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Related Experiment Videos

High-throughput sequence alignment using Graphics Processing Units.

Michael C Schatz1, Cole Trapnell, Arthur L Delcher

  • 1Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA. mschatz@umiacs.umd.edu

BMC Bioinformatics
|December 12, 2007
PubMed
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Researchers need faster DNA sequence alignment tools for high-throughput sequencing. MUMmerGPU, a new program, uses Graphics Processing Units (GPUs) to accelerate sequence alignment, achieving significant speedups over traditional CPU-based methods.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput DNA sequencing technologies generate vast amounts of data requiring efficient analysis.
  • Existing sequence alignment tools face challenges in keeping pace with the increasing data volume.
  • Need for faster, high-throughput alignment tools on inexpensive hardware is critical.

Purpose of the Study:

  • To introduce MUMmerGPU, an open-source, high-throughput, parallel pairwise local sequence alignment program.
  • To leverage Graphics Processing Units (GPUs) for accelerating sequence alignment tasks.
  • To provide a cost-effective and rapid solution for analyzing large-scale sequencing data.

Main Methods:

  • Developed MUMmerGPU utilizing NVIDIA's Compute Unified Device Architecture (CUDA).

Related Experiment Videos

  • Implemented parallel processing of query sequences on GPUs against a reference sequence stored in a suffix tree.
  • Compared performance against serial CPU-based alignment kernels and existing MUMmer versions.

    • Main Results:

    • MUMmerGPU achieves over a 10-fold speedup compared to a serial CPU version.
    • Outperforms the exact alignment component of MUMmer on high-end CPUs by 3.5-fold in total application time.
    • Demonstrates significant speed improvements for aligning reads from Solexa/Illumina, 454, and Sanger sequencing technologies.

    Conclusions:

    • MUMmerGPU offers an ultra-fast, low-cost solution for sequence alignment.
    • Effectively addresses the data analysis demands of modern high-throughput sequencing.
    • Highlights the potential of GPUs for accelerating memory-intensive bioinformatics applications.