Yong Zhang 1 , Tao Liu 1 , Clifford A Meyer 1 , Jérôme Eeckhoute 2 , David S Johnson 3 , Bradley E Bernstein 4 , 5 , Chad Nusbaum 5 , Richard M Myers 6 , Myles Brown 2 , Wei Li , 7 , X Shirley Liu , 1
17 September 2008
MACS performs model-based analysis of ChIP-Seq data generated by short read sequencers.
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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