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Model-based Analysis of ChIP-Seq (MACS)

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.; Bernstein, Bradley E.; ... Show more Show less
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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.
Date issued
2008-09
URI
http://hdl.handle.net/1721.1/59206
Department
Broad Institute of MIT and Harvard
Journal
Genome Biology
Publisher
BioMed Central Ltd
Citation
Genome Biology. 2008 Sep 17;9(9):R137
Version: Final published version
ISSN
1465-6906

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