Data Science

Our works spans three directions: Artificial Intelligence methods to refine knowledge extraction from proteomics data, high dimensional biostatistical tools to assess the significance of biological discoveries, and software development to apply these methodologies to our own research and share them in transparent and reproducible manner. 

Methodological works in artificial intelligence and biostatistics:

  • Multi-omics analysis (notably proteogenomics),
  • Missing values imputation,
  • False discovery rate (FDR) control

Software tools:

  • Proline 
  • Prostar
  • All our other projects are available on our GitHub page including those accessible via public repositories such as the CRAN or BioConductor.

People involved

Thomas Burger
CNRS Researcher
Thomas Burger
Christophe Bruley
CEA Researcher
Christophe Bruley
Véronique Dupierris
CEA Engineer
Véronique Dupierris
Samuel Wieczorek
CEA Engineer
Samuel Wieczorek
Anne-Marie Hesse
INSERM Engineer
Anne-Marie Hesse
Gowtham Seenivasaharagavan
UGA Post-doc
Gowtham Seenivasaharagavan
Alicia Lionneton
UGA PhD
Alicia Lionneton
Nicola De Simone
CNRS PhD
Nicola De Simone
Manon Gaudin
CNRS Engineer
Manon Gaudin

Recent publications

238648 DataScience 1 apa 6 date desc 253 https://www.edyp.fr/web/wp-content/plugins/zotpress/
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Hijazi, H., Manessier, J., Brugiere, S., Ravnsborg, T., Courçon, M., Brule, B., Merienne, K., Jensen, O. N., Hesse, A.-M., Bruley, C., & Pflieger, D. (2025). Mind Your Spectra: Points to be Aware of When Validating the Identification of Isobaric Histone Peptidoforms. Journal of Proteome Research. https://doi.org/10.1021/acs.jproteome.4c01056
Etourneau, L., Fancello, L., Wieczorek, S., Varoquaux, N., & Burger, T. (2025). Penalized likelihood optimization for censored missing value imputation in proteomics. Biostatistics (Oxford, England), 26(1), kxaf006. https://doi.org/10.1093/biostatistics/kxaf006
Pérez Compte, D., Etourneau, L., Hesse, A.-M., Kraut, A., Barthelon, J., Sturm, N., Borges, H., Biennier, S., Courçon, M., de Saint Loup, M., Mignot, V., Costentin, C., Burger, T., Couté, Y., Bruley, C., Decaens, T., Jaquinod, M., Boursier, J., & Brun, V. (2024). Plasma ALS and Gal-3BP differentiate early from advanced liver fibrosis in MASLD patients. Biomarker Research, 12(1), 44. https://doi.org/10.1186/s40364-024-00583-z
Burger, T. (2023). Controlling for false discoveries subsequently to large scale one-way ANOVA testing in proteomics: Practical considerations. Proteomics, 23(18), e2200406. https://doi.org/10.1002/pmic.202200406
Dupierris, V., Hesse, A.-M., Menetrey, J.-P., Bouyssié, D., Burger, T., Couté, Y., & Bruley, C. (2023). Validation of MS/MS Identifications and Label-Free Quantification Using Proline. Methods in Molecular Biology (Clifton, N.J.), 2426, 67–89. https://doi.org/10.1007/978-1-0716-1967-4_4
Etourneau, L., Varoquaux, N., & Burger, T. (2023). Unveiling the Links Between Peptide Identification and Differential Analysis FDR Controls by Means of a Practical Introduction to Knockoff Filters. Methods in Molecular Biology (Clifton, N.J.), 2426, 1–24. https://doi.org/10.1007/978-1-0716-1967-4_1

Highlights

Proline 2.3 Release

Proline Release 2.3 This new release focuses mainly on Isobaric labelling quantification in particular by taking into account purity correction...

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ProteoRE version 2.1 is released

We are pleased to announce the release of ProteoRE 2.1, a user-oriented Galaxy-based instance, for the functional interpretation and exploration...

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Projects

Proline

EDyP

Proline Proline is a landmark software suite for quantitative proteomics allowing to collect, process, visualize and publish proteomics datasets [Bouyssié et al., […]

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ProStaR

EDyP

ProStaR ProStaR, is devoted to the statistical analysis of label-free quantitative proteomics data produced by bottom-up LC-MS/MS experiments. Prostar project has two […]

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