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Related Concept Videos

Parallel Processing01:20

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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Parallel implementation of DNA sequences matching algorithms using PWM on GPU architecture.

Rahul Sharma1, Nitin Gupta, Vipin Narang

  • 1Department of Computer Science and Engineering, College of Engineering Roorkee, India. rahulsrma26@gmail.com

International Journal of Bioinformatics Research and Applications
|May 18, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient parallel algorithm for searching Transcription Factor Of Binding Sites (TFBSs) using Positional Weight Matrices (PWMs). The optimized method significantly accelerates DNA sequence analysis on Graphics Processing Units (GPUs).

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Positional Weight Matrices (PWMs) are crucial for identifying Transcription Factor Of Binding Sites (TFBSs) on DNA.
  • Efficiently processing large-scale PWM data is essential for high-throughput genomic analysis.

Purpose of the Study:

  • To develop and implement an optimized online PWM search algorithm utilizing parallel processing architectures.
  • To enhance the speed and efficiency of TFBS detection on DNA sequences.

Main Methods:

  • Implementation of an online PWM search algorithm on a parallel architecture, specifically leveraging multi-cored Graphics Processing Units (GPUs).
  • Extensive utilization of highly multithreaded architecture and shared memory optimization within the CUDA framework for parallel reduction.

Main Results:

  • Achieved a significant speedup of 230-280x compared to a linear implementation on a GeForce GTX 280 GPU.
  • Demonstrated the effectiveness of shared memory optimization for parallel reduction in CUDA for accelerating PWM searches.

Conclusions:

  • The developed parallel algorithm offers a substantial performance improvement for TFBS detection using PWMs.
  • GPU-based parallel processing is a viable and efficient approach for large-scale bioinformatics tasks like TFBS identification.