No description available.

Identification of the mechanisms by which genes are regulated in eukaryotes is one of the principal challenges of modern biology. The emergence of genome sequencing has facilitated the marked expansion of experimental and computational approaches designed to address this challenge. Integrating and assessing this information remains a major scientific endeavor that requires new and innovative application of technology. Furthermore, our limited understanding of the mechanisms of gene regulation in eukaryotes has undermined our ability to understand the role of genetics in gene regulation. Regulatory variants are thought to be responsible for a considerable amount of the heterogeneity within our population and to be fundamental determinants of health. New experimental approaches offer the opportunity to effectively identify markers of disease susceptibility in gene regulatory regions but the discovery of the molecular mechanism of dysregulation remains difficult and time-consuming. It is here where computational approaches are required to prioritize candidate regulatory variants. To do so requires the development of an extensive control set from which characteristic signals can be identified. This thesis introduces novel approaches for discovering, utilizing, comparing and visualizing regulatory element predictions in completed genomes. This thesis also introduces novel bioinformatics infrastructure for curating regulatory element and variant datasets, and introduces the largest-available, open-access dataset of functional regulatory variants hand-curated from literature. This dataset is used to identify signals which discriminate functional variants from other variants in the promoter regions of human genes using regulatory and population genetics-based computational approaches.

Identification of the mechanisms by which genes are regulated in eukaryotes is one of the principal challenges of modern biology. The emergence of genome sequencing has facilitated the marked expansion of experimental and computational approaches designed to address this challenge. Integrating and assessing this information remains a major scientific endeavor that requires new and innovative application of technology. Furthermore, our limited understanding of the mechanisms of gene regulation in eukaryotes has undermined our ability to understand the role of genetics in gene regulation. Regulatory variants are thought to be responsible for a considerable amount of the heterogeneity within our population and to be fundamental determinants of health. New experimental approaches offer the opportunity to effectively identify markers of disease susceptibility in gene regulatory regions but the discovery of the molecular mechanism of dysregulation remains difficult and time-consuming. It is here where computational approaches are required to prioritize candidate regulatory variants. To do so requires the development of an extensive control set from which characteristic signals can be identified. This thesis introduces novel approaches for discovering, utilizing, comparing and visualizing regulatory element predictions in completed genomes. This thesis also introduces novel bioinformatics infrastructure for curating regulatory element and variant datasets, and introduces the largest-available, open-access dataset of functional regulatory variants hand-curated from literature. This dataset is used to identify signals which discriminate functional variants from other variants in the promoter regions of human genes using regulatory and population genetics-based computational approaches.

No description available.

Thesis (Ph. D.)--University of Hong Kong, 2010. / Includes bibliographical references (leaves 181-207). Also available in print.

Thesis (Ph. D.)--University of Chicago, Dept. of Statistics, August 2001. / Includes bibliographical references. Also available on the Internet.

published_or_final_version / Medicine / Master / Master of Research in Medicine

published_or_final_version / Medicine / Master / Master of Research in Medicine

published_or_final_version / Dentistry / Doctoral / Doctor of Philosophy

published_or_final_version / Clinical Oncology / Master / Master of Philosophy