Source: libmems Maintainer: Debian Med Packaging Team Uploaders: Andreas Tille , Étienne Mollier Section: science Priority: optional Build-Depends: dpkg-dev (>= 1.22.5), debhelper-compat (= 13), d-shlibs (>= 0.106~), pkg-config, libgenome-dev, libboost-filesystem-dev, libboost-iostreams-dev, libboost-program-options-dev, libmuscle-dev Standards-Version: 4.5.1 Vcs-Browser: https://salsa.debian.org/med-team/libmems Vcs-Git: https://salsa.debian.org/med-team/libmems.git Homepage: http://sourceforge.net/p/mauve/code/HEAD/tree/libMems/trunk/ Rules-Requires-Root: no Package: libmems-dev Architecture: any Section: libdevel Depends: libmems1t64 (= ${binary:Version}), ${misc:Depends}, ${devlibs:Depends} Provides: libmems-1.6-dev Description: development 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: libmems1t64 Provides: ${t64:Provides} Architecture: any Multi-Arch: same Section: libs Depends: ${shlibs:Depends}, ${misc:Depends} Pre-Depends: ${misc:Pre-Depends} Conflicts: libmems1 (<< ${source:Version}), libmems-1.6-1, libmems-1.6-1v5 Replaces: libmems1, libmems-1.6-1, libmems-1.6-1v5 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.