Difference between revisions of "MACS"
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|{{#vardefine:url|http://liulab.dfci.harvard.edu/MACS/index.html}} | |{{#vardefine:url|http://liulab.dfci.harvard.edu/MACS/index.html}} | ||
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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. | 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://aluru-sun.ece.iastate.edu/doku.php?id=reptile Upstream documentation] for {{#var:app}}. | [http://aluru-sun.ece.iastate.edu/doku.php?id=reptile Upstream documentation] for {{#var:app}}. | ||
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| − | == | + | ==Environment Modules== |
| − | + | Run <code>module spider {{#var:app}}</code> to find out what environment modules are available for this application. | |
| − | + | ==System Variables== | |
| − | + | * HPC_{{uc:{{#var:app}}}}_DIR - installation directory | |
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* HPC_MACS_BIN - Executable directory | * HPC_MACS_BIN - Executable directory | ||
{{#if: {{#var: exe}}|==How To Run== | {{#if: {{#var: exe}}|==How To Run== | ||
Latest revision as of 19:47, 12 August 2022
Description
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. Upstream documentation for macs.
Environment Modules
Run module spider macs to find out what environment modules are available for this application.
System Variables
- HPC_MACS_DIR - installation directory
- HPC_MACS_BIN - Executable directory
Citation
If you publish research that uses macs you have to cite it as follows: Zhang et al. Model-based Analysis of ChIP-Seq (MACS). Genome Biol (2008) vol. 9 (9) pp. R137