| Literature DB >> 28710041 |
Yann Guitton1, Marie Tremblay-Franco2, Gildas Le Corguillé3, Jean-François Martin2, Mélanie Pétéra4, Pierrick Roger-Mele5, Alexis Delabrière5, Sophie Goulitquer6, Misharl Monsoor3, Christophe Duperier4, Cécile Canlet2, Rémi Servien2, Patrick Tardivel2, Christophe Caron7, Franck Giacomoni8, Etienne A Thévenot9.
Abstract
Metabolomics is a key approach in modern functional genomics and systems biology. Due to the complexity of metabolomics data, the variety of experimental designs, and the multiplicity of bioinformatics tools, providing experimenters with a simple and efficient resource to conduct comprehensive and rigorous analysis of their data is of utmost importance. In 2014, we launched the Workflow4Metabolomics (W4M; http://workflow4metabolomics.org) online infrastructure for metabolomics built on the Galaxy environment, which offers user-friendly features to build and run data analysis workflows including preprocessing, statistical analysis, and annotation steps. Here we present the new W4M 3.0 release, which contains twice as many tools as the first version, and provides two features which are, to our knowledge, unique among online resources. First, data from the four major metabolomics technologies (i.e., LC-MS, FIA-MS, GC-MS, and NMR) can be analyzed on a single platform. By using three studies in human physiology, alga evolution, and animal toxicology, we demonstrate how the 40 available tools can be easily combined to address biological issues. Second, the full analysis (including the workflow, the parameter values, the input data and output results) can be referenced with a permanent digital object identifier (DOI). Publication of data analyses is of major importance for robust and reproducible science. Furthermore, the publicly shared workflows are of high-value for e-learning and training. The Workflow4Metabolomics 3.0 e-infrastructure thus not only offers a unique online environment for analysis of data from the main metabolomics technologies, but it is also the first reference repository for metabolomics workflows.Entities:
Keywords: Data analysis; E-infrastructure; Galaxy; Metabolomics; Repository; Workflow
Mesh:
Year: 2017 PMID: 28710041 DOI: 10.1016/j.biocel.2017.07.002
Source DB: PubMed Journal: Int J Biochem Cell Biol ISSN: 1357-2725 Impact factor: 5.085