Source: libsmithwaterman Maintainer: Debian Med Packaging Team Uploaders: Andreas Tille Section: science Priority: optional Build-Depends: debhelper-compat (= 13), d-shlibs, libdisorder-dev Standards-Version: 4.5.1 Vcs-Browser: https://salsa.debian.org/med-team/libsmithwaterman Vcs-Git: https://salsa.debian.org/med-team/libsmithwaterman.git Homepage: https://github.com/ekg/smithwaterman Rules-Requires-Root: no Package: libsmithwaterman0 Architecture: any Section: libs Depends: ${shlibs:Depends}, ${misc:Depends} Description: determine similar regions between two strings or genomic sequences (lib) The Smith–Waterman algorithm performs local sequence alignment; that is, for determining similar regions between two strings or nucleotide or protein sequences. Instead of looking at the total sequence, the Smith–Waterman algorithm compares segments of all possible lengths and optimizes the similarity measure. . This package contains the dynamic library. Package: libsmithwaterman-dev Architecture: any Section: libdevel Depends: libsmithwaterman0 (= ${binary:Version}), ${shlibs:Depends}, ${misc:Depends}, libdisorder-dev Description: determine similar regions between two strings or genomic sequences (devel) The Smith–Waterman algorithm performs local sequence alignment; that is, for determining similar regions between two strings or nucleotide or protein sequences. Instead of looking at the total sequence, the Smith–Waterman algorithm compares segments of all possible lengths and optimizes the similarity measure. . This is the development package containing the statically linked library and the header files. Package: smithwaterman Architecture: any Depends: ${shlibs:Depends}, ${misc:Depends} Description: determine similar regions between two strings or genomic sequences The Smith–Waterman algorithm performs local sequence alignment; that is, for determining similar regions between two strings or nucleotide or protein sequences. Instead of looking at the total sequence, the Smith–Waterman algorithm compares segments of all possible lengths and optimizes the similarity measure.