Principal Investigator Manolis Kellis (Kamvysselis)
Co-investigator Eric Lander
Understanding the biological signals encoded in a genome is a key challenge of computational biology. These signals are encoded in the four-nucleotide alphabet of DNA and are responsible for all molecular processes in the cell. In particular, the genome contains the blueprint of all protein-coding genes and the regulatory motifs used to coordinate the expression of these genes. Comparative genome analysis of related species provides a general approach for identifying these functional elements, by virtue of their stronger conservation across evolutionary time. In this thesis we address key issues in the comparative analysis of multiple species. We present novel computational methods in four areas (1) the automatic comparative annotation of multiple species and the determination of orthologous genes and intergenic regions (2) the validation of computationally predicted protein-coding genes (3) the systematic de-novo identification of regulatory motifs (4) the determination of combinatorial interactions between regulatory motifs. We applied these methods to the comparative analysis of four yeast genomes, including the best-studied eukaryote, Saccharomyces cerevisiae or baker's yeast. Our results show that nearly a tenth of currently annotated yeast genes are not real, and have refined the structure of hundreds of genes. Additionally, we have automatically discovered a dictionary of regulatory motifs without any previous biological knowledge. These include most previously known regulatory motifs, and a number of novel motifs. We have automatically assigned candidate functions to the majority of motifs discovered, and defined biologically meaningful combinatorial interactions between them. Finally, we defined the regions and mechanisms of rapid evolution, with important biological implications. Results demonstrate the central role of computational tools in modern biology. The analyses presented in this thesis have revealed biological findings that could not have been discovered by traditional genetic methods, regardless of the time or effort spent. The methods presented are general and may present a new paradigm for understanding the genome of any single species. They are currently being applied to a kingdom-wide exploration of fungal genomes, and the comparative analysis of the human genome with that of the mouse and other mammals.