fitgcp 0.0.20150429-1 source package in Ubuntu
Changelog
fitgcp (0.0.20150429-1) unstable; urgency=medium * New upstream checkout * debian/get-orig-source: Fetch latest commit from SVN and create date * Fake watch file * cme fix dpkg-control * debhelper 10 -- Andreas Tille <email address hidden> Mon, 09 Jan 2017 13:27:48 +0100
Upload details
- Uploaded by:
- Debian Med
- Uploaded to:
- Sid
- Original maintainer:
- Debian Med
- Architectures:
- any
- Section:
- misc
- Urgency:
- Medium Urgency
See full publishing history Publishing
Series | Published | Component | Section | |
---|---|---|---|---|
Bionic | release | universe | misc |
Downloads
File | Size | SHA-256 Checksum |
---|---|---|
fitgcp_0.0.20150429-1.dsc | 1.9 KiB | d657b5d9ebbb2f8dbd64e4595591ff68caa85c77bb71332b1972db522728c004 |
fitgcp_0.0.20150429.orig.tar.xz | 8.2 KiB | f18c9c2155dcf7eaf0bedd349d6accbe051110a6958478fcb58507cfa062390e |
fitgcp_0.0.20150429-1.debian.tar.xz | 4.6 KiB | 927f86fd02e70695485dd64abc22ce96da5145101ca3313d8604f6399456cb4e |
Available diffs
No changes file available.
Binary packages built by this source
- fitgcp: fitting genome coverage distributions with mixture models
Genome coverage, the number of sequencing reads mapped to a position in
a genome, is an insightful indicator of irregularities within sequencing
experiments. While the average genome coverage is frequently used within
algorithms in computational genomics, the complete information available
in coverage profiles (i.e. histograms over all coverages) is currently
not exploited to its full extent. Thus, biases such as fragmented or
erroneous reference genomes often remain unaccounted for. Making this
information accessible can improve the quality of sequencing experiments
and quantitative analyses.
.
fitGCP is a framework for fitting mixtures of probability distributions
to genome coverage profiles. Besides commonly used distributions, fitGCP
uses distributions tailored to account for common artifacts. The mixture
models are iteratively fitted based on the Expectation-Maximization
algorithm.