MACS: Model-based Analysis for ChIP-Seq
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 (3.0.0)
1) 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 as the
callvar command. It can
be used to call SNVs and small INDELs directly from alignment
files for ChIP-seq or ATAC-seq. We call
fermi-lite to assemble
the DNA sequence at the enriched genomic regions (binding sites or
accessible DNA) and to refine the alignment when necessary. We
simde as a submodule in order to support fermi-lite
library under non-x64 architectures.
2) HMMRATAC module is added as subcommand
hmmratac. 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,
mono-nucleosomal 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 still underway.
3) 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. We added memory monitoring to the runtime messages.
4) R wrappers for MACS – MACSr for bioconductor.
5) Code cleanup. Reorganize source codes.
6) Unit testing.
7) Switch to Github Action for CI, support multi-arch testing including x64, armv7, aarch64, s390x and ppc64le. We also test on Mac OS 12.
8) 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. (a related bug has been fix #442)
9) BAI index and random access to BAM file now is supported. #449.
10) Support of Python > 3.10 #498
11) The effective genome size parameters have been updated according to deeptools. #508
12) Multiple updates regarding dependencies, anaconda built, CI/CD process.
13) Cython 3 is supported.
14) Documentations for each subcommand can be found under /docs
2) Note: different numpy, scipy, sklearn may give slightly
different results for hmmratac results. The current standard
results for automated testing in
/test directory are from Numpy
1.25.1, Scipy 1.11.1, and sklearn 1.3.0.
MACS3 has been tested using GitHub Actions for every push and PR in the following architectures:
- x86_64 (Python 3.9, 3.10, 3.11, 3.12)
- aarch64 (Python 3.9)
- armv7 (Python 3.9)
- ppc64le (Python 3.9)
- s390x (Python 3.9)
- Apple chips (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
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.
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.|
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.