Development of a methode for joint comparative analysis of omics data (multi-strain/conditions): application to the filamentous fungus Trichoderma reesei

IFPEN conducts research to optimize biotechnological processes in the field of bio-based chemistry and biofuels. A significant part of these improvements is based on a better understanding of the microorganisms used with the help of systems biology. For this purpose, omics data are collected to represent the different regulatory layers of a cell according to given conditions. However, the processing of these data is usually done by stratum and hardly exploits the complementarity of the regulations. For our model organism, a compendium of genomic, transcriptomic and epigenetic data has been collected for two strains and under two different conditions. How can we extract the differential behaviors of a biological system by combining different experimental modalities?
To answer this question, an ambitious and incremental thesis work is envisaged. The aim is to develop a new bioinformatics tool identifying invariant systemic mechanisms in conjunction with those specific to the experimental conditions. A first analysis, based on Bayesian approaches, will be studied to identify the subset of genes jointly invariant across experimental conditions and modalities. A second complementary approach based on source separation will then be evaluated to jointly detect the subsets of variant and invariant genes. We then propose to use these subsets to project the data into a low-dimensional space, densifying the invariant gene data. Thus, a distance from the variant genes to the invariant genes can be computed. This type of joint differential analysis of omics data will improve the understanding of our model organism. 

Keywords: multi-omics data, data integration, differential analysis, bayesian approach, source separation, dimension reduction

• Academic supervisor    Pr MUCCHIELLI-GIORGI Marie-Hélène, Institut de Biologie Intégrative de la cellule – Université Evry Val d’Essonne
• Doctoral School    ED Sciences du végétal - https://www.universite-paris-saclay.fr/ecoles-doctorales/sciences-du-vegetal-du-gene-lecosysteme-seve
• IFPEN supervisor    Dr, CHATAIGNON Aurélie, IFPEN, Sciences et Technologies du Numérique, aurelie.chataignon@ifpen.fr (ORCHID : 0000-0003-0112-3689)
• PhD location    IFP Energies nouvelles, Rueil-Malmaison, France
• Duration and start date    3 years, starting in fourth quarter 2023
• Employer    IFP Energies nouvelles, Rueil-Malmaison, France
• Academic requirements    University Master degree in mathematics, computer sciences
• Language requirements    Fluency in French or English, willingness to learn French
• Other requirements    Applied mathematics, bayesian approach, optimisation, biologie/bioinformatique, data science

To apply, please send your cover letter and CV to the IFPEN supervisor indicated here above.