目錄樹:
331a473f1a
11.0
12.0
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bedlam
bedlam-name-fixes
dive
erik
gitignore
libressl-gopher-test
master
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python-renames
rworkman
urchlay
user/aclemons/updates
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user/anddt/office/gnumeric
user/anddt/office/sigil
user/anddt/system/FontAwesome
user/anddt/system/conky
user/anddt/system/fakeroot
user/anddt/system/hplip-plugin
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willysr
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Matteo Bernardini
09577efff1
academic/cistrome-MACS: Handle content-disposition download.
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2 年之前 | |
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| README | af993d7297 academic/cistrome-MACS: Added (Model-based Analysis of ChIP-Seq). | 11 年之前 |
| References | af993d7297 academic/cistrome-MACS: Added (Model-based Analysis of ChIP-Seq). | 11 年之前 |
| cistrome-MACS.SlackBuild | 09577efff1 academic/cistrome-MACS: Handle content-disposition download. | 2 年之前 |
| cistrome-MACS.info | 4c2e8bb7bd academic/cistrome-MACS: updated homepage and misc cleanups | 4 年之前 |
| slack-desc | ec514e32c4 Multiple: update email, year and slack-desc | 8 年之前 |
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.