Source: libmems
Priority: optional
Maintainer: Debian-Med Packaging Team <debian-med-packaging@lists.alioth.debian.org>
DM-Upload-Allowed: yes
Uploaders: Andreas Tille <tille@debian.org>
Build-Depends: debhelper (>= 6), autotools-dev, d-shlibs (>= 0.38), cdbs, quilt,
 libgenome-1.3-1-dev (>= 1.3.0-2), libclustalw-1.0-1-dev,
 libboost-filesystem-dev, libboost-program-options-dev
Standards-Version: 3.7.3
Section: libs
Homepage: http://asap.ahabs.wisc.edu/software/software-development-libraries/libmems.html
Vcs-Browser: http://svn.debian.org/wsvn/debian-med/trunk/packages/libmems/?rev=0&sc=0
Vcs-Svn: svn://svn.debian.org/svn/debian-med/trunk/packages/libmems/trunk/

Package: libmems-1.5-1-dev
Section: libdevel
Architecture: any
Depends: libmems-1.5-1 (= ${binary:Version}), ${devlibs:Depends}
Provides: libmems-1.5-dev
Conflicts: libmems-1.5-dev
Description: library to support DNA string matching and comparative genomics
 libMems is a freely available software development library to support DNA
 string matching and comparative genomics. Among other things, libMems
 implements an algorithm to perform approximate multi-MUM and multi-MEM
 identification. The algorithm uses spaced seed patterns in conjunction
 with a seed-and-extend style hashing method to identify matches. The method
 is efficient, requiring a maximum of only 16 bytes per base of the largest
 input sequence, and this data can be stored externally (i.e. on disk) to
 further reduce memory requirements.
 .
 This is the development package containing the statically linked
 library and the header files.

Package: libmems-1.5-1
Section: libs
Architecture: any
Depends: ${shlibs:Depends}, ${misc:Depends}
Description: library to support DNA string matching and comparative genomics
 libMems is a freely available software development library to support DNA
 string matching and comparative genomics. Among other things, libMems
 implements an algorithm to perform approximate multi-MUM and multi-MEM
 identification. The algorithm uses spaced seed patterns in conjunction
 with a seed-and-extend style hashing method to identify matches. The method
 is efficient, requiring a maximum of only 16 bytes per base of the largest
 input sequence, and this data can be stored externally (i.e. on disk) to
 further reduce memory requirements.
 .
 This package contains the dynamic library.