The future of metabolomics in ELIXIR

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[1]  Nigel W. Hardy,et al.  A proposed framework for the description of plant metabolomics experiments and their results , 2004, Nature Biotechnology.

[2]  The Standard Metabolic Reporting Structures working group.  Summary recommendations for standardization and reporting of metabolic analyses , 2005 .

[3]  Chris F. Taylor,et al.  Metabolomics standards initiative: ontology working group work in progress , 2007, Metabolomics.

[4]  Nigel W. Hardy,et al.  The metabolomics standards initiative (MSI) , 2007, Metabolomics.

[5]  M. Hirai,et al.  Widely Targeted Metabolomics Based on Large-Scale MS/MS Data for Elucidating Metabolite Accumulation Patterns in Plants , 2008, Plant & cell physiology.

[6]  Chris T. A. Evelo,et al.  The BridgeDb framework: standardized access to gene, protein and metabolite identifier mapping services , 2010, BMC Bioinformatics.

[7]  Egon L. Willighagen,et al.  The Chemical Translation Service—a web-based tool to improve standardization of metabolomic reports , 2010, Bioinform..

[8]  A. Nekrutenko,et al.  Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences , 2010, Genome Biology.

[9]  Kazuki Saito,et al.  KNApSAcK family databases: integrated metabolite-plant species databases for multifaceted plant research. , 2012, Plant & cell physiology.

[10]  Peter D. Karp,et al.  The MetaCyc Database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases , 2007, Nucleic Acids Res..

[11]  Ram Krishnamurthy,et al.  YMDB: the Yeast Metabolome Database , 2011, Nucleic Acids Res..

[12]  Christian Y. A. Brenninkmeijer,et al.  Scientific Lenses over Linked Data: An Approach to Support Task Specific Views of the Data. A Vision , 2012, LISC@ISWC.

[13]  Christoph Steinbeck,et al.  MetaboLights—an open-access general-purpose repository for metabolomics studies and associated meta-data , 2012, Nucleic Acids Res..

[14]  Robert Petryszak,et al.  UniChem: a unified chemical structure cross-referencing and identifier tracking system , 2013, Journal of Cheminformatics.

[15]  Laurent Debrauwer,et al.  MetaboHUB: a national infrastructure dedicated to metabolomics and fluxomics , 2013 .

[16]  Royston Goodacre.  Water, water, every where, but rarely any drop to drink , 2013, Metabolomics.

[17]  David S. Wishart,et al.  HMDB 3.0—The Human Metabolome Database in 2013 , 2012, Nucleic Acids Res..

[18]  Ronan M. T. Fleming,et al.  A community-driven global reconstruction of human metabolism , 2013, Nature Biotechnology.

[19]  Daniel Jacob,et al.  Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics , 2014, Bioinform..

[20]  Mark R. Viant,et al.  Training needs in metabolomics , 2015, Metabolomics.

[21]  Matej Oresic,et al.  Data standards can boost metabolomics research, and if there is a will, there is a way , 2015, Metabolomics.

[22]  Matej Oresic,et al.  COordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access , 2015, Metabolomics.

[23]  Mark R. Viant,et al.  Galaxy-M: a Galaxy workflow for processing and analyzing direct infusion and liquid chromatography mass spectrometry-based metabolomics data , 2016, GigaScience.

[24]  Douglas B. Kell,et al.  The metabolome 18 years on: a concept comes of age , 2016, Metabolomics.

[25]  Tobias Schulze,et al.  SPLASH, a hashed identifier for mass spectra , 2016, Nature Biotechnology.

[26]  P. Karp,et al.  The Time Is Right to Focus on Model Organism Metabolomes , 2016, Metabolites.

[27]  David S. Wishart,et al.  Current and Future Perspectives on the Structural Identification of Small Molecules in Biological Systems , 2016, Metabolites.

[28]  Fabien Jourdan,et al.  A Computational Solution to Automatically Map Metabolite Libraries in the Context of Genome Scale Metabolic Networks , 2016, Front. Mol. Biosci..

[29]  Daniel Schober,et al.  Towards standardized evidence descriptors for metabolite annotations , 2016, ODLS.

[30]  J. Everett,et al.  New Methodology for Known Metabolite Identification in Metabonomics/Metabolomics: Topological Metabolite Identification Carbon Efficiency (tMICE). , 2016, Journal of proteome research.

[31]  Christoph Steinbeck,et al.  Computational tools and workflows in metabolomics: An international survey highlights the opportunity for harmonisation through Galaxy , 2016, Metabolomics.

[32]  Christoph Steinbeck,et al.  ChEBI in 2016: Improved services and an expanding collection of metabolites , 2015, Nucleic Acids Res..

[33]  Ryan Miller,et al.  WikiPathways: capturing the full diversity of pathway knowledge , 2015, Nucleic Acids Res..

[34]  Erik Schultes,et al.  The FAIR Guiding Principles for scientific data management and stewardship , 2016, Scientific Data.

[35]  Juho Rousu,et al.  Critical Assessment of Small Molecule Identification 2016: automated methods , 2017, Journal of Cheminformatics.

[36]  Minoru Kanehisa,et al.  KEGG: new perspectives on genomes, pathways, diseases and drugs , 2016, Nucleic Acids Res..

[37]  Mark R Viant,et al.  How close are we to complete annotation of metabolomes? , 2017, Current opinion in chemical biology.

[38]  Yann Guitton,et al.  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. , 2017, The international journal of biochemistry & cell biology.