Agent skill · AI & Agents

macs3-peak-calling

Poisson-model peak caller for ChIP-seq/ATAC-seq BAMs. MACS3 callpeak finds enriched regions (TF sites or histone marks) vs input/IgG; outputs BED narrowPeak/broadPeak for motif analysis, annotation, and differential binding. Use narrow peaks for TF ChIP-seq and ATAC-seq; broad for H3K27me3, H3K9me3, and other broad marks.

BioTender-maxgithub.com/BioTender-maxGitHub ↗
claude-codeNOASSERTION
Install
npx skills add BioTender-max/awesome-bio-agent-skills --skill macs3-peak-calling --agent claude-code

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

Facts
Files in the skill folder: 1
SKILL.md size: 12 KB
Bundled scripts: none
Path: skills/sciagent/macs3-peak-calling/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 135
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# MACS3 — ChIP-seq and ATAC-seq Peak Caller ## Overview MACS3 (Model-based Analysis of ChIP-seq) identifies regions of significant read enrichment (peaks) from ChIP-seq, ATAC-seq, CUT&RUN, and CUT&TAG experiments. It models the fragment length distribution from paired-end data or estimates it from mono-nucleosomal read shifting in single-end data, then applies a Poisson model to identify fold-enrichment over an input/IgG control. MACS3 produces BED-format narrowPeak (for transcription factors) or broadPeak (for histone marks) files with signal and q-value tracks for visualization in IGV or UCSC Genome Browser. ## When to Use - Calling transcription factor binding peaks from ChIP-seq experiments (use `--nomodel --extsize 200` or let MACS3 estimate fragment length) - Identifying open chromatin regions from ATAC-seq experiments (use `--nomodel --shift -100 --extsize 200 -f BAMPE`) - Calling broad histone modification peaks (H3K27me3, H3K9me3, H3K36me3) with `--broad` - Generating peak signal tracks (bedGraph/bigWig) for genome browser visualization with `-B --SPMR` - Performing differential binding analysis: MACS3 peaks as input to DiffBind or DESeq2 - Use **HMMRATAC** (part of MACS3)

What's inside
Steps it walks through
  1. Overview
  2. When to Use
  3. Prerequisites
  4. Quick Start
  5. Workflow
  6. Step 1: Prepare Input BAM Files
  7. Step 2: Call Narrow Peaks (TF ChIP-seq)
  8. Step 3: Call Broad Peaks (Histone Marks)
  9. Step 4: Call ATAC-seq Peaks
  10. Step 5: Generate Signal Tracks for Visualization
  11. Step 6: Annotate and Analyze Peaks
  12. Key Parameters
  13. Common Recipes
  14. Recipe 1: Batch Peak Calling for Multiple Samples
Commands it runs
Install with pip or conda
pip install macs3
or
conda install -c bioconda macs3
Verify
macs3 --version
macs3 3.0.2
Call peaks for TF ChIP-seq (narrow peaks, with input control)
macs3 callpeak \
wc -l peaks/sample_tf_peaks.narrowPeak
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About this skill
What does the macs3-peak-calling skill do?

Poisson-model peak caller for ChIP-seq/ATAC-seq BAMs. MACS3 callpeak finds enriched regions (TF sites or histone marks) vs input/IgG; outputs BED narrowPeak/broadPeak for motif analysis, annotation, and differential binding. Use narrow peaks for TF ChIP-seq and ATAC-seq; broad for H3K27me3, H3K9me3, and other broad marks.

How do I install it?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill macs3-peak-calling --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 BioTender-max/awesome-bio-agent-skills, a repository with 135 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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