MACS
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Application data |
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| Created by | Yong Zhang, Tao Liu |
|---|---|
| Biological application domain(s) | ChIP-seq |
| Principal bioinformatics method(s) | Peak calling |
| Created at | Dana-Farber Cancer Institute |
| Maintained? | Yes |
| Input format(s) | ELAND, BED, SAM, BAM |
| Output format(s) | BED, WIG |
| Programming language(s) | Python |
| Licence | Artistic License |
| Operating system(s) | platform-independent |
Summary: Model-based Analysis of ChIP-seq data.
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MACS analyzes data generated by short read sequencers such as Solexa's Genome Analyzer. MACS empirically models the shift size of ChIP-seq tags, 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, allowing for more robust predictions.
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