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15.0
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wxWidgets-wip
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Matteo Bernardini
09577efff1
academic/cistrome-MACS: Handle content-disposition download.
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| README | af993d7297 academic/cistrome-MACS: Added (Model-based Analysis of ChIP-Seq). | 10 years ago |
| References | af993d7297 academic/cistrome-MACS: Added (Model-based Analysis of ChIP-Seq). | 10 years ago |
| cistrome-MACS.SlackBuild | 09577efff1 academic/cistrome-MACS: Handle content-disposition download. | 2 years ago |
| cistrome-MACS.info | 4c2e8bb7bd academic/cistrome-MACS: updated homepage and misc cleanups | 4 years ago |
| slack-desc | ec514e32c4 Multiple: update email, year and slack-desc | 8 years ago |
README
MACS: Model-based Analysis of ChIP-Seq
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.