Model-based Analysis of ChIP-Seq (MACS)
Yong Zhang(Dana-Farber Cancer Institute), Tao Liu(Dana-Farber Cancer Institute), Clifford A. Meyer(Dana-Farber Cancer Institute), Jérôme Eeckhoute(Brigham and Women's Hospital), David S. Johnson(Box (United States)), B Bernstein(Broad Institute), Chad Nusbaum(Broad Institute), R Myers(Stanford Medicine), Myles Brown(Brigham and Women's Hospital), Wei Li(Baylor College of Medicine), X. Shirley Liu(Dana-Farber Cancer Institute)
Cited by 19,806Open Access
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.