References

Vu, T.N., Valkenborg, D., Smets, K., et al. (2011). An integrated workflow for robust alignment and simplified quantitative analysis of NMR spectrometry data. BMC Bioinformatics, 12 : 405. doi : 10.1186/1471-2105-12-405

Dieterle et al. (2006). Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics.Analytical Chemistry, 78 (13 ):4281. https://doi.org/10.1021/ac051632c

Tardivel, P., Canlet, C., Lefort, G.,Tremblay-Franco, M., Debrauwer., L., Concordet, D., Servien, R. (2017). ASICS : an automated method for identification and quantification of metabolites in complex 1D 1H NMR spectra. Metabolomics, 13 :109. http://dx.doi.org/10.1007/s11306-017-1244-5

Martin, M., Legat, B., Leenders, J.,Vanwinsberghe, J., Rousseau, R., Boulanger, B., Eilers, H.C., De Tullio, P. (2018). PepsNMR for 1H NMR metabolomic data pre-processing. Analytica Chimica Acta, 1019, 1-13. https://doi.org/10.1016/j.aca.2018.02.067

Guitton et al. (2017). 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. The International Journal of Biochemistry and Cell Biology. https://doi.org/10.1016/j.biocel.2017.07.002

Rinaudo et al. (2016). Biosigner : a new method for the discovery of significant molecular signatures from omics data. Frontiers in Molecular Biosciences. https://doi.org/10.3389/fmolb.2016.00026

Shared statistical history: W4M00001 (http://workflow4metabolomics.org/W4M00001)

Shared statistical history: W4M00003 (http://workflow4metabolomics.org/W4M00003)

Thévenot et al. (2015). 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. 10. https://doi.org/10.1021/acs.jproteome.5b00354

Van Belle et al. (2004). Biostatistics - a methodology for the health sciences. Wiley

Wehrens (2011). Chemometrics with R: multivariate data analysis in the natural sciences and life sciences. Springer

Enis Afgan, Dannon Baker, Bérénice Batut, Marius van den Beek, Dave Bouvier, Martin Čech, John Chilton, Dave Clements, Nate Coraor, Björn Grüning, Aysam Guerler, Jennifer Hillman-Jackson, Vahid Jalili, Helena Rasche, Nicola Soranzo, Jeremy Goecks, James Taylor, Anton Nekrutenko, and Daniel Blankenberg. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update, Nucleic Acids Research, Volume 46, Issue W1, 2 July 2018, Pages W537–W544, doi:10.1093/nar/gky379

Bioconductor : opensource softaware for bioinformatics. (2018). Software packages : xcms (3.7). Repéré à http://bioconductor.org/packages/release/bioc/html/xcms.html

Smith, C. A., Want, E.J., O’Maille, G., Abagyan, R., Siuzdak, G. (2006). XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification. Analytical Chemistry, 78 (3), 779–787. https://doi.org/10.1021/ac051437y

Tautenhahn, R., Boettcher, C., Neumann, S. (2008). Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics, 9, 504. https://doi.org/10.1186/1471-2105-9-504

Dunn, W.B., Broadhust., D., Begley, P., Zelena, E., Francis-McIntyre, S., Anderson, N. (…) Goodacre, R. (2011). Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols, 6 :1060-1083. https://doi.org/10.1038/nprot.2011.335

Van Der Kloet, F.M., Bobeldijk, I., Verheij, E.R.,  Jellema, R.H. (2009). Analytical error reduction using single point calibration for phenotyping. Journal of Research, 5132-5141. https://doi.org/10.1021/pr900499r

Schymanski, E.L., P. Singer, H., Slobodnik, J., M. Ipolyi, I., Oswald, P., Krauss, M. (…) Hollender, J. (2015). Non-target screening with high-resolution mass spectrometry: critical review using a collaborative trial on water analysis.  407 : 6237. https://doi.org/10.1007/s00216-015-8681-7

Viant, M.R., Kurland, I.J., Jones, M.R., Dunn, W.B. (2017). How close are we to complete annotation of metabolomes ?. Chemical Biology, 37, 1-76. https://doi.org/10.1016/j.cbpa.2017.01.001

Guitton et al. (2017). 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. The International Journal of Biochemistry and Cell Biology. https://doi.org/10.1016/j.biocel.2017.07.002

Rinaudo et al. (2016). Biosigner : a new method for the discovery of significant molecular signatures from omics data. Frontiers in Molecular Biosciences. https://doi.org/10.3389/fmolb.2016.00026

Shared statistical history: W4M00001 (http://workflow4metabolomics.org/W4M00001)

Shared statistical history: W4M00003 (http://workflow4metabolomics.org/W4M00003)

Thévenot et al. (2015). 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. 10. https://doi.org/10.1021/acs.jproteome.5b00354

Van Belle et al. (2004). Biostatistics - a methodology for the health sciences. Wiley

Wehrens (2011). Chemometrics with R: multivariate data analysis in the natural sciences and life sciences. Springer

Enis Afgan, Dannon Baker, Bérénice Batut, Marius van den Beek, Dave Bouvier, Martin Čech, John Chilton, Dave Clements, Nate Coraor, Björn Grüning, Aysam Guerler, Jennifer Hillman-Jackson, Vahid Jalili, Helena Rasche, Nicola Soranzo, Jeremy Goecks, James Taylor, Anton Nekrutenko, and Daniel Blankenberg. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update, Nucleic Acids Research, Volume 46, Issue W1, 2 July 2018, Pages W537–W544, doi:10.1093/nar/gky379

www.metexplore.fr  

Thiele,I. and Palsson,B.Ø. (2010) A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat. Protoc., 5, 93–121.

Kanehisa,M., Furumichi,M., Tanabe,M., Sato,Y. and Morishima,K. (2017) KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res., 45, D353–D361.

Caspi,R., Billington,R., Ferrer,L., Foerster,H., Fulcher,C.A., Keseler,I.M., Kothari,A., Krummenacker,M., Latendresse,M., Mueller,L.A., et al. (2016) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res., 44, D471-80.

Heller,S.R., McNaught,A., Pletnev,I., Stein,S. and Tchekhovskoi,D. (2015). InChI, the IUPAC International Chemical Identifier. J. Cheminform., 7, 23. https://doi.org/10.1186/s13321-015-0068-4

Merlet,B., Paulhe,N., Vinson,F., Frainay,C., Chazalviel,M., Poupin,N., Gloaguen,Y., Giacomoni,F. and Jourdan,F. (2016). A Computational Solution to Automatically Map Metabolite Libraries in the Context of Genome Scale Metabolic Networks. Front. Mol. Biosci., 3, 2. https://doi.org/10.3389/fmolb.2016.00002

Wohlgemuth,G., Haldiya,P.K., Willighagen,E., Kind,T. and Fiehn,O. (2010). The Chemical Translation Service--a web-based tool to improve standardization of metabolomic reports. Bioinformatics, 26, 2647–2648. https://doi.org/10.1093/bioinformatics/btq476

http://cts.fiehnlab.ucdavis.edu/ https://www.ebi.ac.uk/chebi/

Kanehisa,M., Furumichi,M., Tanabe,M., Sato,Y., Morishima,K. (2017). KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res., 45, D353–D361. https://dx.doi.org/10.1093%2Fnar%2Fgkw1092  

Kankainen,M., Gopalacharyulu,P., Holm,L. Oresic,M. (2011). MPEA--metabolite pathway enrichment analysis. Bioinformatics, 27, 1878–9. https://doi.org/10.1093/bioinformatics/btr278

Faust,K. and van Helden,J. (2012) Predicting metabolic pathways by sub-network extraction. Methods Mol. Biol., 804, 107–30. https://doi.org/10.1007/978-1-61779-361-5_7

Frainay,C. and Jourdan,F. (2017) Computational methods to identify metabolic sub-networks based on metabolomic profiles. Brief. Bioinform., 18, 43–56. https://doi.org/10.1093/bib/bbv115

Lacroix,V., Cottret,L., Thébault,P. and Sagot,M.-F. (2008) An introduction to metabolic networks and their structural analysis. IEEE/ACM Trans. Comput. Biol. Bioinform., 5, 594–617. https://doi.org/10.1109/TCBB.2008.79