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Model-based Analysis of ChIP-Seq (MACS)

Yong Zhang1, Tao Liu1, Clifford A Meyer1, Jérôme Eeckhoute2, David S Johnson3, Bradley E Bernstein45, Chad Nusbaum5, Richard M Myers6, Myles Brown2, Wei Li7* and X Shirley Liu1*

Author Affiliations

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

2 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

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

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

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

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

7 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

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Genome Biology 2008, 9:R137  doi:10.1186/gb-2008-9-9-r137

Published: 17 September 2008


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.