Evolutionary functional genomics: a bioinformatics approach

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Recent biotechnological advances have resulted in an increasingly accurate characterization of the three major cellular components: genome, transcriptome and proteome. The rapid accumulation of genomic, transcriptomic and proteomic data demand advanced bioinformatic tools to convert the bewildering amount of molecular data into organized knowledge and mathematical modeling of interactions among the cellular components. Software packages from our laboratory have been widely used in molecular phylogenetics that traces the evolutionary history back into time immemorial, and in DNA microarray data analysis that leads to increasingly accurate characterization of how living cells work (or how they fail to work as in cancer). Our research modeling biological and evolutionary processes has enhanced the understanding of the three essential and fundamental biological processes, i.e., genome replication, transcription and translation, shared among all living entities. Comparative genomic analysis carried out in our lab has revealed the pattern of interplay between mutation and selection in shaping the three processes across a wide variety of organisms. The proposed project will lead to (1) better understanding of the molecular adaptation of the human gastric pathogen, Helicobacter pylori, in acidic environment through genomic adaptation, (2) improved methods for analyzing transcriptomic data that monitor how gene expression in vertebrates will respond to environmental chemicals and pharmaceutical products, (3) the secret of retroviruses in shutting down their host translation mechanism yet maintaining the translation of their own proteins, (4) better phylogenetic methods to follow the evolution of genes and genomes and to date speciation and gene duplication events, and (5) widely used software packages contributing to the automation of converting the ever increasing amount of molecular information to organized knowledge. In particular, it will prepare Canadian students to become leaders in bioinformatics research deeply rooted in a fundamental understanding of dynamic cellular processes constantly shaped by mutation and selection.