Model-based Analysis for ChIP-Seq — MACS3 3.0.1 documentation (2024)

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Introduction

With the improvement of sequencing techniques, chromatinimmunoprecipitation followed by high throughput sequencing (ChIP-Seq)is getting popular to study genome-wide protein-DNA interactions. Toaddress the lack of powerful ChIP-Seq analysis method, we presentedthe Model-based Analysis of ChIP-Seq (MACS), foridentifying transcript factor binding sites. MACS captures theinfluence of genome complexity to evaluate the significance ofenriched ChIP regions and MACS improves the spatial resolution ofbinding sites through combining the information of both sequencing tagposition and orientation. MACS can be easily used for ChIP-Seq dataalone, or with a control sample with the increase ofspecificity. Moreover, as a general peak-caller, MACS can also beapplied to any “DNA enrichment assays” if the question to be asked issimply: where we can find significant reads coverage than the randombackground.

Changes for MACS (3.0.2)

Features added

Introduce a new emission model for the hmmratac function. Nowusers can choose the simpler Poisson emission --hmm-type poissoninstead of the default Gaussian emission. As a consequence, thesaved HMM model file in json will include the hmm-type informationas well. Note that in order to be compatible with the HMM modelfile from previous version, if there is no hmm-type information inthe model file, the hmm-type will be assigned as gaussian. #635
See Also
Identifying ChIP-seq enrichment using MACS
Add --cutoff-analysis-steps and --cutoff-analysis-max forcallpeak, bdgpeakcall, and hmmratac so that we canhave finer resolution of the cutoff analysis report. #636 #642
Reduce memory usage of hmmratac during decoding step, bywriting decoding results to a temporary file on disk (filelocation depends on the environmental TEMP setting), then loadingit back while identifying state pathes. This change will decreasethe memory usage dramatically. #628 #640

Bugs fixed

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

Documentation

Update instruction to install macs3 through conda/bioconda
Reorganize MACS3 docs and publish throughhttps://macs3-project.github.io/MACS
Description on various file formats used in MACS3.

Install

The common way to install MACS is throughPYPI) orconda. Please check theINSTALL document for detail.

MACS3 has been tested using GitHub Actions for every push and PR inthe 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. Touse virtual environment is highly recommended. Or you can installafter unzipping the released package downloaded from Github, then usepip install . command. Please note that, we haven’t testedinstallation on any Windows OS, so currently only Linux and Mac OSsystems are supported. Also, for aarch64, armv7, ppc64le and s390x,due to some unknown reason potentially related to the scientificcalculation libraries MACS3 depends on, such as Numpy, Scipy,hmm-learn, scikit-learn, the results from hmmratac subcommand maynot be consistent with the results from x86 or Apple chips. Please beaware.

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

There are currently 14 functions available in MACS3 serving assub-commands. Please click on the link to see the detail descriptionof 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 data.

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

Contribute

Please read our CODE OF CONDUCT and How tocontribute documents. If you have any questions,suggestion/ideas, or just want to have conversions with developers andother users in the community, we recommend using the MACSDiscussionsinstead of posting to ourIssues page.

Ackowledgement

MACS3 project is sponsored byCZI EOSS. And we particularly wantto thank the user community for their supports, feedbacks andcontributions over the years.

Citation

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