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
Published in:Genome biology 2008-09, Vol.9 (9), p.R137-R137, Article R137
Main Authors: Zhang, Yong, Liu, Tao, Meyer, Clifford A, Eeckhoute, Jérôme, Johnson, David S, Bernstein, Bradley E, Nusbaum, Chad, Myers, Richard M, Brown, Myles, Li, Wei, Liu, X Shirley
Format: Article
Language:English
Subjects:
Algorithms
Cell Line, Tumor
Chromatin Immunoprecipitation - methods
Hepatocyte Nuclear Factor 3-alpha - analysis
Hepatocyte Nuclear Factor 3-alpha - genetics
Humans
Method
Models, Genetic
Oligonucleotide Array Sequence Analysis - methods
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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.
ISSN:1474-760X
1465-6906
1474-760X
1465-6914
DOI:10.1186/gb-2008-9-9-r137