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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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Eukaryotes have large genomes compared to prokaryotes. To fit their genomes into a cell, eukaryotic DNA is packaged extraordinarily tightly inside the nucleus. To achieve this, DNA is tightly wound around proteins called histones, which are packaged into nucleosomes that are joined by linker DNA and coil into chromatin fibers. Additional fibrous proteins further compact the chromatin, which is recognizable as chromosomes during certain phases of cell division.
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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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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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Parallel computing in genomic research: advances and applications.

Kary Ocaña1, Daniel de Oliveira2

  • 1National Laboratory of Scientific Computing, Petrópolis, Rio de Janeiro, Brazil.

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|November 26, 2015
PubMed
Summary
This summary is machine-generated.

Genomic experiments generate massive biological big data. High-performance computing (HPC) and parallelism techniques accelerate processing, but require specialized expertise for effective implementation.

Keywords:
cloud computingcluster computinggenomic researchgrid computinghigh-performance computingparallel computing

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genomic experiments generate biological big data (Terabytes-Petabytes).
  • Processing this data on workstations is time-consuming (weeks/months).
  • High-performance computing (HPC) and parallelism offer solutions for faster data processing and analysis.

Purpose of the Study:

  • To systematically review recent literature on genomics and parallel computing.
  • To identify characteristics, benefits, and challenges of using HPC for genomic data analysis.
  • To guide scientists in leveraging parallelism and HPC for genomic experiments.

Main Methods:

  • Systematic literature review of recent research.
  • Analysis of studies involving genomics and parallel computing.
  • Synthesis of findings on HPC implementation in bioinformatics.

Main Results:

  • HPC environments (clouds, grids, clusters, GPUs) can significantly reduce genomic data processing times.
  • Successful implementation requires integrating computational, biological, and mathematical expertise.
  • Various solutions exist to facilitate the use of HPC for genomic experiments.

Conclusions:

  • Parallel computing and HPC are essential for managing and analyzing large-scale genomic data.
  • Scientists need specific expertise to effectively utilize HPC resources for bioinformatics.
  • Understanding the benefits and challenges is crucial for optimizing genomic data processing.