MACS - Revision history

Tanthony@uab.edu: added category bioinformatics

2012-04-04T15:16:49Z

added category bioinformatics

← Older revision		Revision as of 15:16, 4 April 2012
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	[[Category:Software]]		[[Category:Software]][[Category:Bioinformatics]]

Tanthony@uab.edu: added category software

2012-04-04T14:06:39Z

added category software

← Older revision		Revision as of 14:06, 4 April 2012
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	module load macs/macs		module load macs/macs
	</pre>		</pre>


			[[Category:Software]]

Tanthony@uab.edu at 19:50, 2 April 2012

2012-04-02T19:50:05Z

← Older revision		Revision as of 19:50, 2 April 2012
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	'''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>

Tanthony@uab.edu at 18:42, 2 April 2012

2012-04-02T18:42:51Z

← Older revision		Revision as of 18:42, 2 April 2012
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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/


	To load MACS into your environment, use the following module command:		To load MACS into your environment, use the following module command:

Tanthony@uab.edu at 15:10, 20 March 2012

2012-03-20T15:10:17Z

← Older revision		Revision as of 15:10, 20 March 2012
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	Project website: http://liulab.dfci.harvard.edu/MACS/		Project website: http://liulab.dfci.harvard.edu/MACS/


	To load MACS into your environment, use the following module command:		To load MACS into your environment, use the following module command:

Tanthony@uab.edu: added links..

2012-03-20T15:09:59Z

added links..

← Older revision		Revision as of 15:09, 20 March 2012
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	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/

	To load MACS into your environment, use the following module command:		To load MACS into your environment, use the following module command:

Tanthony@uab.edu: created page with generic stub

2012-03-19T14:12:37Z

created page with generic stub

New page

{{Generic_stub}}

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.

To load MACS into your environment, use the following module command:
<pre>
module load macs/macs
</pre>