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Related Concept Videos

Parallel Processing01:20

Parallel Processing

260
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...
260

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Scalable in-memory processing of omics workflows.

Vadim Elisseev1,2, Laura-Jayne Gardiner1, Ritesh Krishna1

  • 1IBM Research Europe, Hartree Centre, Daresbury Laboratory, Keckwick Lane, WarringtonWA4 4AD, Cheshire, UK.

Computational and Structural Biotechnology Journal
|May 6, 2022
PubMed
Summary
This summary is machine-generated.

This study demonstrates in-memory computing for faster metagenomic analysis, comparing file systems and key-value storage for omics data. In-memory key-value storage offers improved flexibility and speed for bioinformatics workflows.

Keywords:
BioinformaticsCloudHPCKey-value storeMachine learningMetagenomics

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Metagenomic sequencing generates vast amounts of omics data, requiring efficient analysis methods.
  • Traditional file systems can be a bottleneck for high-throughput omics data processing.
  • Integrating high-performance computing (HPC) and cloud-native technologies is crucial for modern biological research.

Purpose of the Study:

  • To present a proof-of-concept for in-memory computing in metagenomic data analysis.
  • To compare the performance of POSIX file systems versus key-value storage for omics data.
  • To demonstrate the potential of containerized workflows for real-time analysis of biological data, focusing on antimicrobial resistance (AMR).

Main Methods:

  • Implemented an in-memory computing paradigm for metagenomic read analysis.
  • Benchmarked POSIX file systems against in-memory key-value storage for omics data handling.
  • Developed a bioinformatics and explainable machine learning (ML) workflow for AMR prediction using sewage microbiome data.

Main Results:

  • In-memory key-value storage demonstrated superior flexibility and speed for omics data processing compared to POSIX file systems.
  • The developed workflow successfully predicted population life expectancy based on sewage microbiome data, including AMR contributions.
  • Proof-of-concept validated the integration of HPC and cloud-native technologies for efficient data analysis.

Conclusions:

  • In-memory key-value storage significantly enhances omics data processing efficiency.
  • Containerized, real-time analysis workflows hold promise for public health surveillance, particularly for antimicrobial resistance (AMR).
  • Future real-time analyses can enable rapid risk assessment based on community AMR profiles.