Minnesota Supercomputing Institute
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1.4.1, 1.4.2-1, 2.1.1
Wednesday, April 4, 2018
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.
To run this software interactively in a Linux environment run the commands:
module load macs
macs14 <-t tfile> [-n name] [-g genomesize] [options]
For documentation, refer to the MACS homepage.