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pyPaSWAS: Python-based multi-core CPU and GPU sequence alignment.

Sven Warris1,2, N Roshan N Timal3, Marcel Kempenaar1

  • 1Expertise Centre ALIFE, Institute for Life Science & Technology, Hanze University of Applied Sciences Groningen, Groningen, the Netherlands.

Plos One
|January 3, 2018
PubMed
Summary
This summary is machine-generated.

The new pyPaSWAS tool offers accessible, parallel Smith-Waterman (SW) sequence alignment across multiple hardware platforms, including GPUs. This Python-based application simplifies bioinformatics by enabling accurate alignments and promoting wider adoption of parallel computing.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • High-Performance Computing

Background:

  • Previous Smith-Waterman (SW) sequence alignment tools like PaSWAS were limited to NVIDIA GPUs, hindering wider adoption.
  • OpenCL offers broader hardware compatibility, and Python integration can simplify parallel computing in bioinformatics.

Purpose of the Study:

  • To develop a versatile SW sequence alignment tool that overcomes platform limitations.
  • To enhance the usability and accessibility of parallel computing in bioinformatics through Python.

Main Methods:

  • Developed pyPaSWAS, a novel application implementing parallel SW sequence alignment entirely in Python.
  • Utilized Python libraries for system configuration, I/O, and logging.
  • Ensured compatibility with multi-core systems and GPUs.

Main Results:

  • pyPaSWAS provides accurate SW sequence alignments across diverse hardware platforms.
  • The application supports affine gap penalties and allows inspection of alignment details.
  • Python integration facilitates easier use and further development.

Conclusions:

  • pyPaSWAS offers a user-friendly, Python-based solution for parallel SW sequence alignments on GPUs and multi-core systems.
  • Integrating Python with high-performance parallel languages is a viable strategy for computationally intensive bioinformatics algorithms.
  • This approach can foster greater adoption and extension of parallel computing in the field.