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Understanding the functional impact of genomic variants
Descriptive
TitleInfo
Title
Understanding the functional impact of genomic variants
Name
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Zhou
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Anbo
NamePart
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1991-
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Anbo Zhou
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author
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Veerzi
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Mikeal
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Mikeal Veerzi
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Advisory Committee
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chair
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Xing
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Jinchuan
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Jinchuan Xing
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Advisory Committee
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co-chair
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Brzustowicz
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Linda
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Linda Brzustowicz
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Advisory Committee
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internal member
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(type = family)
Kwan
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Kelvin
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Kelvin Kwan
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Advisory Committee
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outside member
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Rutgers University
Role
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degree grantor
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School of Graduate Studies
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school
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Text
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theses
Genre
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ETD doctoral
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2020
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2020-10
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2020
Language
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English
Abstract
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Structural variations (SV) can lead to DNA rearrangements and frequently cause diseases such as neurological disorders. SVs account for more total nucleotide changes and occur more frequently than single nucleotide polymorphisms (SNPs) (Stankiewicz and Lupski, 2010). As we continue to gain knowledge, SV has surpassed SNPs in its effects on human evolution, population diversity, and genetic diseases (Stankiewicz and Lupski, 2010). Compared to SNP, SV is more challenging to study due to its complex configuration, large size, and repetitive arrangement. Meanwhile, sequencing technologies including Illumina and Oxford Nanopore sequencing platform are being actively developed to generate sequencing data of human whole genomes, which can then be analyzed to study genetic variations. This series of studies aims to employ contemporary sequencing technologies and computational workflows to unravel the functional impact of SVs.
Good tools are prerequisite to the successful execution of a job. My study starts from developing a pipeline construction tool called PipelineDog that can be used throughout the work. PipelineDog is a web-based integrated development environment (IDE) that represents a novel way to arrange and define workflows while promoting code scalability and reusability. I then apply established tools and workflows to analyze a 192-invidual cohort, surveying the large structural genetic etiology of autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) co-occurrence. Lastly, the newly commercialized Nanopore sequencing technique was tested and evaluated on both existing and simulated data. The Nanopore sequencing is anticipated to improve the SV identification, as it generates longer reads and will enrich the SV determining evidence. I improved the overall SV identification accuracy by employing a random forest machine learning model to classify the combined dataset from different workflows. This analysis shed light on how to determine which SV identification workflow to use based on specific use cases for future projects.
Good tools are prerequisite to the successful execution of a job. My study starts from developing a pipeline construction tool called PipelineDog that can be used throughout the work. PipelineDog is a web-based integrated development environment (IDE) that represents a novel way to arrange and define workflows while promoting code scalability and reusability. I then apply established tools and workflows to analyze a 192-invidual cohort, surveying the large structural genetic etiology of autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) co-occurrence. Lastly, the newly commercialized Nanopore sequencing technique was tested and evaluated on both existing and simulated data. The Nanopore sequencing is anticipated to improve the SV identification, as it generates longer reads and will enrich the SV determining evidence. I improved the overall SV identification accuracy by employing a random forest machine learning model to classify the combined dataset from different workflows. This analysis shed light on how to determine which SV identification workflow to use based on specific use cases for future projects.
Subject
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Topic
Pipeline
Subject
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Topic
Quantitative Biomedicine
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Rutgers University Electronic Theses and Dissertations
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ETD_10975
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1 online resource (x, 119 pages) : illustrations
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Ph.D.
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Includes bibliographical references
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School of Graduate Studies Electronic Theses and Dissertations
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rucore10001600001
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NjNbRU
Identifier
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doi:10.7282/t3-ka1c-jv19
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Rights
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The author owns the copyright to this work.
RightsHolder
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Name
FamilyName
Zhou
GivenName
Anbo
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
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2020-05-21 15:14:40
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Name
Anbo Zhou
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
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Type
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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2020-10-31
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2022-10-31
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after October 31st, 2022.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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windows xp
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2020-08-06T11:31:05
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2020-08-06T11:31:05
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