General Context

We are looking for an enthusiastic Ph.D student to work on the development of new methods for comparative pangenomics exploiting language models. This thesis is funded by the CEA.

Prokaryotes (i.e. bacteria and archaea) constitute a fascinating field of living organisms,
representing remarkable diversity and ubiquity. Their impact on the biosphere is immense,
influencing human and animal health, soil and ocean biogeochemistry, and much more.
Large-scale exploration of microbial genomes has helped uncover the molecular
mechanisms underlying their diversity, and particularly the role of Mobile Genetic
Elements (MGE).
In recent years, with the explosion of sequencing projects, several bioinformatics
approaches have been developed based on the pangenome concept, offering solutions for
efficiently managing and exploiting large quantities of data. Pangenomics examines
genetic variability across all available genomes of a given group, usually a species, rather
than relying on a single reference genome or making pairwise comparisons. In terms of
gene content, a distinction is made between the core genome, i.e. the genes present in all
individuals, and the accessory (or variable) genes that are more or less conserved in the
genomes, and therefore likely to explain phenotypic particularities. The development of
pangenomic methods is thus a response to the challenge of massive data in biology,
helping to understand the evolution of microorganisms in relation to epidemiological or
environmental data.

For several years now, the LABGeM laboratory has been working on a model to represent genomic data as a pangenome graph at the gene family level, enabling the compression of information from thousands of genomes while preserving the chromosomal organization of genes. The research works have resulted in developping tools such as PPanGGOLiN and PANORAMA.

Current methods for analyzing genomic contexts have shown their effectiveness in predicting biological functions, but suffer from problems of scaling up to fully exploit the diversity of genomes available in databases. PANORAMA offers one of the first perspectives in comparative pangenomic analysis of genomic contexts in thousands of genomes, but relies on predefined algorithmic rules to identify similar biological systems, which limits its ability to discover completely new ones. New Transformer-based artificial intelligence methods for language models have shown their effectiveness in capturing large-scale semantic relationships through attention mechanisms and are beginning to be used to predict and generate new genomic contexts.

Missions

This thesis proposes to exploit artificial intelligence methods, in particular language models, applied to pangenome graphs. By representing their contents as sequences of sentences, where each word corresponds to a functional unit encoded by a gene family, this approach opens up new prospects for revealing complex patterns through learning on large-scale datasets. This will make it possible to predict missing or uncertain annotations, offering insights into gene function and uncharacterized biological processes. The main objectives of this work will be to :

• build a dataset of annotated pangenome graphs at different functional levels, serving as a basis for model training and validation,

• evaluate different machine learning methods, including language models, in order to identify the best performing approaches,

• apply the developed method to the identification of new biological systems, suchas metabolic pathways, macromolecular or defense systems.

Application 

To apply, send a CV with a covering letter and references before 27 April 2025 to Alexandra CALTEAU (acalteau@genoscope.cns.fr) and David VALLENET (vallenet@genoscope.cns.fr).
The position will be located at the Genoscope in Evry.