.1038/s41467-019-09982-5](https://doi.org/10.1038/s41467-019-09982-5)
  • Network accessline 34
    - **Citation:** Skene PJ, Henikoff JG, Henikoff S. Targeted in situ genome-wide profiling with high efficiency for low cell numbers. Nature Protocols, 13(5):1006-1019, 2018. - **DOI:** [10.1038/nprot.2018.015](https://doi.org/10.1038/nprot.
  • Network accessline 35
    - **DOI:** [10.1038/nprot.2018.015](https://doi.org/10.1038/nprot.2018.015)
  • Network accessline 48
    - **Citation:** Meers MP, Tenenbaum D, Henikoff S. Peak calling by Sparse Enrichment Analysis for CUT&RUN chromatin profiling. Epigenetics & Chromatin, 12(1):42, 2019. - **DOI:** [10.1186/s13072-019-0287-4](https://doi.org/10.1186/s13072-01
  • Network accessline 49
    - **DOI:** [10.1186/s13072-019-0287-4](https://doi.org/10.1186/s13072-019-0287-4)
  • Network accessline 62
    - **Citation:** Nordin A, Zambanini G, Pagella P, Bhatt DK, Bjork P, Nilsson J, Mead P, Boyle AP. The CUT&RUN suspect list of problematic regions of the genome. Genome Biology, 24:185, 2023. - **DOI:** [10.1186/s13059-023-02960-3](https://d
  • Network accessline 63
    - **DOI:** [10.1186/s13059-023-02960-3](https://doi.org/10.1186/s13059-023-02960-3)
  • Network accessline 72
    - **Citation:** Meers MP, Bryson TD, Henikoff JG, Henikoff S. Improved CUT&RUN chromatin profiling tools. eLife, 8:e46314, 2019. - **DOI:** [10.7554/eLife.46314](https://doi.org/10.7554/eLife.46314)
  • Network accessline 73
    - **DOI:** [10.7554/eLife.46314](https://doi.org/10.7554/eLife.46314)
  • Network accessline 88
    - **DOI:** [10.1038/nmeth.1923](https://doi.org/10.1038/nmeth.1923) | **PMID:** 22388286 | **Citations:** ~47,300
  • Network accessline 95
    - **DOI:** [10.1186/gb-2008-9-9-r137](https://doi.org/10.1186/gb-2008-9-9-r137) | **PMID:** 18798982 | **Citations:** ~7,000
  • Network accessline 102
    - **DOI:** [10.1093/bioinformatics/btp352](https://doi.org/10.1093/bioinformatics/btp352) | **PMID:** 19505943 | **Citations:** ~53,700
  • Network accessline 109
    - **URL:** [https://broadinstitute.github.io/picard/](https://broadinstitute.github.io/picard/)
  • Network accessline 116
    - **DOI:** [10.1093/bioinformatics/btq033](https://doi.org/10.1093/bioinformatics/btq033) | **PMID:** 20110278 | **Citations:** ~12,000
  • Network accessline 123
    - **DOI:** [10.1093/nar/gkw257](https://doi.org/10.1093/nar/gkw257) | **PMID:** 27079975 | **Citations:** ~6,100
  • Network accessline 130
    - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z) | **PMID:** 31249361 | **Citations:** ~1,372
  • Network accessline 137
    - **DOI:** [10.1093/bioinformatics/btw354](https://doi.org/10.1093/bioinformatics/btw354) | **PMID:** 27312411 | **Citations:** ~6,800
  • Network accessline 144
    - **URL:** [https://www.bioinformatics.babraham.ac.uk/projects/fastqc/](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)
  • Network accessline 151
    - **DOI:** [10.14806/ej.17.1.200](https://doi.org/10.14806/ej.17.1.200) | **Citations:** ~13,000

skills/pipeline-cutandrun/scripts/Dockerfile

  • Executable scriptline 1
    Executable file
  • Network accessline 5
    LABEL maintainer="Dr. Alex M. Mawla, PhD <https://github.com/ammawla/encode-toolkit>"
  • Dangerous shell operationline 11
    RUN apt-get update && apt-get install -y --no-install-recommends      wget curl gzip pigz unzip      python3 python3-pip python3-dev      openjdk-17-jre-headless      libncurses5-dev libbz2-dev liblzma-dev zlib1g-dev libcurl4-openssl-dev
  • Network accessline 11
    RUN apt-get update && apt-get install -y --no-install-recommends \     wget curl gzip pigz unzip \
  • Network accessline 12
    wget curl gzip pigz unzip \
  • Dangerous shell operationline 17
    && rm -rf /var/lib/apt/lists/*
  • Dangerous shell operationline 20
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 &&      tar xjf samtools-1.19.tar.bz2 && cd samtools-1.19 &&      ./configure --prefix=/usr/local && make -j4 && make install &&      cd .. && rm
  • Network accessline 20
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 && \
  • Dangerous shell operationline 23
    cd .. && rm -rf samtools-1.19*
  • Dangerous shell operationline 26
    RUN wget -q https://github.com/BenLangmead/bowtie2/releases/download/v2.5.4/bowtie2-2.5.4-linux-x86_64.zip &&      unzip -q bowtie2-2.5.4-linux-x86_64.zip -d /opt/ &&      ln -s /opt/bowtie2-2.5.4-linux-x86_64/bowtie2* /usr/local/bin/ &&
  • Network accessline 26
    RUN wget -q https://github.com/BenLangmead/bowtie2/releases/download/v2.5.4/bowtie2-2.5.4-linux-x86_64.zip && \
  • Dangerous shell operationline 29
    rm bowtie2-2.5.4-linux-x86_64.zip
  • Dangerous shell operationline 32
    RUN wget -q https://github.com/arq5x/bedtools2/releases/download/v2.31.0/bedtools-2.31.0.tar.gz &&      tar xzf bedtools-2.31.0.tar.gz && cd bedtools2 &&      make -j4 && cp bin/* /usr/local/bin/ &&      cd .. && rm -rf bedtools2 bedtools-2
  • Network accessline 32
    RUN wget -q https://github.com/arq5x/bedtools2/releases/download/v2.31.0/bedtools-2.31.0.tar.gz && \
  • Dangerous shell operationline 35
    cd .. && rm -rf bedtools2 bedtools-2.31.0.tar.gz
  • Network accessline 38
    RUN wget -q https://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/bedGraphToBigWig \
  • Network accessline 42
    RUN wget -q https://github.com/broadinstitute/picard/releases/download/3.1.1/picard.jar \
  • Dangerous shell operationline 48
    RUN wget -q https://github.com/FredHutch/SEACR/archive/refs/tags/v1.3.tar.gz &&      tar xzf v1.3.tar.gz && mv SEACR-1.3 /opt/SEACR &&      ln -s /opt/SEACR/SEACR_1.3.sh /opt/SEACR/SEACR_1.3.R /usr/local/bin/ &&      rm v1.3.tar.gz
  • Network accessline 48
    RUN wget -q https://github.com/FredHutch/SEACR/archive/refs/tags/v1.3.tar.gz && \
  • Dangerous shell operationline 51
    rm v1.3.tar.gz
  • Dangerous shell operationline 60
    RUN wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz &&      tar xzf 0.6.10.tar.gz && ln -s /TrimGalore-0.6.10/trim_galore /usr/local/bin/ &&      rm 0.6.10.tar.gz
  • Network accessline 60
    RUN wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz && \
  • Dangerous shell operationline 62
    rm 0.6.10.tar.gz
  • Dangerous shell operationline 65
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip &&      unzip -q fastqc_v0.12.1.zip -d /opt/ && chmod +x /opt/FastQC/fastqc &&      ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip
  • Network accessline 65
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip && \
  • Dangerous shell operationline 67
    ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip

skills/pipeline-cutandrun/scripts/main.nf

  • Executable scriptline 1
    #!/usr/bin/env nextflow
  • Dangerous shell operationline 181
    rm qfilt.bam dedup.bam
  • Dangerous shell operationline 240
    rm namesorted.bam

skills/pipeline-dnaseseq/references/03-filtering.md

  • Network accessline 79
    # Download blacklist wget -q https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz
  • Network accessline 80
    wget -q https://github.com/Boyle-Lab/Blacklist/raw/master/lists/hg38-blacklist.v2.bed.gz

skills/pipeline-dnaseseq/references/05-footprinting.md

  • Network accessline 137
    rgt-hint differential      --organism hg38      --bc      --nc 8
  • Network accessline 139
    --bc \     --nc 8 \
  • Network accessline 140
    --nc 8 \
  • Network accessline 161
    # Download Vierstra reference footprints # Available at: https://www.vierstra.org/resources/dgf
  • Network accessline 162
    # Available at: https://www.vierstra.org/resources/dgf

skills/pipeline-dnaseseq/references/literature.md

  • Network accessline 14
    - **Citation:** Thurman RE, Rynes E, Humbert R, Vierstra J, Maurano MT, Haugen E, Sheffield NC, Stergachis AB, Wang H, Vernot B, Garg K, John S, Sandstrom R, Bates D, Boatman L, Canfield TK, Diegel M, Dunn D, Ebersol AK, Frum T, Giste E, Jo
  • Network accessline 15
    - **DOI:** [10.1038/nature11232](https://doi.org/10.1038/nature11232)
  • Network accessline 24
    - **Citation:** John S, Sabo PJ, Thurman RE, Sung MH, Biddie SC, Johnson TA, Hager GL, Stamatoyannopoulos JA. Chromatin accessibility pre-determines glucocorticoid receptor binding patterns. Nature Genetics, 43(3):264-268, 2011. - **DOI:**
  • Network accessline 25
    - **DOI:** [10.1038/ng.759](https://doi.org/10.1038/ng.759)
  • Network accessline 34
    - **Citation:** Vierstra J, Lazar J, Sandstrom R, Halow J, Lee K, Bates D, Diegel M, Dunn D, Neri F, Haugen E, Rynes E, Reynolds A, Nelson J, Johnson A, Frerker M, Buckley M, Kaul R, Meuleman W, Stamatoyannopoulos JA. Global reference mappi
  • Network accessline 35
    - **DOI:** [10.1038/s41586-020-2528-x](https://doi.org/10.1038/s41586-020-2528-x)
  • Network accessline 44
    - **Citation:** ENCODE Project Consortium et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature, 583(7818):699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 45
    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 54
    - **Citation:** Hitz BC, Lee JW, Jolanki O, et al. The ENCODE Uniform Analysis Pipelines. Research Square (preprint), 2023. - **DOI:** [10.21203/rs.3.rs-311127/v1](https://doi.org/10.21203/rs.3.rs-311127/v1)
  • Network accessline 55
    - **DOI:** [10.21203/rs.3.rs-311127/v1](https://doi.org/10.21203/rs.3.rs-311127/v1)
  • Network accessline 67
    - **Citation:** Li Z, Schulz MH, Look T, Begus M, Zenke M, Costa IG. Identification of transcription factor binding sites using ATAC-seq. Genome Research, 29(11):1850-1858, 2019. - **DOI:** [10.1186/s13059-019-1642-2](https://doi.org/10.118
  • Network accessline 68
    - **DOI:** [10.1186/s13059-019-1642-2](https://doi.org/10.1186/s13059-019-1642-2)
  • Network accessline 77
    - **Citation:** Neph S, Vierstra J, Stergachis AB, Reynolds AP, Haugen E, Vernot B, Thurman RE, John S, Sandstrom R, Johnson AK, Maurano MT, Humbert R, Rynes E, Wang H, Vong S, Lee K, Bates D, Diegel M, Roach V, Dunn D, Neri J, Schafer A, H
  • Network accessline 78
    - **DOI:** [10.1038/nature11212](https://doi.org/10.1038/nature11212)
  • Network accessline 93
    - **DOI:** [10.1093/bioinformatics/btp324](https://doi.org/10.1093/bioinformatics/btp324) | **PMID:** 19451168 | **Citations:** ~25,000
  • Network accessline 100
    - **DOI:** [10.1093/bioinformatics/btp352](https://doi.org/10.1093/bioinformatics/btp352) | **PMID:** 19505943 | **Citations:** ~53,700
  • Network accessline 107
    - **URL:** [https://broadinstitute.github.io/picard/](https://broadinstitute.github.io/picard/)
  • Network accessline 114
    - **DOI:** [10.1093/bioinformatics/btq033](https://doi.org/10.1093/bioinformatics/btq033) | **PMID:** 20110278 | **Citations:** ~12,000
  • Network accessline 121
    - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z) | **PMID:** 31249361 | **Citations:** ~1,372
  • Network accessline 128
    - **DOI:** [10.1093/bioinformatics/btw354](https://doi.org/10.1093/bioinformatics/btw354) | **PMID:** 27312411 | **Citations:** ~6,800
  • Network accessline 135
    - **URL:** [https://www.bioinformatics.babraham.ac.uk/projects/fastqc/](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)
  • Network accessline 142
    - **DOI:** [10.14806/ej.17.1.200](https://doi.org/10.14806/ej.17.1.200) | **Citations:** ~13,000

skills/pipeline-dnaseseq/scripts/Dockerfile

  • Executable scriptline 1
    Executable file
  • Network accessline 5
    LABEL maintainer="Dr. Alex M. Mawla, PhD <https://github.com/ammawla/encode-toolkit>"
  • Dangerous shell operationline 11
    RUN apt-get update && apt-get install -y --no-install-recommends      wget curl gzip pigz unzip      python3 python3-pip python3-dev      openjdk-17-jre-headless      libncurses5-dev libbz2-dev liblzma-dev zlib1g-dev libcurl4-openssl-dev
  • Network accessline 11
    RUN apt-get update && apt-get install -y --no-install-recommends \     wget curl gzip pigz unzip \
  • Network accessline 12
    wget curl gzip pigz unzip \
  • Dangerous shell operationline 19
    && rm -rf /var/lib/apt/lists/*
  • Dangerous shell operationline 22
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 &&      tar xjf samtools-1.19.tar.bz2 && cd samtools-1.19 &&      ./configure --prefix=/usr/local && make -j4 && make install &&      cd .. && rm
  • Network accessline 22
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 && \
  • Dangerous shell operationline 25
    cd .. && rm -rf samtools-1.19*
  • Dangerous shell operationline 28
    RUN wget -q https://github.com/lh3/bwa/archive/refs/tags/v0.7.18.tar.gz &&      tar xzf v0.7.18.tar.gz && cd bwa-0.7.18 &&      make -j4 && cp bwa /usr/local/bin/ &&      cd .. && rm -rf bwa-0.7.18 v0.7.18.tar.gz
  • Network accessline 28
    RUN wget -q https://github.com/lh3/bwa/archive/refs/tags/v0.7.18.tar.gz && \
  • Dangerous shell operationline 31
    cd .. && rm -rf bwa-0.7.18 v0.7.18.tar.gz
  • Dangerous shell operationline 34
    RUN wget -q https://github.com/arq5x/bedtools2/releases/download/v2.31.0/bedtools-2.31.0.tar.gz &&      tar xzf bedtools-2.31.0.tar.gz && cd bedtools2 &&      make -j4 && cp bin/* /usr/local/bin/ &&      cd .. && rm -rf bedtools2 bedtools-2
  • Network accessline 34
    RUN wget -q https://github.com/arq5x/bedtools2/releases/download/v2.31.0/bedtools-2.31.0.tar.gz && \
  • Dangerous shell operationline 37
    cd .. && rm -rf bedtools2 bedtools-2.31.0.tar.gz
  • Network accessline 40
    RUN wget -q https://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/bedGraphToBigWig \
  • Dangerous shell operationline 44
    RUN wget -q https://github.com/StamLab/modwt/archive/refs/tags/1.0.tar.gz &&      tar xzf 1.0.tar.gz && make -C modwt-1.0/src &&      cp modwt-1.0/bin/modwt /usr/local/bin/ &&      rm -rf modwt-1.0 1.0.tar.gz
  • Network accessline 44
    RUN wget -q https://github.com/StamLab/modwt/archive/refs/tags/1.0.tar.gz && \
  • Dangerous shell operationline 47
    rm -rf modwt-1.0 1.0.tar.gz
  • Dangerous shell operationline 51
    RUN wget -q https://github.com/Altius/hotspot2/archive/refs/tags/v2.1.2.tar.gz &&      tar xzf v2.1.2.tar.gz && make -C hotspot2-2.1.2 &&      cp hotspot2-2.1.2/bin/* hotspot2-2.1.2/scripts/* /usr/local/bin/ &&      rm -rf hotspot2-2.1.2 v2
  • Network accessline 51
    RUN wget -q https://github.com/Altius/hotspot2/archive/refs/tags/v2.1.2.tar.gz && \
  • Dangerous shell operationline 54
    rm -rf hotspot2-2.1.2 v2.1.2.tar.gz
  • Network accessline 57
    RUN wget -q https://github.com/broadinstitute/picard/releases/download/3.1.1/picard.jar \
  • Dangerous shell operationline 63
    RUN pip3 install --no-cache-dir cutadapt==4.6 &&      wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz &&      tar xzf 0.6.10.tar.gz && ln -s /TrimGalore-0.6.10/trim_galore /usr/local/bin/ &&      rm 0.6.10.tar.gz
  • Network accessline 63
    RUN pip3 install --no-cache-dir cutadapt==4.6 && \     wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz && \
  • Network accessline 64
    wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz && \
  • Dangerous shell operationline 66
    rm 0.6.10.tar.gz
  • Dangerous shell operationline 73
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip &&      unzip -q fastqc_v0.12.1.zip -d /opt/ && chmod +x /opt/FastQC/fastqc &&      ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip
  • Network accessline 73
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip && \
  • Dangerous shell operationline 75
    ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip

skills/pipeline-dnaseseq/scripts/main.nf

  • Executable scriptline 1
    #!/usr/bin/env nextflow
  • Dangerous shell operationline 138
    rm nuclear.bam dedup.bam

skills/pipeline-guide/SKILL.md

  • Network accessline 487
    # Download chain file wget https://hgdownload.soe.ucsc.edu/goldenPath/hg19/liftOver/hg19ToHg38.over.chain.gz
  • Network accessline 488
    wget https://hgdownload.soe.ucsc.edu/goldenPath/hg19/liftOver/hg19ToHg38.over.chain.gz
  • Network accessline 522
    # CRAN available.packages(repos="https://cran.r-project.org")[,"Version"]
  • Network accessline 523
    available.packages(repos="https://cran.r-project.org")[,"Version"]

skills/pipeline-guide/references/literature.md

  • Network accessline 12
    - **Citation:** Di Tommaso P, Chatzou M, Floden EW, Barja PP, Palumbo E, Notredame C. Nextflow enables reproducible computational workflows. *Nature Biotechnology*, 35(4), 316-319, 2017. - **DOI:** [10.1038/nbt.3820](https://doi.org/10.1038
  • Network accessline 13
    - **DOI:** [10.1038/nbt.3820](https://doi.org/10.1038/nbt.3820)
  • Network accessline 22
    - **Citation:** Ewels PA, Peltzer A, Fillinger S, Patel H, Alneberg J, Wilm A, Garcia MU, Di Tommaso P, Nahnsen S. The nf-core framework for community-curated bioinformatics pipelines. *Nature Biotechnology*, 38(3), 276-278, 2020. - **DOI:*
  • Network accessline 23
    - **DOI:** [10.1038/s41587-020-0439-x](https://doi.org/10.1038/s41587-020-0439-x)
  • Network accessline 32
    - **Citation:** Vivian J, Rao AA, Nothaft FA, Ketchum C, Armstrong J, Novak A, Pfeil J, Narkizian J, Deran AD, Musselman-Brown A, Schmidt H, Amstutz P, Craft B, Goldman M, Rober K, Zhu J, Haussler D, Paten B. Toil enables reproducible, open
  • Network accessline 33
    - **DOI:** [10.1038/nbt.3772](https://doi.org/10.1038/nbt.3772)
  • Network accessline 44
    - **Citation:** Kurtzer GM, Sochat V, Bauer MW. Singularity: scientific containers for mobility of compute. *PLoS ONE*, 12(5), e0177459, 2017. - **DOI:** [10.1371/journal.pone.0177459](https://doi.org/10.1371/journal.pone.0177459)
  • Network accessline 45
    - **DOI:** [10.1371/journal.pone.0177459](https://doi.org/10.1371/journal.pone.0177459)
  • Network accessline 64
    - **Citation:** Grüning B, Dale R, Sjodin A, Chapman BA, Rowe J, Tomkins-Tinch CH, Valieris R, Koester J, Bioconda Team. Bioconda: sustainable and comprehensive software distribution for the life sciences. *Nature Methods*, 15(7), 475-476,
  • Network accessline 65
    - **DOI:** [10.1038/s41592-018-0046-7](https://doi.org/10.1038/s41592-018-0046-7)
  • Network accessline 76
    - **Citation:** ENCODE Project Consortium, Moore JE, Purcaro MJ, Pratt HE, Epstein CB, Shoresh N, Adrian J, Kawli T, Davis CA, Dobin A, et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. *Nature*, 583(7818), 699-
  • Network accessline 77
    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)

skills/pipeline-hic/references/literature.md

  • Network accessline 14
    - **Citation:** Lieberman-Aiden E, van Berkum NL, Williams L, Imakaev M, Raber T, Lajoie BR, Dekker J, Bhatt DM, Nussbaum MC, Anton IM, Ahmed K, Gnirke A, Lander ES. Comprehensive mapping of long-range interactions reveals folding principle
  • Network accessline 15
    - **DOI:** [10.1126/science.1181369](https://doi.org/10.1126/science.1181369)
  • Network accessline 24
    - **Citation:** Rao SSP, Huntley MH, Durand NC, Stamenova EK, Bochkov ID, Robinson JT, Sanborn AL, Machol I, Omer AD, Lander ES, Aiden EL. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell, 15
  • Network accessline 25
    - **DOI:** [10.1016/j.cell.2014.11.021](https://doi.org/10.1016/j.cell.2014.11.021)
  • Network accessline 38
    - **Citation:** ENCODE Project Consortium et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature, 583(7818):699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 39
    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 48
    - **Citation:** Hitz BC, Lee JW, Jolanki O, et al. The ENCODE Uniform Analysis Pipelines. Research Square (preprint), 2023. - **DOI:** [10.21203/rs.3.rs-311127/v1](https://doi.org/10.21203/rs.3.rs-311127/v1)
  • Network accessline 49
    - **DOI:** [10.21203/rs.3.rs-311127/v1](https://doi.org/10.21203/rs.3.rs-311127/v1)
  • Network accessline 57
    - **Citation:** Yardimci GG, Ozadam H, Sauria MEG, Ursu O, Yan KK, Yang T, Chakraborty A, Kaul A, Lajoie BR, Song F, Zhan Y, Ay F, Gerstein M, Kundaje A, Li Q, Taylor J, Yue F, Dekker J, Noble WS. Measuring the reproducibility and quality o
  • Network accessline 58
    - **DOI:** [10.1186/s13059-019-1658-7](https://doi.org/10.1186/s13059-019-1658-7)
  • Network accessline 71
    - **Citation:** Durand NC, Shamim MS, Machol I, Rao SSP, Huntley MH, Lander ES, Aiden EL. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Systems, 3(1):95-98, 2016. - **DOI:** [10.1016/j.cels.2016.07.
  • Network accessline 72
    - **DOI:** [10.1016/j.cels.2016.07.002](https://doi.org/10.1016/j.cels.2016.07.002)
  • Network accessline 81
    - **Citation:** Abdennur N, Mirny LA. Cooler: scalable storage for Hi-C data and other genomically labeled arrays. Bioinformatics, 36(1):311-316, 2020. - **DOI:** [10.1093/bioinformatics/btz540](https://doi.org/10.1093/bioinformatics/btz540
  • Network accessline 82
    - **DOI:** [10.1093/bioinformatics/btz540](https://doi.org/10.1093/bioinformatics/btz540)
  • Network accessline 91
    - **Citation:** Open2C, Abdennur N, Abraham S, Fudenberg G, Flyamer IM, Galitsyna AA, Goloborodko A, Imakaev M, Oksuz BA, Venev SV. Pairtools: from sequencing data to chromosome-level 3D maps. PLoS Computational Biology, 20(5):e1012164, 202
  • Network accessline 92
    - **DOI:** [10.1371/journal.pcbi.1012164](https://doi.org/10.1371/journal.pcbi.1012164)
  • Network accessline 101
    - **Citation:** Imakaev M, Fudenberg G, McCord RP, Naumova N, Goloborodko A, Lajoie BR, Dekker J, Mirny LA. Iterative correction of Hi-C data reveals hallmarks of chromosome organization. Nature Methods, 9(10):999-1003, 2012. - **DOI:** [10
  • Network accessline 102
    - **DOI:** [10.1038/nmeth.2148](https://doi.org/10.1038/nmeth.2148)
  • Network accessline 117
    - **DOI:** [10.1093/bioinformatics/btp324](https://doi.org/10.1093/bioinformatics/btp324) | **PMID:** 19451168 | **Citations:** ~25,000
  • Network accessline 124
    - **DOI:** [10.1093/bioinformatics/btp352](https://doi.org/10.1093/bioinformatics/btp352) | **PMID:** 19505943 | **Citations:** ~53,700
  • Network accessline 131
    - **DOI:** [10.1093/bioinformatics/btw354](https://doi.org/10.1093/bioinformatics/btw354) | **PMID:** 27312411 | **Citations:** ~6,800
  • Network accessline 138
    - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z) | **PMID:** 31249361 | **Citations:** ~1,372
  • Network accessline 145
    - **URL:** [https://www.bioinformatics.babraham.ac.uk/projects/fastqc/](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)

skills/pipeline-hic/scripts/Dockerfile

  • Executable scriptline 1
    Executable file
  • Network accessline 3
    LABEL maintainer="Dr. Alex M. Mawla, PhD <https://github.com/ammawla/encode-toolkit>"
  • Dangerous shell operationline 9
    RUN apt-get update && apt-get install -y --no-install-recommends      wget curl gzip pigz unzip      build-essential perl      python3 python3-pip python3-dev      openjdk-17-jre-headless      libncurses5-dev libbz2-dev liblzma-dev zlib1g-d
  • Network accessline 9
    RUN apt-get update && apt-get install -y --no-install-recommends \     wget curl gzip pigz unzip \
  • Network accessline 10
    wget curl gzip pigz unzip \
  • Dangerous shell operationline 16
    && rm -rf /var/lib/apt/lists/*
  • Dangerous shell operationline 19
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 &&      tar xjf samtools-1.19.tar.bz2 && cd samtools-1.19 &&      ./configure --prefix=/usr/local && make -j4 && make install &&      cd .. && rm
  • Network accessline 19
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 && \
  • Dangerous shell operationline 22
    cd .. && rm -rf samtools-1.19*
  • Dangerous shell operationline 25
    RUN wget -q https://github.com/lh3/bwa/archive/refs/tags/v0.7.18.tar.gz &&      tar xzf v0.7.18.tar.gz && cd bwa-0.7.18 &&      make -j4 && cp bwa /usr/local/bin/ &&      cd .. && rm -rf bwa-0.7.18 v0.7.18.tar.gz
  • Network accessline 25
    RUN wget -q https://github.com/lh3/bwa/archive/refs/tags/v0.7.18.tar.gz && \
  • Dangerous shell operationline 28
    cd .. && rm -rf bwa-0.7.18 v0.7.18.tar.gz
  • Network accessline 34
    RUN wget -q https://github.com/aidenlab/Juicebox/releases/download/v2.20.00/juicer_tools.2.20.00.jar \
  • Dangerous shell operationline 41
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip &&      unzip -q fastqc_v0.12.1.zip -d /opt/ && chmod +x /opt/FastQC/fastqc &&      ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip
  • Network accessline 41
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip && \
  • Dangerous shell operationline 43
    ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip
  • Dangerous shell operationline 49
    RUN pip3 install --no-cache-dir cutadapt==4.6 &&      wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz &&      tar xzf 0.6.10.tar.gz && ln -s /TrimGalore-0.6.10/trim_galore /usr/local/bin/ &&      rm 0.6.10.tar.gz
  • Network accessline 49
    RUN pip3 install --no-cache-dir cutadapt==4.6 && \     wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz && \
  • Network accessline 50
    wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz && \
  • Dangerous shell operationline 52
    rm 0.6.10.tar.gz

skills/pipeline-hic/scripts/main.nf

  • Executable scriptline 1
    #!/usr/bin/env nextflow
  • Dangerous shell operationline 195
    rm juicer_medium.txt

skills/pipeline-rnaseq/references/04-signal-tracks.md

  • Network accessline 81
    # Or fetch from UCSC wget https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.chrom.sizes
  • Network accessline 82
    wget https://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/hg38.chrom.sizes

skills/pipeline-rnaseq/references/literature.md

  • Network accessline 14
    - **Citation:** ENCODE Project Consortium et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature, 583(7818):699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 15
    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 24
    - **Citation:** Hitz BC, Lee JW, Jolanki O, et al. The ENCODE Uniform Analysis Pipelines. Research Square (preprint), 2023. - **DOI:** [10.21203/rs.3.rs-311127/v1](https://doi.org/10.21203/rs.3.rs-311127/v1)
  • Network accessline 25
    - **DOI:** [10.21203/rs.3.rs-311127/v1](https://doi.org/10.21203/rs.3.rs-311127/v1)
  • Network accessline 33
    - **Citation:** Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, Szczesniak MW, Gaffney DJ, Elo LL, Zhang X, Mortazavi A. A survey of best practices for RNA-seq data analysis. Genome Biology, 17:13, 2016. - **DOI:*
  • Network accessline 34
    - **DOI:** [10.1186/s13059-016-0881-8](https://doi.org/10.1186/s13059-016-0881-8)
  • Network accessline 47
    - **Citation:** Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 29(1):15-21, 2013. - **DOI:** [10.1093/bioinformatics/bts635](https:
  • Network accessline 48
    - **DOI:** [10.1093/bioinformatics/bts635](https://doi.org/10.1093/bioinformatics/bts635)
  • Network accessline 57
    - **Citation:** Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics, 12:323, 2011. - **DOI:** [10.1186/1471-2105-12-323](https://doi.org/10.1186/1471-2105-12-323)
  • Network accessline 58
    - **DOI:** [10.1186/1471-2105-12-323](https://doi.org/10.1186/1471-2105-12-323)
  • Network accessline 67
    - **Citation:** Bray NL, Pimentel H, Melsted P, Pachter L. Near-optimal probabilistic RNA-seq quantification. Nature Biotechnology, 34(5):525-527, 2016. - **DOI:** [10.1038/nbt.3519](https://doi.org/10.1038/nbt.3519)
  • Network accessline 68
    - **DOI:** [10.1038/nbt.3519](https://doi.org/10.1038/nbt.3519)
  • Network accessline 81
    - **Citation:** Wang L, Wang S, Li W. RSeQC: quality control of RNA-seq experiments. Bioinformatics, 28(16):2184-2185, 2012. - **DOI:** [10.1093/bioinformatics/bts356](https://doi.org/10.1093/bioinformatics/bts356)
  • Network accessline 82
    - **DOI:** [10.1093/bioinformatics/bts356](https://doi.org/10.1093/bioinformatics/bts356)
  • Network accessline 91
    - **Citation:** Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics, 32(19):3047-3048, 2016. - **DOI:** [10.1093/bioinformatics/btw354](https://doi.
  • Network accessline 92
    - **DOI:** [10.1093/bioinformatics/btw354](https://doi.org/10.1093/bioinformatics/btw354)
  • Network accessline 104
    - **Citation:** Frankish A, Diekhans M, Jungreis I, Lagarde J, Loveland JE, Mudge JM, Sisu C, Wright JC, Armstrong J, Barnes I, Berry A, Bignell A, Boix C, Carbonell Sala S, Cunningham F, Di Domenico T, Donaldson S, Fiddes IT, Garcia Giron
  • Network accessline 105
    - **DOI:** [10.1093/nar/gkaa1087](https://doi.org/10.1093/nar/gkaa1087)
  • Network accessline 120
    - **DOI:** [10.1093/bioinformatics/btp352](https://doi.org/10.1093/bioinformatics/btp352) | **PMID:** 19505943 | **Citations:** ~53,700
  • Network accessline 127
    - **URL:** [https://broadinstitute.github.io/picard/](https://broadinstitute.github.io/picard/)
  • Network accessline 134
    - **URL:** [https://www.bioinformatics.babraham.ac.uk/projects/fastqc/](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)
  • Network accessline 141
    - **DOI:** [10.14806/ej.17.1.200](https://doi.org/10.14806/ej.17.1.200) | **Citations:** ~13,000
  • Network accessline 148
    - **Citation:** Teng M, Love MI, Davis CA, Djebali S, Dobin A, Graveley BR, Li S, Mason CE, Olson S, Pervouchine D, Sloan CA, Wei X, Zhan L, Irizarry RA. A benchmark for RNA-seq quantification pipelines. Genome Biology, 17:74, 2016. - **DOI
  • Network accessline 149
    - **DOI:** [10.1186/s13059-016-0940-1](https://doi.org/10.1186/s13059-016-0940-1)

skills/pipeline-rnaseq/scripts/Dockerfile

  • Executable scriptline 1
    Executable file
  • Dangerous shell operationline 10
    RUN apt-get update && apt-get install -y --no-install-recommends      build-essential wget curl git ca-certificates perl unzip bc      zlib1g-dev libbz2-dev liblzma-dev libncurses5-dev libcurl4-openssl-dev      python3 python3-pip python3-d
  • Network accessline 10
    RUN apt-get update && apt-get install -y --no-install-recommends \     build-essential wget curl git ca-certificates perl unzip bc \
  • Network accessline 11
    build-essential wget curl git ca-certificates perl unzip bc \
  • Dangerous shell operationline 14
    && rm -rf /var/lib/apt/lists/*
  • Dangerous shell operationline 17
    RUN wget -q https://github.com/alexdobin/STAR/archive/refs/tags/2.7.11b.tar.gz &&      tar xzf 2.7.11b.tar.gz && cp STAR-2.7.11b/bin/Linux_x86_64_static/STAR /usr/local/bin/ &&      rm -rf STAR-2.7.11b 2.7.11b.tar.gz
  • Network accessline 17
    RUN wget -q https://github.com/alexdobin/STAR/archive/refs/tags/2.7.11b.tar.gz && \
  • Dangerous shell operationline 19
    rm -rf STAR-2.7.11b 2.7.11b.tar.gz
  • Dangerous shell operationline 22
    RUN wget -q https://github.com/deweylab/RSEM/archive/refs/tags/v1.3.3.tar.gz &&      tar xzf v1.3.3.tar.gz && cd RSEM-1.3.3 && make -j4 && make install &&      cd .. && rm -rf RSEM-1.3.3 v1.3.3.tar.gz
  • Network accessline 22
    RUN wget -q https://github.com/deweylab/RSEM/archive/refs/tags/v1.3.3.tar.gz && \
  • Dangerous shell operationline 24
    cd .. && rm -rf RSEM-1.3.3 v1.3.3.tar.gz
  • Dangerous shell operationline 27
    RUN wget -q https://github.com/pachterlab/kallisto/releases/download/v0.50.1/kallisto_linux-v0.50.1.tar.gz &&      tar xzf kallisto_linux-v0.50.1.tar.gz && cp kallisto/kallisto /usr/local/bin/ &&      rm -rf kallisto kallisto_linux-v0.50.1.
  • Network accessline 27
    RUN wget -q https://github.com/pachterlab/kallisto/releases/download/v0.50.1/kallisto_linux-v0.50.1.tar.gz && \
  • Dangerous shell operationline 29
    rm -rf kallisto kallisto_linux-v0.50.1.tar.gz
  • Dangerous shell operationline 32
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 &&      tar xjf samtools-1.19.tar.bz2 && cd samtools-1.19 &&      ./configure --prefix=/usr/local && make -j4 && make install &&      cd .. && rm
  • Network accessline 32
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 && \
  • Dangerous shell operationline 35
    cd .. && rm -rf samtools-1.19*
  • Network accessline 38
    RUN wget -q https://hgdownload.soe.ucsc.edu/admin/exe/linux.x86_64/bedGraphToBigWig \
  • Dangerous shell operationline 46
    RUN wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz &&      tar xzf 0.6.10.tar.gz && ln -s /TrimGalore-0.6.10/trim_galore /usr/local/bin/ &&      rm 0.6.10.tar.gz
  • Network accessline 46
    RUN wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz && \
  • Dangerous shell operationline 48
    rm 0.6.10.tar.gz
  • Dangerous shell operationline 51
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip &&      unzip -q fastqc_v0.12.1.zip -d /opt/ && chmod +x /opt/FastQC/fastqc &&      ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip
  • Network accessline 51
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip && \
  • Dangerous shell operationline 53
    ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip

skills/pipeline-rnaseq/scripts/main.nf

  • Executable scriptline 1
    #!/usr/bin/env nextflow

skills/pipeline-wgbs/SKILL.md

  • Network accessline 228
    The primary output is per-CpG methylation in the ENCODE bedMethyl layout (https://www.encodeproject.org/data-standards/wgbs/):
  • Network accessline 229
    (https://www.encodeproject.org/data-standards/wgbs/):

skills/pipeline-wgbs/references/04-methylation-calling.md

  • Network accessline 3
    MethylDackel (formerly PileOMeth) extracts per-CpG methylation levels from bisulfite-aligned BAM files. This workflow then converts its bedGraph output to the ENCODE bedMethyl layout (https://www.encodeproject.org/data-standards/wgbs/).
  • Network accessline 5
    ENCODE bedMethyl layout (https://www.encodeproject.org/data-standards/wgbs/).

skills/pipeline-wgbs/references/literature.md

  • Network accessline 14
    - **Citation:** Lister R, Pelizzola M, Dowen RH, Hawkins RD, Hon G, Tonti-Filippini J, Nott JR, Lee L, Ye Z, Ngo QM, Edsall L, Antosiewicz-Bourget J, Stewart R, Ruotti V, Millar AH, Thomson JA, Ren B, Ecker JR. Human DNA methylomes at base
  • Network accessline 15
    - **DOI:** [10.1038/nature08514](https://doi.org/10.1038/nature08514)
  • Network accessline 24
    - **Citation:** Schultz MD, He Y, Whitaker JW, Hariharan M, Mukamel EA, Leung D, Rajagopal N, Nery JR, Urich MA, Chen H, Lin S, Lin Y, Jung I, Schmitt AD, Selvaraj S, Ren B, Sejnowski TJ, Wang W, Ecker JR. Human body epigenome maps reveal n
  • Network accessline 25
    - **DOI:** [10.1038/nature14248](https://doi.org/10.1038/nature14248)
  • Network accessline 34
    - **Citation:** ENCODE Project Consortium et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature, 583(7818):699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 35
    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 44
    - **Citation:** Hitz BC, Lee JW, Jolanki O, et al. The ENCODE Uniform Analysis Pipelines. Research Square (preprint), 2023. - **DOI:** [10.21203/rs.3.rs-311127/v1](https://doi.org/10.21203/rs.3.rs-311127/v1)
  • Network accessline 45
    - **DOI:** [10.21203/rs.3.rs-311127/v1](https://doi.org/10.21203/rs.3.rs-311127/v1)
  • Network accessline 53
    - **Citation:** Foox J, Nordlund J, Lalonde E, Lim S, Deschamps-Francoeur G, Trognitz F, Holmes M, Close MC, et al. Performance assessment of DNA sequencing platforms in the ABRF Next-Generation Sequencing Study. Nature Biotechnology, 39(9)
  • Network accessline 54
    - **DOI:** [10.1038/s41587-021-01049-5](https://doi.org/10.1038/s41587-021-01049-5)
  • Network accessline 67
    - **Citation:** Krueger F, Andrews SR. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics, 27(11):1571-1572, 2011. - **DOI:** [10.1093/bioinformatics/btr167](https://doi.org/10.1093/bioinformat
  • Network accessline 68
    - **DOI:** [10.1093/bioinformatics/btr167](https://doi.org/10.1093/bioinformatics/btr167)
  • Network accessline 77
    - **Citation:** Pedersen BS, Eyring K, De S, Yang IV, Schwartz DA. Fast and accurate alignment of long bisulfite-seq reads. arXiv:1401.1129, 2014. - **DOI:** [10.48550/arXiv.1401.1129](https://doi.org/10.48550/arXiv.1401.1129)
  • Network accessline 78
    - **DOI:** [10.48550/arXiv.1401.1129](https://doi.org/10.48550/arXiv.1401.1129)
  • Network accessline 86
    - **Citation:** Ryan DP. MethylDackel: A (mostly) universal methylation extractor for BS-seq experiments. GitHub, 2023. - **URL:** [https://github.com/dpryan79/MethylDackel](https://github.com/dpryan79/MethylDackel)
  • Network accessline 87
    - **URL:** [https://github.com/dpryan79/MethylDackel](https://github.com/dpryan79/MethylDackel)
  • Network accessline 98
    - **Citation:** Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics, 32(19):3047-3048, 2016. - **DOI:** [10.1093/bioinformatics/btw354](https://doi.
  • Network accessline 99
    - **DOI:** [10.1093/bioinformatics/btw354](https://doi.org/10.1093/bioinformatics/btw354)
  • Network accessline 113
    - **DOI:** [10.1093/bioinformatics/btp352](https://doi.org/10.1093/bioinformatics/btp352) | **PMID:** 19505943 | **Citations:** ~53,700
  • Network accessline 120
    - **URL:** [https://broadinstitute.github.io/picard/](https://broadinstitute.github.io/picard/)
  • Network accessline 127
    - **URL:** [https://www.bioinformatics.babraham.ac.uk/projects/fastqc/](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)
  • Network accessline 134
    - **DOI:** [10.14806/ej.17.1.200](https://doi.org/10.14806/ej.17.1.200) | **Citations:** ~13,000
  • Network accessline 141
    - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z) | **PMID:** 31249361 | **Citations:** ~1,372
  • Network accessline 152
    - **Citation:** Hansen KD, Langmead B, Irizarry RA. BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions. Genome Biology, 13(10):R83, 2012. - **DOI:** [10.1186/gb-2012-13-10-r83](https://doi.org/10.1186
  • Network accessline 153
    - **DOI:** [10.1186/gb-2012-13-10-r83](https://doi.org/10.1186/gb-2012-13-10-r83)
  • Network accessline 162
    - **Citation:** Peters TJ, Buckley MJ, Statham AL, Pidsley R, Samaras K, Lord RV, Clark SJ, Molloy PL. De novo identification of differentially methylated regions in the human genome. Epigenetics & Chromatin, 8:6, 2015. - **DOI:** [10.1186/
  • Network accessline 163
    - **DOI:** [10.1186/1756-8935-8-6](https://doi.org/10.1186/1756-8935-8-6)

skills/pipeline-wgbs/scripts/Dockerfile

  • Executable scriptline 1
    Executable file
  • Network accessline 5
    LABEL maintainer="Dr. Alex M. Mawla, PhD <https://github.com/ammawla/encode-toolkit>"
  • Dangerous shell operationline 11
    RUN apt-get update && apt-get install -y --no-install-recommends      wget curl unzip gzip pigz      build-essential perl      python3 python3-pip      openjdk-17-jre-headless      libncurses5-dev libbz2-dev liblzma-dev zlib1g-dev libcurl4-
  • Network accessline 11
    RUN apt-get update && apt-get install -y --no-install-recommends \     wget curl unzip gzip pigz \
  • Network accessline 12
    wget curl unzip gzip pigz \
  • Dangerous shell operationline 18
    && rm -rf /var/lib/apt/lists/*
  • Dangerous shell operationline 21
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 &&      tar xjf samtools-1.19.tar.bz2 && cd samtools-1.19 &&      ./configure --prefix=/usr/local && make -j4 && make install &&      cd .. && rm
  • Network accessline 21
    RUN wget -q https://github.com/samtools/samtools/releases/download/1.19/samtools-1.19.tar.bz2 && \
  • Dangerous shell operationline 24
    cd .. && rm -rf samtools-1.19*
  • Dangerous shell operationline 27
    RUN wget -q https://github.com/samtools/htslib/releases/download/1.19/htslib-1.19.tar.bz2 &&      tar xjf htslib-1.19.tar.bz2 && cd htslib-1.19 &&      ./configure --prefix=/usr/local && make -j4 && make install && ldconfig &&      cd .. &&
  • Network accessline 27
    RUN wget -q https://github.com/samtools/htslib/releases/download/1.19/htslib-1.19.tar.bz2 && \
  • Dangerous shell operationline 30
    cd .. && rm -rf htslib-1.19*
  • Dangerous shell operationline 33
    RUN wget -q https://github.com/BenLangmead/bowtie2/releases/download/v2.5.4/bowtie2-2.5.4-linux-x86_64.zip &&      unzip -q bowtie2-2.5.4-linux-x86_64.zip -d /opt/ &&      ln -s /opt/bowtie2-2.5.4-linux-x86_64/bowtie2* /usr/local/bin/ &&
  • Network accessline 33
    RUN wget -q https://github.com/BenLangmead/bowtie2/releases/download/v2.5.4/bowtie2-2.5.4-linux-x86_64.zip && \
  • Dangerous shell operationline 36
    rm bowtie2-2.5.4-linux-x86_64.zip
  • Dangerous shell operationline 39
    RUN wget -q https://github.com/FelixKrueger/Bismark/archive/v0.24.2.tar.gz &&      tar xzf v0.24.2.tar.gz && mv Bismark-0.24.2 /opt/bismark &&      ln -s /opt/bismark/bismark /usr/local/bin/ &&      ln -s /opt/bismark/deduplicate_bismark /u
  • Network accessline 39
    RUN wget -q https://github.com/FelixKrueger/Bismark/archive/v0.24.2.tar.gz && \
  • Dangerous shell operationline 44
    rm v0.24.2.tar.gz
  • Dangerous shell operationline 47
    RUN pip3 install --no-cache-dir cutadapt==4.6 &&      wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz &&      tar xzf 0.6.10.tar.gz && ln -s /TrimGalore-0.6.10/trim_galore /usr/local/bin/ &&      rm 0.6.10.tar.gz
  • Network accessline 47
    RUN pip3 install --no-cache-dir cutadapt==4.6 && \     wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz && \
  • Network accessline 48
    wget -q https://github.com/FelixKrueger/TrimGalore/archive/0.6.10.tar.gz && \
  • Dangerous shell operationline 50
    rm 0.6.10.tar.gz
  • Dangerous shell operationline 53
    RUN wget -q https://github.com/dpryan79/MethylDackel/archive/0.6.1.tar.gz &&      tar xzf 0.6.1.tar.gz && cd MethylDackel-0.6.1 &&      make CFLAGS="-O3 -Wall" LIBBIGWIG="-lBigWig" && cp MethylDackel /usr/local/bin/ &&      cd .. && rm -rf
  • Network accessline 53
    RUN wget -q https://github.com/dpryan79/MethylDackel/archive/0.6.1.tar.gz && \
  • Dangerous shell operationline 56
    cd .. && rm -rf MethylDackel-0.6.1*
  • Dangerous shell operationline 59
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip &&      unzip -q fastqc_v0.12.1.zip -d /opt/ && chmod +x /opt/FastQC/fastqc &&      ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip
  • Network accessline 59
    RUN wget -q https://www.bioinformatics.babraham.ac.uk/projects/fastqc/fastqc_v0.12.1.zip && \
  • Dangerous shell operationline 61
    ln -s /opt/FastQC/fastqc /usr/local/bin/ && rm fastqc_v0.12.1.zip

skills/pipeline-wgbs/scripts/main.nf

  • Executable scriptline 1
    #!/usr/bin/env nextflow
  • Network accessline 192
    # Convert each context to ENCODE bedMethyl (https://www.encodeproject.org/data-standards/wgbs/):

skills/publication-trust/references/literature.md

  • Network accessline 12
    - **Citation:** Ioannidis JPA. Why most published research findings are false. *PLoS Medicine*, 2(8), e124, 2005. - **DOI:** [10.1371/journal.pmed.0020124](https://doi.org/10.1371/journal.pmed.0020124)
  • Network accessline 13
    - **DOI:** [10.1371/journal.pmed.0020124](https://doi.org/10.1371/journal.pmed.0020124)
  • Network accessline 22
    - **Citation:** Baker M. 1,500 scientists lift the lid on reproducibility. *Nature*, 533(7604), 452-454, 2016. - **DOI:** [10.1038/533452a](https://doi.org/10.1038/533452a)
  • Network accessline 23
    - **DOI:** [10.1038/533452a](https://doi.org/10.1038/533452a)
  • Network accessline 32
    - **Citation:** Steen RG, Casadevall A, Fang FC. Why has the number of scientific retractions increased? *PLoS ONE*, 8(7), e68397, 2013. - **DOI:** [10.1371/journal.pone.0068397](https://doi.org/10.1371/journal.pone.0068397)
  • Network accessline 33
    - **DOI:** [10.1371/journal.pone.0068397](https://doi.org/10.1371/journal.pone.0068397)
  • Network accessline 42
    - **Citation:** Bik EM, Casadevall A, Fang FC. The prevalence of inappropriate image duplication in biomedical research publications. *mBio*, 7(3), e00809-16, 2016. - **DOI:** [10.1128/mBio.00809-16](https://doi.org/10.1128/mBio.00809-16)
  • Network accessline 43
    - **DOI:** [10.1128/mBio.00809-16](https://doi.org/10.1128/mBio.00809-16)
  • Network accessline 54
    - **Citation:** Conesa A, Madrigal P, Tarazona S, Gomez-Cabrero D, Cervera A, McPherson A, Szczesniak MW, Gaffney DJ, Elo LL, Zhang X, Mortazavi A. A survey of best practices for RNA-seq data analysis. *Genome Biology*, 17, 13, 2016. - **DO
  • Network accessline 55
    - **DOI:** [10.1186/s13059-016-0881-8](https://doi.org/10.1186/s13059-016-0881-8)
  • Network accessline 64
    - **Citation:** Landt SG, Marinov GK, Kundaje A, Kheradpour P, Pauli F, Batzoglou S, Bernstein BE, Bickel P, Brown JB, Cayting P, Chen Y, DeSalvo G, Epstein C, Fisher-Aylor KI, Euskirchen G, Gerstein M, Gertz J, Hartemink AJ, Hoffman MM, Iy
  • Network accessline 65
    - **DOI:** [10.1101/gr.136184.111](https://doi.org/10.1101/gr.136184.111)
  • Network accessline 74
    - **Citation:** Amemiya HM, Kundaje A, Boyle AP. The ENCODE Blacklist: identification of problematic regions of the genome. *Scientific Reports*, 9(1), 9354, 2019. - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-4
  • Network accessline 75
    - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z)

skills/quality-assessment/SKILL.md

  • Network accessline 268
    CUT&RUN and CUT&Tag generate artifacts at specific genomic regions (distinct from the ENCODE Blacklist). These are regions with apparent enrichment that is not target-specific: - Use the CUT&RUN suspect list (Nordin et al. 2023) IN ADDITION
  • Network accessline 269
    - Use the CUT&RUN suspect list (Nordin et al. 2023) IN ADDITION to the ENCODE Blacklist - Available at: https://github.com/Boyle-Lab/CUT-RUN_suspect_list - Particularly important for H3K4me3 and H3K27me3 CUT&RUN data
  • Network accessline 270
    - Available at: https://github.com/Boyle-Lab/CUT-RUN_suspect_list
  • Network accessline 348
    Before interpreting any peak-based quality metric, confirm that the ENCODE Blacklist has been applied: - **Blacklist v2 regions**: High-signal artifacts (satellite repeats, centromeric regions, high-copy sequences) - **Available at**: https
  • Network accessline 349
    - **Blacklist v2 regions**: High-signal artifacts (satellite repeats, centromeric regions, high-copy sequences) - **Available at**: https://github.com/Boyle-Lab/Blacklist/ - **hg38**: `hg38-blacklist.v2.bed.gz` (910 regions)
  • Network accessline 350
    - **Available at**: https://github.com/Boyle-Lab/Blacklist/

skills/quality-assessment/references/literature.md

  • Network accessline 38
    ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia.   *Genome Research*, 22(9), 1813-1831, 2012. - **DOI:** [10.1101/gr.136184.111](https://doi.org/10.1101/gr.136184.111)
  • Network accessline 39
    *Genome Research*, 22(9), 1813-1831, 2012. - **DOI:** [10.1101/gr.136184.111](https://doi.org/10.1101/gr.136184.111) - **PMID:** 22955991 | **PMC:** PMC3431496
  • Network accessline 40
    - **DOI:** [10.1101/gr.136184.111](https://doi.org/10.1101/gr.136184.111)
  • Network accessline 59
    - **Citation:** Marinov GK, Kundaje A, Park PJ, Wold BJ. Large-scale quality analysis   of published ChIP-seq data. *G3: Genes, Genomes, Genetics*, 4(2), 209-223, 2014. - **DOI:** [10.1534/g3.113.008680](https://doi.org/10.1534/g3.113.00868
  • Network accessline 60
    of published ChIP-seq data. *G3: Genes, Genomes, Genetics*, 4(2), 209-223, 2014. - **DOI:** [10.1534/g3.113.008680](https://doi.org/10.1534/g3.113.008680) - **PMID:** 24347632 | **PMC:** PMC3931556
  • Network accessline 61
    - **DOI:** [10.1534/g3.113.008680](https://doi.org/10.1534/g3.113.008680)
  • Network accessline 78
    removal on ChIP quality metrics in ChIP-seq and ChIP-exo data. *Frontiers in Genetics*,   5, 75, 2014. - **DOI:** [10.3389/fgene.2014.00075](https://doi.org/10.3389/fgene.2014.00075)
  • Network accessline 79
    5, 75, 2014. - **DOI:** [10.3389/fgene.2014.00075](https://doi.org/10.3389/fgene.2014.00075) - **PMID:** 24782889 | **PMC:** PMC3988386
  • Network accessline 80
    - **DOI:** [10.3389/fgene.2014.00075](https://doi.org/10.3389/fgene.2014.00075)
  • Network accessline 105
    native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding   proteins, and nucleosome position. *Nature Methods*, 10(12), 1213-1218, 2013. - **DOI:** [10.1038/nmeth.2688](https://doi.org/10.1038/nmeth.2688)
  • Network accessline 106
    proteins, and nucleosome position. *Nature Methods*, 10(12), 1213-1218, 2013. - **DOI:** [10.1038/nmeth.2688](https://doi.org/10.1038/nmeth.2688) - **PMID:** 24097267 | **PMC:** PMC3959825
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    - **DOI:** [10.1038/nmeth.2688](https://doi.org/10.1038/nmeth.2688)
  • Network accessline 123
    - **Citation:** Yan F, Powell DR, Curtis DJ, Wong NC. From reads to insight: a hitchhiker's   guide to ATAC-seq data analysis. *Genome Biology*, 21(1), 22, 2020. - **DOI:** [10.1186/s13059-020-1929-3](https://doi.org/10.1186/s13059-020-1929
  • Network accessline 124
    guide to ATAC-seq data analysis. *Genome Biology*, 21(1), 22, 2020. - **DOI:** [10.1186/s13059-020-1929-3](https://doi.org/10.1186/s13059-020-1929-3) - **PMID:** 32014034 | **PMC:** PMC6996192
  • Network accessline 125
    - **DOI:** [10.1186/s13059-020-1929-3](https://doi.org/10.1186/s13059-020-1929-3)
  • Network accessline 155
    Szczesniak MW, Gaffney DJ, Elo LL, Zhang X, Mortazavi A. A survey of best practices for   RNA-seq data analysis. *Genome Biology*, 17, 13, 2016. - **DOI:** [10.1186/s13059-016-0881-8](https://doi.org/10.1186/s13059-016-0881-8)
  • Network accessline 156
    RNA-seq data analysis. *Genome Biology*, 17, 13, 2016. - **DOI:** [10.1186/s13059-016-0881-8](https://doi.org/10.1186/s13059-016-0881-8) - **PMID:** 26813401 | **PMC:** PMC4728800
  • Network accessline 157
    - **DOI:** [10.1186/s13059-016-0881-8](https://doi.org/10.1186/s13059-016-0881-8)
  • Network accessline 189
    Mason CE. The SEQC2 epigenomics quality control (EpiQC) study. *Genome Biology*,   22, 307, 2021. - **DOI:** [10.1186/s13059-021-02529-2](https://doi.org/10.1186/s13059-021-02529-2)
  • Network accessline 190
    22, 307, 2021. - **DOI:** [10.1186/s13059-021-02529-2](https://doi.org/10.1186/s13059-021-02529-2) - **PMID:** 34872606 | **PMC:** PMC8650396
  • Network accessline 191
    - **DOI:** [10.1186/s13059-021-02529-2](https://doi.org/10.1186/s13059-021-02529-2)
  • Network accessline 210
    - **Citation:** Schwartzman O, Tanay A. Single-cell epigenomics: techniques and emerging   applications. *Nature Reviews Genetics*, 16, 716-726, 2015. - **DOI:** [10.1038/nrg3980](https://doi.org/10.1038/nrg3980)
  • Network accessline 211
    applications. *Nature Reviews Genetics*, 16, 716-726, 2015. - **DOI:** [10.1038/nrg3980](https://doi.org/10.1038/nrg3980) - **PMID:** 26460349
  • Network accessline 212
    - **DOI:** [10.1038/nrg3980](https://doi.org/10.1038/nrg3980)
  • Network accessline 239
    Noble WS, Mirny LA. Measuring the reproducibility and quality of Hi-C data.   *Genome Biology*, 20(1), 57, 2019. - **DOI:** [10.1186/s13059-019-1658-7](https://doi.org/10.1186/s13059-019-1658-7)
  • Network accessline 240
    *Genome Biology*, 20(1), 57, 2019. - **DOI:** [10.1186/s13059-019-1658-7](https://doi.org/10.1186/s13059-019-1658-7) - **PMID:** 30890172 | **PMC:** PMC6423771
  • Network accessline 241
    - **DOI:** [10.1186/s13059-019-1658-7](https://doi.org/10.1186/s13059-019-1658-7)
  • Network accessline 270
    - **Citation:** Nordin A, Zambanini G, Pagella P, Cantu C. The CUT&RUN suspect list of   problematic regions of the genome. *Genome Biology*, 24(1), 185, 2023. - **DOI:** [10.1186/s13059-023-03027-3](https://doi.org/10.1186/s13059-023-03027
  • Network accessline 271
    problematic regions of the genome. *Genome Biology*, 24(1), 185, 2023. - **DOI:** [10.1186/s13059-023-03027-3](https://doi.org/10.1186/s13059-023-03027-3) - **PMID:** 37563719 | **PMC:** PMC10416431
  • Network accessline 272
    - **DOI:** [10.1186/s13059-023-03027-3](https://doi.org/10.1186/s13059-023-03027-3)
  • Network accessline 297
    - **Citation:** Amemiya HM, Kundaje A, Boyle AP. The ENCODE Blacklist: identification of   problematic regions of the genome. *Scientific Reports*, 9(1), 9354, 2019. - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019
  • Network accessline 298
    problematic regions of the genome. *Scientific Reports*, 9(1), 9354, 2019. - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z) - **PMID:** 31249361 | **PMC:** PMC6597582
  • Network accessline 299
    - **DOI:** [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z)
  • Network accessline 325
    - **Citation:** Ewels P, Magnusson M, Lundin S, Kaller M. MultiQC: summarize analysis   results for multiple tools and samples. *Bioinformatics*, 32(19), 3047-3048, 2016. - **DOI:** [10.1093/bioinformatics/btw354](https://doi.org/10.1093/bi
  • Network accessline 326
    results for multiple tools and samples. *Bioinformatics*, 32(19), 3047-3048, 2016. - **DOI:** [10.1093/bioinformatics/btw354](https://doi.org/10.1093/bioinformatics/btw354) - **PMID:** 27312411 | **PMC:** PMC5039924
  • Network accessline 327
    - **DOI:** [10.1093/bioinformatics/btw354](https://doi.org/10.1093/bioinformatics/btw354)

skills/regulatory-elements/SKILL.md

  • Network accessline 36
    - **ENCODE Project Consortium 2020** (Nature, ~1,656 citations): Registry of 926,535 human cCREs. Introduces the SCREEN web server. [DOI](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 37
    - **Kundaje et al. 2015** (Nature, ~5,810 citations): Roadmap Epigenomics — integrative analysis of 111 reference human epigenomes. Chromatin state maps across tissues. Disease variants enriched in tissue-specific epigenomic marks. [DOI](ht
  • Network accessline 38
    - **Ernst & Kellis 2012** (Nature Methods, ~2,294 citations): ChromHMM — multivariate hidden Markov model for chromatin state discovery from combinatorial histone modification patterns. [DOI](https://doi.org/10.1038/nmeth.1906)
  • Network accessline 39
    - **Hnisz et al. 2013** (Cell, ~3,215 citations): Super-enhancer catalog across human cell types. Disease-associated variation enriched in super-enhancers of disease-relevant cells. [DOI](https://doi.org/10.1016/j.cell.2013.09.053)
  • Network accessline 40
    - **Whyte et al. 2013** (Cell, ~2,500 citations): Defined super-enhancers as large enhancer clusters occupied by master TFs and Mediator. Introduced the ROSE algorithm. [DOI](https://doi.org/10.1016/j.cell.2013.03.035)
  • Network accessline 41
    - **Shlyueva et al. 2014** (Nature Reviews Genetics, ~1,200 citations): Authoritative review of enhancer sequence properties, chromatin signatures, genome-wide prediction, and high-throughput activity assays. [DOI](https://doi.org/10.1038/n
  • Network accessline 42
    - **Schoenfelder & Fraser 2019** (Nature Reviews Genetics, ~869 citations): How enhancer-promoter interactions are established through 3D genome architecture (TADs, CTCF loops). [DOI](https://doi.org/10.1038/s41576-019-0128-0)
  • Network accessline 43
    - **Visel et al. 2007** (Nucleic Acids Research, ~1,079 citations): VISTA Enhancer Browser — in vivo transgenic mouse validation of enhancers. 4,500+ experiments. [DOI](https://doi.org/10.1093/nar/gkl822)
  • Network accessline 44
    - **Gasperini et al. 2019** (Cell, ~465 citations): CRISPRi screen of 5,920 candidate enhancers with scRNA-seq readout. Identified 664 enhancer-gene pairs. Established the "crisprQTL" framework. [DOI](https://doi.org/10.1016/j.cell.2018.11.
  • Network accessline 45
    - **Yao et al. 2024** (Nature Methods, ~26 citations): ENCODE4 Functional Characterization Centers — 108 CRISPRi screens, >540,000 perturbations. Pre-designed sgRNAs targeting 3.27M ENCODE SCREEN cCREs. [DOI](https://doi.org/10.1038/s41592-
  • Network accessline 46
    - **Nasser et al. 2021** (Nature, ~468 citations): ABC model enhancer-gene maps in 131 cell types. [DOI](https://doi.org/10.1038/s41586-021-03446-x)
  • Network accessline 47
    - **Heintzman et al. 2007** (Nature Genetics, ~2,300 citations): Discovered that H3K4me1 marks enhancers while H3K4me3 marks promoters — the foundational chromatin signature for distinguishing regulatory element classes. [DOI](https://doi.o
  • Network accessline 48
    - **Rada-Iglesias et al. 2011** (Nature, ~1,200 citations): Identified "poised enhancers" marked by H3K4me1+H3K27me3 (without H3K27ac) in hESCs. These activate during differentiation by gaining H3K27ac. [DOI](https://doi.org/10.1038/nature0
  • Network accessline 49
    - **Amemiya et al. 2019** (Scientific Reports, ~1,372 citations): ENCODE Blacklist — regions producing artifactual signal across ChIP-seq, ATAC-seq, and DNase-seq. Must be filtered before any regulatory element classification. [DOI](https:/
  • Network accessline 126
    **ENCODE Blacklist filtering (required)**: Before using any peak or signal files, remove regions overlapping the ENCODE Blacklist (Amemiya et al. 2019, Scientific Reports, 1,372 citations). Blacklisted regions produce artifactual signal in
  • Network accessline 127
    - Human GRCh38: `hg38-blacklist.v2.bed.gz` from [Boyle-Lab/Blacklist](https://github.com/Boyle-Lab/Blacklist)

skills/regulatory-elements/references/literature.md

  • Network accessline 36
    encyclopaedias of DNA elements in the human and mouse genomes. *Nature*, 583(7818),   699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 37
    699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4) - **PMID:** 32728248 | **PMC:** PMC7410830
  • Network accessline 38
    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 58
    the Encyclopedia of DNA Elements (ENCODE) data portal. *Nucleic Acids Research*, 48(D1),   D882-D889, 2020. - **DOI:** [10.1093/nar/gkz1062](https://doi.org/10.1093/nar/gkz1062)
  • Network accessline 59
    D882-D889, 2020. - **DOI:** [10.1093/nar/gkz1062](https://doi.org/10.1093/nar/gkz1062) - **PMID:** 31713622 | **PMC:** PMC7061942
  • Network accessline 60
    - **DOI:** [10.1093/nar/gkz1062](https://doi.org/10.1093/nar/gkz1062)
  • Network accessline 84
    - **Citation:** Thurman RE, Rynes E, Humbert R, Vierstra J, Maurano MT, et al. The accessible   chromatin landscape of the human genome. *Nature*, 489(7414), 75-82, 2012. - **DOI:** [10.1038/nature11232](https://doi.org/10.1038/nature11232)
  • Network accessline 85
    chromatin landscape of the human genome. *Nature*, 489(7414), 75-82, 2012. - **DOI:** [10.1038/nature11232](https://doi.org/10.1038/nature11232) - **PMID:** 22955617 | **PMC:** PMC3721348
  • Network accessline 86
    - **DOI:** [10.1038/nature11232](https://doi.org/10.1038/nature11232)
  • Network accessline 101
    common disease-associated variation in regulatory DNA. *Science*, 337(6099),   1190-1195, 2012. - **DOI:** [10.1126/science.1222794](https://doi.org/10.1126/science.1222794)
  • Network accessline 102
    1190-1195, 2012. - **DOI:** [10.1126/science.1222794](https://doi.org/10.1126/science.1222794) - **PMID:** 22955828 | **PMC:** PMC3771521
  • Network accessline 103
    - **DOI:** [10.1126/science.1222794](https://doi.org/10.1126/science.1222794)
  • Network accessline 127
    framework for mapping gene regulation via cellular genetic screens. *Cell*, 176(1-2),   377-390, 2019. - **DOI:** [10.1016/j.cell.2018.11.029](https://doi.org/10.1016/j.cell.2018.11.029)
  • Network accessline 128
    377-390, 2019. - **DOI:** [10.1016/j.cell.2018.11.029](https://doi.org/10.1016/j.cell.2018.11.029) - **PMID:** 30612741 | **PMC:** PMC6435518
  • Network accessline 129
    - **DOI:** [10.1016/j.cell.2018.11.029](https://doi.org/10.1016/j.cell.2018.11.029)
  • Network accessline 156
    signatures of transcriptional promoters and enhancers in the human genome. *Nature   Genetics*, 39(3), 311-318, 2007. - **DOI:** [10.1038/ng1966](https://doi.org/10.1038/ng1966)
  • Network accessline 157
    Genetics*, 39(3), 311-318, 2007. - **DOI:** [10.1038/ng1966](https://doi.org/10.1038/ng1966) - **PMID:** 17277777
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    - **DOI:** [10.1038/ng1966](https://doi.org/10.1038/ng1966)
  • Network accessline 175
    - **Citation:** Ernst J, Kheradpour P, Mikkelsen TS, Shoresh N, et al. Mapping and analysis of   chromatin state dynamics in nine human cell types. *Nature*, 473(7345), 43-49, 2011. - **DOI:** [10.1038/nature09906](https://doi.org/10.1038/n
  • Network accessline 176
    chromatin state dynamics in nine human cell types. *Nature*, 473(7345), 43-49, 2011. - **DOI:** [10.1038/nature09906](https://doi.org/10.1038/nature09906) - **PMID:** 21441907 | **PMC:** PMC3088773
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    - **DOI:** [10.1038/nature09906](https://doi.org/10.1038/nature09906)
  • Network accessline 200
    - **Citation:** Shen Y, Yue F, McCleary DF, Ye Z, et al. A map of the cis-regulatory   sequences in the mouse genome. *Nature*, 488(7409), 116-120, 2012. - **DOI:** [10.1038/nature11243](https://doi.org/10.1038/nature11243)
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    sequences in the mouse genome. *Nature*, 488(7409), 116-120, 2012. - **DOI:** [10.1038/nature11243](https://doi.org/10.1038/nature11243) - **PMID:** 22763441 | **PMC:** PMC4041622
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    - **DOI:** [10.1038/nature11243](https://doi.org/10.1038/nature11243)
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    - **Citation:** Andersson R, Gebhard C, Miguel-Escalada I, et al. An atlas of active enhancers   across human cell types and tissues. *Nature*, 507(7493), 455-461, 2014. - **DOI:** [10.1038/nature12787](https://doi.org/10.1038/nature12787)
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    across human cell types and tissues. *Nature*, 507(7493), 455-461, 2014. - **DOI:** [10.1038/nature12787](https://doi.org/10.1038/nature12787) - **PMID:** 24670763 | **PMC:** PMC5215096
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    - **DOI:** [10.1038/nature12787](https://doi.org/10.1038/nature12787)
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    - **Citation:** Visel A, Blow MJ, Li Z, Zhang T, et al. ChIP-seq accurately predicts   tissue-specific activity of enhancers. *Nature*, 457(7231), 854-858, 2009. - **DOI:** [10.1038/nature07730](https://doi.org/10.1038/nature07730)
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    tissue-specific activity of enhancers. *Nature*, 457(7231), 854-858, 2009. - **DOI:** [10.1038/nature07730](https://doi.org/10.1038/nature07730) - **PMID:** 19212405 | **PMC:** PMC2745234
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    - **DOI:** [10.1038/nature07730](https://doi.org/10.1038/nature07730)

skills/scientific-writing/SKILL.md

  • Network accessline 171
    Bowtie2 as the underlying aligner. Duplicate reads were removed using Bismark deduplicate_bismark. Methylation calls were extracted using MethylDackel (v[version]; https://github.com/dpryan79/MethylDackel)
  • Network accessline 172
    Bismark deduplicate_bismark. Methylation calls were extracted using MethylDackel (v[version]; https://github.com/dpryan79/MethylDackel) with parameters [--minDepth X --mergeContext]. Bisulfite conversion
  • Network accessline 173
    MethylDackel (v[version]; https://github.com/dpryan79/MethylDackel)
  • Network accessline 483
    All sequencing data used in this study are publicly available through the ENCODE Project portal (https://www.encodeproject.org) under the
  • Network accessline 484
    the ENCODE Project portal (https://www.encodeproject.org) under the
  • Network accessline 519
    Gene Expression Omnibus (GEO) under accession [GSE number]. Processed data are available at the same accession. Previously published ENCODE data used in this study are available at https://www.encodeproject.org
  • Network accessline 520
    data are available at the same accession. Previously published ENCODE data used in this study are available at https://www.encodeproject.org under accessions listed in Supplementary Table S1.
  • Network accessline 521
    data used in this study are available at https://www.encodeproject.org
  • Network accessline 594
    | pairtools | Open2C et al. | PLOS Computational Biology | 2024 | 10.1371/journal.pcbi.1012164 | | cooler | Abdennur N & Mirny LA | Bioinformatics | 2020 | 10.1093/bioinformatics/btz540 | | Juicer | Durand NC et al. | Cell Systems | 2016 |
  • Network accessline 595
    | cooler | Abdennur N & Mirny LA | Bioinformatics | 2020 | 10.1093/bioinformatics/btz540 | | Juicer | Durand NC et al. | Cell Systems | 2016 | 10.1016/j.cels.2016.07.002 | | HiCCUPS | Rao SSP et al. | Cell | 2014 | 10.1016/j.cell.2014.11.02
  • Network accessline 596
    | Juicer | Durand NC et al. | Cell Systems | 2016 | 10.1016/j.cels.2016.07.002 |

skills/scientific-writing/references/literature.md

  • Network accessline 14
    - **Citation:** ENCODE Project Consortium et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature, 583(7818):699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
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    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
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    - **Citation:** Landt SG, Marinov GK, Kundaje A, et al. ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Research, 22(9):1813-1831, 2012. - **DOI:** [10.1101/gr.136184.111](https://doi.org/10.1101/gr.136184.11
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    - **DOI:** [10.1101/gr.136184.111](https://doi.org/10.1101/gr.136184.111)
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    - **DOI:** [10.1186/s13059-016-0881-8](https://doi.org/10.1186/s13059-016-0881-8)
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    - **DOI:** [10.1371/journal.pcbi.1003285](https://doi.org/10.1371/journal.pcbi.1003285)
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    - **DOI:** [10.1038/sdata.2016.18](https://doi.org/10.1038/sdata.2016.18)
  • Network accessline 72
    - **Citation:** Rougier NP, Droettboom M, Bing PE. Ten simple rules for better figures. PLoS Computational Biology, 10(9):e1003833, 2014. - **DOI:** [10.1371/journal.pcbi.1003833](https://doi.org/10.1371/journal.pcbi.1003833)
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    - **DOI:** [10.1371/journal.pcbi.1003833](https://doi.org/10.1371/journal.pcbi.1003833)
  • Network accessline 86
    - **URL:** [https://www.encodeproject.org/help/citing-encode/](https://www.encodeproject.org/help/citing-encode/)

skills/scrna-meta-analysis/SKILL.md

  • Network accessline 42
    - **Mawla, van der Meulen & Huising 2019** (Diabetes): Foundational cross-study meta-analysis framework. Introduced TIN-based quality assessment for scRNA-seq, demonstrated detection-limit artifacts, and proposed guidance for when to use si
  • Network accessline 43
    - **Tran et al. 2020** (Genome Biology, 854 citations): Benchmarked 14 batch-correction methods across 5 scenarios. Recommends Harmony first (fastest), then LIGER and Seurat 3 as alternatives. Evaluated using kBET, LISI, ASW, and ARI metric
  • Network accessline 44
    - **Luecken & Theis 2019** (Molecular Systems Biology, 1,631 citations): Current best practices for scRNA-seq analysis — QC, normalization, batch correction, feature selection, dimensionality reduction, clustering, and differential expressi
  • Network accessline 45
    - **Andreatta et al. 2023** (Nature Communications): STACAS — semi-supervised integration that leverages prior cell type knowledge. Outperforms unsupervised methods when partial cell type labels are available. Particularly relevant when int
  • Network accessline 46
    - **Zappia et al. 2025** (Nature Methods): Benchmarked feature selection methods for integration. Confirms highly variable gene selection is effective; provides guidance on number of features, batch-aware selection, and interaction with int
  • Network accessline 47
    - **Stuart et al. 2019** (Cell, 8,400+ citations): Seurat v3 — CCA-based anchor identification for cross-dataset integration. The most widely used integration framework. [DOI](https://doi.org/10.1016/j.cell.2019.05.031)
  • Network accessline 48
    - **Korsunsky et al. 2019** (Nature Methods, 3,200+ citations): Harmony — fast, scalable iterative soft clustering for batch correction. Works in PCA space, preserving biological variance while removing batch effects. [DOI](https://doi.org/
  • Network accessline 49
    - **Macosko et al. 2015** (Cell): Drop-seq — original species-mixing experiment quantifying ambient RNA contamination at 0.26–2.44% of reads per cell. Critical control for interpreting cross-cell-type transcript detection. [DOI](https://doi
  • Network accessline 50
    - **Squair et al. 2021** (Nature Communications, 700+ citations): Demonstrated that pseudobulk differential expression dramatically outperforms single-cell-level tests (Wilcoxon, MAST, etc.) for multi-sample comparisons. Now the recommended
  • Network accessline 51
    - **Young & Beber 2020** (Genome Biology, SoupX): Ambient RNA removal from droplet-based scRNA-seq. Essential preprocessing step to remove contaminating transcripts from lysed cells before integration. - **Lopez et al. 2018** (Nature Method
  • Network accessline 52
    - **Lopez et al. 2018** (Nature Methods, 2,700+ citations): scVI — deep generative model for single-cell transcriptomics. Provides a probabilistic framework for batch correction, visualization, clustering, and differential expression, accou
  • Network accessline 53
    - **Xu et al. 2021** (Molecular Systems Biology): scANVI — semi-supervised variant of scVI for cell type annotation during integration. Leverages existing cell state annotations to improve both integration quality and automatic annotation t
  • Network accessline 54
    - **Luecken et al. 2022** (Nature Methods, 700+ citations): Benchmarked 68 method+preprocessing combinations across 85 batches (>1.2 million cells) in 13 atlas-level integration tasks. Found scANVI, Scanorama, scVI, and scGen perform best o
  • Network accessline 55
    - **Xu et al. 2023** (Cell, CellHint): Automatic cell-type harmonization across datasets. Uses predictive clustering trees to resolve differences in annotation resolution and technical biases. Applied to 12 tissues from 38 datasets (~3.7M c
  • Network accessline 56
    - **Domínguez Conde et al. 2022** (Science, CellTypist): Automated cross-tissue cell type annotation using machine learning. Surveyed 16 tissues from 12 donors (~360,000 cells). CellTypist provides pre-trained models for rapid, reproducible

skills/scrna-meta-analysis/references/literature.md

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    - **DOI:** [10.1172/jci.insight.131661](https://doi.org/10.1172/jci.insight.131661)
  • Network accessline 24
    - **Citation:** Hao Y, Hao S, Andersen-Nissen E, Mauck WM III, Zheng S, Butler A, Lee MJ, Wilk AJ, Darby C, Zager M, Hoffman P, Stoeckius M, Papalexi E, Mimitou EP, Jain J, Srivastava A, Stuart T, Fleming LM, Yeung B, Rogers AJ, McElrath JM
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    - **DOI:** [10.1016/j.cell.2021.04.048](https://doi.org/10.1016/j.cell.2021.04.048)
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    - **Citation:** Lotfollahi M, Naber A, Theis FJ. Mapping single-cell data to reference atlases by transfer learning. *Nature Biotechnology*, 40(1), 121-130, 2022. - **DOI:** [10.1038/s41587-021-01001-7](https://doi.org/10.1038/s41587-021-01
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    - **DOI:** [10.1038/s41587-021-01001-7](https://doi.org/10.1038/s41587-021-01001-7)
  • Network accessline 44
    - **Citation:** Luecken MD, Buttner M, Chaichoompu K, Danese A, Interlandi M, Mueller MF, Strobl DC, Zappia L, Dugas M, Colome-Tatche M, Theis FJ. Benchmarking atlas-level data integration in single-cell genomics. *Nature Methods*, 19(1), 4
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    - **DOI:** [10.1038/s41592-018-0229-2](https://doi.org/10.1038/s41592-018-0229-2)
  • Network accessline 90
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    - **DOI:** [10.1038/s41587-021-01033-z](https://doi.org/10.1038/s41587-021-01033-z)
  • Network accessline 112
    - **Citation:** Squair JW, Gautier M, Kathe C, Anderson MA, James ND, Hutson TH, Hudelle R, Khodadadifar T, Phillips AA, Asboth L, Conon TH, Hutbert V, Romber N, Kosber E, Aeber V, Arvanian V, Schwab ME, Bhatt DH, Courtine G, De La Fuente A
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    - **DOI:** [10.1038/s41467-021-25960-2](https://doi.org/10.1038/s41467-021-25960-2)

skills/search-encode/references/literature.md

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    Project. *Science*, 306(5696), 636-640, 2004. - **DOI:** [10.1126/science.1105136](https://doi.org/10.1126/science.1105136) - **PMID:** 15499007 | **PMC:** PMC3232742
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    - **DOI:** [10.1126/science.1105136](https://doi.org/10.1126/science.1105136)
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    - **Citation:** The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in   the human genome. *Nature*, 489(7414), 57-74, 2012. - **DOI:** [10.1038/nature11247](https://doi.org/10.1038/nature11247)
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    the human genome. *Nature*, 489(7414), 57-74, 2012. - **DOI:** [10.1038/nature11247](https://doi.org/10.1038/nature11247) - **PMID:** 22955616 | **PMC:** PMC3439153
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    - **DOI:** [10.1038/nature11247](https://doi.org/10.1038/nature11247)
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    Expanded encyclopaedias of DNA elements in the human and mouse genomes. *Nature*,   583(7818), 699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
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    583(7818), 699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4) - **PMID:** 32728249 | **PMC:** PMC7410828
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    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
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    Narayanan AK, Ho M, Lee BT, et al. ENCODE data at the ENCODE portal. *Nucleic Acids   Research*, 44(D1), D726-D732, 2016. - **DOI:** [10.1093/nar/gkv1160](https://doi.org/10.1093/nar/gkv1160)
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    Research*, 44(D1), D726-D732, 2016. - **DOI:** [10.1093/nar/gkv1160](https://doi.org/10.1093/nar/gkv1160) - **PMID:** 26527727 | **PMC:** PMC4702836
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    - **DOI:** [10.1093/nar/gkv1160](https://doi.org/10.1093/nar/gkv1160)
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    Strattan JS, Jolanki O, Tanaka FY, Cherry JM. The Encyclopedia of DNA elements (ENCODE):   data portal update. *Nucleic Acids Research*, 46(D1), D794-D801, 2018. - **DOI:** [10.1093/nar/gkx1081](https://doi.org/10.1093/nar/gkx1081)
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    data portal update. *Nucleic Acids Research*, 46(D1), D794-D801, 2018. - **DOI:** [10.1093/nar/gkx1081](https://doi.org/10.1093/nar/gkx1081) - **PMID:** 29126249 | **PMC:** PMC5753278
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    - **DOI:** [10.1093/nar/gkx1081](https://doi.org/10.1093/nar/gkx1081)
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    the Encyclopedia of DNA Elements (ENCODE) data portal. *Nucleic Acids Research*, 48(D1),   D882-D889, 2020. - **DOI:** [10.1093/nar/gkz1062](https://doi.org/10.1093/nar/gkz1062)
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    D882-D889, 2020. - **DOI:** [10.1093/nar/gkz1062](https://doi.org/10.1093/nar/gkz1062) - **PMID:** 31713622 | **PMC:** PMC7061942
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    - **DOI:** [10.1093/nar/gkz1062](https://doi.org/10.1093/nar/gkz1062)
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    encyclopaedias of DNA elements in the human and mouse genomes. *Nature*, 583(7818),   699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
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    699-710, 2020. - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4) - **PMID:** 32728248 | **PMC:** PMC7410830
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    - **DOI:** [10.1038/s41586-020-2493-4](https://doi.org/10.1038/s41586-020-2493-4)
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    encyclopaedias of DNA elements in the human and mouse genomes. *Nature*, 583(7818),   693-698, 2020. - **DOI:** [10.1038/s41586-020-2489-0](https://doi.org/10.1038/s41586-020-2489-0)
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    693-698, 2020. - **DOI:** [10.1038/s41586-020-2489-0](https://doi.org/10.1038/s41586-020-2489-0) - **PMID:** 32728247 | **PMC:** PMC7410826
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    - **DOI:** [10.1038/s41586-020-2489-0](https://doi.org/10.1038/s41586-020-2489-0)

skills/setup/SKILL.md

  • Network accessline 121
    1. Get API credentials from https://www.encodeproject.org/profile/ (requires ENCODE account)
  • Access to secretsline 127
    [Credential-related source omitted]
  • Network accessline 234
    |---------|-------|-----| | "Server not found" | Claude not restarted after config change | Restart Claude Desktop / reload Claude Code | | "uvx not found" | uv not installed | `curl -LsSf https://astral.sh/uv/install.sh \| sh` |
  • Network accessline 235
    | "Server not found" | Claude not restarted after config change | Restart Claude Desktop / reload Claude Code | | "uvx not found" | uv not installed | `curl -LsSf https://astral.sh/uv/install.sh \| sh` | | Timeout errors | Slow connection o
  • Dangerous shell operationline 236
    | "uvx not found" | uv not installed | `curl -LsSf https://astral.sh/uv/install.sh \| sh` |
  • Network accessline 236
    | "uvx not found" | uv not installed | `curl -LsSf https://astral.sh/uv/install.sh \| sh` |

skills/setup/references/literature.md

  • Network accessline 78
    Strattan JS, Jolanki O, Tanaka FY, Cherry JM. The Encyclopedia of DNA elements (ENCODE):   data portal update. *Nucleic Acids Research*, 46(D1), D794-D801, 2018. - **DOI:** [10.1093/nar/gkx1081](https://doi.org/10.1093/nar/gkx1081)
  • Network accessline 79
    data portal update. *Nucleic Acids Research*, 46(D1), D794-D801, 2018. - **DOI:** [10.1093/nar/gkx1081](https://doi.org/10.1093/nar/gkx1081) - **PMID:** 29126249 | **PMC:** PMC5753278
  • Network accessline 80
    - **DOI:** [10.1093/nar/gkx1081](https://doi.org/10.1093/nar/gkx1081)
  • Access to secretsline 93
    [Credential-related source omitted]

skills/single-cell-encode/SKILL.md

  • Network accessline 118
    **ENCODE Blacklist filtering (required for scATAC-seq)**: Before any downstream analysis of scATAC-seq peaks or fragments, remove reads/peaks overlapping ENCODE Blacklist regions (Amemiya et al. 2019, Scientific Reports, 1,372 citations). T

skills/single-cell-encode/references/literature.md

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    - **Citation:** Zheng GXY, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R, Ziraldo SB, Wheeler TD, McDermott GP, Zhu J, Gregory MT, Shuga J, Montesclaros L, Underwood JG, Masquelier DA, Nishimura SY, Schnall-Levin M, Wyatt PW, Hindson C
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  • Network accessline 22
    - **Citation:** Cusanovich DA, Hill AJ, Aghamirzaie D, Daza RM, Pliner HA, Berletch JB, Filippova GN, Huang X, Christiansen L, DeWitt WS, Lee C, Regalado SG, Read DF, Steemers FJ, Disteche CM, Trapnell C, Shendure J. A single-cell atlas of
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    - **Citation:** Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM III, Hao Y, Stoeckius M, Smibert P, Satija R. Comprehensive integration of single-cell data. *Cell*, 177(7), 1888-1902.e21, 2019. - **DOI:** [10.1016/j.cell.
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    - **DOI:** [10.1186/s13059-017-1382-0](https://doi.org/10.1186/s13059-017-1382-0)
  • Network accessline 64
    - **Citation:** Granja JM, Corces MR, Pierce SE, Bagdatli ST, Chouber H, Zheng H, Zhu M, Dumm RE, Chang HY, Greenleaf WJ. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. *Nature Genetics*,
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    - **DOI:** [10.1038/s41592-019-0619-0](https://doi.org/10.1038/s41592-019-0619-0)
  • Network accessline 86
    - **Citation:** Luecken MD, Theis FJ. Current best practices in single-cell RNA-seq analysis: a tutorial. *Molecular Systems Biology*, 15(6), e8746, 2019. - **DOI:** [10.15252/msb.20188746](https://doi.org/10.15252/msb.20188746)
  • Network accessline 87
    - **DOI:** [10.15252/msb.20188746](https://doi.org/10.15252/msb.20188746)
  • Network accessline 98
    - **Citation:** Mawla AM, Huising MO. Navigating the depths and avoiding the shallows of pancreatic islet cell transcriptomes. *JCI Insight*, 4(17), e131661, 2019. - **DOI:** [10.1172/jci.insight.131661](https://doi.org/10.1172/jci.insight.
  • Network accessline 99
    - **DOI:** [10.1172/jci.insight.131661](https://doi.org/10.1172/jci.insight.131661)
  • Network accessline 108
    - **Citation:** The Tabula Sapiens Consortium, Jones RC, Karkanias J, Krasnow MA, Pisco AO, Quake SR, Salzman J, Yosef N, Bber B, Beechem J, Bein D, Bui T, Burber M, Chen J, Cho P, Choi M, D'Angelo NJ, D'Augustin RC, Danaher T, Debnath M, D
  • Network accessline 109
    - **DOI:** [10.1126/science.abl4896](https://doi.org/10.1126/science.abl4896)

skills/track-experiments/SKILL.md

  • Network accessline 60
    | `replication_type` | Replicate strategy | isogenic, anisogenic | | `life_stage` | Developmental stage | adult, embryonic, child | | `url` | ENCODE Portal URL | https://www.encodeproject.org/experiments/ENCSR123ABC/ |
  • Network accessline 61
    | `life_stage` | Developmental stage | adult, embryonic, child | | `url` | ENCODE Portal URL | https://www.encodeproject.org/experiments/ENCSR123ABC/ | | `notes` | User-provided notes | H3K27ac reference for islet enhancer study |
  • Network accessline 62
    | `url` | ENCODE Portal URL | https://www.encodeproject.org/experiments/ENCSR123ABC/ |

skills/track-experiments/references/literature.md

  • Network accessline 32
    FAIR Guiding Principles for scientific data management and stewardship. *Scientific Data*,   3, 160018, 2016. - **DOI:** [10.1038/sdata.2016.18](https://doi.org/10.1038/sdata.2016.18)
  • Network accessline 33
    3, 160018, 2016. - **DOI:** [10.1038/sdata.2016.18](https://doi.org/10.1038/sdata.2016.18) - **PMID:** 26978244 | **PMC:** PMC4792175
  • Network accessline 34
    - **DOI:** [10.1038/sdata.2016.18](https://doi.org/10.1038/sdata.2016.18)
  • Network accessline 60
    - **Citation:** Baker M. 1,500 scientists lift the lid on reproducibility. *Nature*, 533(7604),   452-454, 2016. - **DOI:** [10.1038/533452a](https://doi.org/10.1038/533452a)
  • Network accessline 61
    452-454, 2016. - **DOI:** [10.1038/533452a](https://doi.org/10.1038/533452a) - **PMID:** 27225100
  • Network accessline 62
    - **DOI:** [10.1038/533452a](https://doi.org/10.1038/533452a)
  • Network accessline 83
    Ioannidis JP, Taufer M. Enhancing reproducibility for computational methods. *Science*,   354(6317), 1240-1241, 2016. - **DOI:** [10.1126/science.aah6168](https://doi.org/10.1126/science.aah6168)
  • Network accessline 84
    354(6317), 1240-1241, 2016. - **DOI:** [10.1126/science.aah6168](https://doi.org/10.1126/science.aah6168) - **PMID:** 27940837
  • Network accessline 85
    - **DOI:** [10.1126/science.aah6168](https://doi.org/10.1126/science.aah6168)
  • Network accessline 117
    Strattan JS, Jolanki O, Tanaka FY, Cherry JM. The Encyclopedia of DNA elements (ENCODE):   data portal update. *Nucleic Acids Research*, 46(D1), D794-D801, 2018. - **DOI:** [10.1093/nar/gkx1081](https://doi.org/10.1093/nar/gkx1081)
  • Network accessline 118
    data portal update. *Nucleic Acids Research*, 46(D1), D794-D801, 2018. - **DOI:** [10.1093/nar/gkx1081](https://doi.org/10.1093/nar/gkx1081) - **PMID:** 29126249 | **PMC:** PMC5753278
  • Network accessline 119
    - **DOI:** [10.1093/nar/gkx1081](https://doi.org/10.1093/nar/gkx1081)
  • Network accessline 147
    information about a microarray experiment (MIAME)-toward standards for microarray data.   *Nature Genetics*, 29(4), 365-371, 2001. - **DOI:** [10.1038/ng1201-365](https://doi.org/10.1038/ng1201-365)
  • Network accessline 148
    *Nature Genetics*, 29(4), 365-371, 2001. - **DOI:** [10.1038/ng1201-365](https://doi.org/10.1038/ng1201-365) - **PMID:** 11726920
  • Network accessline 149
    - **DOI:** [10.1038/ng1201-365](https://doi.org/10.1038/ng1201-365)

skills/ucsc-browser/SKILL.md

  • Network accessline 30
    - **ENCODE Project Consortium 2020** (Nature, ~1,656 citations): Registry of 926,535 human cCREs hosted on UCSC as `encodeCcreCombined` track. [DOI](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 31
    - **Nassar et al. 2023** (Nucleic Acids Research): The UCSC Genome Browser database: 2023 update. [DOI](https://doi.org/10.1093/nar/gkac1072)
  • Network accessline 36
    **Base URL**: `https://api.genome.ucsc.edu`
  • Network accessline 47
    # List all tracks for hg38 curl "https://api.genome.ucsc.edu/list/tracks?genome=hg38"
  • Network accessline 48
    curl "https://api.genome.ucsc.edu/list/tracks?genome=hg38"
  • Network accessline 50
    # Search for ENCODE-specific tracks curl "https://api.genome.ucsc.edu/search?search=encode+regulation&genome=hg38&categories=trackDb"
  • Network accessline 51
    curl "https://api.genome.ucsc.edu/search?search=encode+regulation&genome=hg38&categories=trackDb"
  • Network accessline 53
    # Get schema (field definitions) for a track curl "https://api.genome.ucsc.edu/list/schema?genome=hg38;track=encodeCcreCombined"
  • Network accessline 54
    curl "https://api.genome.ucsc.edu/list/schema?genome=hg38;track=encodeCcreCombined"
  • Network accessline 81
    # Get all cCREs in a 100kb window curl "https://api.genome.ucsc.edu/getData/track?genome=hg38;track=encodeCcreCombined;chrom=chr1;start=1000000;end=1100000"
  • Network accessline 82
    curl "https://api.genome.ucsc.edu/getData/track?genome=hg38;track=encodeCcreCombined;chrom=chr1;start=1000000;end=1100000"
  • Network accessline 84
    # Use jsonOutputArrays for named fields (recommended) curl "https://api.genome.ucsc.edu/getData/track?genome=hg38;track=encodeCcreCombined;chrom=chr1;start=1000000;end=1100000;jsonOutputArrays=1"
  • Network accessline 85
    curl "https://api.genome.ucsc.edu/getData/track?genome=hg38;track=encodeCcreCombined;chrom=chr1;start=1000000;end=1100000;jsonOutputArrays=1"
  • Network accessline 116
    # Get TF rPeak clusters in a region curl "https://api.genome.ucsc.edu/getData/track?genome=hg38;track=TFrPeakClusters;chrom=chr1;start=1000000;end=1100000;jsonOutputArrays=1"
  • Network accessline 117
    curl "https://api.genome.ucsc.edu/getData/track?genome=hg38;track=TFrPeakClusters;chrom=chr1;start=1000000;end=1100000;jsonOutputArrays=1"
  • Network accessline 139
    # Get sequence for a region curl "https://api.genome.ucsc.edu/getData/sequence?genome=hg38;chrom=chr1;start=1000000;end=1000500"
  • Network accessline 140
    curl "https://api.genome.ucsc.edu/getData/sequence?genome=hg38;chrom=chr1;start=1000000;end=1000500"
  • Network accessline 142
    # Get reverse complement curl "https://api.genome.ucsc.edu/getData/sequence?genome=hg38;chrom=chr1;start=1000000;end=1000500;revComp=1"
  • Network accessline 143
    curl "https://api.genome.ucsc.edu/getData/sequence?genome=hg38;chrom=chr1;start=1000000;end=1000500;revComp=1"
  • Network accessline 157
    # DNase clusters (95 cell types) curl "https://api.genome.ucsc.edu/getData/track?genome=hg38;track=wgEncodeRegDnaseClustered;chrom=chr1;start=1000000;end=1100000;jsonOutputArrays=1"
  • Network accessline 158
    curl "https://api.genome.ucsc.edu/getData/track?genome=hg38;track=wgEncodeRegDnaseClustered;chrom=chr1;start=1000000;end=1100000;jsonOutputArrays=1"
  • Network accessline 168
    # Download UCSC tools (macOS example) # Available at: https://hgdownload.gi.ucsc.edu/admin/exe/
  • Network accessline 169
    # Available at: https://hgdownload.gi.ucsc.edu/admin/exe/
  • Network accessline 171
    # Extract ENCODE cCREs for a region from hosted bigBed bigBedToBed https://hgdownload.gi.ucsc.edu/gbdb/hg38/encode3/encodeCcreCombined.bb \
  • Network accessline 172
    bigBedToBed https://hgdownload.gi.ucsc.edu/gbdb/hg38/encode3/encodeCcreCombined.bb \
  • Network accessline 175
    # Extract TF rPeak clusters bigBedToBed https://hgdownload.gi.ucsc.edu/gbdb/hg38/bbi/ENCODE4/TFrPeakClusters.bb \
  • Network accessline 176
    bigBedToBed https://hgdownload.gi.ucsc.edu/gbdb/hg38/bbi/ENCODE4/TFrPeakClusters.bb \
  • Network accessline 179
    # Summarize bigWig signal over regions bigWigSummary http://path/to/signal.bw chr1 1000000 1100000 10
  • Network accessline 180
    bigWigSummary http://path/to/signal.bw chr1 1000000 1100000 10
  • Network accessline 201
    3. For each peak region, query UCSC for regulatory context:    curl "https://api.genome.ucsc.edu/getData/track?genome=hg38;track=encodeCcreCombined;chrom=CHR;start=START;end=END;jsonOutputArrays=1"
  • Network accessline 202
    curl "https://api.genome.ucsc.edu/getData/track?genome=hg38;track=encodeCcreCombined;chrom=CHR;start=START;end=END;jsonOutputArrays=1"
  • Network accessline 204
    4. Check which TFs bind at each peak:    curl "https://api.genome.ucsc.edu/getData/track?genome=hg38;track=TFrPeakClusters;chrom=CHR;start=START;end=END;jsonOutputArrays=1"
  • Network accessline 205
    curl "https://api.genome.ucsc.edu/getData/track?genome=hg38;track=TFrPeakClusters;chrom=CHR;start=START;end=END;jsonOutputArrays=1"
  • Network accessline 207
    5. Get DNA sequence for motif analysis:    curl "https://api.genome.ucsc.edu/getData/sequence?genome=hg38;chrom=CHR;start=START;end=END"
  • Network accessline 208
    curl "https://api.genome.ucsc.edu/getData/sequence?genome=hg38;chrom=CHR;start=START;end=END"
  • Network accessline 258
    "file_format": "bigWig",   "output_type": "fold change over control",   "download_url": "https://www.encodeproject.org/files/ENCFF200BW/@@download/ENCFF200BW.bigWig",
  • Network accessline 259
    "output_type": "fold change over control",   "download_url": "https://www.encodeproject.org/files/ENCFF200BW/@@download/ENCFF200BW.bigWig",   "s3_uri": "s3://encode-public/2021/06/14/ENCFF200BW.bigWig",
  • Network accessline 260
    "download_url": "https://www.encodeproject.org/files/ENCFF200BW/@@download/ENCFF200BW.bigWig",
  • Network accessline 274
    GET https://api.genome.ucsc.edu/getData/track?genome=hg38&track=encodeCcreCombined&chrom=chr1&start=1000000&end=1100000
  • Network accessline 292
    GET https://api.genome.ucsc.edu/getData/sequence?genome=hg38&chrom=chr1&start=1020500&end=1021200
  • Network accessline 361
    "file_size": 131386573,     "file_size_human": "125.3 MB",     "download_url": "https://www.encodeproject.org/files/ENCFF456DEF/@@download/ENCFF456DEF.bigWig",
  • Network accessline 362
    "file_size_human": "125.3 MB",     "download_url": "https://www.encodeproject.org/files/ENCFF456DEF/@@download/ENCFF456DEF.bigWig",     "preferred_default": true
  • Network accessline 363
    "download_url": "https://www.encodeproject.org/files/ENCFF456DEF/@@download/ENCFF456DEF.bigWig",

skills/ucsc-browser/references/literature.md

  • Network accessline 10
    - **Citation:** Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, Haussler D. The human genome browser at UCSC. *Genome Research*, 12(6), 996-1006, 2002. - **DOI:** [10.1101/gr.229102](https://doi.org/10.1101/gr.229102)
  • Network accessline 11
    - **DOI:** [10.1101/gr.229102](https://doi.org/10.1101/gr.229102)
  • Network accessline 20
    - **Citation:** Karolchik D, Hinrichs AS, Furey TS, Roskin KM, Sugnet CW, Haussler D, Kent WJ. The UCSC Table Browser data retrieval tool. *Nucleic Acids Research*, 32(Database issue), D493-D496, 2004. - **DOI:** [10.1093/nar/gkh103](https:
  • Network accessline 21
    - **DOI:** [10.1093/nar/gkh103](https://doi.org/10.1093/nar/gkh103)
  • Network accessline 30
    - **Citation:** Hinrichs AS, Karolchik D, Baertsch R, Barber GP, Bejerano G, Clawson H, Diekhans M, Furey TS, Harte RA, Hsu F, Hillman-Jackson J, Kuhn RM, Pedersen JS, Pohl A, Raney BJ, Rosenbloom KR, Siepel A, Smith KE, Sugnet CW, Sultan-Q
  • Network accessline 31
    - **DOI:** [10.1093/nar/gkj144](https://doi.org/10.1093/nar/gkj144)
  • Network accessline 40
    - **Citation:** Kent WJ, Zweig AS, Barber G, Hinrichs AS, Karolchik D. BigWig and BigBed: enabling browsing of large distributed datasets. *Bioinformatics*, 26(17), 2204-2207, 2010. - **DOI:** [10.1093/bioinformatics/btq351](https://doi.org
  • Network accessline 41
    - **DOI:** [10.1093/bioinformatics/btq351](https://doi.org/10.1093/bioinformatics/btq351)
  • Network accessline 50
    - **Citation:** Raney BJ, Dreszer TR, Barber GP, Clawson H, Fujita PA, Wang T, Nguyen N, Paten B, Zweig AS, Karolchik D, Kent WJ. Track data hubs enable visualization of user-defined genome-wide annotations on the UCSC Genome Browser. *Bioi
  • Network accessline 51
    - **DOI:** [10.1093/bioinformatics/btt637](https://doi.org/10.1093/bioinformatics/btt637)
  • Network accessline 60
    - **Citation:** Haeussler M, Zweig AS, Tyner C, Speir ML, Rosenbloom KR, Raney BJ, Lee CM, Lee BT, Hinrichs AS, Gonzalez JN, Gibson D, Diekhans M, Clawson H, Casper J, Barber GP, Haussler D, Kuhn RM, Kent WJ. The UCSC Genome Browser databas
  • Network accessline 61
    - **DOI:** [10.1093/nar/gky1095](https://doi.org/10.1093/nar/gky1095)
  • Network accessline 70
    - **Citation:** Navarro Gonzalez J, Zweig AS, Speir ML, Schmelter D, Rosenbloom KR, Raney BJ, Powell CC, Nassar LR, Maulding ND, Lee CM, Lee BT, Hinrichs AS, Fyfe AC, Fernandes JD, Diekhans M, Clawson H, Casper J, Benet-Pages A, Barber GP,
  • Network accessline 71
    - **DOI:** [10.1093/nar/gkaa1070](https://doi.org/10.1093/nar/gkaa1070)

skills/variant-annotation/SKILL.md

  • Network accessline 44
    - **ENCODE Project Consortium 2020** (Nature, ~1,656 citations): Registry of 926,535 human cCREs. The primary resource for variant-to-regulatory-element annotation. [DOI](https://doi.org/10.1038/s41586-020-2493-4)
  • Network accessline 45
    - **Boyle et al. 2012** (Genome Research, ~2,623 citations): RegulomeDB — integrates ENCODE ChIP-seq, DNase-seq, eQTLs to score non-coding variants on a 1–6 scale. [DOI](https://doi.org/10.1101/gr.137323.112)
  • Network accessline 46
    - **Kircher et al. 2014** (Nature Genetics, ~5,719 citations): CADD — Combined Annotation-Dependent Depletion. SVM trained on 14.7M simulated vs. high-frequency alleles, pre-computes scores for all 8.6B possible human SNVs. [DOI](https://do
  • Network accessline 47
    - **Rentzsch et al. 2019** (Nucleic Acids Research, ~1,500 citations): CADD v1.4/v1.6 update with GRCh38 support. [DOI](https://doi.org/10.1093/nar/gky1016)
  • Network accessline 48
    - **Finucane et al. 2015** (Nature Genetics, ~2,253 citations): Stratified LD Score Regression (S-LDSC) — partitions SNP heritability into functional categories using GWAS summary statistics. Foundational for quantifying how much disease he
  • Network accessline 49
    - **Iotchkova et al. 2019** (Nature Genetics, ~157 citations): GARFIELD — GWAS enrichment in regulatory annotations with LD correction, allele frequency, and TSS distance confounders. [DOI](https://doi.org/10.1038/s41588-018-0322-6)
  • Network accessline 50
    - **Wang et al. 2020** (JRSS-B, ~500 citations): SuSiE — Sum of Single Effects model for Bayesian fine-mapping, outputs credible sets per independent signal. [DOI](https://doi.org/10.1111/rssb.12388)
  • Network accessline 51
    - **Weissbrod et al. 2020** (Nature Genetics, ~200 citations): PolyFun — leverages ENCODE/Roadmap functional annotations as prior causal probabilities for fine-mapping. PolyFun+SuSiE identified 32% more causal variants than SuSiE alone. [DO
  • Network accessline 52
    - **Nasser et al. 2021** (Nature, ~468 citations): ABC model enhancer-gene maps in 131 cell types. Links 5,036 GWAS signals to 2,249 genes. >20-fold enrichment of causal variants in cell-type-specific predicted enhancers. [DOI](https://doi.
  • Network accessline 53
    - **Broekema et al. 2020** (Open Biology, ~107 citations): Practical review of post-GWAS fine-mapping and gene prioritization strategies. [DOI](https://doi.org/10.1098/rsob.190221)
  • Network accessline 54
    - **Schaub et al. 2012** (Genome Research, ~675 citations): Early framework linking ENCODE functional data with GWAS disease associations. Functional annotations for up to 80% of reported associations. [DOI](https://doi.org/10.1101/gr.13612
  • Network accessline 55
    - **Maurano et al. 2012** (Science, ~2,800 citations): Systematic demonstration that disease-associated variants concentrate in DNase I hypersensitive sites. 88% of variant-containing DHSs are active during fetal development. Enabled de nov
  • Network accessline 56
    - **Amemiya et al. 2019** (Scientific Reports, ~1,372 citations): ENCODE Blacklist — regions producing artifactual signal in functional genomics. Must filter before annotation. [DOI](https://doi.org/10.1038/s41598-019-45839-z)
  • Network accessline 161
    **Pre-annotation filter**: Before annotating, filter out variants overlapping ENCODE Blacklist regions (Amemiya et al. 2019, Scientific Reports, 1,372 citations). Blacklisted regions produce artifactual signal in functional genomics assays.
  • Network accessline 162
    - Human GRCh38: `hg38-blacklist.v2.bed.gz` from [Boyle-Lab/Blacklist](https://github.com/Boyle-Lab/Blacklist)
  • Network accessline 405
    POST https://rest.ensembl.org/vep/human/region
  • Network accessline 414
    GET https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=clinvar&term=rs7903146
  • Network accessline 434
    GET https://www.ebi.ac.uk/gwas/rest/api/singleNucleotidePolymorphisms/rs7903146

skills/variant-annotation/references/literature.md

  • Network accessline 12
    - **Citation:** McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GRS, Thormann A, Flicek P, Cunningham F. The Ensembl Variant Effect Predictor. *Genome Biology*, 17(1), 122, 2016. - **DOI:** [10.1186/s13059-016-0974-4](https://doi.org/10.1186/s1
  • Network accessline 13
    - **DOI:** [10.1186/s13059-016-0974-4](https://doi.org/10.1186/s13059-016-0974-4)
  • Network accessline 22
    - **Citation:** Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. *Nucleic Acids Research*, 38(16), e164, 2010. - **DOI:** [10.1093/nar/gkq603](https://doi.org/10.1093/nar/g
  • Network accessline 23
    - **DOI:** [10.1093/nar/gkq603](https://doi.org/10.1093/nar/gkq603)
  • Network accessline 32
    - **Citation:** Cingolani P, Platts A, Wang LL, Coon M, Nguyen T, Wang L, Land SJ, Lu X, Ruden DM. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster
  • Network accessline 33
    - **DOI:** [10.4161/fly.19695](https://doi.org/10.4161/fly.19695)
  • Network accessline 44
    - **Citation:** Kircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J. A general framework for estimating the relative pathogenicity of human genetic variants. *Nature Genetics*, 46(3), 310-315, 2014. - **DOI:** [10.1038/ng.2892](h
  • Network accessline 45
    - **DOI:** [10.1038/ng.2892](https://doi.org/10.1038/ng.2892)
  • Network accessline 54
    - **Citation:** Rentzsch P, Witten D, Cooper GM, Shendure J, Kircher M. CADD: predicting the deleteriousness of variants throughout the human genome. *Nucleic Acids Research*, 47(D1), D886-D894, 2019. - **DOI:** [10.1093/nar/gky1016](https:
  • Network accessline 55
    - **DOI:** [10.1093/nar/gky1016](https://doi.org/10.1093/nar/gky1016)
  • Network accessline 64
    - **Citation:** Ioannidis NM, Rothstein JH, Pejaver V, Middha S, McDonnell SK, Baheti S, Musolf A, Li Q, Holzinger E, Karyadi D, Cannon-Albright LA, Teerlink CC, Stanford JL, Isaacs WB, Xu J, Cooney KA, Lange EM, Schleutker J, Carpten JD, P
  • Network accessline 65
    - **DOI:** [10.1016/j.ajhg.2016.08.016](https://doi.org/10.1016/j.ajhg.2016.08.016)
  • Network accessline 76
    - **Citation:** Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, Grody WW, Hegde M, Lyon E, Spector E, Voelkerding K, Rehm HL, ACMG Laboratory Quality Assurance Committee. Standards and guidelines for the interpretation of seque
  • Network accessline 77
    - **DOI:** [10.1038/gim.2015.30](https://doi.org/10.1038/gim.2015.30)
  • Network accessline 86
    - **Citation:** Landrum MJ, Lee JM, Benson M, Brown GR, Chao C, Chitipiralla S, Gu B, Hart J, Hoffman D, Jang W, Karapetyan K, Katz K, Liu C, Maddipatla Z, Malheiro A, McDaniel K, Ovetsky M, Riley G, Zhou G, Holmes JB, Kattman BL, Maglott D
  • Network accessline 87
    - **DOI:** [10.1093/nar/gkx1153](https://doi.org/10.1093/nar/gkx1153)
  • Network accessline 98
    - **Citation:** Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alfoldi J, Wang Q, Collins RL, Laricchia KM, Ganna A, Birnbaum DP, Gauthier LD, Brand H, Solomonson M, Watts NA, Rhodes D, Singer-Berk M, England EM, Seaby EG, Kosmicki JA, W
  • Network accessline 99
    - **DOI:** [10.1038/s41586-020-2308-7](https://doi.org/10.1038/s41586-020-2308-7)
  • Network accessline 110
    - **Citation:** Cooper GM, Shendure J. Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data. *Nature Reviews Genetics*, 12(9), 628-640, 2011. - **DOI:** [10.1038/nrg3046](https://doi.org/10.1038/nrg3046)
  • Network accessline 111
    - **DOI:** [10.1038/nrg3046](https://doi.org/10.1038/nrg3046)

skills/visualization-workflow/SKILL.md

  • Network accessline 20
    | Reference | Journal | Key Contribution | DOI | Citations | |-----------|---------|-----------------|-----|-----------| | Ramirez et al. (2016) | Nucleic Acids Research | deepTools2: next-generation server for deep-sequencing data analysis
  • Network accessline 21
    |-----------|---------|-----------------|-----|-----------| | Ramirez et al. (2016) | Nucleic Acids Research | deepTools2: next-generation server for deep-sequencing data analysis; heatmaps, profiles, correlation, PCA | [10.1093/nar/gkw257]
  • Network accessline 22
    | Ramirez et al. (2016) | Nucleic Acids Research | deepTools2: next-generation server for deep-sequencing data analysis; heatmaps, profiles, correlation, PCA | [10.1093/nar/gkw257](https://doi.org/10.1093/nar/gkw257) | ~3,800 |
  • Network accessline 23
    | Robinson et al. (2011) | Nature Biotechnology | Integrative Genomics Viewer (IGV): interactive exploration of large genomic datasets | [10.1038/nbt.1754](https://doi.org/10.1038/nbt.1754) | ~10,000 |
  • Network accessline 24
    | Kent et al. (2002) | Genome Research | The Human Genome Browser at UCSC: foundation for track-based genomic visualization | [10.1101/gr.229102](https://doi.org/10.1101/gr.229102) | ~8,000 |
  • Network accessline 25
    | Ramirez et al. (2014) | Nucleic Acids Research | deepTools: flexible platform for exploring deep-sequencing data; original computeMatrix/plotHeatmap framework | [10.1093/nar/gku365](https://doi.org/10.1093/nar/gku365) | ~2,500 |
  • Network accessline 26
    | Amemiya et al. (2019) | Scientific Reports | ENCODE Blacklist: comprehensive identification of artifact regions to exclude from visualization | [10.1038/s41598-019-45839-z](https://doi.org/10.1038/s41598-019-45839-z) | ~1,372 |
  • Network accessline 178
    new genome hg38 load https://www.encodeproject.org/files/ENCFF.../@@download/ENCFF....bigWig
  • Network accessline 179
    genome hg38 load https://www.encodeproject.org/files/ENCFF.../@@download/ENCFF....bigWig load https://www.encodeproject.org/files/ENCFF.../@@download/ENCFF....bigBed
  • Network accessline 180
    load https://www.encodeproject.org/files/ENCFF.../@@download/ENCFF....bigWig
  • Network accessline 181
    load https://www.encodeproject.org/files/ENCFF.../@@download/ENCFF....bigBed
  • Network accessline 198
    <div id="igv-div"></div> <script src="https://cdn.jsdelivr.net/npm/igv@2.15.0/dist/igv.min.js"></script>
  • Network accessline 199
    <script src="https://cdn.jsdelivr.net/npm/igv@2.15.0/dist/igv.min.js"></script>
  • Network accessline 205
    {             name: "H3K27ac Signal",             url: "https://www.encodeproject.org/files/ENCFF.../@@download/ENCFF....bigWig",
  • Network accessline 206
    name: "H3K27ac Signal",             url: "https://www.encodeproject.org/files/ENCFF.../@@download/ENCFF....bigWig",             type: "wig",
  • Network accessline 207
    url: "https://www.encodeproject.org/files/ENCFF.../@@download/ENCFF....bigWig",
  • Network accessline 211
    {             name: "ATAC Peaks",             url: "https://www.encodeproject.org/files/ENCFF.../@@download/ENCFF....bigBed",
  • Network accessline 212
    name: "ATAC Peaks",             url: "https://www.encodeproject.org/files/ENCFF.../@@download/ENCFF....bigBed",             type: "annotation",
  • Network accessline 213
    url: "https://www.encodeproject.org/files/ENCFF.../@@download/ENCFF....bigBed",
  • Network accessline 337
    https://genome.ucsc.edu/cgi-bin/hgTracks?db=hg38&hubUrl=https://yourserver.edu/hub.txt

skills/visualization-workflow/references/literature.md

  • Network accessline 32
    - **Citation:** Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, Haussler D.   The human genome browser at UCSC. *Genome Research*, 12(6), 996-1006, 2002. - **DOI:** [10.1101/gr.229102](https://doi.org/10.1101/gr.229102)
  • Network accessline 33
    The human genome browser at UCSC. *Genome Research*, 12(6), 996-1006, 2002. - **DOI:** [10.1101/gr.229102](https://doi.org/10.1101/gr.229102) - **PMID:** 12045153 | **PMC:** PMC186604
  • Network accessline 34
    - **DOI:** [10.1101/gr.229102](https://doi.org/10.1101/gr.229102)
  • Network accessline 52
    - **Citation:** Robinson JT, Thorvaldsdottir H, Winckler W, Guttman M, Lander ES, Getz G,   Mesirov JP. Integrative genomics viewer. *Nature Biotechnology*, 29(1), 24-26, 2011. - **DOI:** [10.1038/nbt.1754](https://doi.org/10.1038/nbt.1754)
  • Network accessline 53
    Mesirov JP. Integrative genomics viewer. *Nature Biotechnology*, 29(1), 24-26, 2011. - **DOI:** [10.1038/nbt.1754](https://doi.org/10.1038/nbt.1754) - **PMID:** 21221095 | **PMC:** PMC3346182
  • Network accessline 54
    - **DOI:** [10.1038/nbt.1754](https://doi.org/10.1038/nbt.1754)
  • Network accessline 74
    high-performance genomics data visualization and exploration. *Briefings in Bioinformatics*,   14(2), 178-192, 2013. - **DOI:** [10.1093/bib/bbs017](https://doi.org/10.1093/bib/bbs017)
  • Network accessline 75
    14(2), 178-192, 2013. - **DOI:** [10.1093/bib/bbs017](https://doi.org/10.1093/bib/bbs017) - **PMID:** 22517427 | **PMC:** PMC3603213
  • Network accessline 76
    - **DOI:** [10.1093/bib/bbs017](https://doi.org/10.1093/bib/bbs017)
  • Network accessline 104
    Dundar F, Manke T. deepTools2: a next generation web server for deep-sequencing data   analysis. *Nucleic Acids Research*, 44(W1), W160-W165, 2016. - **DOI:** [10.1093/nar/gkw257](https://doi.org/10.1093/nar/gkw257)
  • Network accessline 105
    analysis. *Nucleic Acids Research*, 44(W1), W160-W165, 2016. - **DOI:** [10.1093/nar/gkw257](https://doi.org/10.1093/nar/gkw257) - **PMID:** 27079975 | **PMC:** PMC4987876
  • Network accessline 106
    - **DOI:** [10.1093/nar/gkw257](https://doi.org/10.1093/nar/gkw257)
  • Network accessline 135
    Mirny LA, Park PJ, Gehlenborg N. HiGlass: web-based visual exploration and analysis of   genome interaction maps. *Genome Biology*, 19(1), 125, 2018. - **DOI:** [10.1186/s13059-018-1486-1](https://doi.org/10.1186/s13059-018-1486-1)
  • Network accessline 136
    genome interaction maps. *Genome Biology*, 19(1), 125, 2018. - **DOI:** [10.1186/s13059-018-1486-1](https://doi.org/10.1186/s13059-018-1486-1) - **PMID:** 30143029 | **PMC:** PMC6109259
  • Network accessline 137
    - **DOI:** [10.1186/s13059-018-1486-1](https://doi.org/10.1186/s13059-018-1486-1)
  • Network accessline 163
    - **Citation:** Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations   in multidimensional genomic data. *Bioinformatics*, 32(18), 2847-2849, 2016. - **DOI:** [10.1093/bioinformatics/btw313](https://doi.org/10.1093/b
  • Network accessline 164
    in multidimensional genomic data. *Bioinformatics*, 32(18), 2847-2849, 2016. - **DOI:** [10.1093/bioinformatics/btw313](https://doi.org/10.1093/bioinformatics/btw313) - **PMID:** 27207943
  • Network accessline 165
    - **DOI:** [10.1093/bioinformatics/btw313](https://doi.org/10.1093/bioinformatics/btw313)
  • Network accessline 190
    - **Citation:** Robinson MD, Oshlack A. A scaling normalization method for differential   expression analysis of RNA-seq data. *Genome Biology*, 11(3), R25, 2010. - **DOI:** [10.1186/gb-2010-11-3-r25](https://doi.org/10.1186/gb-2010-11-3-r2
  • Network accessline 191
    expression analysis of RNA-seq data. *Genome Biology*, 11(3), R25, 2010. - **DOI:** [10.1186/gb-2010-11-3-r25](https://doi.org/10.1186/gb-2010-11-3-r25) - **PMID:** 20196867 | **PMC:** PMC2864565
  • Network accessline 192
    - **DOI:** [10.1186/gb-2010-11-3-r25](https://doi.org/10.1186/gb-2010-11-3-r25)
  • Network accessline 221
    integration of genomic datasets with the R/Bioconductor package biomaRt. *Nature   Protocols*, 4(8), 1184-1191, 2009. - **DOI:** [10.1038/nprot.2009.97](https://doi.org/10.1038/nprot.2009.97)
  • Network accessline 222
    Protocols*, 4(8), 1184-1191, 2009. - **DOI:** [10.1038/nprot.2009.97](https://doi.org/10.1038/nprot.2009.97) - **PMID:** 19617889 | **PMC:** PMC3159387
  • Network accessline 223
    - **DOI:** [10.1038/nprot.2009.97](https://doi.org/10.1038/nprot.2009.97)

How the signals fit together

Facts from a static read of the files at the pinned commit. They describe what the code does, not why.

Some files could not be read; the facts below still hold.

Declared and executed: 4 facts
Signal confidence: 496 facts

and 446 more

Only the first 500 facts are listed.

Works with

Agent compatibility, from the files in this repository
AgentStatusEvidenceInstall to
Claude CodeNativeManifest .claude-plugin/marketplace.json.claude/skills/<skill-name>/
CodexCompatibleStandard SKILL.md plugin/skills/accessibility-aggregation/SKILL.md.agents/skills/<skill-name>/
CursorCompatibleStandard SKILL.md plugin/skills/accessibility-aggregation/SKILL.md.cursor/skills/<skill-name>/
Gemini CLICompatibleStandard SKILL.md plugin/skills/accessibility-aggregation/SKILL.md.gemini/skills/<skill-name>/
GitHub CopilotCompatibleStandard SKILL.md plugin/skills/accessibility-aggregation/SKILL.md.github/skills/<skill-name>/
OpenCodeCompatibleStandard SKILL.md plugin/skills/accessibility-aggregation/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.

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