期刊
GENOME BIOLOGY
卷 9, 期 9, 页码 -出版社
BMC
DOI: 10.1186/gb-2008-9-9-r137
关键词
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资金
- NIH [HG004069, HG004270, DK074967]
- NATIONAL HUMAN GENOME RESEARCH INSTITUTE [R01HG004069, U01HG004270] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES [R56DK074967, R01DK074967] Funding Source: NIH RePORTER
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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