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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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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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Related Experiment Video

Updated: Apr 18, 2026

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens
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HBLAST: Parallelised sequence similarity--A Hadoop MapReducable basic local alignment search tool.

Aisling O'Driscoll1, Vladislav Belogrudov1, John Carroll2

  • 1Department of Computing, Cork Institute of Technology, Rossa Avenue, Bishopstown, Cork, Ireland.

Journal of Biomedical Informatics
|January 28, 2015
PubMed
Summary
This summary is machine-generated.

Hadoop Blast (HBlast) offers a scalable solution for genomic sequence alignment, overcoming computational bottlenecks. This approach enhances BLAST search performance on memory-constrained hardware, aiding rapid clinical diagnostics.

Keywords:
Big dataBioinformaticsCloud computingGenomicsHadoopSequence alignment

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Genomic database growth creates bottlenecks in sequence alignment.
  • High Performance Computing (HPC) is often required but cost-prohibitive.
  • Existing parallel solutions struggle with Big Data scalability and memory constraints.

Purpose of the Study:

  • To develop a scalable parallelized BLAST algorithm for Big Data.
  • To address the limitations of existing solutions in processing large genomic datasets.
  • To enable efficient sequence alignment on memory-constrained hardware.

Main Methods:

  • Implemented a parallelized BLAST algorithm using the Hadoop framework.
  • Introduced "virtual partitioning" for flexible segmentation of databases and query sequences.
  • Utilized a "divide and conquer" parallelization strategy.

Main Results:

  • Hadoop Blast (HBlast) demonstrates improved scalability over existing methods.
  • Achieved a well-balanced computational workload with minimal database segmentation.
  • Showcased enhanced BLAST search performance on memory-constrained hardware.

Conclusions:

  • HBlast offers an efficient and scalable solution for sequence alignment in computational biology.
  • The algorithm's performance on affordable hardware has significant implications for clinical diagnostics.
  • Enables faster and more accurate identification of pathogenic DNA for in-field testing.