Functional annotation aims at identifying genome elements involved in a particular molecular or cellular function. Our works in this area fit in two different axis.
First we aim at identifying genomic signals responsible for gene expression regulation in a given condition (a cell type, a specific treatment, …). Gene expression is tightly controlled to ensure a wide variety of cell types and functions. These controls take place at several levels (transcriptomic, post-transcriptomic, …) and are associated with different genomic regions (promoters, enhancers, 3’UTRs, etc.). In this framework, we develop statistical and computational approaches for identifying and modelling the transcription-factor binding-sites responsible for a particular expression profile (Lajoie et al., 2012). Moreover, we aim at building a global model to predict gene expression from the different regulatory regions associated with the genes (Bessière et al., submitted).différentes séquences régulatrices (promoters, enhancers, UTR, …).
In the second axis, our aim is to improve the sensibility of the tools dedicated to protein annotation, and most notably to identify protein domains, which constitute the functional units of the proteins. For this, we develop methods for improving the sensibility of Hidden Markov Models (HMMs) or for identifying new domain families not yet referenced in protein domain databases (Terrapon et al. 2009, Ghouila et al. 2014, Menichelli et al. submitted). Another part of our works involves the prediction of ligand binding sites in protein, thanks to the prediction of the 3D structure of the target protein (Roche et al. 2016).