Régulation Génomique Computationnelle

Keywords: regulatory genomics, transcription, bioinformatics, statistics, machine learning, precision medicine.

Many loci can be transcribed outside annotated protein-coding gene promoters to generate various RNAs e.g. enhancer RNAs, microRNAs and various long non-coding RNAs. On the other hand, genome-wide association studies showed that trait-associated loci, including those linked to human diseases, can be found outside canonical protein-coding regions. These recent findings suggest that the non-coding regions of the human genome harbor a plethora of functionally significant elements which can drastically impact genome regulations and functions. Only the exploration of the genome « dark matter » has just started and many discoveries have yet to be made. In that context, our team develop computational and statistical methods to analyze high-throughput data, delineate relevant genomic regions and identify novel regulatory elements, with the ultimate goal of uncovering genetic features relevant for medical genomics.

The « Computational Regulatory Genomics » team was established in October 2014 at the Computational Biology Institute of Montpellier. We work in a highly interdisciplinary environment at the interface of computer science, statistics and biology. Members of the group have different backgrounds and can be affiliated to IGMM (biology), LIRMM (computer science) and/or IMAG (statistics).

We are engaged in several international collaborative research programs with notably a CNRS International Associated Laboratory (joined with Wyeth W. Wasserman, UBC, Vancouver, Canada) and the FANTOM consortium (based at RIKEN Yokohama, Japan).


BioRxiv preprints

Gene nucleotide composition accurately predicts expression and is linked to topological chromatin domains. Chloe Bessiere, May Taha, Florent Petitprez, Jimmy Vandel, Jean-Michel Marin, Laurent Brehelin, Sophie Lebre, Charles-Henri Lecellier. bioRxiv 117499; doi: https://doi.org/10.1101/117499

Modeling transcription factor combinatorics in promoters and enhancers. Jimmy Vandel, Oceane Cassan, Sophie Lebre, Charles-Henri Lecellier, Laurent Brehelin. bioRxiv 197418; doi: https://doi.org/10.1101/197418

Human enhancers associated with immune response harbour specific sequence composition, activity, and genome organization. Charles-Henri Lecellier, Wyeth W Wasserman, Remo Rohs, Anthony Mathelier. bioRxiv 078477; doi: https://doi.org/10.1101/078477