[med-svn] [kmc] 06/08: Add myself (KDM) as uploader, trim libkmc blurb

[Kevin Murray]

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daube-guest pushed a commit to branch daube/libkmcdev
in repository kmc.

commit 4e106faeb4a0c178286b3f9c75ffa7cbfe7f547e
Author: Kevin Murray <spam at kdmurray.id.au>
Date:   Thu Oct 1 18:43:35 2015 +1000

    Add myself (KDM) as uploader, trim libkmc blurb
---
 debian/control | 20 +++-----------------
 1 file changed, 3 insertions(+), 17 deletions(-)

diff --git a/debian/control b/debian/control
index 4f33305..0548aa3 100644
--- a/debian/control
+++ b/debian/control
@@ -2,7 +2,8 @@ Source: kmc
 Maintainer: Debian Med Packaging Team <debian-med-packaging at lists.alioth.debian.org>
 Uploaders: Andreas Tille <tille at debian.org>,
            Jorge Soares <j.s.soares at gmail.com>,
-           Sascha Steinbiss <sascha at steinbiss.name>
+           Sascha Steinbiss <sascha at steinbiss.name>,
+           Kevin Murray <spam at kdmurray.id.au>
 Section: science
 Priority: optional
 Build-Depends: debhelper (>= 9),
@@ -44,6 +45,7 @@ Description: count kmers in genomic sequences
  order of the size of input data (usually it is smaller).
 
 Package: libkmc-dev
+Section: libdevel
 Architecture: any
 Depends: ${shlibs:Depends},
          ${misc:Depends}
@@ -52,22 +54,6 @@ Description: count kmers in genomic sequences
  consecutive k symbols) in a set of reads. K-mer counting is
  important for many bioinformatics applications, e.g. developing de Bruijn
  graph assemblers.
- Building de Bruijn graphs is a commonly used approach for genome
- assembly with data from second-generation sequencing.
- Unfortunately, sequencing errors (frequent in practice)
- result in huge memory requirements for de Bruijn graphs, as well
- as long build time. One of the popular approaches to handle this
- problem is filtering the input reads in such a way that unique k-mers
- (very likely obtained as a result of an error) are discarded.
- .
- Thus, KMC scans the raw reads and produces a compact representation
- of all non-unique reads accompanied with number of their occurrences.
- The algorithm implemented in KMC makes use mostly of disk space rather
- than RAM, which allows one to use KMC even on rather typical personal
- computers. When run on high-end servers (what is necessary for KMC
- competitors) it outperforms them in both memory requirements and
- speed of computation. The disk space necessary for computation is in
- order of the size of input data (usually it is smaller).
  .
  This package contains the C++ library headers for accessing kmc's output
  files.

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