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

Stephens, Zachary

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https://hdl.handle.net/2142/113916 Copy

Description

Title

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

Author(s)

Stephens, Zachary

Issue Date

2021-12-03

Director of Research (if dissertation) or Advisor (if thesis)

Iyer, Ravishankar K

Doctoral Committee Chair(s)

Iyer, Ravishankar K

Committee Member(s)

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

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

bioinformatics structural variants long read sequencing

Abstract

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.

Graduation Semester

2021-12

Type of Resource

Thesis

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

Copyright and License Information

Copyright 2021 Zachary Stephens

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