Agent skill · Data & Analytics

bio-chipseq-motif-analysis

De novo motif discovery and known motif enrichment analysis using HOMER and MEME-ChIP. Identify transcription factor binding motifs in ChIP-seq, ATAC-seq, or other genomic peak data. Use when finding enriched DNA motifs in peak sequences.

FreedomIntelligencegithub.com/FreedomIntelligenceGitHub ↗
claude-codeships scripts
Install
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-chipseq-motif-analysis --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 3
SKILL.md size: 10 KB
Bundled scripts: yes
Path: skills/bio-chipseq-motif-analysis/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,909
Language: Python
Read our review of the source →

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

## Version Compatibility Reference examples tested with: BioPython 1.83+, bedtools 2.31+, matplotlib 3.8+, pandas 2.2+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - CLI: `<tool> --version` then `<tool> --help` to confirm flags If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Motif Analysis **"Find enriched motifs in my ChIP-seq peaks"** → Discover de novo DNA-binding motifs and test for known TF motif enrichment in peak sequences. - CLI: `findMotifsGenome.pl peaks.bed hg38 output/` (HOMER), `meme-chip -db JASPAR peaks.fa` (MEME) Identify DNA sequence motifs enriched in ChIP-seq or ATAC-seq peaks to discover transcription factor binding sites. ## Tool Comparison | Tool | Strengths | Use Case | |------|-----------|----------| | HOMER | Fast, comprehensive, built-in databases | General motif analysis | | MEME-ChIP | Multiple algorithms, web interface | Publication-quality | | MEME | De novo discovery only | Simple discovery | | FIMO | Known motif scanning | Ge

What's inside
Steps it walks through
  1. Version Compatibility
  2. Tool Comparison
  3. HOMER
  4. Installation
  5. De Novo Motif Discovery
  6. Key Options
  7. Output Files
  8. Known Motif Enrichment Only
  9. Scan for Specific Motifs
  10. Motif Comparison
  11. Create Custom Motif
  12. MEME Suite
  13. Extract Sequences from Peaks
  14. MEME (De Novo Discovery)
Ships with 2 files
  • examples/motif_analysis.sh
  • usage-guide.md
Commands it runs
conda install -c bioconda homer
Configure genome (required once)
perl /path/to/homer/configureHomer.pl -install hg38
perl /path/to/homer/configureHomer.pl -install mm10
Basic motif finding
findMotifsGenome.pl peaks.bed hg38 output_dir/ -size 200
With background regions
findMotifsGenome.pl peaks.bed hg38 output_dir/ -size 200 -bg background.bed
Specify motif lengths to search
findMotifsGenome.pl peaks.bed hg38 output_dir/ -size 200 -len 8,10,12
More from OpenClaw-Medical-Skills
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About this skill
What does the bio-chipseq-motif-analysis skill do?

De novo motif discovery and known motif enrichment analysis using HOMER and MEME-ChIP. Identify transcription factor binding motifs in ChIP-seq, ATAC-seq, or other genomic peak data. Use when finding enriched DNA motifs in peak sequences.

How do I install it?

Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-chipseq-motif-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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