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Related Experiment Videos

Accelerating protein classification using suffix trees.

B Dorohonceanu1, C G Nevill-Manning

  • 1Computer Science Department, Rutgers University, Piscataway, NJ 07310, USA. dbogdan@cs.rutgers.edu

Proceedings. International Conference on Intelligent Systems for Molecular Biology
|September 8, 2000
PubMed
Summary
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This study introduces a novel suffix tree method to accelerate protein region searches. This approach significantly speeds up analyses by efficiently excluding large protein segments, improving computational efficiency.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics and Proteomics

Background:

  • Position-specific scoring matrices (PSSMs) are crucial for identifying conserved protein regions.
  • Current methods for PSSM searches can be computationally intensive.
  • The need for faster algorithms to analyze large protein sequence datasets is growing.

Purpose of the Study:

  • To develop and present an accelerated method for searching protein sequences using PSSMs.
  • To leverage suffix tree data structures for enhanced search efficiency.
  • To reduce the computational resources required for identifying conserved protein regions.

Main Methods:

  • Implemented a search acceleration technique utilizing a suffix tree data structure.
  • Integrated early termination strategies for scoring matrix evaluation within the suffix tree framework.

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  • Optimized suffix tree node storage to minimize memory usage (17 bytes per input symbol).

    • Main Results:

    • The suffix tree-based method significantly accelerates PSSM searches, achieving speedups of up to tenfold.
    • The method efficiently prunes large portions of the sequence space, reducing unnecessary computations.
    • Memory requirements for the suffix tree were optimized, making the approach practical for large datasets.

    Conclusions:

    • The proposed suffix tree method offers a substantial improvement in the speed of PSSM searches.
    • This technique provides a computationally efficient solution for identifying conserved protein regions.
    • The optimized memory footprint makes this method suitable for large-scale bioinformatics analyses.