Date of Award


Degree Type


Degree Name

Doctor of Philosophy in Computer Science


Computer Science and Statistics

First Advisor

Noah M. Daniels


Comparative cancer genomics can elucidate similarities in genome variation across different types of cancer. Current approaches are implemented through the identification of mutations in specific pathways present in multiple cancers. However, the major shortcoming of these approaches is in identifying specific genes that are similar rather than determining broad-scale patterns of variation in the genome. Similarities found in these patterns across cancers may be linked to similarities in driver mutations and thus lead to treatment approaches with a higher success rate. Identification of such similarities requires comparison of whole genomes and is not always easy to do using traditional alignment-based approaches due to mutated genomes being unalignable.

In this work, we first analyze the robustness of k-mer frequency-based alignment-free approaches to accurately identify similarities between whole-genome sequences and determine their applicability in identifying patterns of variation in a genome.



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