MACS: Difference between revisions

From Cheaha
Jump to navigation Jump to search
(created page with generic stub)
 
(added category bioinformatics)
 
(5 intermediate revisions by the same user not shown)
Line 2: Line 2:




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.
[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/
====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

This page is a Generic stub.

You can help by expanding this page..


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
Retrieved from "https://docs.uabgrid.uab.edu/w/index.php?title=MACS&oldid=4040"