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Prefiltering Model for Homology Detection Algorithms on GPU.

Germán Retamosa1, Luis de Pedro1, Ivan González1

  • 1High Performance Computing and Networking Department, Universidad Autonóma de Madrid, Madrid, Spain.

Evolutionary Bioinformatics Online
|December 24, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a new heuristic model using graphics processing units (GPUs) to accelerate homology detection in sequence databases. The GPU-based approach significantly speeds up analysis, making it essential for next-generation sequencing systems.

Keywords:
NCBI BLASTNVIDIA CUDAcomputational biologynext-generation sequencingparallel programmingperformance analysis

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Traditional homology detection algorithms face challenges with speed and accuracy due to complex statistical models.
  • Accelerating these algorithms is crucial for efficient biological sequence analysis.

Purpose of the Study:

  • To develop and implement a novel heuristic model for prefiltering sequence databases.
  • To enhance the speed and efficiency of homology detection in large biological datasets.

Main Methods:

  • Implementation of a heuristic model leveraging NVIDIA's graphics processing units (GPUs) and multicore processors.
  • Development of a prefiltering application designed to reduce sequence database size.

Main Results:

  • The GPU-based prefiltering model significantly reduces sequence databases by 50%–95% without losing significant sequences.
  • GPU hardware accelerates existing homology detection tools like Basic Local Alignment Search Tool for Proteins (BLASTP) by up to a factor of 4.

Conclusions:

  • The implemented GPU-accelerated heuristic model offers substantial speedups for homology detection.
  • This prefiltering application is a valuable component for next-generation sequencing systems, improving overall analysis efficiency.