Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023)

This is a computational genomics research program aimed at solving algorithmic and computational problems in order to improve our understanding of genome evolution. Currently, most genome evolution reconstruction methods are limited to the use of sequence-conserved portions of genomes, paying less attention to the structure of components. However, current discoveries in the life sciences are shedding new light on the complexity of gene structure and its importance for gene function and evolution. In particular, the secondary structure of non-coding RNAs (ncRNAs) and the architecture of eukaryotic coding genes constitute valuable information for the reconstruction of gene evolution and ancestral states.

The long-term objective of this program is to develop an integrative approach for reconstructing gene and genome evolution and ancestral states using non-coding and coding genes, while making use of ncRNA folding structure and gene architecture information. For this proposal, the two five-year objectives focus on developing models and algorithms for reconstructing the evolution and ancestral states of ncRNA secondary structures and coding gene architectures.

The methodology to reach these objectives consists in first designing improved gene evolution models that take into account full information about coding gene architecture and ncRNA secondary structure. Second, we will consider various bio-inspired optimization problems under the improved evolutionary models, study their theoretical complexity and design appropriate algorithmic solutions. We will use species trees, gene homology relations and architecture/structure information in an integrative approach to reconstruct evolutionary histories and infer ancestral states. We will develop user-friendly software that will be made available and maintained for the community.

The impacts of this program will be, first, the contribution to a better understanding of gene and genome structure evolution and the availability of efficient programs and software for computational analyses of these structures. Second, the program will contribute to the training of highly qualified personnel in a growing, demanding, highly multidisciplinary field. Third, it will yield theories and algorithms that are likely to reveal interesting links with more general problems on multiscale structures, trees and graphs.