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Parallel pattern identification in biological sequences on clusters.

Chun-Hsi Huang1, Sanguthevar Rajasekaran

  • 1Department of Computer Science and Engineering, U-155, University of Connecticut, Storrs, CT 06269, USA. huang@cse.uconn.edu

IEEE Transactions on Nanobioscience
|September 24, 2004
PubMed
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Identifying tandem repeats in genomes is crucial for understanding diseases. This study presents a new parallel algorithm that efficiently finds these patterns on low-cost clusters, reducing computation time and communication overhead.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Tandem repeats are common in genomes and linked to human neurological diseases.
  • Identifying these repetitive sequences is computationally demanding.
  • Existing parallel methods require specialized hardware and low-latency networks.

Purpose of the Study:

  • To develop a communication-efficient parallel algorithm for tandem repeat identification.
  • To leverage cost-effective clusters for accelerating biological sequence analysis.

Main Methods:

  • Designed a parallel algorithm with five computation/communication phases.
  • Algorithm focuses on minimizing inter-processor communication overhead.
  • Implementation targets low-cost cluster environments.

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Main Results:

  • The algorithm achieves O(n) computation time per phase.
  • Communication complexity is reduced to O(p) message units.
  • Demonstrates potential for significant speedups on clusters.

Conclusions:

  • The proposed algorithm offers an efficient solution for tandem repeat identification.
  • Low communication overhead is key for effective parallelization on clusters.
  • This approach enhances the feasibility of large-scale genomic analyses.