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

Pre-flight Interview

Settle these with the user before writing any analysis code.

decisions:
  - id: D1
    param: assayType
    kind: required
    source: user
    ask: "What produced these reads - a transcription-factor ChIP, a histone mark, or open-chromatin (ATAC)?"
    default: null

  - id: D2
    param: controlSample
    kind: required
    source: data
    ask: "Is there a matched input or IgG control for this sample?"
    default: "none - background is estimated locally instead"

  - id: D3
    param: effectiveGenomeSize
    kind: derived
    source: upstream
    ask: "Which genome were the reads aligned to?"
    default: "carried from the alignment reference"

  - id: D4
    param: peakShape
    kind: optional
    source: user
    depends_on: [D1]
    ask: "Are the enriched regions sharp summits or broad domains?"
    default: "narrow for TF and ATAC; broad for spreading histone marks"

  - id: D5
    param: significanceThreshold
    kind: required
    source: user
    ask: "How strong must enrichment be before a region is called a peak?"
    default: "q-value 0.05"

  - id: D6
    param: fragmentModel
    kind: optional
    source: user
    depends_on: [D1]
    ask: "Should fragment length be modelled from the data, or fixed because the assay's signal is not paired summits?"
    default: "model for ChIP; fixed shift/extension for ATAC"

  - id: D7
    param: duplicateHandling
    kind: optional
    source: user
    depends_on: [D1]
    ask: "Are identical read positions PCR duplicates, or real independent insertions?"
    default: "one per position for ChIP; keep all for ATAC"

  - id: D8
    param: signalTracks
    kind: optional
    source: user
    ask: "Emit normalized coverage tracks alongside the peak calls?"
    default: "peaks only"

D1 is asked first because four later settings follow from it. ATAC in particular inverts three ChIP defaults at once - no fragment model, a negative shift, and duplicates kept - so running ATAC through ChIP defaults produces peaks that look plausible and are wrong.

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)"
    
    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

Output	Format	Description
*_peaks.narrowPeak	BED6+4	Narrow peaks with signal, p-value, q-value, summit offset
*_peaks.broadPeak	BED6+3	Broad peaks (when --broad): chrom, start, end, signal, p-val, q-val
*_summits.bed	BED3+2	Peak summit positions (1 bp) with score; use for motif analysis
*_treat_pileup.bdg	bedGraph	Treatment signal track (when -B)
*_control_lambda.bdg	bedGraph	Control/local lambda track (when -B)
*_model.r	R script	Fragment size model; run Rscript *_model.r to plot

Troubleshooting

Problem	Cause	Solution
Very few peaks called	Stringent q-value or low read depth	Relax to -p 1e-3; check sequencing depth (≥10M aligned reads recommended)
Too many peaks (>100k)	Threshold too loose or no input control	Add --control input.bam; use -q 0.01; filter on signalValue
Peak calling fails with "no reads"	BAM file is not sorted or indexed	Run samtools sort and samtools index before MACS3
ATAC-seq peaks in mitochondria	High mtDNA content	Filter: `samtools view -h chip.bam
Fragment model fails	Too few reads or unusual read length	Add --nomodel --extsize 200 to skip modeling
bedGraph output very large	High coverage data without normalization	Add --SPMR to normalize to signal per million reads
--broad misses narrow peaks	Signal is actually sharp	Check ChIP target: TFs and H3K4me3 need narrow mode
gsize mismatch	Using wrong genome size for assembly	Use hs for hg19/hg38, mm for mm9/mm10; or provide exact integer

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