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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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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Flexible, fast and accurate sequence alignment profiling on GPGPU with PaSWAS.

Sven Warris1, Feyruz Yalcin2, Katherine J L Jackson3

  • 1Institute for Life Science & Technology & Hanze Research Center Energy, Hanze University of Applied Sciences Groningen, 9747 AS, Zernikeplein 11, Groningen, The Netherlands.

Plos One
|April 2, 2015
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Summary
This summary is machine-generated.

PaSWAS software accelerates sequence alignment using graphics processing units, enabling detailed analysis of next-generation sequencing data. This parallel Smith-Waterman implementation provides essential alignment details for complex biological research.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Large-scale sequence alignment is crucial for next-generation sequencing (NGS) data analysis.
  • Existing Smith-Waterman (SW) algorithm implementations face speed, flexibility, and accuracy limitations.
  • Current GPU-based SW tools often lack necessary alignment details for downstream analysis.

Purpose of the Study:

  • To develop a fast and flexible parallel Smith-Waterman (SW) alignment software.
  • To leverage NVIDIA-based general-purpose graphics processing units (GPGPUs) for high-speed sequence alignments.
  • To enable retrieval of detailed alignment information, including scores, gaps, and mismatches.

Main Methods:

  • Development of the Parallel SW Alignment Software (PaSWAS).
  • Implementation utilizing NVIDIA GPGPUs for parallel computation.
  • Validation through two distinct biological use cases: tag recovery and isotype assignment.

Main Results:

  • PaSWAS achieves high-speed sequence alignments using GPGPU acceleration.
  • The software successfully retrieves detailed alignment metrics (score, gaps, mismatches).
  • Demonstrated utility in tag recovery for NGS data and isotype assignment for immunoglobulin sequences.

Conclusions:

  • PaSWAS offers a versatile and efficient solution for large-scale sequence alignment.
  • The tool enhances NGS data analysis by providing crucial alignment details.
  • The parallel SW implementation is suitable for diverse bioinformatics applications.