MACS: Model-based Analysis for ChIP-Seq

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Introduction

With the advancement of sequencing technologies, Chromatin Immunoprecipitation followed by high-throughput sequencing (ChIP-Seq) has become a popular method for studying genome-wide protein-DNA interactions. With the purpose of addressing the need for a robust ChIP-Seq analysis tool, we introduce Model-based Analysis of ChIP-Seq (MACS), a powerful tool for identifying transcription factor binding sites. MACS accounts for the complexity of the genome to assess the significance of enriched ChIP regions and enhances the spatial resolution of binding sites by integrating both sequencing tag position and orientation. MACS can be readily applied to ChIP-Seq data alone, or in conjunction with a control sample, thus enhancing specificity. Furthermore, as a versatile peak-caller, MACS can be employed in any “DNA enrichment assay” to answer the fundamental question: Where are the regions with significant read coverage compared to random background?

Changes for MACS 3.1.0

Features and performance

Major callpeak speedup: Jacob Schreiber’s auto-optimize work in PR #756 measured 41–64× faster results across four default workflows (BAM, FRAG, BAM with control, and BAMPE). The speedup varies by workload, and large inputs can use more peak memory.
Added --threads to supported commands for a per-run CPU budget. Set MACS3_NUM_THREADS to choose a default, or pass an execution policy from the Python API. See CPU budgets and process backends.
Added --process-backend {auto,serial} to callpeak and callvar.

Fixes

Automatic duplicate filtering now uses the control sample’s own threshold for control reads.
Corrected lower-tail Poisson probabilities for very large expected counts.
FRAG input may end without a trailing newline. Truncated BAM alignment records are rejected as invalid input.
Improved reliability and cleanup when peak-calling workers fail or multiple operations run concurrently.

Documentation

Clarified the Poisson tail convention used for reported p-values.

Acknowledgements

Thanks to Jacob Schreiber (PR #756), Cindy Krafft (PR #753, PR #777, and PR #789), Michael R. Crusoe (PR #754), and Apoorva Garg (PR #793) for their contributions to this release.

Install

Install MACS3 from PyPI or, when the desired version is available, Bioconda. MACS3 3.1.0 requires Python 3.12 or later. See the installation guide for supported environments, build requirements, and source-install instructions.

GitHub Actions tests each code change on x86_64 Linux and macOS with Python 3.12, 3.13, and 3.14, and on aarch64 Ubuntu 24.04 with Python 3.12. The armv7, ppc64le, and s390x Ubuntu 24.04 tests use Python 3.12 and run through a separate manually triggered workflow.

We recommend a virtual environment. Install from PyPI with python -m pip install macs3, or install from an unpacked source distribution with python -m pip install .. Windows is not currently supported. On aarch64, armv7, ppc64le, and s390x, hmmratac results may differ from results on x86 or Apple Silicon; validate cross-architecture comparisons.

Usage

Example for regular peak calling on TF ChIP-seq:

macs3 callpeak -t ChIP.bam -c Control.bam -f BAM -g hs -n test -B -q 0.01

Example for broad peak calling on Histone Mark ChIP-seq:

macs3 callpeak -t ChIP.bam -c Control.bam --broad -g hs --broad-cutoff 0.1

Example for peak calling on ATAC-seq (paired-end mode):

macs3 callpeak -f BAMPE -t ATAC.bam -g hs -n test -B -q 0.01

Example for peak calling on ATAC-seq with HMMATAC:

macs3 hmmratac -i ATAC.bam -f BAMPE -n test

There are currently 14 functions available in MACS3 serving as sub-commands. Please click on the link to see the detail description of the subcommands.

Subcommand	Description
`callpeak`	Main MACS3 Function to call peaks from alignment results.
`bdgpeakcall`	Call peaks from bedGraph file.
`bdgbroadcall`	Call nested broad peaks from bedGraph file.
`bdgcmp`	Comparing two signal tracks in bedGraph format.
`bdgopt`	Operate the score column of bedGraph file.
`cmbreps`	Combine bedGraph files of scores from replicates.
`bdgdiff`	Differential peak detection based on paired four bedGraph files.
`filterdup`	Remove duplicate reads, then save in BED/BEDPE format file.
`predictd`	Predict d or fragment size from alignment results. In case of PE data, report the average insertion/fragment size from all pairs.
`pileup`	Pileup aligned reads (single-end) or fragments (paired-end)
`randsample`	Randomly choose a number/percentage of total reads, then save in BED/BEDPE format file.
`refinepeak`	Take raw reads alignment, refine peak summits.
`callvar`	Call variants in given peak regions from the alignment BAM files.
`hmmratac`	Dedicated peak calling based on Hidden Markov Model for ATAC-seq or scATAC-seq data.

For advanced usage, for example, to run macs3 in a modular way, please read the advanced usage. There is a Q&A document where we collected some common questions from users.

Contribute

Please read our Code of Conduct and Contributing guide. For questions, suggestions, or ideas, we recommend using the MACS Discussions instead of posting to our Issues page.

Ackowledgement

MACS3 project is sponsored by through EOSS2 (2020-2022) and EOSS4 (2021-2025). And we particularly want to thank the user community for their supports, feedbacks and contributions over the years.

Citation

For MACS version 2 and 3, please cite our 2026 paper MACS3: A Peak-calling Platform for Bulk and Single-cell Regulatory Genomics

If you are using MACS version 1, please cite our 2008 paper Model-based Analysis of ChIP-Seq (MACS)