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Novel Sequence Discovery by Subtractive Genomics
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Pairwise sequence alignment for very long sequences on GPUs.

Junjie Li1, Sanjay Ranka1, Sartaj Sahni1

  • 1Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL-32611, USA.

International Journal of Bioinformatics Research and Applications
|July 4, 2014
PubMed
Summary
This summary is machine-generated.

We developed new GPU algorithms for the Smith-Waterman algorithm, significantly speeding up pairwise sequence alignment for very long DNA or protein sequences. These methods achieve an order of magnitude faster alignment compared to existing GPU approaches.

Keywords:
CUDAGPUSmith–Waterman algorithmbioinformaticsgraphics processing unitslocal alignmentpairwise sequence alignment

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • High-Performance Computing

Background:

  • Pairwise sequence alignment is fundamental in bioinformatics.
  • The Smith-Waterman algorithm is a standard for local sequence alignment.
  • Aligning very long sequences is computationally intensive.

Purpose of the Study:

  • To develop novel single-GPU parallelizations of the Smith-Waterman algorithm.
  • To enable efficient alignment of very long biological sequences.
  • To reduce computational time for pairwise sequence alignment.

Main Methods:

  • Single-GPU parallelization strategies for the Smith-Waterman algorithm.
  • Implementation of algorithms for determining alignment score and actual alignment.
  • Experimental evaluation on a single GPU.

Main Results:

  • Developed novel parallel algorithms for Smith-Waterman on a single GPU.
  • Achieved an order of magnitude reduction in runtime compared to existing GPU algorithms.
  • Algorithms are suitable for aligning very long sequences.

Conclusions:

  • The novel GPU parallelizations offer significant speedups for Smith-Waterman.
  • Efficient alignment of very long sequences is now feasible.
  • These methods advance high-performance computing in bioinformatics.