Model-based Analysis of ChIP-Seq (MACS)

• Published in: Genome biology Vol. 9; no. 9; p. R137
• Main Authors: Zhang, Yong, Liu, Tao, Meyer, Clifford A, Eeckhoute, Jérôme, Johnson, David S, Bernstein, Bradley E, Nusbaum, Chad, Myers, Richard M, Brown, Myles, Li, Wei, Liu, X Shirley
• Format: Journal Article
• Language: English
• Published: London BioMed Central 17.09.2008
• Subjects: Algorithms; Animal Genetics and Genomics; binding sites; Bioinformatics; Biomedical and Life Sciences; Cell Line, Tumor; Chromatin Immunoprecipitation - methods; Evolutionary Biology; genome; Hepatocyte Nuclear Factor 3-alpha - analysis; Hepatocyte Nuclear Factor 3-alpha - genetics; Human Genetics; Humans; Life Sciences; Method; Microbial Genetics and Genomics; Models, Genetic; Oligonucleotide Array Sequence Analysis - methods; Plant Genetics and Genomics; Poisson distribution; prediction; False Discovery Rate; False Discovery Rate Estimate; Candidate Peak; Additional Data File; Peak Summit
• ISSN: 1474-760X; 1465-6906; 1474-7596; 1474-760X; 1465-6914
• DOI: 10.1186/gb-2008-9-9-r137

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.