Model-based Analysis of ChIP-Seq (MACS)

被引:11424
作者
Zhang, Yong [2 ]
Liu, Tao [2 ]
Meyer, Clifford A. [2 ]
Eeckhoute, Jerome [3 ,4 ,5 ]
Johnson, David S. [6 ]
Bernstein, Bradley E. [7 ,8 ,9 ,10 ,11 ]
Nussbaum, Chad [10 ,11 ]
Myers, Richard M. [12 ]
Brown, Myles [3 ,4 ,5 ]
Li, Wei [1 ]
Liu, X. Shirley [2 ]
机构
[1] Baylor Coll Med, Dept Mol & Cellular Biol, Dan L Duncan Canc Ctr, Div Biostat, Houston, TX 77030 USA
[2] Harvard Univ, Dana Farber Canc Inst, Dept Biostat & Computat Biol, Sch Publ Hlth, Boston, MA 02115 USA
[3] Brigham & Womens Hosp, Dana Farber Canc Inst, Dept Med Oncol, Div Mol & Cellular Onocl, Boston, MA 02115 USA
[4] Brigham & Womens Hosp, Dept Med, Boston, MA 02115 USA
[5] Harvard Univ, Sch Med, Boston, MA 02115 USA
[6] Gene Secur Network Inc, Redwood City, CA 94063 USA
[7] Harvard Univ, Sch Med, Dept Pathol, Charlestown, MA 02129 USA
[8] Massachusetts Gen Hosp, Ctr Canc Res, Charlestown, MA 02129 USA
[9] Massachusetts Gen Hosp, Mol Pathol Unit, Charlestown, MA 02129 USA
[10] Broad Inst Harvard, Cambridge, MA 02142 USA
[11] MIT, Cambridge, MA 02142 USA
[12] Stanford Univ, Med Ctr, Dept Genet, Stanford, CA 94305 USA
关键词
D O I
10.1186/gb-2008-9-9-r137
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
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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页数:9
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