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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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MetHoS: a platform for large-scale processing, storage and analysis of metabolomics data.

Konstantinos Tzanakis1, Tim W Nattkemper2, Karsten Niehaus3

  • 1International Research Training Group "Computational Methods for the Analysis of the Diversity and Dynamics of Genomes", Faculty of Technology, Bielefeld University, Bielefeld, Germany. ktzan@cebitec.uni-bielefeld.de.

BMC Bioinformatics
|July 8, 2022
PubMed
Summary

MetHoS is a new web-based platform for processing and analyzing large-scale metabolomics data from mass spectrometry. It uses Big Data frameworks for scalable, integrative analysis of thousands of experiments, enabling untargeted metabolomic studies.

Keywords:
Distributed analysisDistributed storageLarge-scale metabolomicsMass spectrometry dataParallel processing

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Area of Science:

  • Biochemistry
  • Bioinformatics
  • Computational Biology

Background:

  • Mass spectrometry has generated vast amounts of metabolomics data, necessitating efficient analysis tools.
  • Existing software often struggles with processing large numbers of datasets from diverse studies.
  • There is a need for integrated solutions to handle thousands of metabolomics experiments cohesively.

Purpose of the Study:

  • To introduce MetHoS, an automated web-based platform for large-scale metabolomics data management and analysis.
  • To provide a scalable solution for processing, storing, and analyzing numerous mass spectrometry-based metabolomics datasets.
  • To enable integrative analysis across diverse metabolomics studies.

Main Methods:

  • MetHoS utilizes Big Data frameworks for parallel and distributed processing, storage, and analysis.
  • The platform is designed for high scalability, capable of handling extensive datasets across computer clusters.
  • Thousands of experiments from the MetaboLights database were used for a large-scale proof-of-concept study.

Main Results:

  • MetHoS successfully processed, stored, and statistically analyzed thousands of metabolomics experiments.
  • The platform demonstrated scalability and efficiency in handling large volumes of mass spectrometry data.
  • Proof-of-concept study confirmed the platform's capability for integrative, large-scale metabolomics analysis.

Conclusions:

  • MetHoS is well-suited for large-scale processing, storage, and analysis of metabolomics data.
  • The platform facilitates untargeted metabolomic analyses on extensive datasets.
  • MetHoS is freely available, encouraging its adoption for metabolomics research.