MACS: Model-based Analysis for ChIP-Seq

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With the improvement of sequencing techniques, chromatin immunoprecipitation followed by high throughput sequencing (ChIP-Seq) is getting popular to study genome-wide protein-DNA interactions. To address the lack of powerful ChIP-Seq analysis method, we presented the Model-based Analysis of ChIP-Seq (MACS), for identifying transcript factor binding sites. MACS captures the influence of genome complexity to evaluate the significance of enriched ChIP regions and MACS improves the spatial resolution of binding sites through combining the information of both sequencing tag position and orientation. MACS can be easily used for ChIP-Seq data alone, or with a control sample with the increase of specificity. Moreover, as a general peak-caller, MACS can also be applied to any “DNA enrichment assays” if the question to be asked is simply: where we can find significant reads coverage than the random background.

Changes for MACS (3.0.2)

Features added

  1. Introduce a new emission model for the hmmratac function. Now users can choose the simpler Poisson emission --hmm-type poisson instead of the default Gaussian emission. As a consequence, the saved HMM model file in json will include the hmm-type information as well. Note that in order to be compatible with the HMM model file from previous version, if there is no hmm-type information in the model file, the hmm-type will be assigned as gaussian. #635

  2. Add --cutoff-analysis-steps and --cutoff-analysis-max for callpeak, bdgpeakcall, and hmmratac so that we can have finer resolution of the cutoff analysis report. #636 #642

  3. Reduce memory usage of hmmratac during decoding step, by writing decoding results to a temporary file on disk (file location depends on the environmental TEMP setting), then loading it back while identifying state pathes. This change will decrease the memory usage dramatically. #628 #640

Bugs fixed

  1. Use -O3 instead of -Ofast for compatibility. #637


  1. Update instruction to install macs3 through conda/bioconda

  2. Reorganize MACS3 docs and publish through

  3. Description on various file formats used in MACS3.


The common way to install MACS is through PYPI) or conda. Please check the INSTALL document for detail.

MACS3 has been tested using GitHub Actions for every push and PR in the following architectures:

  • x86_64 (Ubuntu 22, Python 3.9, 3.10, 3.11)

  • aarch64 (Ubuntu 22, Python 3.10)

  • armv7 (Ubuntu 22, Python 3.10)

  • ppc64le (Ubuntu 22, Python 3.10)

  • s390x (Ubuntu 22, Python 3.10)

  • Apple chips (Mac OS 13, Python 3.11)

In general, you can install through PyPI as pip install macs3. To use virtual environment is highly recommended. Or you can install after unzipping the released package downloaded from Github, then use pip install . command. Please note that, we haven’t tested installation on any Windows OS, so currently only Linux and Mac OS systems are supported. Also, for aarch64, armv7, ppc64le and s390x, due to some unknown reason potentially related to the scientific calculation libraries MACS3 depends on, such as Numpy, Scipy, hmm-learn, scikit-learn, the results from hmmratac subcommand may not be consistent with the results from x86 or Apple chips. Please be aware.


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

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.




Main MACS3 Function to call peaks from alignment results.


Call peaks from bedGraph file.


Call nested broad peaks from bedGraph file.


Comparing two signal tracks in bedGraph format.


Operate the score column of bedGraph file.


Combine bedGraph files of scores from replicates.


Differential peak detection based on paired four bedGraph files.


Remove duplicate reads, then save in BED/BEDPE format file.


Predict d or fragment size from alignment results. In case of PE data, report the average insertion/fragment size from all pairs.


Pileup aligned reads (single-end) or fragments (paired-end)


Randomly choose a number/percentage of total reads, then save in BED/BEDPE format file.


Take raw reads alignment, refine peak summits.


Call variants in given peak regions from the alignment BAM files.


Dedicated peak calling based on Hidden Markov Model for ATAC-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.


Please read our CODE OF CONDUCT and How to contribute documents. If you have any questions, suggestion/ideas, or just want to have conversions with developers and other users in the community, we recommend using the MACS Discussions instead of posting to our Issues page.


MACS3 project is sponsored by CZI EOSS. And we particularly want to thank the user community for their supports, feedbacks and contributions over the years.


2008: Model-based Analysis of ChIP-Seq (MACS)