161 rue Ada
Understanding the DNA determinants implicated in gene expression
regulation is of prime importance for genomic medicine and the
discovery of new therapeutic strategies in cancers and infectious
diseases. We and others have recently demonstrated the existence of
instructions for gene expression lying at the level of DNA sequence in
human. We have specifically shown that the nucleotide composition of
specific and large DNA regions (hundreds of nucleotides) act as a
major determinant of gene regulation [Bessiere-2018, Lecellier-2018,
Vandel-2019]. However, an accurate characterization of these regions
is still missing. Specifically, computational methods are needed for
identifying both the precise composition and the location of these
regions in the genomes, a combined problem of feature-selection and
sequence-segmentation. The aim of our project is to develop
statistical and machine learning methods to solve this problem and to
identify regulatory regions that could be implicated in specific
diseases. The identified regions and their compositions will further
be validated with genetic analyses and reporter assays thanks to
expert collaborators. These features will also be compared along the
tree of life in order to evaluate their conservation/evolution.
The candidate should have strong programming skill and good knowledge
in one or several of the following topics: probabilistic models and
statistic, motif discovery, regulatory genomics. Being familiar with
genetics, genomics and/or gene expression regulation is an advantage.
The candidate will work in a young, scientifically stimulating, team
established in October 2014 at the Computational Biology Institute of
Montpellier to work at the interface of computer science, statistics
and biology (http://www.igmm.cnrs.fr/en/service/biogenese-micro-arns/).
Candidates should send a CV and the name and e-mails of 2 references.
[Bessiere-2018] Probing instructions for expression regulation in gene
nucleotide compositions. Bessière C, Taha M, Petitprez F, Vandel J,
Marin JM, Bréhélin L, Lèbre S, Lecellier CH. PLoS Comput Biol. (2018)
[Lecellier-2018] Human Enhancers Harboring Specific Sequence Composition,
Activity, and Genome Organization Are Linked to the Immune Response.
Lecellier CH, Wasserman WW, Mathelier A. Genetics. (2018)
[Vandel-2019] Probing transcription factor combinatorics in different
promoter classes and in enhancers. Vandel J., Cassan O., Lebre S.,
Lecellier CH, Brehelin L. BMC Genomics. (2019)