[Debian-med-packaging] Bug#808895: ITP: lofreq -- sensitive variant calling from sequencing data
Afif Elghraoui
afif at ghraoui.name
Thu Dec 24 07:39:22 UTC 2015
Package: wnpp
Severity: wishlist
Owner: Debian Med Packaging Team <debian-med-packaging at lists.alioth.debian.org>
* Package name : lofreq
Version : 2.1.2
Upstream Author : Andreas Wilm and Niranjan Nagarajan <lofreq-help at lists.sourceforge.net>
* URL : http://csb5.github.io/lofreq
* License : MIT
Programming Lang: C, Python
Description : sensitive variant calling from sequencing data
LoFreq* (i.e. LoFreq version 2) is a fast and sensitive
variant-caller for inferring SNVs and indels from next-generation
sequencing data. It makes full use of base-call qualities and other
sources of errors inherent in sequencing (e.g. mapping or base/indel
alignment uncertainty), which are usually ignored by other methods or
only used for filtering.
.
LoFreq* can run on almost any type of aligned sequencing data
(e.g. Illumina, IonTorrent or Pacbio) since no machine- or
sequencing-technology dependent thresholds are used. It automatically
adapts to changes in coverage and sequencing quality and can
therefore be applied to a variety of data-sets e.g.
viral/quasispecies, bacterial, metagenomics or somatic data.
.
LoFreq* is very sensitive; most notably, it is able to predict
variants below the average base-call quality (i.e. sequencing error
rate). Each variant call is assigned a p-value which allows for
rigorous false positive control. Even though it uses no
approximations or heuristics, it is very efficient due to several
runtime optimizations and also provides a (pseudo-)parallel
implementation. LoFreq* is generic and fast enough to be applied to
high-coverage data and large genomes. On a single processor it takes
a minute to analyze Dengue genome sequencing data with nearly 4000X
coverage, roughly one hour to call SNVs on a 600X coverage E.coli
genome and also roughly an hour to run on a 100X coverage human exome
dataset.
This package will be maintained by the Debian Med team.
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