Many aspects of genome evolution are best captured by numerical quantities. Examples include genome size, eukaryotic intron density, and so-called phylogenetic profiles (number of genes within the same homolog family across related organisms). I will describe computational methods tailored for the analysis of such numerical characters evolving along a known evolutionary tree. The presentation focuses on inferring general evolutionary trends, as well as information about ancestral states. I will discuss probabilistic frameworks applied to the evolutionary analysis of gene repertoire and exon-intron architecture. Analyses of intron evolution point to intron-rich eukaryotic ancestors. Reconstructions of genome-wide gene repertoires among Archaea reveal the persistence of streamlining processes, punctuated with occasional surges of gene content that (presumably) demarcate episodes of rapid adaptations and population bottlenecks. Perhaps surprisingly, both cases thus illustrate the formative influence of loss in the history of contemporary genomes.

The advances in experimental techniques are allowing the collection of large amount of biological data. The accurate analysis of the available information is important to model biological processes and reduce the costs of the experiments, performing selected computationally driven tests. Two interesting examples of this trend are the large amount of human genetic variation data from high-throughput sequencing experiments and the increasing number of macromolecular structures known at atomic level deposited in the Protein Data Bank. In this talk, I will summarize the results of my research activity during the last years. First, I will present new algorithms for the prediction of effects of single amino acid polymorphisms (SAPs), describing different methods for the prediction of the protein stability change upon single point mutation [1] and for the prediction of their effects on human health [2,3] using protein sequence and structural information. In the last part of the talk I will present…

Many proteins recognize specific sequence motifs in DNA and form stable protein-DNA complexes to perform a range of biological functions associated with transcription, repair and packaging. Although, DNA sequences recognized by most proteins are relatively easily described by small sequence motifs, similar motif-like alphabet in proteins is rarely observed. Sequence and structural elements of proteins interact with DNA in a complex, cooperative and dynamic manner. We have been investigating sequence, structural, evolutionary and thermodynamic patterns of DNA-binding proteins to establish the broad principles of DNA-recognition by them. Our studies have revealed several interesting features useful for understanding and predicting DNA-binding proteins, their binding sites and pinpoint the most significant players in the stability of these complexes.We have developed these leads into novel algorithms and web-based tools for public use.

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Les champignons possèdent une très grande variété d'enzymes auxnombreuses applications potentielles. Nos travaux visent à étudier l'ensemble de cette diversité enzymatique afin de comprendre commentelle a pu prendre place et se maintenir au cours du temps. En utilisantdes méthodes d'apprentissage et de fouille de données nous essayonségalement de voir dans quelle mesure la capacité enzymatique deschampignons permet de rendre compte de leur évolution

A major challenge facing biological research is the assignment of a function to uncharacterized genomic sequences. I will describe an approach for the functional characterization of gene products, which combines high-throughput bioinformatics and low-throughput, targeted experiments. The prediction of gene functions is based on the logic analysis of the evolutionary relationships existing between known and unknown genes involved in the same metabolic process. Bioinformatic predictions are validated in tageted experiments through the structural and functional characterization of gene products. Examples will be presented illustrating the application of such method to the study of the enzymes involved in purine catabolism. However, the method has a general applicability in the basic task of finding a function for unknown genes

A major challenge facing biological research is the assignment of a function to uncharacterized genomic sequences. I will describe an approach for the functional characterization of gene products, which combines high-throughput bioinformatics and low-throughput, targeted experiments. The prediction of gene functions is based on the logic analysis of the evolutionary relationships existing between known and unknown genes involved in the same metabolic process. Bioinformatic predictions are validated in tageted experiments through the structural and functional characterization of gene products. Examples will be presented illustrating the application of such method to the study of the enzymes involved in purine catabolism. However, the method has a general applicability in the basic task of finding a function for unknown genes