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Related Concept Videos

Parallel Processing01:20

Parallel Processing

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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 computing for genome sequence processing.

You Zou1, Yuejie Zhu1, Yaohang Li2

  • 1Hunan Provincial Key Lab of Bioinformatics, School of Computer Science and Engineering at Central South University, Changsha, China.

Briefings in Bioinformatics
|April 6, 2021
PubMed
Summary
This summary is machine-generated.

This review explores parallel computing technologies for processing vast amounts of genome data. It analyzes programming models and tools for applications like genome sequence alignment and SNP calling.

Keywords:
cluster computinggenome sequence processingheterogeneous computingmulti-core computingparallel computing

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • The rapid expansion of genome sequencing generates massive datasets, creating significant data processing challenges.
  • Existing solutions involve big data storage, efficient algorithms, and parallel computing.

Purpose of the Study:

  • To review and analyze popular parallel programming technologies for genome sequence processing.
  • To provide a reference for selecting high-performance computing tools in genomics.

Main Methods:

  • Introduction of three common parallel computing models based on hardware architectures.
  • Classification and analysis of each model's features.
  • Discussion of parallel computing applications in genome sequence alignment, SNP calling, preprocessing, and pattern detection.

Main Results:

  • Summary of tools and algorithms for each application, detailing principles, hardware platforms, and efficiency.
  • Analysis of programming models for different hardware and applications.

Conclusions:

  • Parallel computing offers essential solutions for managing and analyzing large-scale genomic data.
  • Understanding programming models and tools is crucial for advancing genomic research and applications.