[Cdd-commits] r535 - projects/med/trunk/debian-med/tasks

[CDD Subversion Commit]

Author: tille
Date: Sun Feb  3 00:03:10 2008
New Revision: 535

Modified:
   projects/med/trunk/debian-med/tasks/bio
Log:
Fixed spacing


Modified: projects/med/trunk/debian-med/tasks/bio
==============================================================================
--- projects/med/trunk/debian-med/tasks/bio	(original)
+++ projects/med/trunk/debian-med/tasks/bio	Sun Feb  3 00:03:10 2008
@@ -32,7 +32,8 @@
 Why:         This is only recommended to not necessarily flood the system with TeX
              packages - perhaps target for -doc?
 
-Depends:     biosquid, gff2ps, mipe, melting, ncbi-epcr, ncbi-tools-bin, ncbi-tools-x11, perlprimer, primer3, readseq, tigr-glimmer
+Depends:     biosquid, gff2ps, mipe, melting, ncbi-epcr, ncbi-tools-bin, ncbi-tools-x11,
+             perlprimer, primer3, readseq, tigr-glimmer
 Why:         Tools for the molecular biologist.
 
 Suggests:    mozilla-biofox
@@ -82,18 +83,18 @@
 License: non-free for commercial purpose (http://meme.nbcr.net/meme/COPYRIGHT.html)
 WNPP: 
 Pkg-Description: motif discovery and search
-  MEME is a tool for discovering motifs in a group of related DNA or protein
-  sequences.  A motif is a sequence pattern that occurs repeatedly in a group
-  of related protein or DNA sequences. MEME represents motifs as position-dependent
-  letter-probability matrices which describe the probability of each possible
-  letter at each position in the pattern. Individual MEME motifs do not contain
-  gaps. Patterns with variable-length gaps are split by MEME into two or more
-  separate motifs.
-  .
-  MEME takes as input a group of DNA or protein sequences (the training set)
-  and outputs as many motifs as requested. MEME uses statistical modeling
-  techniques to automatically choose the best width, number of occurrences,
-  and description for each motif.
+ MEME is a tool for discovering motifs in a group of related DNA or protein
+ sequences.  A motif is a sequence pattern that occurs repeatedly in a group
+ of related protein or DNA sequences. MEME represents motifs as position-dependent
+ letter-probability matrices which describe the probability of each possible
+ letter at each position in the pattern. Individual MEME motifs do not contain
+ gaps. Patterns with variable-length gaps are split by MEME into two or more
+ separate motifs.
+ .
+ MEME takes as input a group of DNA or protein sequences (the training set)
+ and outputs as many motifs as requested. MEME uses statistical modeling
+ techniques to automatically choose the best width, number of occurrences,
+ and description for each motif.
 
 Depends: vienna-rna
 Homepage: http://www.tbi.univie.ac.at/~ivo/RNA/