ATAC Seq
SkillAI & modelsATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the ATAC Seq skill
What this skill tells your AI
The instructions your AI receives, as published by biotender-max/awesome-bio-agent-skills in skills/bioclaw/atac-seq/SKILL.md and read by ahel’s review.
Version Compatibility
Reference examples assume:
macs33.0+samtools1.18+deepTools3.5+
Verify the runtime first:
- CLI:
macs3 --version,samtools --version,bamCoverage --version
Overview
Use this skill when the user needs:
- bulk ATAC-seq QC
- peak calling
- accessibility counting
- differential accessibility
- motif deviation or footprint follow-up
When To Use This Skill
- the task is bulk ATAC-seq rather than ChIP-seq
- TSS enrichment, fragment periodicity, or FRiP need review
- the output should include peaks, counts, and downstream accessibility summaries
Quick Route
- paired-end bulk ATAC: use
BAMPE - call peaks without control using ATAC-specific settings
- if TSS enrichment is poor, stop and flag data quality before interpretation
Progressive Disclosure
- Read technical_reference.md for QC gates and assay-specific caveats.
- Read commands_and_thresholds.md for peak-calling commands, thresholds, and output conventions.
Prerequisites
| Check | Guidance |
|---|---|
| uniquely mapped reads | >= 20M preferred for strong bulk ATAC |
| TSS enrichment | > 7 acceptable, > 10 strong |
| FRiP | > 0.2 often strong for good bulk ATAC |
Expected Inputs
- paired-end ATAC BAM or FASTQ
- reference genome
- sample groups for comparisons
Expected Outputs
results/peaks/sample_peaks.narrowPeakresults/matrix/consensus_peak_counts.tsvresults/diff_accessibility.tsvfigures/tss_enrichment.pdffigures/fragment_size_distribution.pdf
Starter Pattern
macs3 callpeak \
-t atac.bam \
-f BAMPE \
-g hs \
-n sample \
--nomodel \
--shift -100 \
--extsize 200 \
-q 0.01 \
--outdir results/peaks
Key Parameters
| Parameter | Typical value | Notes |
|---|---|---|
-f | BAMPE | paired-end ATAC should use fragment-aware mode |
--nomodel | on | standard for ATAC |
--shift | -100 | common Tn5 offset convention |
--extsize | 200 | common first-pass extension |
-q | 0.01 | starting FDR threshold |
Workflow
1. Validate assay QC
Review:
- TSS enrichment
- fragment size periodicity
- duplication
- mapped read depth
2. Call peaks with ATAC-specific settings
Use fragment-aware paired-end mode and Tn5-aware shifting or equivalent settings.
3. Build a consensus peak matrix
Merge peaks across samples, count fragments into consensus intervals, then produce a peak-by-sample matrix.
4. Test differential accessibility
Use replicate-aware statistics and report both effect size and adjusted significance.
5. Run motif or footprint follow-up
Only after peak quality and read depth support it.
Output Artifacts
results/
├── peaks/
│ ├── sample_peaks.narrowPeak
│ └── sample_summits.bed
├── matrix/
│ └── consensus_peak_counts.tsv
└── diff_accessibility.tsv
qc/
├── tss_enrichment.tsv
└── fragment_metrics.tsv
figures/
├── tss_enrichment.pdf
└── fragment_size_distribution.pdf
Quality Review
- TSS enrichment below
7should trigger caution. - Strong nucleosome periodicity supports a good bulk ATAC library.
- FRiP below
0.1is usually weak and needs scrutiny. - Footprinting should not be trusted on low-depth or poor-quality libraries.
Anti-Patterns
- using generic ChIP peak-calling defaults for ATAC
- running footprinting on weak libraries
- skipping TSS enrichment review
- merging peaks from mixed reference builds
Related Skills
- ChIP Seq
- Gene Regulatory Networks
- Multiome And scATAC
Optional Supplements
deeptoolspysam
Signals
- GitHub stars
- 178
- Forks
- 32
- Last commit
- Jul 2026
Advanced
- Catalog kind
- skill
- Gateway key
atac-seq- Source
- github.com/biotender-max/awesome-bio-agent-skills