Source: unanimity Maintainer: Debian Med Packaging Team Uploaders: Afif Elghraoui , Andreas Tille Section: science Testsuite: autopkgtest-pkg-python Priority: optional Build-Depends: debhelper (>= 12~), dh-python, cmake, swig, python-setuptools, python-all-dev, python-numpy, libboost-dev, libhts-dev, libpbbam-dev (>= 0.18.0+dfsg-1~), libpbcopper-dev, libseqan2-dev, pandoc Standards-Version: 4.3.0 Vcs-Browser: https://salsa.debian.org/med-team/unanimity Vcs-Git: https://salsa.debian.org/med-team/unanimity.git Homepage: https://github.com/PacificBiosciences/unanimity Package: unanimity Architecture: any Depends: ${shlibs:Depends}, ${misc:Depends}, ${python:Depends} Description: generate and process accurate consensus nucleotide sequences Unanimity provides a set of tools for consensus sequences from Pacific Biosciences sequencing data: * Circular Consensus Calling ccs takes multiple reads of the same SMRTbell sequence and combines them, employing a statistical model, to produce one high quality consensus sequence. * Minor variant caller juliet identifies minor variants from aligned ccs reads. Package: python-consensuscore2 Architecture: any Section: python Depends: ${shlibs:Depends}, ${misc:Depends}, ${python:Depends} Description: generate consensus sequences for PacBio data -- Python 2 ConsensusCore2 embodies core C++ routines underlying the Arrow HMM algorithm for PacBio multi-sequence consensus. Arrow is the successor to the Quiver model---a CRF model that was embodied in the ConsensusCore C++ library. Compared to Quiver, the Arrow model is more statistically principled and easier and more robust to train. . This package installs the library for Python 2.