Hox transcription factors are extensively investigated in diverse fields of molecular and evolutionary biology. Hox genes belong to the family of homeobox transcription factors characterised by a 60 amino acids region called homeodomain. These genes are evolutionary conserved and play crucial roles in the development of animals. In particular, they are involved in the specification of segmental identity, and in the tetrapod limb differentiation. In vertebrates, this family of genes can be divided into 14 groups of homology. Common methods to classify Hox proteins focus on the homeodomain. Classification is however hampered by the high conservation of this short domain. Since phylogenetic tree reconstruction is time-consuming, it is not suitable to classify the growing number of Hox sequences. The first goal of this thesis is therefore to design an automated approach to classify vertebrate Hox proteins in their groups of homology. This approach classifies Hox proteins on the basis of their scores for a combination of protein generalised profiles. The resulting program, HoxPred, combines predictive accuracy and time efficiency. We used this program to detect and classify Hox genes in several teleost fish genomes. In particular, it allowed us to clarify the evolutionary history of the HoxC1a genes in teleosts. Overall, HoxPred could efficiently contribute to the bioinformatics toolbox commonly used to annotate vertebrate Hox sequences. This program was then evaluated in non-vertebrate species. Although not intended for the classification of Hox proteins in distantly related species, HoxPred showed a high accuracy in bilaterians. It has also given insights into the evolutionary relationships between bilaterian posterior Hox genes, which are notoriously difficult to classify with phylogenetic trees.<p><p>As transcription factors, Hox proteins regulate target genes by specifically binding DNA on cis-regulatory elements. Only a few of these target genes have been identified so far. The second goal of this work was to evaluate whether it is possible to apply computational approaches to detect Hox cis-regulatory elements in genomic sequences. Regulatory Sequence Analysis Tools (RSAT) is a suite of bioinformatics tools dedicated to the detection of cis-regulatory elements in genomes. We participated to the development of matrix-based pattern matching approaches in RSAT. After having performed a statistical validation of the pattern-matching scores, we focused on a study case based on the vertebrate HoxB1 protein, which binds DNA with its cofactors Pbx and Meis. This study aimed at predicting combinations of cis-regulatory elements for these three transcription factors. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished