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+MACS: Model-based Analysis of ChIP-Seq
+
+Next generation parallel sequencing technologies made chromatin
+immunoprecipitation followed by sequencing (ChIP-Seq) a popular
+strategy to study genome-wide protein-DNA interactions, while creating
+challenges for analysis algorithms. We present Model-based Analysis of
+ChIP-Seq (MACS) on short reads sequencers such as Genome Analyzer
+(Illumina / Solexa). MACS empirically models the length of the
+sequenced ChIP fragments, which tends to be shorter than sonication or
+library construction size estimates, 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 sequence, allowing for more sensitive and robust
+prediction. MACS compares favorably to existing ChIP-Seq peak-finding
+algorithms, is publicly available open source, and can be used for
+ChIP-Seq with or without control samples.