Dissertation/Thesis Abstract

Tumor versus matched-normal sequencing analysis and data integration
by Sanborn, J. Zachary, Ph.D., University of California, Santa Cruz, 2012, 155; 3551072
Abstract (Summary)

Cancer research is a study of tragic complexity. The more we discover about this disease, the more we realize how much we have to learn. It relentlessly unravels new complications, justifying the difficulty we have had in ridding cancer from the human experience. Nevertheless, we strive to conquer it. To accomplish this noble goal, we require tools to understand a patient's specific tumor in as comprehensive a manner as possible. The methods presented here aim to provide a complete assessment of a tumor's genome, discovering the alterations made to the patient's germline genome that may have given rise to cancer. Mutations both large and small are identified and integrated using various techniques to discover the events and elements specific to the tumor genome. The methods will be used on whole genome sequencing data of several tumors, revealing the structural events that occurred in multiple tumors. Double minute chromosomes in GBM tumors will be discovered computationally and confirmed experimentally. Finally, techniques that expose tumor clonality will be described and used to infer whole genome, allele-specific copy number profiles of all major clones comprising a tumor population from a single tumor biopsy.

Indexing (document details)
Advisor: Haussler, David
Commitee: Pourmand, Nader, Stuart, Joshua M.
School: University of California, Santa Cruz
Department: Biomolecular Engineering and Bioinformatics
School Location: United States -- California
Source: DAI-B 74/06(E), Dissertation Abstracts International
Subjects: Genetics, Bioinformatics, Oncology
Keywords: Algorithms, Cancer, Data integration, Genomics, Oncogenesis, Sequencing, Tumor
Publication Number: 3551072
ISBN: 978-1-267-88521-0
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