MACS: Model-based Analysis for ChIP-Seq

Latest Release:

• Github:

• PyPI:

• Bioconda:

• Debian Med:

Introduction

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. hmmratac now output narrowPeak format output. The summit position and the peak score columns reported in the narrowPeak output represents the position with highest foldchange value (pileup vs average background).

3. 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

4. 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

5. Fix instructions for preparing narrowPeak files for uploading to UCSC browser, with the --trackline option in callpeak. #653

6. For gappedPeak output, set thickStart and thickEnd columns as 0, according to UCSC definition.

Bugs fixed

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

Documentation

1. Update instruction to install macs3 through conda/bioconda

2. Reorganize MACS3 docs and publish through https://macs3-project.github.io/MACS

3. Description on various file formats used in MACS3.

Install

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.

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 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 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 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.

Ackowledgement

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

Citation

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

Other useful links

• Cistrome

• bedTools

• UCSC toolkits

• deepTools