Agent skill · Data & Analytics

bio-atac-seq-footprinting

Detect transcription factor binding sites through footprinting analysis in ATAC-seq data using TOBIAS. Use when identifying TF occupancy patterns within accessible regions, as TF binding protects DNA from Tn5 cutting.

FreedomIntelligencegithub.com/FreedomIntelligenceGitHub ↗
claude-codeships scripts
Install
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-atac-seq-footprinting --agent claude-code

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

Facts
Files in the skill folder: 3
SKILL.md size: 7 KB
Bundled scripts: yes
Path: skills/bio-atac-seq-footprinting/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: bedtools 2.31+, matplotlib 3.8+, numpy 1.26+, pandas 2.2+, pyBigWig 0.3+, samtools 1.19+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters - CLI: `<tool> --version` then `<tool> --help` to confirm flags If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # TF Footprinting **"Identify TF binding footprints in my ATAC-seq data"** → Detect protected DNA regions within accessible chromatin where bound transcription factors block Tn5 insertion. - CLI: `TOBIAS ATACorrect` → `TOBIAS FootprintScores` → `TOBIAS BINDetect` ## TOBIAS Workflow **Goal:** Identify transcription factor binding footprints within accessible chromatin regions. **Approach:** Correct Tn5 insertion bias, compute per-base footprint scores, then detect bound/unbound TF motif sites using the three-step TOBIAS pipeline. ```bash # 1. Correct Tn5 bias tobias ATACorrect \ --bam sampl

What's inside
Steps it walks through
  1. Version Compatibility
  2. TOBIAS Workflow
  3. TOBIAS Differential Footprinting
  4. Download JASPAR Motifs
  5. Prepare Input Files
  6. HINT-ATAC Alternative
  7. PIQ Footprinting
  8. Aggregate Footprint Plots
  9. Python: Custom Footprint Analysis
  10. Scan for Motifs
  11. Interpret Footprint Depth
  12. Quality Considerations
  13. Differential TF Activity
  14. TOBIAS Output Files
Ships with 2 files
  • examples/run_tobias.sh
  • usage-guide.md
Commands it runs
tobias ATACorrect \
tobias FootprintScores \
tobias BINDetect \
Compare conditions
Output includes:
Download JASPAR motifs
wget https://jaspar.genereg.net/download/data/2022/CORE/JASPAR2022_CORE_vertebrates_non-redundant_pfms_jaspar.txt
mv JASPAR2022_CORE_vertebrates_non-redundant_pfms_jaspar.txt JASPAR_motifs.pfm
Ensure BAM is sorted and indexed
samtools sort -@ 8 sample.bam -o sample.sorted.bam
More from OpenClaw-Medical-Skills
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About this skill
What does the bio-atac-seq-footprinting skill do?

Detect transcription factor binding sites through footprinting analysis in ATAC-seq data using TOBIAS. Use when identifying TF occupancy patterns within accessible regions, as TF binding protects DNA from Tn5 cutting.

How do I install it?

Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-atac-seq-footprinting --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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