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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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The gputools package enables GPU computing in R.

Joshua Buckner1, Justin Wilson, Mark Seligman

  • 1Department of Psychiatry and Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA. bucknerj@umich.edu

Bioinformatics (Oxford, England)
|October 24, 2009
PubMed
Summary
This summary is machine-generated.

Researchers can accelerate microarray gene expression analysis in R using graphics processing units (GPUs). This implementation offers a cost-effective, high-performance alternative to traditional cluster hardware for complex computational tasks.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • High-Performance Computing

Background:

  • The R statistical environment lacks default parallelism, often necessitating expensive hardware for large-scale analyses.
  • Graphics Processing Units (GPUs) offer a powerful and affordable computational resource.
  • Microarray gene expression analysis involves computationally intensive tasks.

Purpose of the Study:

  • To implement commonly used microarray gene expression analysis functions in R for GPU utilization.
  • To provide R users with an accessible method for leveraging GPU acceleration.
  • To offer a cost-effective alternative to traditional parallel computing solutions.

Main Methods:

  • Development of R functions utilizing Nvidia's CUDA toolkit for GPU computation.
  • Integration of GPU-accelerated functions into an R package.
  • Testing and validation of implemented functions on GPU-equipped hardware.

Main Results:

  • Successful implementation of key microarray analysis functions for GPU execution.
  • Demonstrated performance improvements for R users with Nvidia GPUs.
  • Availability of the 'gputools' R package for public use.

Conclusions:

  • GPU acceleration provides a significant performance enhancement for R-based gene expression analysis.
  • The 'gputools' package democratizes access to high-performance computing for R users.
  • This approach reduces the need for expensive specialized hardware for complex biological data analysis.