macs3-peak-calling

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) for nucleosome-resolution ATAC-seq peak calling
• Use SPP or HOMER as alternatives; MACS3 is the ENCODE-recommended standard

Prerequisites

• Python packages: macs3 (Python ≥ 3.8)
• Input: Sorted BAM files (with index) from ChIP-seq or ATAC-seq alignment (e.g., using STAR or Bowtie2)
• Optional: Input/IgG control BAM for background normalization

Check before installing: The tool may already be available in the current environment (e.g., inside a pixi / conda env). Run command -v macs3 first and skip the install commands below if it returns a path. When running inside a pixi project, invoke the tool via pixi run macs3 rather than bare macs3.

# Install with pip or conda
pip install macs3
# or
conda install -c bioconda macs3

# Verify
macs3 --version
# macs3 3.0.2

Quick Start

# Call peaks for TF ChIP-seq (narrow peaks, with input control)
macs3 callpeak \
    -t chip.bam \
    -c input.bam \
    -f BAM \
    -g hs \
    -n sample_tf \
    --outdir peaks/ \
    -q 0.05

# Output: peaks/sample_tf_peaks.narrowPeak
wc -l peaks/sample_tf_peaks.narrowPeak

Workflow

Step 1: Prepare Input BAM Files

MACS3 requires sorted, indexed BAM files from genome alignment.

# Sort and index ChIP and control BAMs (if not already done)
samtools sort -@ 8 chip_raw.bam -o chip.bam
samtools sort -@ 8 input_raw.bam -o input.bam
samtools index chip.bam
samtools index input.bam

# Check read counts
echo "ChIP reads: $(samtools view -c -F 4 chip.bam)"
echo "Input reads: $(samtools view -c -F 4 input.bam)"

Step 2: Call Narrow Peaks (TF ChIP-seq)

Use the default mode for transcription factor binding site identification.

# TF ChIP-seq with input control
macs3 callpeak \
    -t chip.bam \
    -c input.bam \
    -f BAM \
    -g hs \
    -n tf_chip \
    --outdir peaks/ \
    -q 0.05 \
    --keep-dup auto

echo "Peaks called: $(wc -l < peaks/tf_chip_peaks.narrowPeak)"
echo "Summit file: peaks/tf_chip_summits.bed"

# Without input control (less recommended)
macs3 callpeak \
    -t chip.bam \
    -f BAM \
    -g hs \
    -n tf_noinput \
    --outdir peaks/ \
    --nolambda

Step 3: Call Broad Peaks (Histone Marks)

Use --broad for spread histone modifications like H3K27me3 or H3K36me3.

# H3K27me3 broad histone mark
macs3 callpeak \
    -t h3k27me3.bam \
    -c input.bam \
    -f BAM \
    -g hs \
    -n h3k27me3 \
    --outdir peaks/ \
    --broad \
    --broad-cutoff 0.1 \
    -q 0.05

echo "Broad peaks: $(wc -l < peaks/h3k27me3_peaks.broadPeak)"

# H3K4me3 (sharp mark — use narrow peaks)
macs3 callpeak \
    -t h3k4me3.bam \
    -c input.bam \
    -f BAM \
    -g hs \
    -n h3k4me3 \
    --outdir peaks/ \
    -q 0.05

Step 4: Call ATAC-seq Peaks

ATAC-seq requires special handling for the Tn5 insertion site.

# ATAC-seq with paired-end BAM (recommended)
macs3 callpeak \
    -t atac.bam \
    -f BAMPE \
    -g hs \
    -n atac_sample \
    --outdir peaks/ \
    --nomodel \
    --nolambda \
    -q 0.05 \
    --keep-dup all

echo "ATAC peaks: $(wc -l < peaks/atac_sample_peaks.narrowPeak)"

# Single-end ATAC-seq: shift reads to center on Tn5 cut site
macs3 callpeak \
    -t atac_se.bam \
    -f BAM \
    -g hs \
    -n atac_se \
    --outdir peaks/ \
    --nomodel \
    --shift -100 \
    --extsize 200 \
    --keep-dup all

Step 5: Generate Signal Tracks for Visualization

Produce bedGraph and bigWig files for genome browser visualization.

# Generate bedGraph normalized to million reads (SPMR)
macs3 callpeak \
    -t chip.bam \
    -c input.bam \
    -f BAM \
    -g hs \
    -n chip_track \
    --outdir tracks/ \
    -B \
    --SPMR \
    --keep-dup auto

# Convert bedGraph to bigWig for IGV/UCSC
# Requires bedGraphToBigWig and chrom.sizes
sort -k1,1 -k2,2n tracks/chip_track_treat_pileup.bdg > tracks/chip_sorted.bdg
bedGraphToBigWig tracks/chip_sorted.bdg genome/hg38.chrom.sizes tracks/chip.bw

echo "BigWig track: tracks/chip.bw"

Step 6: Annotate and Analyze Peaks

Parse narrowPeak output and annotate peaks to genomic features.

import pandas as pd

# Load narrowPeak file
# Columns: chrom, start, end, name, score, strand, signalValue, pValue, qValue, peak
cols = ["chrom", "start", "end", "name", "score", "strand",
        "signalValue", "pValue", "qValue", "peak"]
peaks = pd.read_csv("peaks/tf_chip_peaks.narrowPeak", sep="\t",
                    header=None, names=cols)

print(f"Total peaks: {len(peaks)}")
print(f"Peaks on chr1: {(peaks['chrom'] == 'chr1').sum()}")
print(f"Median peak width: {(peaks['end'] - peaks['start']).median():.0f} bp")
print(f"Peaks with q-value < 0.01: {(peaks['qValue'] > 2).sum()}")  # -log10(q) > 2

# Filter high-confidence peaks
high_conf = peaks[peaks["qValue"] > 2].copy()  # q < 0.01
high_conf["width"] = high_conf["end"] - high_conf["start"]
print(f"\nHigh-confidence peaks: {len(high_conf)}")
high_conf.to_csv("high_confidence_peaks.bed", sep="\t", index=False, header=False,
                 columns=["chrom", "start", "end", "name", "score", "strand"])

Key Parameters

| Parameter | Default | Range/Options | Effect | |-----------|---------|---------------|--------| | -t / --treatment | required | BAM/BED/SAM | ChIP or ATAC treatment file | | -c / --control | — | BAM/BED/SAM | Input/IgG control; omit --nolambda if absent | | -g / --gsize | required | hs, mm, ce, dm, or integer | Effective genome size; hs=2.7e9 (human), mm=1.87e9 (mouse) | | -q / --qvalue | 0.05 | 0–1 | FDR threshold for peak calling | | -p / --pvalue | — | 0–1 | P-value cutoff (use instead of q-value for strict control) | | --broad | off | flag | Call broad peaks for diffuse histone marks | | --broad-cutoff | 0.1 | 0–1 | Q-value cutoff for broad region merging | | --nomodel | off | flag | Skip fragment length modeling; required for ATAC-seq | | --extsize | 200 | 50–1000 | Fragment extension size when --nomodel is set | | --shift | 0 | -500–500 | Read shift in bp; use -100 with --extsize 200 for ATAC-seq | | --keep-dup | 1 | auto, all, integer | Duplicate handling; auto uses Poisson model, all keeps all (ATAC-seq) | | -B / --bdg | off | flag | Write bedGraph signal tracks | | --SPMR | off | flag | Normalize bedGraph to signal per million reads |

Common Recipes

Recipe 1: Batch Peak Calling for Multiple Samples

#!/bin/bash
# Call peaks for multiple ChIP-seq samples with the same input
INPUT="input.bam"
GENOME="hs"
OUTDIR="peaks"
mkdir -p "$OUTDIR"

SAMPLES=(H3K4me3 H3K27ac H3K27me3 CTCF)
MODES=(narrow narrow broad narrow)

for i in "${!SAMPLES[@]}"; do
    sample="${SAMPLES[$i]}"
    mode="${MODES[$i]}"
    echo "Calling peaks: $sample ($mode)"