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PyOPA - optimal pairwise sequence alignments

Project Description

This python package provides a fast implementation to compute

  • optimal pairwise alignments of molecular sequences
  • ML distance estimates of pairwise alignments.

The implementation uses Farrar's algorithm <http://bioinformatics.oxfordjournals.org/content/23/2/156.abstract>_ to compute the optimal pairwise alignment using SSE vectorization operations. This package implements the Smith-Waterman and Needleman-Wunsch algorithm to compute the local and global sequence alignments.

Example

import pyopa
log_pam1_env = pyopa.read_env_json(os.path.join(pyopa.matrix_dir(), 'logPAM1.json'))
s1 = pyopa.Sequence('GCANLVSRLENNSRLLNRDLIAVKINADVYKDPNAGALRL')
s2 = pyopa.Sequence('GCANPSTLETNSQLVNRELIAVKINPRVYKGPNLGAFRL')

# super fast check whether the alignment reaches a given min-score
min_score = 100
pam250_env = pyopa.generate_env(log_pam1_env, 250, min_score)
pyopa.align_short(s1, s2, pam250_env)
Release History

Release History

This version
History Node

0.7.0

History Node

0.6

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
pyopa-0.7.0-cp27-cp27mu-macosx_10_11_x86_64.whl (6.8 MB) Copy SHA256 Checksum SHA256 cp27 Wheel Sep 30, 2016
pyopa-0.7.0-cp35-cp35m-macosx_10_11_x86_64.whl (6.8 MB) Copy SHA256 Checksum SHA256 cp35 Wheel Sep 30, 2016
pyopa-0.7.0.tar.gz (8.0 MB) Copy SHA256 Checksum SHA256 Source Sep 30, 2016

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