Research

I am specialized in the development and application of statistical learning methods (latent variable models, multivariate analysis, sparse methods and regularization) for ecology and life sciences more particularly around omics data and microbial ecology application.

Research topics and selected associated publications

• Latent variable models in particular for omics data (see Aubert 2017; Aubert and Robin 2021; Wang et al. 2019)
  keywords: Latent Block Models, Overdispersed Count Data, Coupled Hidden Markov-Model

• Applications in microbial ecology
  plant-microbial communities interactions - soil microbial communities - food microbial communities (see Lejal et al. 2021; Romdhane et al. 2022; Thieffry et al. 2024; Lecomte et al. 2024; Zancarini et al. 2024; Fournier et al. 2025)
  keywords: multivariate analysis, count data, co-occurrences network inference, machine learning, metabarcoding

• Statistical methods for analyzing transcriptomics data (see Dillies et al. 2012; Mary-Huard et al. 2008; Magniette et al. 2008; Chassé et al. 2018; Cao et al. 2021; Aubert et al. 2004)
  keywords: normalization, false discovery rate control, experimental design, sea-urchin traductome, cheese metatranscriptome

• GenexEnvironment prediction (see Washburn et al. 2024)
  keywords: machine learning, reproducibility, competition

Some founded projects

PEPR Maths Vives: projet cible AgroStat (2024-2028)

Statistics for the evolution and dynamics of populations and species of agronomic interest
Leaders: Arnaud Estoup (INRAE) and Jean-Michel Marin (Université de Montpellier)
keywords: Interdisciplinary, mathematical and statistical methods, artificial intelligence, machine learning, modelling, massive/heterogeneous data, global change, population biology, agrosystems, agroecology, ecosystems, biological invasions, epidemiology, biodiversity, control, monitoring

HOLOPRO - Flagship project INRAE Métaprogramme - HOLOFLUX (2026 - 2029)

LEGacy of long-term Organic fertilisation Strategies: microbial dynamics as drivers of ecosystem services

Coordinateur : P. Piveteau (UR OPAALE, INRAE Rennes) Partenaires : UMR Agroécologie, UMR ECOSYS, UMR IJPB, UMR LIPME, GENOSOL, SMART

Keywords: Nexus organic fertiliser/soil/plant/environment; Ecosystem services; long term impacts; microbial fluxes; microbial engineering

MYCOMIX - BioSphera (2024 - 2027)

Les associations variétales influencent-elles la composition et la structure des communautés fongiques de la phyllosphère et de la résidusphère ? Partenaires : UR BIOGER (INRAE Versailles-Saclay), Valérie Laval, Frédérique Suffert, Angélique Gautier

EINACT - INRAE Métaprogramme - HOLOFLUX (2025- )

Efficacité, Impact et extension d’une Nouvelle Approche d’ingénierie écologique des CommunauTés microbiennes
Coordinateur : A. Spor, UMR AgroEcologie, INRAE Dijon
Partenaires : UMR AgroEcologie (INRAE Dijon), UR Riverly (INRAE Lyon-Grenoble-Auvergne-Rhône-Alpes), Pathologie Végétale (INRAE PACA)
keywords: Bioremédiation, Impact Ecologique, Multifonctionnalité, Relation Composition-Fonction, Croissance et santé de la plante

Some past funded projects

ANR EcoNet

EcoNet (2019-2023): Advanced statistical modelling of ecological networks (leader: C. Matias)
Partenaires : LPSM (Sorbonne Université), LBBE (Lyon), ISEM (Montpellier), IEES (Paris), Evo-Eco-Paléo (Lille)

ANR project NGB

NGB (2018–2022): Next Generation Biomonitoring of change in ecosystems structure and function (leader: D. Bohan)

DeepPhenomic - INRAE Métaprogramme - Digit-Bio

DeepPhenomic (2022-2024) : Améliorer les performances de sélection chez les bovins laitiers grâce à la sélection phénomique (leaders : P. Croiseau, UMR GABI, Jouy-en-Josas and T. Mary-Huard, UMR GQE, Le Moulon, UMR MIA-PS, Palaiseau)
Partenaires : UMR GABI (Jouy-en-Josas), UMR GQE (Le Moulon), UMR AGAP (Montpellier), Institut Elliance
keywords: sélection phénomiques, sélection génomiques, deep learning, régression fonctionnelle

References

Aubert, J. 2017. “Analyse Statistique de Données Biologiques à Haut Débit.” PhD thesis.

Aubert, J., A. Bar-Hen, J.-J. Daudin, and S.* Robin. 2004. “Determination of the Regulated Genes in Microarray Experiments Using Local FDR.” BMC Bioinformatics 5: np. https://doi.org/10.1186/1471-2105-5-125.

Aubert, Schbath, J.*, and S. Robin. 2021. “Model‐based Biclustering for Overdispersed Count Data with Application in Microbial Ecology.” Methods in Ecology and Evolution 12: 1050–61. https://doi.org/10.1111/2041-210X.13582.

Cao, W., J. Aubert, M.-B. Maillard, et al. 2021. “Fine-Tuning of Process Parameters Modulates Specific Metabolic Bacterial Activities and Aroma Compound Production in Semi-Hard Cheese.” Journal of Agricultural and Food Chemistry, ahead of print. https://doi.org/10.1021/acs.jafc.1c01634.

Chassé, H., J. Aubert, S. Boulben, et al. 2018. “Translatome Analysis at the Egg-to-Embryo Transition in Sea Urchin.” Nucleic Acids Research 46: 4607–21. https://doi.org/10.1093/nar/gky258.

Dillies, M.-A., A. Rau, J. Aubert, et al. 2012. “A Comprehensive Evaluation of Normalization Methods for Illumina High-Throughput RNA Sequencing Data Analysis.” Briefings in Bioinformatics 14: 671–83. https://doi.org/10.1093/bib/bbs046.

Fournier, Paola, Lucile Pellan, Julie Aubert, Patrice This, and Corinne Vacher. 2025. “A new scenario of pathogen-microbiota interactions involving the oomycete Plasmopara viticola.” FEMS Microbiology Ecology 101 (12): fiaf111. https://doi.org/10.1093/femsec/fiaf111.

Lecomte, Maxime, Wenfan Cao, Julie Aubert, et al. 2024. “Revealing the Dynamics and Mechanisms of Bacterial Interactions in Cheese Production with Metabolic Modelling.” Metab. Eng. 83 (May): 24–38.

Lejal, E., J. Chiquet, J. Aubert, et al. 2021. “Temporal Patterns in Ixodes Ricinus Microbial Communities: An Insight into Tick-Borne Microbe Interactions.” Microbiome 9: 153. https://doi.org/10.1186/s40168-021-01051-8.

Magniette, M.-L., J. Aubert, A. Bar-Hen, et al. 2008. “Normalization for Triple-Target Microarray Experiments.” BMC Bioinformatics 9: 216. https://doi.org/10.1186/1471-2105-9-216.

Mary-Huard, T., J. Aubert, N. Mansouri, O. Sandra, and J.-J. Daudin. 2008. “Statistical Methodology for the Analysis of Dye-Switch Microarray Experiments.” BMC Bioinformatics 9: np. https://doi.org/10.1186/1471-2105-9-98.

Romdhane, S., A. Spor, J. Aubert, et al. 2022. “Unraveling Negative Biotic Interactions Determining Soil Microbial Community Assembly and Functioning.” The International Society of Microbiologial Ecology Journal 16: 296–306. https://doi.org/10.1038/s41396-021-01076-9.

Thieffry, Sylvia, Julie Aubert, Marion Devers-Lamrani, et al. 2024. “Engineering Multi-Degrading Bacterial Communities to Bioremediate Soils Contaminated with Pesticides Residues.” J. Hazard. Mater. 471 (134454): 134454.

Wang, Lebarbier, X.*, J. Aubert, and S. Robin. 2019. “Variational Inference for Coupled Hidden Markov Models Applied to the Joint Detection of Copy Number Variations.” The International Journal of Biostatistics 15. https://doi.org/10.1515/ijb-2018-0023.

Washburn, Jacob D, José Ignacio Varela, Alencar Xavier, et al. 2024. “Global Genotype by Environment Prediction Competition Reveals That Diverse Modeling Strategies Can Deliver Satisfactory Maize Yield Estimates.” Genetics, November.

Zancarini, Anouk, Christine Le Signor, Sébastien Terrat, et al. 2024. “Medicago Truncatula Genotype Drives the Plant Nutritional Strategy and Its Associated Rhizosphere Bacterial Communities.” New Phytol., November.