1. Stanstrup J., Broeckling C.D., Helmus R., Hoffmann N., Mathé E., Naake T., Nicolotti L., Peters K., Rainer J., Salek R., Schulze T., Schymanski E.L., Stravs M.A., Thévenot E.A., Treutler H., Weber R., Willighagen E., Witting M., Neumann S. The metaRbolomics toolbox in Bioconductor and beyond. Metabolites, 9, doi:10.3390/metabo9100200

  2. Fall F., Lenuzza N., Lamy E., Brollo M., Naline E., Devillier P., Thévenot E., and Grassin-Delyle S. (2019). A split-range acquisition method for the non-targeted metabolomic profiling of human plasma with hydrophilic interaction chromatography - high-resolution mass spectrometry. Journal of Chromatography B, 1128, doi:10.1016/j.jchromb.2019.121780

  3. Emami Khoonsari P., Moreno P., Bergmann S., Burman J., Capuccini M., Carone M., Cascante M., de Atauri P., Foguet C., Gonzalez-Beltran A., Hankemeier T., Haug K., He S., Herman S., Johnson D., Kale N., Larsson A., Neumann S., Peters K., Pireddu L., Rocca-Serra P., Roger P., Rueedi R., Ruttkies C., Sadawi N., Salek R.M., Sansone S.A., Schober D., Selivanov V., Thévenot E.A., van Vliet M., Zanetti G., Steinbeck C., Kultima K. and Spjuth O. (2019). Interoperable and scalable data analysis with microservices: applications in metabolomics. Bioinformatics, 35:3752-3760, doi:10.1093/bioinformatics/btz160

  4. Peters K., Bradbury J., Bergmann S., Capuccini M., Cascante M., de Atauri P., Ebbels T.M.D., Foguet C., Glen R., Gonzalez-Beltran A., Günther U.L., Handakas E., Hankemeier T., Haug K., Herman S., Holub P., Izzo M., Jacob D., Johnson D., Jourdan F., Kale N., Karaman I., Khalili B., Emami Khonsari P., Kultima K., Lampa S., Larsson A., Ludwig C., Moreno P., Neumann S., Novella J.A., O'Donovan C., Pearce J.T.M., Peluso A., Piras M.E., Pireddu L., Reed M.A.C., Rocca-Serra P., Roger P., Rosato A., Rueedi R., Ruttkies C., Sadawi N., Salek R.M., Sansone S.A., Selivanov V., Spjuth O., Schober D., Thévenot E.A., Tomasoni M., van Rijswijk M., van Vliet M., Viant M.R., Weber R.J.M., Zanetti G. and Steinbeck C. (2019). PhenoMeNal: processing and analysis of metabolomics data in the cloud. Gigascience, 8, doi:10.1093/gigascience/giy149

  5. Souard F., Delporte C., Stoffelen P., Thévenot E.A., Noret N., Dauvergne B., Kauffmann J.-M., Van Antwerpen P. and Stévigny C. (2017). Metabolomics fingerprint of coffee species determined by untargeted-profiling study using LC-HRMS. Food Chemistry, 245:603-612. doi:10.1016/j.foodchem.2017.10.022

  6. van Rijswijk M., Beirnaert C., Caron C., Cascante M., Dominguez V., Dunn W., Ebbels T., Giacomoni F., Gonzalez-Beltran A., Hankemeier T., Haug K., Izquierdo-Garcia J., Jimenez R., Jourdan F., Kale N., Klapa M., Kohlbacher O., Koort K., Kultima K., Le Corguillé G., Moschonas N., Neumann S., O'Donovan C., Reczko M., Rocca-Serra P., Rosato A., Salek R., Sansone S., Satagopam V., Schober D., Shimmo R., Spicer R., Spjuth O., Thévenot E., Viant M., Weber R., Willighagen E., Zanetti G. and Steinbeck C. (2017). The future of metabolomics in ELIXIR. F1000Research, doi:10.12688/f1000research.12342.1

  7. Delabrière A., Hohenester U.M., Colsch B., Junot C., Fenaille F. and Thévenot E.A. (2017). proFIA: a data preprocessing workflow for flow injection analysis coupled to high-resolution mass spectrometry. Bioinformatics, 33:3767-3775. doi:10.1093/bioinformatics/btx458 [HAL-pdf]

  8. Guitton Y., Tremblay-Franco M., Le Corguillé G., Martin J.-F., Pétéra M., Roger-Mele P., Delabrière A., Goulitquer S., Monsoor M., Duperier C., Canlet C., Servien R., Tardivel P., Caron C., Giacomoni F. and Thévenot, E.A. (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. International Journal of Biochemistry and Cell Biology, 93:89-101. doi:10.1016/j.biocel.2017.07.002 [HAL-pdf]

  9. Rinaudo P., Boudah S., Junot C. and Thévenot E.A. (2016). biosigner: a new method for the discovery of significant molecular signatures from omics data. Frontiers in Molecular Biosciences, 3. doi:10.3389/fmolb.2016.00026

  10. Thévenot E.A., Roux A., Xu Y., Ezan E. and Junot C. (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, 14:3322-3335. doi:10.1021/acs.jproteome.5b00354

  11. Roux A., Thévenot E., Seguin F., Olivier M.-F. and Junot C. (2015). Impact of collection conditions on the metabolite content of human urine samples as analyzed by liquid chromatography coupled to mass spectrometry and nuclear magnetic resonance spectroscopy. Metabolomics, 11:1095:1105. doi:10.1007/s11306-014-0764-5

  12. Giacomoni F., Le Corguillé G., Monsoor M., Landi M., Pericard P., Pétéra M., Duperier C., Tremblay-Franco M., Martin J.-F., Jacob D., Goulitquer S., Thévenot E.A. and Caron C. (2015). Workflow4Metabolomics: A collaborative research infrastructure for computational metabolomics. Bioinformatics, 31:1493-1495. doi:10.1093/bioinformatics/btu813

  13. Lenuzza N., Duval X., Nicolas G., Thévenot E., Job S., Videau O., Narjoz C., Loriot M.-A., Beaune P., Becquemont L., Mentré F., Funck-Brentano C., Alavoine L., Arnaud P., Delaforge M. and Bénech H. (2014). Safety and pharmacokinetics of the CIME combination of drugs and their metabolites after a single oral dosing in healthy volunteers. European Journal of Drug Metabolism and Pharmacokinetics, 1-14. doi:10.1007/s13318-014-0239-0

  14. Lacombe O., Videau O., Chevillon D., Guyot A.-C., Contreras C., Blondel S., Nicolas L., Ghettas A., Benech H., Thevenot E., Pruvost A., Bolze S., Krzaczkowski L., Prevost C. and Mabondzo A. (2011). In vitro primary human and animal cell-based blood-brain barrier models as a screening tool in drug discovery. Molecular Pharmaceutics, 8:651-663. doi:10.1021/mp1004614

  15. Videau O., Pitarque S., Troncale S., Hery P., Thevenot E., Delaforge M. and Benech H. (2011). Can a cocktail designed for phenotyping pharmacokinetics and metabolism enzymes in human can be used efficiently in rats. Xenobiotica, 42:349-354. doi:10.3109/00498254.2011.625453

  16. Videau O., Delaforge M., Levi M., Thévenot E., Gal O., Becquemont L., Beaune P., Lirsac P., Grassi J. and Bénech H. (2010). Biochemical and analytical developments of the CIME cocktail for drug fate assessment in humans. Rapid Communications in Mass Spectrometry,24:2407-2419. doi:10.1002/rcm.4641

  17. Cote F., Thevenot E., Fligny C., Fromes Y., Darmon M., Ripoche M.A., Bayard E., Hanoun N., Saurini F., Lechat P., Dandolo L., Hamon M., Mallet J. and Vodjdani G. (2003). Disruption of the nonneuronal tph1 gene demonstrates the importance of peripheral serotonin in cardiac function. Proceedings of the National Academy of Sciences USA, 100:13525-30. doi:10.1073/pnas.2233056100

  18. Thevenot E., Cote F., Colin P., He Y., Leblois H., Perricaudet M., Mallet J. and Vodjdani G. (2003). Targeting conditional gene modification into the serotonin neurons of the dorsal raphe nucleus by viral delivery of the Cre recombinase. Molecular and Cellular Neuroscience,24:139-47. doi:10.1016/S1044-7431(03)00131-3

  19. Cote F., Schussler N., Boularand S., Peirotes A., Thevenot E., Mallet J. and Vodjdani G. (2002). Involvement of NF-Y and Sp1 in basal and cAMP-stimulated transcriptional activation of the tryptophan hydroxylase (TPH) gene in the pineal gland. Journal of Neurochemistry,81:673-85. doi:10.1046/j.1471-4159.2002.00890.x

  20. De Gois S., Houhou L., Oda Y., Corbex M., Pajak F., Thevenot E., Vodjdani G., Mallet J. and Berrard S. (2000). Is RE1/NRSE a common cis-regulatory sequence for ChAT and VAChT genes? Journal of Biological Chemistry, 275:36683-90. doi:10.1074/jbc.M006895200

Book chapter

  1. Viral vectors for in vivo gene transfer. (2009). Thévenot E., Dufour N. and Déglon N. In Nanoscience: Nanotechnology and Nanobiology (Lahmani M., Boisseau P., Houdy P. eds.), Springer (Original French edition by Belin, 2007). doi:10.1007/978-3-540-88633-4\_23


  1. Workflow4Metabolomics 2.0: New workflows for LC-HRMS, GC-MS, and NMR data processing, statistical analysis, and annotation (MetaboNews, Issue 46, June 2015)
  2. MetaboHUB: The French infrastructure for metabolomics and fluxomics (MetaboNews, Issue 37, Sept. 2014)


  1. Data sciences for deep phenotyping and precision medicine (2019), Center of Modeling, Simulation, and Interactions, Nice.
  2. Data sciences for deep phenotyping and precision medicine (2019), bilille Bioinformatics Platform, Lille.
  3. Statistical workflows for computational metabolomics (2016), 'metaRbolomics: The R toolbox for Metabolomics' workshop session from the Metabolomics Society Conference, Dublin.
  4. The Workflow4Metabolomics online infrastructure for users and developers (2016), 'Computational workflows and workflow engines' workshop session from the Metabolomics Society Conference, Dublin.
  5. biosigner: a new method and module for signature discovery from omics data (2016). RFMF, Montpellier.
  6. Meeting the statisticians' and experimenters' needs for reproducible workflows with Bioconductor and Galaxy: the example of the ropls and biosigner package integration into the Workflow4metabolomics computational infrastructure (2015), European Bioconductor Developers' Meeting, Cambridge.
  7. Statistical methods for biomarker discovery (2015), Metabomeeting, Cambridge.
  8. The Worfklow4metabolomics infrastructure: meeting the workflow challenge (2015). Metabomeeting, Cambridge.
  9. Urine metabolomics for biomarker discovery: Data analysis strategies to study human cohorts (2015). Clinical Biology Conference, Hammamet.
  10. Signal processing and data analysis applied to the study of the physiological variations of the urinary metabolome (2015). Chemometrics workshop, DIM Analytics, Paris.
  11. Biostatistics for biomarker discovery and phenotype prediction (2014). Merlion Metabolomics Workshop, Singapore.
  12. Statistical approaches to study the physiological variability of the urinary metabolome (2014). RFMF, Lyon.