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MACS

MACS -- Model-based Analysis of ChIP-Seq

MACS: Model-based Analysis for ChIP-Seq

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Latest Release:

Github:
PyPI:
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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.

Please note that current MACS3 is still in alpha stage. However, we utilize Github Action to implement the CI (Continous Integration) to make sure that the main branch passes unit testing on certain functions and subcommands to reproduce the correct outputs. We will add more new features in the future.

Recent Changes for MACS (3.0.0b1)

3.0.0b1

    The first beta version of MACS3, with HMMRATAC feature recently added.
   
* New features from alpha7:

1) HMMRATAC module is added
HMMRATAC is a dedicated software to analyze ATAC-seq data. The
basic idea behind HMMRATAC is to digest ATAC-seq data according to
the fragment length of read pairs into four signal tracks: short
fragments, mononucleosomal fragments, di-nucleosomal fragments and
tri-nucleosomal fragments. Then integrate the four tracks again
using Hidden Markov Model to consider three hidden states: open
region, nucleosomal region, and background region. The orginal
paper was published in 2019 written in JAVA, by Evan Tarbell. We
implemented it in Python/Cython and optimize the whole process
using existing MACS functions and hmmlearn. Now it can run much
faster than the original JAVA version. Note: evaluation of the
peak calling results is underway.

2) Multiple updates regarding dependencies, anaconda built, CI/CD
process.

Install

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

MACS3 has been tested in CI for every push and PR in the following architectures:

x86_64
aarch64
armv7
ppc64le
s390x

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 twelve functions available in MAC3 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 output.
`bdgbroadcall`	Call broad peaks from bedGraph output.
`bdgcmp`	Comparing two signal tracks in bedGraph format.
`bdgopt`	Operate the score column of bedGraph file.
`cmbreps`	Combine BEDGraphs of scores from replicates.
`bdgdiff`	Differential peak detection based on paired four bedGraph files.
`filterdup`	Remove duplicate reads, then save in BED/BEDPE format.
`predictd`	Predict d or fragment size from alignment results.
`pileup`	Pileup aligned reads (single-end) or fragments (paired-end)
`randsample`	Randomly choose a number/percentage of total reads.
`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 recommand you use the MACS Discussions instead of posting to our Issues page.

Ackowledgement

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.