M. Baker, In biomarkers we trust?, Nature Biotechnology, vol.125, issue.3, pp.297-304, 2005.
DOI : 10.1016/S0009-9236(02)17625-9

R. , D. B. Damjanovich, L. Darzi, A. Nicholson, J. Takats et al., Intraoperative Tissue Identification Using Rapid Evaporative Ionization Mass Spectrometry, Science Translational Medicine, vol.5, pp.194-93, 2013.

J. Gomez-perez, S. Bechhofer, G. Klyne, and C. Goble, Using a suite of ontologies for preserving workflow-centric research objects, Web Semantics: Science, Services and Agents on the World Wide Web, vol.32, pp.16-42, 2015.

C. Sieb and B. Wiswedel, KNIME: the Konstanz Information Miner, Proceedings 4th Annual Industrial Simulation Conference (ISC), Workshop on Multi-Agent Systems and Simulation, 2006.

P. Lukasse, P. Moerland, and T. Griffin, Multi-omic data analysis using Galaxy, Nature Biotechnology, vol.33, pp.137-139, 2015.

C. Sonnenschein, J. Cravedi, B. Rubin, A. Soto, and D. Zalko, Effects of low doses of Bisphenol A on the metabolome of perinatally exposed CD-1 mice, Environmental Health Perspectives, vol.121, pp.586-593, 2013.

S. Seymour, L. Nuwaysir, B. Lefebvre, F. Kuhlmann, J. Roark et al., A crossplatform toolkit for mass spectrometry and proteomics, Nature Biotechnology, vol.30, pp.918-920, 2012.

M. Carrillo, M. Gallardo, M. Blasco, P. Greenberg, P. Snyder et al., Personal omics profiling reveals dynamic molecular and medical phenotypes, Cell, vol.148, pp.1293-1307, 2012.

M. Cuperlovic-culf, D. Barnett, A. Culf, and I. Chute, Cell culture metabolomics: applications and future directions, Drug Discovery Today, vol.15, issue.15-16, pp.610-621, 2010.
DOI : 10.1016/j.drudis.2010.06.012

R. Davidson, R. Weber, H. Liu, A. Sharma-oates, and M. Viant, Galaxy-M: a Galaxy workflow for processing and analyzing direct infusion and liquid chromatography mass spectrometry-based metabolomics data, GigaScience, vol.5, issue.1, pp.1-9, 2016.
DOI : 10.1186/s13742-016-0115-8

URL : http://doi.org/10.1186/s13742-016-0115-8

R. Alcantara, M. Darsow, M. Guedj, and M. Ashburner, ChEBI: a database and ontology for chemical entities of biological interest, Nucleic Acids Research, vol.36, pp.344-350, 2008.

A. Delabriere, U. Hohenester, C. Junot, and E. Thevenot, proFIA: A data preprocessing workflow for Flow Injection Analysis coupled to High-Resolution Mass Spectrometry, Bioinformatics
DOI : 10.1093/bioinformatics/btx458

F. Dieterle, A. Ross, G. Schlotterbeck, and H. Senn, H NMR Metabonomics, Analytical Chemistry, vol.78, issue.13, pp.4281-4290, 2006.
DOI : 10.1021/ac051632c

C. Tonon and T. , Towards deciphering dynamic changes and evolutionary mechanisms involved in the adaptation to low salinities in Ectocarpus (brown algae), The Plant Journal, vol.71, pp.366-377, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01208656

R. Goodacre, Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry, Nature Protocols, vol.6, pp.1060-1083, 2011.

D. Etalo, D. Vos, R. Joosten, M. Hall, and R. , Spatially Resolved Plant Metabolomics: Some Potentials and Limitations of Laser-Ablation Electrospray
DOI : 10.1104/pp.15.01176

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4634093

D. Blankenberg, I. Albert, J. Taylor, W. Miller, W. Kent et al., Galaxy: A platform for interactive large-scale genome analysis, Genome Research, vol.15, pp.1451-1455, 2005.

J. Goecks, A. Nekrutenko, J. Taylor, and T. Team, Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences, Genome Biology, vol.11, issue.8, p.86, 2010.
DOI : 10.1186/gb-2010-11-8-r86

E. Horst, R. Kaliyaperumal, R. Luo, T. Lee, T. Lam et al., Rocca-Serra P. From peer-reviewed to peer-reproduced in scholarly publishing: The complementary roles of data models and workflows in bioinformatics, PLoS ONE, vol.10, p.127612, 2015.

T. , W. M. Neumann, S. Rocca-serra, P. Maguire, E. Gonzalez-beltran et al., MetaboLights: an open-access general-purpose repository for metabolomics studies and associated meta-data, Nucleic Acids Research, vol.41, pp.781-786, 2013.

P. Elliott, Human metabolic phenotype diversity and its association with diet and blood pressure, Nature, vol.453, pp.396-400, 2008.

R. Taguchi, K. Saito, and T. Nishioka, MassBank: a public repository for sharing mass spectral data for life sciences, Journal of Mass Spectrometry, vol.45, pp.703-714, 2010.

D. Hull, K. Wolstencroft, R. Stevens, C. Goble, M. Pocock et al., Taverna: a tool for building and running workflows of services, Nucleic Acids Research, vol.34, issue.Web Server, pp.729-732, 2006.
DOI : 10.1093/nar/gkl320

G. Bandhakavi, S. Smith, L. Griffin, and T. , Flexible and accessible Workflows for Improved Proteogenomic Analysis Using the Galaxy Framework, Journal of Proteome Research, vol.13, pp.5898-5908, 2014.

J. Rudney and T. Griffin, Metaproteomic analysis using the Galaxy framework, Proteomics, vol.15, pp.3553-3565, 2015.

C. Johnson, J. Ivanisevic, H. Benton, and G. Siuzdak, Bioinformatics: The Next Frontier of Metabolomics, Analytical Chemistry, vol.87, issue.1, pp.147-156, 2015.
DOI : 10.1021/ac5040693

C. Johnson, J. Ivanisevic, and G. Siuzdak, Metabolomics: beyond biomarkers and towards mechanisms, Nature Reviews Molecular Cell Biology, vol.36, issue.7, pp.451-459, 2016.
DOI : 10.1093/nar/gkq329

R. Spicer, M. Williams, X. Li, R. Salek, J. Griffin et al., MetaboLights: An Open-Access Database Repository for Metabolomics Data, Current Protocols in Bioinformatics, 2016.

M. Kanehisa and G. S. Kegg, KEGG: Kyoto Encyclopedia of Genes and Genomes, Nucleic Acids Research, vol.28, issue.1, pp.27-30, 2000.
DOI : 10.1093/nar/28.1.27

D. Kell and S. Oliver, The metabolome 18 years on: a concept comes of age, Metabolomics, vol.8, issue.6
DOI : 10.1038/nprot.2013.004

F. Kloet, I. Bobeldijk, E. Verheij, and R. Jellema, Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping, Metabolomics, vol.12, p.148, 2016.

G. Db, the Golm Metabolome Database, Bioinformatics, vol.21, pp.1635-1638, 2005.

C. Kuhl, R. Tautenhahn, C. Bottcher, T. Larson, and S. Neumann, CAMERA: An Integrated Strategy for Compound Spectra Extraction and Annotation of Liquid Chromatography/Mass Spectrometry Data Sets, Analytical Chemistry, vol.84, issue.1, pp.283-289, 2012.
DOI : 10.1021/ac202450g

J. Leipzig, A review of bioinformatic pipeline frameworks, Briefings in Bioinformatics, vol.18, pp.530-536, 2017.
DOI : 10.1093/bib/bbw020

S. Leonelli, N. Smirnoff, J. Moore, C. Cook, and R. Bastow, Making open data work for plant scientists, Journal of Experimental Botany, vol.64, issue.14, pp.4109-4117, 2013.
DOI : 10.1093/jxb/ert273

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3808334

D. Li, S. Heiling, I. Baldwin, and E. Gaquerel, Illuminating a plant???s tissue-specific metabolic diversity using computational metabolomics and information theory, Proceedings of the National Academy of Sciences, vol.27, issue.3, pp.7610-7618, 2016.
DOI : 10.1016/0031-9422(92)83120-N

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5127351

J. Nicholson, J. Lindon, and E. Holmes, 'Metabonomics': understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data, Xenobiotica, vol.13, issue.11, pp.1181-1189, 1999.
DOI : 10.1016/0079-6565(89)80008-1

B. Mons, H. Van-haagen, C. Chichester, P. Hoen, J. Den-dunnen et al., The value of data, Nature Genetics, vol.30, issue.4, pp.281-283, 2011.
DOI : 10.1002/(SICI)1098-1004(200001)15:1<7::AID-HUMU4>3.0.CO;2-N

S. Oliver, M. Winson, D. Kell, and F. Baganz, Systematic functional analysis of the yeast genome, Trends in Biotechnology, vol.16, issue.9, pp.373-378, 1998.
DOI : 10.1016/S0167-7799(98)01214-1

G. Patti, R. Tautenhahn, and G. Siuzdak, Meta-analysis of untargeted metabolomic data from multiple profiling experiments, Nature Protocols, vol.2, issue.3, pp.508-516, 2012.
DOI : 10.1021/ac103011b

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3683249

T. Peng, A. Royer, Y. Guitton, L. Bizec, B. Dervilly-pinel et al., Serum-based metabolomics characterization of pigs treated with ractopamine, Metabolomics, vol.8, issue.6, pp.77-91, 2017.
DOI : 10.1038/nprot.2013.004

R. Peyraud, K. Schneider, P. Kiefer, S. Massou, J. Vorholt et al., Genome-scale reconstruction and system level investigation of the metabolic network of Methylobacterium extorquens AM1, BMC Systems Biology, vol.5, issue.1, p.189, 2011.
DOI : 10.1006/mben.2001.0188

P. Rinaudo, S. Boudah, C. Junot, and E. Thevenot, biosigner: A New Method for the Discovery of Significant Molecular Signatures from Omics Data, Frontiers in Molecular Biosciences, vol.67, 2016.
DOI : 10.1111/j.1467-9868.2005.00503.x

D. Rolin, Metabolomics Coming of Age with its Technological Diversity, Advances in Botanical Research, vol.2013, pp.67-69

A. Roux, Y. Xu, J. Heilier, M. Olivier, E. Ezan et al., Annotation of the Human Adult Urinary Metabolome and Metabolite Identification Using Ultra High Performance Liquid Chromatography Coupled to a Linear Quadrupole Ion Trap-Orbitrap Mass Spectrometer, Analytical Chemistry, vol.84, issue.15, pp.6429-6437, 2012.
DOI : 10.1021/ac300829f

R. Abagyan and G. Siuzdak, METLIN: A Metabolite Mass Spectral Database, Therapeutic Drug Monitoring, vol.27, pp.747-751, 2005.

C. Smith, E. Want, G. O-'maille, R. Abagyan, and G. Siuzdak, XCMS:?? Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification, Analytical Chemistry, vol.78, issue.3, pp.779-787, 2006.
DOI : 10.1021/ac051437y

R. Nair, K. Sumner, S. Subramaniam, and S. , Metabolomics Workbench: An international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools, Nucleic Acids Research, vol.44, pp.463-470, 2016.

J. Lindon, P. Marriott, A. Nicholls, M. Reily, J. Thaden et al., Proposed minimum reporting standards for chemical analysis, Metabolomics, vol.3, pp.211-221, 2007.

P. Tardivel, R. Servien, and D. Concordet, Familywise Error Rate Control With a Lasso Estimator

R. Tautenhahn, C. Bottcher, and S. Neumann, Highly sensitive feature detection for high resolution LC/MS, BMC Bioinformatics, vol.9, issue.1, p.504, 2008.
DOI : 10.1186/1471-2105-9-504

URL : http://doi.org/10.1186/1471-2105-9-504

R. Tautenhahn, G. Patti, D. Rinehart, and G. Siuzdak, XCMS Online: A Web-Based Platform to Process Untargeted Metabolomic Data, Analytical Chemistry, vol.84, issue.11, pp.5035-5039, 2012.
DOI : 10.1021/ac300698c

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3703953

E. Thevenot, A. Roux, Y. Xu, E. Ezan, and C. Junot, Analysis of the Human Adult Urinary Metabolome Variations with Age, Body Mass Index, and Gender by Implementing a Comprehensive Workflow for Univariate and OPLS Statistical Analyses, Journal of Proteome Research, vol.14, issue.8, pp.3322-3335, 2015.
DOI : 10.1021/acs.jproteome.5b00354

F. Moran, N. Mulder, T. Nyrnen, K. Rother, M. Schneider et al., Best practices in bioinformatics training for life scientists, Briefings in Bioinformatics, vol.14, pp.528-537, 2013.

R. Weber, C. Winder, L. Larcombe, W. Dunn, and M. Viant, Training needs in metabolomics, Metabolomics, vol.1, issue.4, pp.784-786, 2015.
DOI : 10.1007/s11306-005-1111-7

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4475540

R. Wehrens, G. Weingart, and F. Mattivi, metaMS: An open-source pipeline for GC???MS-based untargeted metabolomics, Journal of Chromatography B, vol.966, pp.109-116, 2014.
DOI : 10.1016/j.jchromb.2014.02.051

. Mdm, M. Rudler, F. Lamari, F. Sedel, D. Thabut et al., Cerebrospinal fluid metabolomics highlights dysregulation of energy metabolism in overt hepatic encephalopathy, Journal of Hepatology, vol.65, pp.1120-1130, 2016.

H. Vogel and L. Querengesser, HMDB: the Human Metabolome Database, Nucleic Acids Research, vol.35, pp.521-526, 2007.

J. Xia, N. Psychogios, N. Young, and D. Wishart, MetaboAnalyst: a web server for metabolomic data analysis and interpretation, Nucleic Acids Research, vol.37, issue.Web Server, pp.652-660, 2009.
DOI : 10.1093/nar/gkp356

L. For, Within the history, the files must then be grouped as a data collection 2016) for further parallel processing by the xcms.xcmsSet tool. The use of a data collection therefore speeds up this computer intensive step After peak detection, the collection of xset.RData outputs, together with a sampleMetadata file indicating the classes, are merged with the xcms.xcmsSet Merger tool before the grouping step (xcms.group) More details about the use of data collection for LC- MS data preprocessing can be found: 1) in the following tutorial: http://download.workflow4metabolomics.org/docs/170510_galaxy_xcms_dataset_col lection.m4v 2) and on the 'W4M_sacurine-subset_parallel-preprocessing' public history: https://galaxy.workflow4metabolomics.org/history/list_published 1.2.1.2. NMR NMR preprocessing tools currently work with Bruker files. Each sample directory should be organized with acquisition run and process numbered " 1 " (Table 1 and Fig. 8; upper right) Sample directories should then be gathered in a single parent directory, which should in turn be zipped before upload into W4M. 1.2.2. Preprocessed data (for normalization, quality control, statistical analysis, and annotation tools