Model-based analysis of ChIP-Seq (MACS)

  • Genome Biol. 2008;9(9):R137. doi: 10.1186/gb-2008-9-9-r137.
Yong Zhang  1 ,  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
Affiliations
  • 1. Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA 02115, USA.
PMID: 18798982 DOI: 10.1186/gb-2008-9-9-r137
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.