---
name: atac-seq-4b62a90dc467
description: ATAC-seq processing with assay QC, MACS3 peak calling, consensus peak matrices, differential accessibility, and motif or footprint follow-up.
tool_type: mixed
primary_tool: MACS3
---

# ATAC Seq

## 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

- Read [technical_reference.md](technical_reference.md) for QC gates and assay-specific caveats.
- Read [commands_and_thresholds.md](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.narrowPeak`
- `results/matrix/consensus_peak_counts.tsv`
- `results/diff_accessibility.tsv`
- `figures/tss_enrichment.pdf`
- `figures/fragment_size_distribution.pdf`

## Starter Pattern

```bash
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

```text
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`


---
Source attribution: Runchuan-BU
Original project: https://github.com/Runchuan-BU/BioClaw
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Dave AI.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Dave AI resource metadata

Creator / publisher: Runchuan-BU
Original project: https://github.com/Runchuan-BU/BioClaw
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Education & Research
Roles: Business Intelligence Analyst, Data Scientist, Researcher
Specialties: Specialty not specified
Tools: Deeptools, Macs3, Samtools
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 27fd2f86098a3583ffc0898c94e21f62eccce40e36aa54162a3166e1b790db78