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# Model-based analysis of ChIP-Seq (MACS, [1]).

Intro from the MACS website

Next generation parallel sequencing technologies made chromatin immunoprecipitation followed by sequencing (ChIP-Seq) a popular strategy to study genome-wide protein-DNA interactions, while creating challenges for analysis algorithms. We present Model-based Analysis of ChIP-Seq (MACS) on short reads sequencers such as Genome Analyzer (Illumina / Solexa). MACS empirically models the length of the sequenced ChIP fragments, which tends to be shorter than sonication or library construction size estimates, and uses it to improve the spatial resolution of predicted binding sites. MACS also uses a dynamic Poisson distribution to effectively capture local biases in the genome sequence, allowing for more sensitive and robust prediction. MACS compares favorably to existing ChIP-Seq peak-finding algorithms, is publicly available open source, and can be used for ChIP-Seq with or without control samples.

Other, related articles: [2]


  1. Yong Zhang, Tao Liu, Clifford A Meyer, Jérôme Eeckhoute, David S Johnson, Bradley E Bernstein, Chad Nusbaum, Richard M Myers, Myles Brown, Wei Li, X Shirley Liu
    Model-based analysis of ChIP-Seq (MACS).
    Genome Biol: 2008, 9(9);R137
    [PubMed:18798982] ##WORLDCAT## [DOI] (I p)

  2. Yan-Feng Zhang, Bing Su
    Peak identification for ChIP-seq data with no controls.
    Dongwuxue Yanjiu: 2012, 33(E5-6);E121-8
    [PubMed:23266983] ##WORLDCAT## [DOI] (P p)

    Jianxing Feng, Tao Liu, Bo Qin, Yong Zhang, Xiaole Shirley Liu
    Identifying ChIP-seq enrichment using MACS.
    Nat Protoc: 2012, 7(9);1728-40
    [PubMed:22936215] ##WORLDCAT## [DOI] (I p)

    Jianxing Feng, Tao Liu, Yong Zhang
    Using MACS to identify peaks from ChIP-Seq data.
    Curr Protoc Bioinformatics: 2011, Chapter 2;2.14.1-2.14.14
    [PubMed:21633945] ##WORLDCAT## [DOI] (I p)

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