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<h1 class="title toc-ignore">Selected publications</h1>
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<ul>
<li><p>Karaki D., Dechaumet S., Damont A., Colsch B., Fenaille F.,
Souloumiac A. and Thévenot E.A. (2024) Non-Negative Matrix Factorization
of SWATH DIA Data Improves Global Metabolite Identification. In,
<em>European Signal Processing Conference (EUSIPCO)</em>. Lyon (<a
href="https://eurasip.org/Proceedings/Eusipco/Eusipco2024/pdfs/0002387.pdf"
class="uri">https://eurasip.org/Proceedings/Eusipco/Eusipco2024/pdfs/0002387.pdf</a>)</p></li>
<li><p>Hajjar G., Barros Santos M.C., Bertrand-Michel J., Canlet C.,
Castelli F., Creusot N., Dechaumet S., Diémé B., Giacomoni F., Giraudeau
P., Guitton Y., Thévenot E., Tremblay-Franco M., Junot C., Jourdan F.,
Fenaille F., Comte B., Pétriacq P. and Pujos-Guillot E. (2023)
Scaling-up metabolomics: Current state and perspectives. <em>TrAC Trends
in Analytical Chemistry</em>, <strong>167</strong>:117225 (<a
href="https://doi.org/10.1016/j.trac.2023.117225"
class="uri">https://doi.org/10.1016/j.trac.2023.117225</a>)</p></li>
</ul>
<!-- - Ghazaly C., Biletska K, Thévenot E.A., Devillier P., Naline E., Grassin-Delyle S. and Scorsone E. (2023). Assessment of an e-nose performance for the detection of COVID-19 specific biomarkers. *Journal of Breath Research*, **17**:2 (<https://doi.org/10.1088/1752-7163/acb9b2>) -->
<ul>
<li><p>Paulhe N., Canlet C., Damont A., Peyriga L., Durand S., Deborde
C., Alves S., Bernillon S., Berton T., Bir R., Bouville A., Cahoreau E.,
Centeno D., Costantino, R., Debrauwer L., Delabrière A., Duperier C.,
Emery S., Flandin A., Hohenester U., Jacob D., Joly C., Jousse C.,
Lagree M., Lamari N., Lefebvre M., Lopez-Piffet C., Lyan B., Maucourt
M., Migne C., Olivier M.F., Rathahao-Paris E., Petriacq P., Pinelli J.,
Roch L., Roger P., Roques S., Tabet J.C., Tremblay-Franco M., Traikia
M., Warnet A., Zhendre V., Rolin D., Jourdan F., Thévenot E., Moing A.,
Jamin E., Fenaille F., Junot C., Pujos-Guillot E., and Giacomoni F.
(2022). PeakForest: a multi-platform digital infrastructure for
interoperable metabolite spectral data and metadata management.
<em>Metabolomics</em>, <strong>18</strong> (<a
href="https://doi.org/10.1007/s11306-022-01899-3"
class="uri">https://doi.org/10.1007/s11306-022-01899-3</a>)</p></li>
<li><p>Roquencourt C., Grassin-Delyle S. and Thévenot, E. A. (2022).
ptairMS: real-time processing and analysis of PTR-TOF-MS data for
biomarker discovery in exhaled breath. <em>Bioinformatics</em>,
<strong>38</strong>:1930-1937 (<a
href="https://doi.org/10.1093/bioinformatics/btac031"
class="uri">https://doi.org/10.1093/bioinformatics/btac031</a>)</p></li>
<li><p>Imbert A., Rompais M., Selloum M., Castelli F., Mouton-Barbosa
E., Brandolini-Bunlon M., Chu-Van E., Joly C., Hirschler A., Roger P.,
Burger T., Leblanc S., Sorg T., Ouzia S., Vandenbrouck Y., Medigue C.,
Junot C., Ferro M., Pujos-Guillot E., de Peredo A. G., Fenaille F.,
Carapito C., Herault Y. and Thévenot, E. A. (2021). ProMetIS, deep
phenotyping of mouse models by combined proteomics and metabolomics
analysis. <em>Scientific Data</em>, <strong>8</strong> (<a
href="https://doi.org/10.1038/s41597-021-01095-3"
class="uri">https://doi.org/10.1038/s41597-021-01095-3</a>)</p></li>
<li><p>Comte B., Monnerie S., Brandolini-Bunlon M., Canlet C., Castelli
F., Chu-Van E., Colsch B., Fenaille F., Joly C., Jourdan F., Lenuzza N.,
Lyan B., Martin J., Migné C., Morais J.A., Pétéra M., Poupin N., Vinson
F., Thévenot E., Junot C., Gaudreau P. and Pujos-Guillot E (2021).
Multiplatform metabolomics for an integrative exploration of metabolic
syndrome in older men. <em>EBioMedicine</em>, <strong>69</strong> (<a
href="https://doi.org/10.1016/j.ebiom.2021.103440"
class="uri">https://doi.org/10.1016/j.ebiom.2021.103440</a>)</p></li>
<li><p>Grassin-Delyle S., Roquencourt C., Moine P., Saffroy G., Carn S.,
Heming N., Fleuriet J., Salvator H., Naline E., Couderc L.-J., Devillier
P., Thevenot E. A. and Annane D. (2021). Metabolomics of exhaled breath
in critically ill COVID-19 patients: A pilot study.
<em>EBioMedicine</em>, <strong>63</strong> (<a
href="https://doi.org/10.1016/j.ebiom.2020.103154"
class="uri">https://doi.org/10.1016/j.ebiom.2020.103154</a>)</p></li>
<li><p>Stanstrup J., Broeckling C.D., Helmus R., Hoffmann N., Mathe 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. <em>Metabolites</em>, <strong>9</strong> (<a
href="https://doi.org/10.3390/metabo9100200"
class="uri">https://doi.org/10.3390/metabo9100200</a>)</p></li>
<li><p>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.
<em>Bioinformatics</em>, <strong>35</strong>:3752-3760 (<a
href="https://doi.org/10.1093/bioinformatics/btz160"
class="uri">https://doi.org/10.1093/bioinformatics/btz160</a>)</p></li>
<li><p>Peters K., Bradbury J., Bergmann S., Capuccini M., Cascante M.,
de Atauri P., Ebbels T.M.D., Foguet C., Glen R., Gonzalez-Beltran A.,
Gunther 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. <em>Gigascience</em>, <strong>8</strong> (<a
href="https://doi.org/10.1093/gigascience/giy149"
class="uri">https://doi.org/10.1093/gigascience/giy149</a>)</p></li>
<li><p>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 Corguille 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.
<em>F1000Research</em>, (<a
href="https://doi.org/10.12688/f1000research.12342.1"
class="uri">https://doi.org/10.12688/f1000research.12342.1</a>)</p></li>
<li><p>Delabriere A., Hohenester U.M., Colsch B., Junot C., Fenaille F.
and Thévenot E.A. (2017). <em>proFIA</em>: a data preprocessing workflow
for flow injection analysis coupled to high-resolution mass
spectrometry. <em>Bioinformatics</em>, <strong>33</strong>:3767-3775 (<a
href="https://doi.org/10.1093/bioinformatics/btx458"
class="uri">https://doi.org/10.1093/bioinformatics/btx458</a>)</p></li>
<li><p>Guitton Y., Tremblay-Franco M., Le Corguille G., Martin J.-F.,
Petera M., Roger-Mele P., Delabriere 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.
<em>International Journal of Biochemistry and Cell Biology</em>,
<strong>93</strong>:89-101 (<a
href="https://doi.org/10.1016/j.biocel.2017.07.002"
class="uri">https://doi.org/10.1016/j.biocel.2017.07.002</a>)</p></li>
<li><p>Rinaudo P., Boudah S., Junot C. and Thévenot E.A. (2016).
<em>biosigner</em>: a new method for the discovery of significant
molecular signatures from omics data. <em>Frontiers in Molecular
Biosciences</em>, <strong>3</strong> (<a
href="https://doi.org/10.3389/fmolb.2016.00026"
class="uri">https://doi.org/10.3389/fmolb.2016.00026</a>)</p></li>
<li><p>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. <em>Journal of Proteome
Research</em>, <strong>14</strong>:3322-3335 (<a
href="https://doi.org/10.1021/acs.jproteome.5b00354"
class="uri">https://doi.org/10.1021/acs.jproteome.5b00354</a>)</p></li>
<li><p>Giacomoni F., Le Corguille G., Monsoor M., Landi M., Pericard P.,
Petera 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.
<em>Bioinformatics</em>, <strong>31</strong>:1493-1495 (<a
href="https://doi.org/10.1093/bioinformatics/btu813"
class="uri">https://doi.org/10.1093/bioinformatics/btu813</a>)</p></li>
<li><p>Lenuzza N., Duval X., Nicolas G., Thévenot E., Job S., Videau O.,
Narjoz C., Loriot M.-A., Beaune P., Becquemont L., Mentre F.,
Funck-Brentano C., Alavoine L., Arnaud P., Delaforge M. and Benech H.
(2016). Safety and pharmacokinetics of the CIME combination of drugs and
their metabolites after a single oral dosing in healthy volunteers.
<em>European Journal of Drug Metabolism and Pharmacokinetics</em>,
<strong>41</strong>:125-138 (<a
href="https://doi.org/10.1007/s13318-014-0239-0"
class="uri">https://doi.org/10.1007/s13318-014-0239-0</a>)</p></li>
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