Algorithms for decoding cancer genomes : phylogenetic inference and haplotype assembly
- The field of cancer genomics is expanding rapidly due to major advancements in the sequencing technologies. Only a decade ago, the cost and limited throughput of DNA sequencing made the study of cancer genome alterations at base-pair resolution infeasible. Today, whole-genome sequencing of tumor populations is commonplace. Cancer evolves through cycles of cell damage and a series of clonal expansions, marking the genome with new alterations along the way. To unravel the life history of cancer genomes, new computational methods are needed to take advantage of the wealth of whole genome sequence data now available. In this dissertation, I first describe my work developing computational methods for studying the role of early neoplasias in breast cancer evolution and show how these methods can reveal robust clonal lineages and identify cancer progenitor mutations. Next, I describe a probabilistic approach for haplotype reconstruction of an invasive breast carcinoma genome using long DNA fragments from Moleculo sequencing technology. I show how cancer-specific aneuploidies can be leveraged to achieve megabase-length haplotypes with high accuracy. Finally, I demonstrate applications of phase information for detecting false somatic variant calls, and for identifying and phasing segmental duplications.
|Type of resource
|electronic; electronic resource; remote
|1 online resource.
|Stanford University, Department of Computer Science.
|Dill, David L
|Dill, David L
|Statement of responsibility
|Submitted to the Department of Computer Science.
|Thesis (Ph.D.)--Stanford University, 2015.
- © 2015 by Dorna KashefHaghighi
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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