MACS3 3.0.0a2

Model Based Analysis for ChIP-Seq data

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.

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 (3.0.0a2)

3.0.0a2

* New features

1) Speed/memory optimization.  Use the cykhash to replace python
dictionary. Use buffer (10MB) to read and parse input file (not
available for BAM file parser). And many optimization tweaks.

2) Code cleanup. Reorganize source codes.

3) Unit testing.

4) R wrappers for MACS -- MACSr

5) Switch to Github Action for CI, support multi-arch testing
including x64, armv7, aarch64, s390x and ppc64le.

6) MACS tag-shifting model has been refined. Now it will use a
naive peak calling approach to find ALL possible paired peaks at +
and - strand, then use all of them to calculate the
cross-correlation.

7) Call variants in peak regions directly from BAM files. The
function was originally developed under code name SAPPER. Now
SAPPER has been merged into MACS. Also, `simde` has been added as
a submodule in order to support fermi-lite library under non-x64
architectures.

Install

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

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.

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.

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.

Other useful links

• Cistrome
• bedTools
• UCSC toolkits
• deepTools