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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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A Rapid High-throughput Method for Mapping Ribonucleoproteins (RNPs) on Human pre-mRNA
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permGPU: Using graphics processing units in RNA microarray association studies.

Ivo D Shterev1, Sin-Ho Jung, Stephen L George

  • 1Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27705, USA.

BMC Bioinformatics
|June 18, 2010
PubMed
Summary
This summary is machine-generated.

permGPU dramatically accelerates microarray association studies using graphics processing units (GPUs) for permutation resampling. This open-source tool enhances computational efficiency for complex genetic analyses.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genetics

Background:

  • Microarray association studies frequently use computationally intensive methods like permutation and cross-validation.
  • These analyses are often embarrassingly parallel, making them suitable for scalable computing solutions.
  • Developing efficient, multi-core processor-compatible approaches is crucial for handling large datasets.

Purpose of the Study:

  • To develop a high-performance computing solution for permutation resampling in microarray association studies.
  • To leverage graphics processing units (GPUs) for accelerating computationally demanding analyses.
  • To provide a scalable and efficient tool for genetic association analysis.

Main Methods:

  • Developed permGPU, a CUDA-based implementation utilizing GPUs for permutation resampling.
  • Evaluated performance using permutation resampling for various test statistics in microarray association studies.
  • Conducted extensive simulations comparing permGPU on an NVIDIA GTX 280 card against an optimized C/C++ solution on a Linux server.

Main Results:

  • permGPU demonstrated a dramatic increase in performance for permutation resampling.
  • Significant speedup was observed when using permGPU on a GPU compared to a conventional CPU-based C/C++ implementation.
  • The tool's applicability was illustrated within the context of multiple test statistics.

Conclusions:

  • permGPU offers a substantial performance improvement for permutation resampling in microarray association studies.
  • The software is available as an open-source standalone application and an R extension package.
  • The current version supports six test statistics for diverse trait types including binary, quantitative, and time-to-event data.