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

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)"
    
    if [ "$mode" == "broad" ]; then
        BROAD_FLAG="--broad --broad-cutoff 0.1"
    else
        BROAD_FLAG=""
    fi
    
    macs3 callpeak \
        -t "${sample}.bam" \
        -c "$INPUT" \
        -f BAM \
        -g "$GENOME" \
        -n "$sample" \
        --outdir "$OUTDIR" \
        $BROAD_FLAG \
        -q 0.05 \
        --keep-dup auto
    
    echo "$sample: $(wc -l < $OUTDIR/${sample}_peaks.*Peak) peaks"
done

Recipe 2: Reproducible Peaks with IDR (Irreproducible Discovery Rate)

# Call peaks on individual replicates (lenient thresholds for IDR)
for rep in rep1 rep2; do
    macs3 callpeak \
        -t "chip_${rep}.bam" \
        -c input.bam \
        -f BAM \
        -g hs \
        -n "tf_${rep}" \
        --outdir peaks/ \
        -p 0.1 \
        --keep-dup auto
done

# Run IDR to find reproducible peaks
# pip install idr
idr --samples peaks/tf_rep1_peaks.narrowPeak peaks/tf_rep2_peaks.narrowPeak \
    --input-file-type narrowPeak \
    --output-file peaks/tf_idr_peaks.txt \
    --idr-threshold 0.05 \
    --plot

echo "IDR peaks: $(wc -l < peaks/tf_idr_peaks.txt)"

Expected Outputs

Troubleshooting

References

• MACS3 GitHub: macs3-project/MACS — source code, documentation, and changelog
• Zhang Y et al. (2008) "Model-based Analysis of ChIP-Seq (MACS)" — Genome Biology 9:R137. DOI:10.1186/gb-2008-9-9-r137
• ENCODE ATAC-seq pipeline — ENCODE standardized ATAC-seq workflow using MACS3
• IDR framework — irreproducible discovery rate for reproducible peak calls