[med-svn] r11736 - trunk/packages/rostlab/profisis/trunk/debian

[Laszlo Kajan]

Author: lkajan-guest
Date: 2012-07-11 14:11:12 +0000 (Wed, 11 Jul 2012)
New Revision: 11736

Modified:
   trunk/packages/rostlab/profisis/trunk/debian/control
Log:
reviewed long description

Modified: trunk/packages/rostlab/profisis/trunk/debian/control
===================================================================
--- trunk/packages/rostlab/profisis/trunk/debian/control	2012-07-11 13:48:52 UTC (rev 11735)
+++ trunk/packages/rostlab/profisis/trunk/debian/control	2012-07-11 14:11:12 UTC (rev 11736)
@@ -1,6 +1,6 @@
 Source: profisis
 Section: science
-Priority: optional
+Priority: extra
 Maintainer: Debian Med Packaging Team <debian-med-packaging at lists.alioth.debian.org>
 DM-Upload-Allowed: yes
 Uploaders: Cedric Staniewski <cedric at gmx.ca>
@@ -12,15 +12,11 @@
 
 Package: profisis
 Architecture: all
-Depends: ${shlibs:Depends}, ${misc:Depends}, libconfig-inifiles-perl, profnet-isis
+Depends: ${misc:Depends}, ${perl:Depends}, libconfig-inifiles-perl, profnet-isis
 Recommends: librg-utils-perl, pp-popularity-contest
-Description: protein-protein interaction sites identified from sequence
- Profisis (ISIS) is a machine learning-based method that identifies interacting
- residues from sequence alone. Although the method is developed using transient
- protein–protein interfaces from complexes of experimentally known 3D
- structures, it never explicitly uses 3D information. Instead, predicted
- structural features are combined with evolutionary information. The strongest
- predictions of the method reached over 90% accuracy in a cross-validation
- experiment. The results suggest that despite the significant diversity in the
- nature of protein–protein interactions, they all share common basic principles
- and that these principles are identifiable from sequence alone.
+Description: prediction of protein-protein interaction sites from sequence
+ Profisis (ISIS) identifies interacting protein residues in protein-protein
+ interfaces from sequence alone.
+ .
+ The strongest predictions of the method reached over 90% accuracy in a
+ cross-validation experiment.