fitgcp 0.0.20130418-2 source package in Ubuntu

Changelog

fitgcp (0.0.20130418-2) unstable; urgency=medium


  [ James McCoy ]
  * Move debian/upstream to debian/upstream/metadata

  [ Charles Plessy ]
  * Build-depend on python instead of python-all-dev.
  * Rely on ${python:Depends} for the dependency on Python.
  * Normalised debian/control with config-model-edit.
  * Conforms with Policy 3.9.6.

  [ Andreas Tille ]
  * Assume Build-Depends: python-pysam to make implicitly clear that
    the package can exist only on those architectures where python-pysam
    is available (thanks to Charles Plessy for the hint)

 -- Andreas Tille <email address hidden>  Tue, 30 Sep 2014 13:46:48 +0200

Upload details

Uploaded by:
Debian Med on 2014-09-30
Uploaded to:
Sid
Original maintainer:
Debian Med
Architectures:
any
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Yakkety release on 2016-04-22 universe misc
Xenial release on 2015-10-22 universe misc
Wily release on 2015-05-05 universe misc

Downloads

File Size SHA-256 Checksum
fitgcp_0.0.20130418-2.dsc 1.9 KiB fee0eebfc921a8313cf9889b30cdd3d11bda76de0892717a6b60d226976fc102
fitgcp_0.0.20130418.orig.tar.xz 8.2 KiB 421fef43d89debbcb318a9a9e490087146dbf14429f1059a14d126fdfa2e6a14
fitgcp_0.0.20130418-2.debian.tar.xz 4.4 KiB 4256677b04da42827f61d0c4601b1b9fa8c83c79fb17a63d106d786efae1297f

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.