Current Research in Computational BiologyThe goal of my thesis work is to develop a formal statistical framework for analyzing the spatial organization of genes within and across genomes. One main component of this work is to identify modules of genes for which spatial proximity is significantly conserved in the genomes of a number of species, either for functional or historical reasons. This task is often approached by identifying chromosomal regions that have arisen from a single region in a common ancestor. In closely related genomes, these regions are characterized by identical gene content and order. However, in more distantly related genomes, homologous regions must be detected by searching for gene clusters, pairs of regions with similar, but not identical, gene content and scrambled gene order. Identification of gene clusters is an essential prerequisite for many types of comparative genomics analyses. Applications of this work include operon prediction, identification of horizontal transfer events, discovery and analysis of large-scale or whole-genome duplications, reconstruction of ancestral gene order, ortholog detection, and the generation of novel features for distance-based phylogeny reconstruction. Summer 2004 - Present.
Advisor: Dannie Durand,
Deparrtments of Biological Sciences and Computer Science,
Carnegie Mellon University
Previous Research in Computational Biology
Publications and Reports:
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