References

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W4M/NMR - Processing

  • 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

W4M/NMR - Spectra annotation

  • 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

W4M/NMR - File Reading & Pre-Processing

  • 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

W4M/NMR - Statistical Analysis

  • 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

W4M/NMR - Upload files

  • 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

W4M/LC-MS - Pre-Processing

W4M/LC-MS - Processing

  • 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

W4M/LC-MS - Annotation

  • 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

W4M/LC-MS - Statistical Analysis

  • 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

W4M/LC-MS - Upload files

  • 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

MetExplore - Metabolic network content

  • 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.

MetExplore - Metabolomic data mapping

MetExplore - Pathway based analysis of metabolomics data

MetExplore - Network based analysis of metabolomics data

  • 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