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Model-based Analysis of ChIP-Seq (MACS)

Yong Zhang1* email, Tao Liu1* email, Clifford A Meyer1 email, Jérôme Eeckhoute2 email, David S Johnson3 email, Bradley E Bernstein4,5 email, Chad Nussbaum5 email, Richard M Myers6 email, Myles Brown2 email, Wei Li7 email and X Shirley Liu1 email

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

Division 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

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

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

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

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

Division 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.


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