Date of Award

Summer 8-2026

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

Program/Concentration

Computer Science

Committee Director

Desh Ranjan

Committee Member

Harold Riethman

Committee Member

Mohammad Zubair

Abstract

Telomeres are the protective caps of the human chromosomes and are critical for genome stability. Dysfunctional telomeres caused by their erosion with age and cell proliferation as well as by defects in their maintenance is a major early event leading to genome changes and cancer. Subtelomeres possess the critical role of regulating adjacent telomeres. Due to their complex repeat structure and high variance from one person to another, these areas have not been analyzed in detail. We present a set of computational and machine learning tools to aid in the understanding of subtelomere structure and its rearrangements in cancer.

Initially, we present a computational assembly method that utilizes the latest ultralong sequencing reads to accurately assemble subtelomere areas. Then we define the structural elements of subtelomere features previously associated with functional telomere and subtelomere properties and note their variation in the new T2T genome references. Using these elements as a guiding map, we develop a biologically aware method capable of haplotype resolution that maps telomere-terminal DNA sequence reads to single chromosome ends, providing a digital readout of haplotype-specific telomere lengths. Because a single critically short telomere in a cell can initiate genome instability, this work is important for understanding how telomere lengths are regulated.

With the availability of large datasets of matched normal and cancer genomic DNA sequence from individual patients and the cloud infrastructure for high-performance computational analysis, we utilize our subtelomere structural elements to select short-read datasets containing sequence information from the telomere and subtelomere areas of these genomes. Because telomere loss and telomeric instability is believed to be an early step in cancer formation, these datasets may have clues to the origins and potentially the treatment of cancer. We define features that represent each component of a chromosome end and apply machine learning methods to distinguish between normal and cancer samples.

Our results demonstrate that subtelomere features hold vital information reflected in genomic DNA changes that occur in cancer. The cancer-associated signals of subtelomere features proved to be highly valuable in distinguishing normal from tumor tissue. Chromosome end analysis constitutes an important research path for cancer genomics.

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DOI

10.25777/2xsy-g459

ISBN

9798193217046

ORCID

0000-0002-7548-4375

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