# MACS: Model-based Analysis for ChIP-Seq

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[![PyPI
download](https://img.shields.io/pypi/dm/macs3?label=pypi%20downloads)](https://pypistats.org/packages/macs3)

Latest Release:
* Github: [![Github Release](https://img.shields.io/github/v/release/macs3-project/MACS)](https://github.com/macs3-project/MACS/releases)
* PyPI: [![PyPI Release](https://img.shields.io/pypi/v/macs3.svg)](https://pypi.org/project/MACS3/)
* Bioconda:[![Bioconda Badge](https://anaconda.org/bioconda/macs3/badges/version.svg)](https://anaconda.org/bioconda/macs3)
* Debian Med: [![Debian Stable](https://img.shields.io/debian/v/macs/stable?label=debian%20stable)](https://packages.debian.org/stable/macs)[![Debian Unstable](https://img.shields.io/debian/v/macs/sid?label=debian%20sid)](https://packages.debian.org/sid/macs)

## 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 **M**odel-based **A**nalysis of **C**hIP-**S**eq (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) 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

3) 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

### 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](https://pypi.org/project/macs3/)) or
[conda](https://anaconda.org/macs3/macs3). Please check the
[INSTALL](docs/INSTALL.md) 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`](docs/callpeak.md) | Main MACS3 Function to call peaks from alignment results.
[`bdgpeakcall`](docs/bdgpeakcall.md) | Call peaks from bedGraph file.
[`bdgbroadcall`](docs/bdgbroadcall.md) | Call nested broad peaks from bedGraph file.
[`bdgcmp`](docs/bdgcmp.md) | Comparing two signal tracks in bedGraph format.
[`bdgopt`](docs/bdgopt.md) | Operate the score column of bedGraph file.
[`cmbreps`](docs/cmbreps.md) | Combine bedGraph files of scores from replicates.
[`bdgdiff`](docs/bdgdiff.md) | Differential peak detection based on paired four bedGraph files.
[`filterdup`](docs/filterdup.md) | Remove duplicate reads, then save in BED/BEDPE format file.
[`predictd`](docs/predictd.md) | Predict d or fragment size from alignment results. In case of PE data, report the average insertion/fragment size from all pairs.
[`pileup`](docs/pileup.md) | Pileup aligned reads (single-end) or fragments (paired-end)
[`randsample`](docs/randsample.md) | Randomly choose a number/percentage of total reads, then save in BED/BEDPE format file.
[`refinepeak`](docs/refinepeak.md) | Take raw reads alignment, refine peak summits.
[`callvar`](docs/callvar.md) | Call variants in given peak regions from the alignment BAM files.
[`hmmratac`](docs/hmmratac.md) | 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](docs/Advanced_Step-by-step_Peak_Calling.md). There is a
[Q&A](docs/qa.md) document where we collected some common questions
from users.

## Contribute

Please read our [CODE OF CONDUCT](CODE_OF_CONDUCT.md) and [How to
contribute](CONTRIBUTING.md) 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](https://github.com/macs3-project/MACS/discussions)
instead of posting to our
[Issues](https://github.com/macs3-project/MACS/issues) page.

## Ackowledgement

MACS3 project is sponsored by
[CZI EOSS](https://chanzuckerberg.com/eoss/). 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)](https://genomebiology.biomedcentral.com/articles/10.1186/gb-2008-9-9-r137)

## Other useful links

 * [Cistrome](http://cistrome.org/)
 * [bedTools](http://code.google.com/p/bedtools/)
 * [UCSC toolkits](http://hgdownload.cse.ucsc.edu/admin/exe/)
 * [deepTools](https://github.com/deeptools/deepTools/)


```{toctree}
:maxdepth: 2
:hidden:

docs/INSTALL.md
docs/subcommands_index.md
docs/fileformats_index.md
docs/Advanced_Step-by-step_Peak_Calling.md
docs/cutoffanalysis.md
docs/qa.md
docs/tutorial.md
CODE_OF_CONDUCT.md
CONTRIBUTING.md