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A Practical Guide to Phylogenetics for Nonexperts
12:00

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Published on: February 5, 2014

PLAST: parallel local alignment search tool for database comparison.

Van Hoa Nguyen1, Dominique Lavenier

  • 1INRIA/IRISA, Campus de Beaulieu, 35042 Rennes Cedex, France. vhnguyen@irisa.fr

BMC Bioinformatics
|October 14, 2009
PubMed
Summary
This summary is machine-generated.

A new parallel algorithm, PLAST, accelerates genomic sequence similarity searching by leveraging modern microprocessor features like SIMD and multicore processing. This approach offers significant speedups over existing methods with comparable accuracy.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Sequence similarity searching is crucial in molecular biology, especially with the rise of next-generation sequencing.
  • Modern microprocessors increasingly feature parallel architectures (e.g., multi-core).
  • There is a growing need for faster algorithms to handle large biological datasets.

Purpose of the Study:

  • To design an effective algorithm for sequence similarity searching that utilizes the parallel capabilities of modern microprocessors.
  • To develop a method that can process vast amounts of genomic data efficiently.

Main Methods:

  • Developed and implemented a parallel algorithm named PLAST.
  • Exploited Single Instruction, Multiple Data (SIMD) programming model (SSE instruction set).
  • Utilized the multithreading concept (multicore processing).

Main Results:

  • PLAST software was created for comparing large genomic databases.
  • Tests on an 8-processor server showed speedups of 3 to 6 times compared to multithreaded BLAST.
  • The algorithm maintained a similar level of accuracy to existing methods.

Conclusions:

  • A parallel algorithmic approach, informed by microprocessor architecture, significantly speeds up similarity searches.
  • The developed method preserves the standard sensitivity required for similarity search problems.
  • PLAST offers an efficient solution for handling large-scale genomic data on contemporary hardware.