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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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Dana-Farber Cancer Institute
1 shared publicationDana-Farber Cancer Institute
1 shared publicationDana-Farber Cancer Institute
1 shared publicationBroad Institute
1 shared publicationBroad Institute
1 shared publicationStanford Medicine
1 shared publicationBrigham and Women's Hospital
1 shared publicationBaylor College of Medicine
1 shared publicationDana-Farber Cancer Institute
1 shared publication