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Developing a nanopore sequencing data processing pipeline for structure variation identification
Descriptive
TitleInfo
Title
Developing a nanopore sequencing data processing pipeline for structure variation identification
Name
(type = personal)
NamePart
(type = family)
Lin
NamePart
(type = given)
Timothy
NamePart
(type = date)
1986-
DisplayForm
Timothy Lin
Role
RoleTerm
(authority = RULIB)
author
Name
(type = personal)
NamePart
(type = family)
Xing
NamePart
(type = given)
Jinchuan
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Jinchuan Xing
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
chair
Name
(type = personal)
NamePart
(type = family)
Shah
NamePart
(type = given)
Premal
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Premal Shah
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Advisory Committee
Role
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(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Ellison
NamePart
(type = given)
Christopher
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Christopher Ellison
Affiliation
Advisory Committee
Role
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(authority = RULIB)
internal member
Name
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NamePart
Rutgers University
Role
RoleTerm
(authority = RULIB)
degree grantor
Name
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NamePart
School of Graduate Studies
Role
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school
TypeOfResource
Text
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theses
OriginInfo
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2019
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(type = degree)
2019-10
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2019
Language
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(authority = ISO 639-3:2007);
(type = text)
English
Abstract
(type = abstract)
Many genomic sequencing technologies have been developed since the Human Genome Project. These next-generation sequencing (NGS) technologies from various companies reshaped the genomics field and have improved rapidly. However, NGS has limitations for certain applications due to its short read length. The third generation of sequencing technology uses single molecule real-time sequencer that can generate long reads. Recently Oxford Nanopore entered the market with the release of its MinION sequencer. Oxford Nanopore's unique third generation sequencing technology allows for much longer read length than NGS technologies, potentially addressing some of the limitations of NGS. Due to the novelty of nanopore sequencing technology, the available tools for aligning long read data and detecting structural variants have not been thoroughly evaluated. Here we evaluate the performance of several alignment and structural variation detection tools on long read MinION data.
Subject
(authority = RUETD)
Topic
Microbiology and Molecular Genetics
Subject
(authority = LCSH)
Topic
Nanopores
Subject
(authority = LCSH)
Topic
Nucleotide sequence
RelatedItem
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
(type = RULIB)
ETD
Identifier
ETD_10071
PhysicalDescription
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InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (vi, 41 pages) : illustrations
Note
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M.S.
Note
(type = bibliography)
Includes bibliographical references
RelatedItem
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TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
Identifier
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rucore10001600001
Location
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(displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier
(type = doi)
doi:10.7282/t3-f6me-bg19
Genre
(authority = ExL-Esploro)
ETD graduate
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Rights
RightsDeclaration
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The author owns the copyright to this work.
RightsHolder
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Name
FamilyName
Lin
GivenName
Timothy
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
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(point = start)
2019-06-11 15:58:06
AssociatedEntity
Name
Timothy Lin
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
AssociatedObject
Type
License
Name
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.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical
RULTechMD
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ContentModel
ETD
OperatingSystem
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windows xp
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1.5
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2019-08-26T15:09:36
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2019-08-26T15:09:36
ApplicationName
Microsoft® Word 2016
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Statistical Profile