Model-based Analysis of ChIP-Seq (MACS)
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gb-2008-9-9-r137.pdf
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Author(s)
Zhang, Yong
Liu, Tao
Meyer, Clifford A.
Eeckhoute, Jerome
Johnson, David S.
Nusbaum, Chad
Myers, Richard M.
Brown, Myles
Li, Wei
Liu, Xiaole S.
Date Issued
September 2008
Journal
Genome Biology
Publisher
BioMed Central Ltd
Citation
Genome Biology. 2008 Sep 17;9(9):R137
Version
Final published version
Abstract
We present Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer. MACS empirically models the shift size of ChIP-Seq tags, 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, allowing for more robust predictions. MACS compares favorably to existing ChIP-Seq peak-finding algorithms, and is freely available.
MIT Department
Broad Institute of MIT and Harvard
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1186/gb-2008-9-9-r137
