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PARALIGN: rapid and sensitive sequence similarity searches powered by parallel computing technology.

Per Eystein Saebø1, Sten Morten Andersen, Jon Myrseth

  • 1Centre for Molecular Biology and Neuroscience, Institute of Medical Microbiology, University of Oslo and Rikshospitalet-Radiumhospitalet, HF NO-0027 Oslo, Norway.

Nucleic Acids Research
|June 28, 2005
PubMed
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PARALIGN is a fast and sensitive tool for identifying distantly related DNA and protein sequences. It uses novel multimedia technology for high-speed searches, comparable to BLAST, and is available online and for academic download.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Identifying distantly related sequences is crucial for understanding biological function and evolution.
  • Existing tools like BLAST offer speed, while Smith-Waterman provides sensitivity, but a tool combining both has been lacking.

Purpose of the Study:

  • To introduce PARALIGN, a novel software tool for rapid and sensitive similarity searching in biological sequence databases.
  • To leverage parallel computing for enhanced search performance.

Main Methods:

  • Implementation of two algorithms: accelerated Smith-Waterman and the novel ParAlign algorithm.
  • Exploitation of multimedia technology (a form of parallel computing) for high-speed processing.
  • Design for efficient execution on computer clusters using the Message Passing Interface (MPI) standard.

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Main Results:

  • The ParAlign algorithm achieves sensitivity comparable to Smith-Waterman.
  • PARALIGN demonstrates speed comparable to BLAST for protein sequence searches.
  • The software is optimized for parallel processing on modern multi-core processors and computer clusters.

Conclusions:

  • PARALIGN offers a significant advancement in sequence similarity searching, balancing speed and sensitivity.
  • The tool's accessibility via a public web service and free academic download promotes its widespread adoption.
  • The innovative use of multimedia technology in bioinformatics software opens new avenues for computational efficiency.