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 on 2017-01-09
Uploaded to:
Sid
Original maintainer:
Debian Med
Architectures:
any
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Artful release on 2017-04-20 universe misc
Zesty release on 2017-03-31 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.