---
name: chip-seq-d36e66f98d92
description: ChIP-seq peak calling and downstream interpretation with MACS3, signal track export, annotation, motif analysis, and differential binding review.
tool_type: mixed
primary_tool: MACS3
---

# ChIP Seq

## Version Compatibility

Reference examples assume:

- `macs3` 3.0+
- `samtools` 1.18+
- `deepTools` 3.5+

Before using commands, verify the installed environment:

- CLI: `macs3 --version`, `samtools --version`, `bamCoverage --version`
- If flags differ, inspect `--help` and adapt rather than forcing the example unchanged.

## Overview

Use this skill for:

- narrow or broad peak calling
- input-normalized signal tracks
- peak annotation
- motif follow-up
- differential binding review when replicates exist

## When To Use This Skill

- the user has aligned ChIP and optional input BAM files
- the deliverable includes peaks, browser tracks, or motif results
- the assay is TF ChIP or histone-mark ChIP and needs standard peak-centric processing

## Quick Route

- TF or narrow marks: use narrow peak mode first.
- H3K27me3, H3K36me3, or other broad marks: use `--broad`.
- Paired-end BAM: prefer `-f BAMPE`.
- No input control: still possible, but report the limitation explicitly.

## Progressive Disclosure

- Read [technical_reference.md](technical_reference.md) for QC gates, narrow-versus-broad logic, and replicate handling.
- Read [commands_and_thresholds.md](commands_and_thresholds.md) for MACS3 commands, parameter defaults, and output file conventions.

## Prerequisites

| Requirement | Narrow TF-style | Broad histone-style |
|---|---:|---:|
| usable uniquely mapped reads | `>= 10M` | `>= 20M` |
| matched input recommended | yes | yes |
| biological replicates recommended | `>= 2` | `>= 2` |

## Expected Inputs

- `chip.bam`
- `input.bam` when available
- reference genome build
- chromosome sizes if bigWig export is needed

## Expected Outputs

- `results/peaks/sample_peaks.narrowPeak` or `.broadPeak`
- `results/peaks/sample_summits.bed`
- `results/tracks/sample_treat_pileup.bw`
- `results/annotation/peak_annotation.tsv`
- `qc/chip_qc_summary.tsv`

## Starter Pattern

```bash
macs3 callpeak \
  -t chip.bam \
  -c input.bam \
  -f BAMPE \
  -g hs \
  -n sample \
  -q 0.01 \
  --outdir results/peaks
```

## Key Parameters

| Parameter | Typical value | Meaning |
|---|---|---|
| `-f` | `BAM` or `BAMPE` | paired-end should use `BAMPE` |
| `-g` | `hs`, `mm`, or numeric | effective genome size |
| `-q` | `0.01` or `0.05` | FDR cutoff for narrow peaks |
| `--broad` | broad marks only | broad peak mode |
| `--broad-cutoff` | `0.1` | broad-peak FDR cutoff |
| `-B --SPMR` | enabled for tracks | bedGraph for normalized signal |

## Workflow

### 1. Validate BAMs and replicate structure

Check:

- mapped read counts
- duplicate burden
- whether input control exists
- whether the mark is narrow or broad

### 2. Call peaks with MACS3

- narrow marks: `-q 0.01` is a good starting point
- broad marks: use `--broad --broad-cutoff 0.1`
- paired-end: `-f BAMPE`

### 3. Export signal tracks

Use `-B --SPMR`, sort the resulting bedGraph, then convert to bigWig for browser use.

### 4. Annotate and inspect peaks

Map peaks to promoters, gene bodies, or distal intervals and review top loci in a genome browser or track plot.

### 5. Run motif or differential follow-up

Only after peak quality looks credible and replicate structure supports the downstream question.

## Output Artifacts

```text
results/
├── peaks/
│   ├── sample_peaks.narrowPeak
│   ├── sample_summits.bed
│   └── sample_model.r
├── tracks/
│   ├── sample_treat_pileup.bdg
│   └── sample_treat_pileup.bw
└── annotation/
    └── peak_annotation.tsv
qc/
└── chip_qc_summary.tsv
```

## Quality Review

- TF ChIP-seq FRiP:
  - `< 0.01` poor
  - `0.01-0.05` usable but weak
  - `> 0.05` generally solid
- Histone broad-mark FRiP often differs; compare within assay type rather than against TF expectations.
- Use replicate concordance when available. Do not trust a single noisy replicate just because peaks were called.
- Check that top peaks occur in plausible loci and not only blacklisted or artifactual regions.

## Anti-Patterns

- treating broad and narrow marks with the same peak-calling setup
- calling peaks on unsorted or low-quality BAMs
- presenting motif hits without showing peak quality
- hiding that no input control was available

## Related Skills

- ATAC Seq
- Methylation Analysis
- Gene Regulatory Networks

## 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 Mega AI Hub.

## 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.



# Mega AI Hub 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, Healthcare & Life Sciences
Roles: Clinical Research, Data Scientist, Researcher
Specialties: Specialty not specified
Tools: Macs3, Deeptools, Samtools
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: d3723d28230a09a46fe6f06bb0eca805053026a20dd77404f041cf01335a6bce