MACS: Difference between revisions
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[http://liulab.dfci.harvard.edu/MACS/ 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. | [http://liulab.dfci.harvard.edu/MACS/ 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. | ||
Project website: http://liulab.dfci.harvard.edu/MACS/ | '''Project website:''' http://liulab.dfci.harvard.edu/MACS/ | ||
====Load SGE module==== | |||
To load MACS into your environment, use the following module command: | To load MACS into your environment, use the following module command: | ||
<pre> | <pre> | ||
module load macs/macs | module load macs/macs | ||
</pre> | </pre> | ||
[[Category:Software]][[Category:Bioinformatics]] | |||
Latest revision as of 15:16, 4 April 2012
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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.
Project website: http://liulab.dfci.harvard.edu/MACS/
Load SGE module
To load MACS into your environment, use the following module command:
module load macs/macs