Model-based Analysis of ChIP-Seq (MACS) View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2008-11

AUTHORS

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

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. More... »

PAGES

r137

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/gb-2008-9-9-r137

DOI

http://dx.doi.org/10.1186/gb-2008-9-9-r137

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1027608848

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/18798982


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