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Related Experiment Videos

Multiple alignment of sequences on parallel computers

S Date1, R Kulkarni, B Kulkarni

  • 1Center for Development of Advanced Computing, University of Poona, Pune, India.

Computer Applications in the Biosciences : CABIOS
|August 1, 1993
PubMed
Summary
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A new software package enables efficient multiple sequence alignment for proteins and nucleic acids on parallel computers. This tool achieves near-linear speed gains, aiding in protein classification and analysis.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Multiple sequence alignment is crucial for understanding protein and nucleic acid function and evolution.
  • Existing alignment tools may face limitations with large datasets or long sequences.
  • Parallel computing offers a potential solution for accelerating complex bioinformatics tasks.

Purpose of the Study:

  • To develop a high-performance software package for multiple sequence alignment.
  • To leverage parallel computing for handling large-scale sequence data.
  • To utilize the software for analyzing biological sequences and classifying proteins.

Main Methods:

  • Development of a software package on a transputer-based parallel computer (C-DAC).
  • Implementation of a data parallelization strategy using the farming approach.

Related Experiment Videos

  • Testing the software with large datasets, including hemoglobin and cytochrome c sequences.

    • Main Results:

    • The software efficiently performs multiple alignment of protein and nucleic acid sequences.
    • Achieved near-linear speedup with an increase in the number of processors (transputers).
    • Identified the signature sequence for cytochrome c as PGTKMXF.
    • Demonstrated the utility of the multiple alignment score (S) for protein categorization.

    Conclusions:

    • The developed software package is effective for large-scale multiple sequence alignment.
    • Parallel computing significantly enhances the speed and scalability of sequence alignment.
    • The alignment score parameter (S) provides a valuable metric for protein subfamily classification.