ATAC Seq

SkillAI & models

ATAC-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.

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:

  • macs3 3.0+
  • samtools 1.18+
  • deepTools 3.5+

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

Prerequisites

CheckGuidance
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.narrowPeak
  • results/matrix/consensus_peak_counts.tsv
  • results/diff_accessibility.tsv
  • figures/tss_enrichment.pdf
  • figures/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

ParameterTypical valueNotes
-fBAMPEpaired-end ATAC should use fragment-aware mode
--nomodelonstandard for ATAC
--shift-100common Tn5 offset convention
--extsize200common first-pass extension
-q0.01starting 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 7 should trigger caution.
  • Strong nucleosome periodicity supports a good bulk ATAC library.
  • FRiP below 0.1 is 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

  • deeptools
  • pysam

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