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A parallel implementation of the Smith-Waterman algorithm for massive sequences searching.

Hsien-Yu Liao1, Meng-Lai Yin, Yi Cheng

  • 1Dept. of Electr. & Comput. Eng., California State Polytech. Univ., Pomona, CA, USA.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|February 3, 2007
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This study presents a parallel implementation of the Smith-Waterman algorithm using FPGA technology. The new method significantly accelerates biological sequence searching while maintaining high sensitivity.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Algorithm Optimization

Background:

  • Biological sequence searching is crucial in bioinformatics.
  • The Smith-Waterman algorithm offers high sensitivity but suffers from poor performance.
  • Traditional sequential implementations limit real-world applications.

Purpose of the Study:

  • To develop a high-performance parallel implementation of the Smith-Waterman algorithm.
  • To leverage FPGA technology for massive parallelism in sequence analysis.
  • To overcome the performance limitations of the traditional Smith-Waterman algorithm.

Main Methods:

  • Developed a parallel implementation methodology for the Smith-Waterman algorithm.
  • Utilized Field-Programmable Gate Arrays (FPGAs) for hardware acceleration.

Related Experiment Videos

  • Applied dynamic programming principles within a parallel architecture.

    • Main Results:

    • Achieved significant speedup compared to traditional sequential implementations.
    • Maintained the high sensitivity characteristic of the Smith-Waterman algorithm.
    • Demonstrated the effectiveness of FPGA-based parallelization for bioinformatics tasks.

    Conclusions:

    • FPGA-based parallel implementation offers a viable solution for efficient biological sequence searching.
    • The presented methodology enhances the practical applicability of the Smith-Waterman algorithm.
    • Massive parallelism through FPGAs can overcome computational bottlenecks in bioinformatics.