CLUSTERING OF CANCEROUS GENOMES USING EFFICIENT DISTANCE CALCULATIONS

Author

Yana Hrytsenko, University of Rhode IslandFollow

Date of Award

2022

Degree Type

Dissertation

Degree Name

Doctor of Philosophy in Computer Science

Department

Computer Science and Statistics

First Advisor

Noah M. Daniels

Abstract

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.

Recommended Citation

Hrytsenko, Yana, "CLUSTERING OF CANCEROUS GENOMES USING EFFICIENT DISTANCE CALCULATIONS" (2022). Open Access Dissertations. Paper 1397.
https://digitalcommons.uri.edu/oa_diss/1397