The joint IGMM/LIRMM/IMAG “Computational Regulatory Genomics” develops machine learning and statistical methods to integrate and interpret diverse types of genomics data, delineate relevant genomic regions and identify novel regulatory elements, with the ultimate goal of uncovering genetic features relevant to medical genomics.
More specifically, 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 show 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 dramatically impact genome regulations and functions but yet remain to be explored. Their study is at the core of Computational Regulatory Genomics” team’s project.
This team was created in October 2014 at the Computational Biology Institute of Montpellier and works in a highly interdisciplinary environment at the interface of computer science, statistics and biology. The members of the group have different backgrounds and are affiliated to the Institute of Molecular Genetics of Montpellier (IGMM; biology), the Laboratory of Informatics, Robotics and Microelectronics of Montpellier (LIRMM; computer science) and the Montpellier Alexander Grothendieck Institute (IMAG, statistics). The IGMM/LIRMM/IMAG team is supported by the CNRS through the interdisciplinary programs of the MITI (PRIME, AI for Science, Digital Health).
Moreover, the team is / was engaged in several international collaborative research programs, notably through a CNRS International Associated Laboratory (1) with Pr. Wyeth W. Wasserman, University of British Columbia, Vancouver, Canada and the FANTOM consortium (2)(based at RIKEN Yokohama, Japan).
Regulatory genomics, transcription, medical genomics, bioinformatics, statistics, machine learning.
Laurent Bréhélin CR CNRS, LIRMM
Quentin Bouvier PhD student, IGMM
Sophie Lèbre MCF, IMAG & LIRMM
Charles Lecellier DR CNRS, IGMM & LIRMM
Mathilde Robin Engineer, LIRMM & IRCM
Océane Cassan Post-doc, LIRMM
Christophe Vroland Post-doc, IGMM
Kevin Yauy MD, PhD, Univ. Montpellier
Previous lab members
Amadou Kide Abdallahi, Master Student, from University Paris 7
Florent Petitprez, Master Student from Ecole Polytechnique
Manu Saraswat, Master Student from BiTS Pilani Goa
May Taha, PhD Student from IGMM & IMAG
Jimmy Vandel, Post-Doc