Sensitive detection of complex and repetitive structural variation with long read sequencing data

DNA sequencing has become a ubiquitous part of individualized medicine, playing central roles in the discovery, diagnosis, and treatment of disease. As sequencing technologies mature and become more affordable, it is expected that patient genotyping will soon become a standard practice of care across the world. The most common kind of genetic variation are single nucleotide variants (SNVs), followed by small insertions and deletions, however, roughly half of all sequence differences that differentiate individuals are in the form of larger, less frequent events called structural variants (SVs). While a large variety of analytical methods have been developed to detect SVs, they remain the most poorly characterized. SVs are challenging to detect with high sensitivity in part due to the limited ability of short read sequencing data to span large events or to identify breakpoint coordinates with high confidence. The aim of this dissertation was to develop computational methods for detecting SVs and cxSVs which are applicable to clinical use cases. That is, developing specialized methods for characterizing types of SVs that are relevant to clinical genotyping but unaddressed or insufficiently described by existing tools. Additionally it is crucial that these methods are computationally efficient, as to support the rapidly growing amounts of sequence data generated from individualized medicine.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2022

Author dc:creator

• Stephens, Zachary

Contributors dc:contributor

• Iyer, Ravishankar K
• Hwu, Wen-mei
• Robinson, Gene E
• Sinha, Saurabh
• Kocher, Jean-Pierre
• Shomorony, Ilan

• Engineering

Statement dc:rights

• Copyright 2021 Zachary Stephens

Language dc:language

en, eng

Handle dc:identifier

http://hdl.handle.net/2142/113916

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F113916