Model-based Analysis of ChIP-Seq (MACS)

Model-based Analysis of ChIP-Seq (MACS)

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 dy...

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Bibliographic Details
Main Authors: 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.
Other Authors: Broad Institute of MIT and Harvard
Format: Article
Language:English
Published: BioMed Central Ltd 2010
Online Access:http://hdl.handle.net/1721.1/59206
Description
Summary: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.

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