Metadata-Version: 2.1
Name: MACS3
Version: 3.0.0a1
Summary: Model Based Analysis for ChIP-Seq data
Home-page: http://github.com/taoliu/MACS/
Author: Tao Liu
Author-email: vladimir.liu@gmail.com
License: UNKNOWN
Description: # MACS: Model-based Analysis for ChIP-Seq
        
        ![Status](https://img.shields.io/pypi/status/macs3.svg) ![License](https://img.shields.io/github/license/macs3-project/MACS) ![Programming languages](https://img.shields.io/github/languages/top/macs3-project/MACS) ![CI x64](https://github.com/macs3-project/MACS/workflows/CI%20x64/badge.svg) ![CI non x64](https://github.com/macs3-project/MACS/workflows/CI%20non%20x64,%20python%203.7/badge.svg)
        
        [![PyPI download](https://img.shields.io/pypi/dm/macs3?label=pypi%20downloads)](https://pypistats.org/packages/macs3) [![Bioconda download](https://img.shields.io/conda/dn/bioconda/macs3?label=bioconda%20downloads)](https://anaconda.org/bioconda/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) ![PyPI Python Version](https://img.shields.io/pypi/pyversions/MACS3) ![PyPI Format](https://img.shields.io/pypi/format/macs3)](https://pypi.org/project/macs3/)
        * Bioconda: [![Bioconda Release](https://img.shields.io/conda/v/bioconda/macs3) ![Bioconda Platform](https://img.shields.io/conda/pn/bioconda/macs3)](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*.
        
        **Please note that current MACS3 is still in alpha stage, although 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. More new featuer will be added soon.**
        
        ## Recent Changes for MACS (3.0.0a1)
        
        ### 3.0.0a1
        	* Features
        	
        	1) Speed/memory optimization, including using the cykhash to
            replace python dictionary
        
        	2) Code cleanup
        
        	3) Unit testing
        
        	4) R wrappers for MACS
        
            5) Switching to Github Action for CI, support multi-arch testing.
        
        ## Install
        
        The common way to install MACS is through
        [PYPI](https://pypi.org/project/macs3/)) or
        [conda](https://anaconda.org/bioconda/macs3). Please check the
        [INSTALL](./docs/INSTALL.md) document for detail.
        
        ## Usage
        
        Example for regular peak calling on TF ChIP-seq:
        
        `macs2 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:
        
        `macs2 callpeak -t ChIP.bam -c Control.bam --broad -g hs --broad-cutoff 0.1`
        
        Example for peak calling on ATAC-seq (paired-end mode):
        
        `macs2 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`](./docs/callpeak.md) | Main MACS3 Function to call peaks from alignment results.
        [`bdgpeakcall`](./docs/bdgpeakcall.md) | Call peaks from bedGraph output.
        [`bdgbroadcall`](./docs/bdgbroadcall.md) | Call broad peaks from bedGraph output.
        [`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 BEDGraphs 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.
        [`predictd`](./docs/predictd.md) | Predict d or fragment size from alignment results.
        [`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.
        [`refinepeak`](./docs/refinepeak.md) | Take raw reads alignment, refine peak summits.
        
        For advanced usage, for example, to run `macs3` in a modular way,
        please read the [advanced usage](./docs/advanced_usage.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.
        
        ## 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.
        
        ## 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/)
        
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Cython
Requires-Python: >=3.6
Description-Content-Type: text/markdown
