Literature DB >> 28710041

Create, run, share, publish, and reference your LC-MS, FIA-MS, GC-MS, and NMR data analysis workflows with the Workflow4Metabolomics 3.0 Galaxy online infrastructure for metabolomics.

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.
Copyright © 2017 Elsevier Ltd. All rights reserved.

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


  25 in total

1.  A Divergent Selection on Breast Meat Ultimate pH, a Key Factor for Chicken Meat Quality, is Associated With Different Circulating Lipid Profiles.

Authors:  Stéphane Beauclercq; Sandrine Mignon-Grasteau; Angélique Petit; Quentin Berger; Antoine Lefèvre; Sonia Métayer-Coustard; Sophie Tesseraud; Patrick Emond; Cécile Berri; Elisabeth Le Bihan-Duval
Journal:  Front Physiol       Date:  2022-06-22       Impact factor: 4.755

2.  LipidSuite: interactive web server for lipidomics differential and enrichment analysis.

Authors:  Ahmed Mohamed; Michelle M Hill
Journal:  Nucleic Acids Res       Date:  2021-07-02       Impact factor: 16.971

3.  The future of metabolomics in ELIXIR.

Authors:  Merlijn van Rijswijk; Charlie Beirnaert; Christophe Caron; Marta Cascante; Victoria Dominguez; Warwick B Dunn; Timothy M D Ebbels; Franck Giacomoni; Alejandra Gonzalez-Beltran; Thomas Hankemeier; Kenneth Haug; Jose L Izquierdo-Garcia; Rafael C Jimenez; Fabien Jourdan; Namrata Kale; Maria I Klapa; Oliver Kohlbacher; Kairi Koort; Kim Kultima; Gildas Le Corguillé; Pablo Moreno; Nicholas K Moschonas; Steffen Neumann; Claire O'Donovan; Martin Reczko; Philippe Rocca-Serra; Antonio Rosato; Reza M Salek; Susanna-Assunta Sansone; Venkata Satagopam; Daniel Schober; Ruth Shimmo; Rachel A Spicer; Ola Spjuth; Etienne A Thévenot; Mark R Viant; Ralf J M Weber; Egon L Willighagen; Gianluigi Zanetti; Christoph Steinbeck
Journal:  F1000Res       Date:  2017-09-06

Review 4.  Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling.

Authors:  Miroslava Cuperlovic-Culf
Journal:  Metabolites       Date:  2018-01-11

5.  KniMet: a pipeline for the processing of chromatography-mass spectrometry metabolomics data.

Authors:  Sonia Liggi; Christine Hinz; Zoe Hall; Maria Laura Santoru; Simone Poddighe; John Fjeldsted; Luigi Atzori; Julian L Griffin
Journal:  Metabolomics       Date:  2018-03-16       Impact factor: 4.290

6.  The ChemicalToolbox: reproducible, user-friendly cheminformatics analysis on the Galaxy platform.

Authors:  Simon A Bray; Xavier Lucas; Anup Kumar; Björn A Grüning
Journal:  J Cheminform       Date:  2020-06-01       Impact factor: 5.514

7.  NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data.

Authors:  Qingxia Yang; Yunxia Wang; Ying Zhang; Fengcheng Li; Weiqi Xia; Ying Zhou; Yunqing Qiu; Honglin Li; Feng Zhu
Journal:  Nucleic Acids Res       Date:  2020-07-02       Impact factor: 16.971

8.  Improve your Galaxy text life: The Query Tabular Tool.

Authors:  James E Johnson; Praveen Kumar; Caleb Easterly; Mark Esler; Subina Mehta; Arthur C Eschenlauer; Adrian D Hegeman; Pratik D Jagtap; Timothy J Griffin
Journal:  F1000Res       Date:  2018-10-05

9.  Differential Effects of Post-Weaning Diet and Maternal Obesity on Mouse Liver and Brain Metabolomes.

Authors:  Sofiane Safi-Stibler; Etienne A Thévenot; Luc Jouneau; Mélanie Jouin; Alexandre Seyer; Hélène Jammes; Delphine Rousseau-Ralliard; Christine Baly; Anne Gabory
Journal:  Nutrients       Date:  2020-05-28       Impact factor: 5.717

10.  LC-HRMS data as a result of untargeted metabolomic profiling of human cerebrospinal fluid.

Authors:  Florence Mehl; Héctor Gallart-Ayala; Ioana Konz; Tony Teav; Aikaterini Oikonomidi; Gwendoline Peyratout; Vera van der Velpen; Julius Popp; Julijana Ivanisevic
Journal:  Data Brief       Date:  2018-10-27
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.