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Related Experiment Videos

D-ASSIRC: distributed program for finding sequence similarities in genomes.

Pierre Vincens1, Anne Badel-Chagnon, Cécile André

  • 1Département de Biologie (FR 36), Ecole Normale Supérieure, 46 rue d'Ulm, 75230 Paris Cedex 05, France. vincens@biologie.ens.fr

Bioinformatics (Oxford, England)
|April 6, 2002
PubMed
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This study enhances the Accelerated Search for SImilar Regions in Chromosomes (ASSIRC) tool for genome analysis. New parallel processing strategies significantly reduce running time, particularly the ASS strategy with many processors for efficient genomic similarity searches.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Identifying similar genomic regions is crucial for biological research.
  • Existing tools like Accelerated Search for SImilar Regions in Chromosomes (ASSIRC) aid in this process.
  • Improving the efficiency of these tools through parallelization is essential for analyzing large genomes.

Purpose of the Study:

  • To present and evaluate novel strategies for parallelizing the ASSIRC program.
  • To assess the performance gains achieved by distributing computational tasks across multiple processing units.
  • To optimize the search for genomic similarities using enhanced computational approaches.

Main Methods:

  • Development of D-ASSIRC, a parallelized version of ASSIRC.
  • Implementation of three distinct task-sharing strategies: ASS, AGD, and ALD.

Related Experiment Videos

  • Evaluation of running time efficiency based on the number of processing units utilized.

    • Main Results:

    • The ASS strategy demonstrated significant speedups, reducing runtime by a factor of 12 with 16 processing units.
    • The ALD strategy proved more efficient with a smaller number of processors (four to six).
    • All proposed strategies showed efficiency for diverse genomic research applications, including large-scale sequence similarity detection.

    Conclusions:

    • The parallelized D-ASSIRC tool offers improved performance for locating genomic similarities.
    • Different parallelization strategies are suitable for varying numbers of processing units, providing flexibility.
    • These advancements are valuable for accelerating genomic research and large genome analysis.