[HTML][HTML] Model-based analysis of ChIP-Seq (MACS)

Y Zhang, T Liu, CA Meyer, J Eeckhoute, DS Johnson… - Genome biology, 2008 - Springer
Y Zhang, T Liu, CA Meyer, J Eeckhoute, DS Johnson, BE Bernstein, C Nusbaum, RM Myers
Genome biology, 2008Springer
Abstract 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.
Abstract
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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