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Related Experiment Video

Updated: Feb 1, 2026

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
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PhenoMeNal: processing and analysis of metabolomics data in the cloud.

Kristian Peters1, James Bradbury2, Sven Bergmann3,4

  • 1Leibniz Institute of Plant Biochemistry, Stress and Developmental Biology, Weinberg 3, 06120 Halle (Saale), Germany.

Gigascience
|December 12, 2018
PubMed
Summary

PhenoMeNal offers a cloud-based solution for metabolomics data analysis, integrating diverse tools into reproducible workflows. This platform enhances accessibility and interoperability for researchers in biological and biomedical domains.

Keywords:
NMRcloud computingcomputational workflowsdata analysise-infrastructuresgalaxymass spectrometrymetabolomicsstandardizationstatistics

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Area of Science:

  • * Computational Biology
  • * Bioinformatics
  • * Systems Biology

Background:

  • * Metabolomics involves the comprehensive study of small molecules to understand organism metabolism.
  • * The field is expanding rapidly with diverse applications in biomedical and biotechnological domains.
  • * Computational intensity necessitates open data formats, repositories, and analysis tools, yet current methods are often incompatible.

Purpose of the Study:

  • * To present PhenoMeNal, an advanced Infrastructure-as-a-Service (IaaS) solution for metabolomics data analysis.
  • * To provide a cloud-based platform that integrates and harmonizes existing open-source tools.
  • * To enable workflow-oriented, interoperable, reproducible, and shareable metabolomics data analysis.

Main Methods:

  • * PhenoMeNal integrates open-source metabolomics tools packaged as Docker containers.
  • * Deployment is managed via a Kubernetes orchestration framework.
  • * Standardized analysis workflows are provided through user interfaces like Galaxy and Jupyter.

Main Results:

  • * PhenoMeNal offers a scalable cloud e-infrastructure for metabolomics data analysis.
  • * It harmonizes software installation and configuration, providing ready-to-use scientific workflows.
  • * The platform ensures reproducible, shareable, and interoperable data analysis through standard formats and versioning.

Conclusions:

  • * PhenoMeNal serves as a crucial solution for cloud e-infrastructures in metabolomics research.
  • * It delivers accessible, user-friendly web interfaces adaptable to various cloud environments.
  • * The platform facilitates workflow-driven, reproducible, and shareable metabolomics analysis, applicable to other 'omics domains.