Model-based Analysis of ChIP-Seq (MACS)

Yong Zhang^*¹ , Tao Liu^*¹ , Clifford A Meyer¹ , Jérôme Eeckhoute² , David S Johnson³ , Bradley E Bernstein⁴^,5 , Chad Nussbaum⁵ , Richard M Myers⁶ , Myles Brown² , Wei Li⁷ and X Shirley Liu¹

¹Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, 44 Binney Street, Boston, MA 02115, USA

²Division of Molecular and Cellular Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute and Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 44 Binney Street, Boston, MA 02115, USA

³Gene Security Network, Inc., 2686 Middlefield Road, Redwood City, CA 94063, USA

⁴Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital and Department of Pathology, Harvard Medical School, 13th Street, Charlestown, MA 02129, USA

⁵Broad Institute of Harvard and MIT, 7 Cambridge Center, Cambridge, MA, 02142, USA

⁶Department of Genetics, Stanford University Medical Center, Stanford, CA 94305, USA

⁷Division of Biostatistics, Dan L Duncan Cancer Center, Department of Molecular and Cellular Biology, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA

author email corresponding author email* Contributed equally

Genome Biology 2008, 9:R137doi:10.1186/gb-2008-9-9-r137


Published:	17 September 2008

Subject areas: Bioinformatics, Molecular biology

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.