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Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics.

Franck Giacomoni1, Gildas Le Corguillé1, Misharl Monsoor1

  • 1INRA, UMR 1019, PFEM, 63122 Saint Genes Champanelle, CNRS, UPMC, FR2424, ABiMS, Station Biologique, 29680 Roscoff, INRA, UMR 1331, PF MetaToul-AXIOM, Toxalim, F-31027 Toulouse, INRA, Metabolome Facility of Bordeaux Functional Genomics Center, IBVM, 33140 Villenave d'Ornon and CEA, LIST, Laboratory for Data Analysis and Smart Systems (LADIS), MetaboHUB Paris, F-91191 Gif-sur-Yvette, France.

Bioinformatics (Oxford, England)
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Summary

Workflow4Metabolomics (W4M) is a new open-source platform for computational metabolomics. It offers a collaborative environment for integrating, evaluating, and running analysis workflows for complex biological data.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Metabolomics

Background:

  • The field of computational metabolomics is complex and rapidly evolving.
  • There is a need for collaborative infrastructures to integrate diverse algorithms for data analysis.
  • New algorithms for pre-processing, statistical analysis, and annotation require efficient evaluation and integration.

Purpose of the Study:

  • To develop a collaborative online platform for computational metabolomics.
  • To provide an infrastructure for integrating, evaluating, and running analysis workflows.
  • To facilitate the use of diverse computational tools for metabolomics data analysis.

Main Methods:

  • Development of Workflow4Metabolomics (W4M), an open-source platform.
  • W4M is built upon the Galaxy web-based platform technology.
  • The platform enables ergonomic integration, exchange, and execution of modules and workflows.
  • A downloadable virtual machine is available for local installation.

Main Results:

  • W4M is the first fully open-source and collaborative online platform for computational metabolomics.
  • It serves as a virtual research environment for users.
  • The platform allows for the chaining of algorithms to build ad hoc workflows.

Conclusions:

  • W4M addresses the need for collaborative infrastructures in computational metabolomics.
  • It facilitates the integration and evaluation of a large volume of new algorithms.
  • The platform supports the creation of customized workflows for users, enhancing research capabilities.