# MACS: Model-based Analysis for ChIP-Seq

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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/macs3)

## Introduction

With the advancement of sequencing technologies, Chromatin
Immunoprecipitation followed by high-throughput sequencing (ChIP-Seq)
has become a popular method for studying genome-wide protein-DNA
interactions. With the purpose of addressing the need for a robust
ChIP-Seq analysis tool, we introduce **M**odel-based **A**nalysis of
**C**hIP-**S**eq (MACS), a powerful tool for identifying transcription
factor binding sites. MACS accounts for the complexity of the genome
to assess the significance of enriched ChIP regions and enhances the
spatial resolution of binding sites by integrating both sequencing tag
position and orientation. MACS can be readily applied to ChIP-Seq data
alone, or in conjunction with a control sample, thus enhancing
specificity. Furthermore, as a versatile peak-caller, MACS can be
employed in any "DNA enrichment assay" to answer the fundamental
question: *Where are the regions with significant read coverage
compared to random background?*

## Changes for MACS (3.0.4) 

### Features added

1) `hmmratac` now supports `-f FRAG`, enabling direct processing of
single-cell fragment files.

Note: because FRAG uses a different internal subsampling
implementation from `BEDPE`/`BAMPE`, results can differ slightly
between collapsed `FRAG` input and standard paired-end inputs.

2) Added `--barcodes` and `--max-count` to `hmmratac` so peak calling
can be restricted to a barcode subset and/or capped by fragment count.

For example, to call accessible regions for one cell type in an
scATAC-seq dataset: `--barcodes celltype1_barcodes.txt --max-count 2`.

### Bugs fixed

1) `PETrackII.pileup_bdg` now wraps pileup outputs in `array.array`
before passing them to `bedGraphTrackI`, preventing runtime type
errors when writing bedGraph tracks from FRAG inputs.

2) `PETrackII.sample_percent*` now correctly allows zero-percent
downsampling, preserving expected CLI behavior when the balance target
contains no fragments.

3) Fixed an overflow path in `PETrackI` when updating total track
length by explicitly casting `int32` values to `ulonglong`.

4) Fixed a `pvalue_stat` issue that could yield `nan` when a region
with the same p-value exceeded 2e9 bp (int32 limit).

### Documentation

1) Cleaned up source-code docstrings and expanded API docs for key
Signal and IO modules.

2) Added a Jupyter notebook demo showing single-cell ATAC-seq
processing with the MACS3 API.
	
## 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, 3.12, 3.13)
 * 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 3.9, 3.10, 3.11, 3.12, 3.13)

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`

Example for peak calling on ATAC-seq with HMMATAC:

`macs3 hmmratac -i ATAC.bam -f BAMPE -n test`

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 or scATAC-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's Essential Open Source Software for Science](https://chanzuckerberg.github.io/open-science/badges/CZI-EOSS.svg)](https://czi.co/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: 3
:hidden:

docs/INSTALL.md
docs/subcommands_index.md
docs/fileformats_index.md
docs/tutorial.md
docs/qa.md
CODE_OF_CONDUCT.md
CONTRIBUTING.md
docs/api/index
