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A Practical Guide to Phylogenetics for Nonexperts
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Published on: February 5, 2014

A parallel pairwise local sequence alignment algorithm.

Sanghamitra Bandyopadhyay1, Ramkrishna Mitra

  • 1Machine Intelligence Unit, Indian Statistical Institute, Kolkata 700108, India. sanghami@isical.ac.in

IEEE Transactions on Nanobioscience
|April 16, 2009
PubMed
Summary
This summary is machine-generated.

Researchers developed RPAlign, a parallel algorithm for sequence alignment, overcoming computational limits. This tool significantly speeds up alignment using cluster computers, maintaining high sensitivity for large and distantly related sequences.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Heuristic methods are often used for sequence alignment due to the computational expense of exact algorithms like Smith-Waterman (SW).
  • Existing parallelization strategies primarily focus on database searching via fragmentation, not on the alignment process itself.
  • This leads to a loss of sensitivity in detecting biological sequence relationships.

Purpose of the Study:

  • To develop a parallel algorithm, RPAlign, that leverages cluster computing for efficient sequence alignment.
  • To address the time and space constraints that limit the use of sensitive algorithms like SW.
  • To improve the detection of distantly related sequences and enable alignment of megabase-scale sequences.

Main Methods:

  • RPAlign utilizes cluster computers to perform parallel sequence alignment.
  • The algorithm first identifies potentially alignable regions within sequences.
  • Subsequent alignment is performed on these identified regions.

Main Results:

  • RPAlign reduces computation time by up to 9x with BLAST and 99x with SW.
  • The algorithm maintains sensitivity comparable to the original methods.
  • It enables sensitive alignment of distantly related sequences missed by BLAST.
  • RPAlign efficiently aligns megabase-scale sequences where SW is intractable.

Conclusions:

  • RPAlign offers a computationally efficient and sensitive solution for sequence alignment challenges.
  • The parallel approach overcomes limitations of traditional methods for large-scale and divergent sequence analysis.
  • This method enhances the utility of sensitive alignment algorithms like SW in practical research settings.