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Parallel computation of multiple biological sequence comparisons.

D E Foulser1, N G Core

  • 1Department of Computer Science, Yale University New Haven, Connecticut 06520.

Computers and Biomedical Research, an International Journal
|August 1, 1990
PubMed
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This study presents an efficient parallel method for comparing multiple DNA sequences using a tree data structure. The developed graphics program visualizes large datasets, aiding in the analysis of sequence similarities and structures.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Multiple sequence alignment is crucial for understanding evolutionary relationships and functional genomics.
  • Existing methods may face computational challenges with large datasets.
  • Efficient algorithms are needed to handle the growing volume of genomic data.

Purpose of the Study:

  • To introduce a novel parallel algorithm for efficient multiple DNA sequence comparison.
  • To develop a complementary graphics program for visualizing large-scale sequence comparison results.
  • To assess the performance and scalability of the parallel algorithm.

Main Methods:

  • Implementation of a parallel algorithm based on a conceptual tree data structure for DNA sequences.
  • Utilizing an Encore Multimax computer for performance evaluation with 11 sequences (over 4000 bases).

Related Experiment Videos

  • Development of a graphics program for visualizing multiple sequence alignment output.

    • Main Results:

    • The parallel algorithm demonstrated efficient processor utilization on the Encore Multimax.
    • Timing data highlighted the significant impact of computer system architecture on parallel program performance.
    • The graphics program effectively displayed large volumes of comparison data in a single plot, revealing known sequence structures.

    Conclusions:

    • The presented parallel method offers an efficient approach for multiple DNA sequence comparison.
    • The visualization tool enhances the interpretability of complex sequence alignment data.
    • Further optimization may be influenced by specific parallel computing environments.