Metadata-Version: 2.1
Name: ssw-py
Version: 1.0.1
Summary: Python bindings for Complete-Striped-Smith-Waterman-Library (SSW) project
Home-page: https://github.com/libnano/ssw-py
Author: Nick Conway
Author-email: a.grinner@gmail.com
License: MIT
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Cython
Classifier: Topic :: Scientific/Engineering
Description-Content-Type: text/markdown
License-File: LICENSE.md

# ssw-py: Striped Smith-Waterman SIMD accelerated Python Package for Use in Genomic Applications

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This library uses the excellent source code from this is
[original source repository](https://github.com/mengyao/Complete-Striped-Smith-Waterman-Library)

Please cite this [PLOS ONE paper](http://dx.plos.org/10.1371/journal.pone.0082138) by Zhao et al. 2013 when using this software.

## Overview

**ssw-py** provides a fast implementation of the Smith-Waterman algorithm,
which uses the Single-Instruction Multiple-Data (SIMD) instructions to parallelize
the algorithm at the instruction level.

Using `ssw.AlignmentMgr`, you can compute the Smith-Waterman score, alignment location and traceback path
([CIGAR](https://genome.sph.umich.edu/wiki/SAM#What_is_a_CIGAR.3F)) of the
optimal alignment accurately; and return the sub-optimal alignment score and
location heuristically.

Note: When Striped Smith-Waterman opens a gap, the gap open penalty alone is applied.

## Installation

from [PyPi](https://pypi.org/project/ssw-py/)

    $ pip install ssw-py


or from source

    $ python setup.py install

## Documentation
See [documentation](https://libnano.github.io/ssw-py/) for help on using these
bindings.
