| Name | ssw-py JSON |
| Version |
1.0.1
JSON |
| download |
| home_page | https://github.com/libnano/ssw-py |
| Summary | Python bindings for Complete-Striped-Smith-Waterman-Library (SSW) project |
| upload_time | 2023-03-14 14:42:16 |
| maintainer | |
| docs_url | None |
| author | Nick Conway |
| requires_python | |
| license | MIT |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# ssw-py: Striped Smith-Waterman SIMD accelerated Python Package for Use in Genomic Applications
<a href="https://github.com/libnano/ssw-py/actions/" rel="actions"></a>
<a href="http://www.gnu.org/licenses/gpl-2.0.html" rel="license"></a>
<a href="https://pypi.python.org/pypi/ssw-py" rel="pypi"></a>
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.
Raw data
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