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Reduced representations for efficient analysis of genomic data

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TitleInfo
Title
Reduced representations for efficient analysis of genomic data
SubTitle
from microarray to high-throughput sequencing
Name (type = personal)
NamePart (type = family)
Mahmud
NamePart (type = given)
Md Pavel
NamePart (type = date)
1981-
DisplayForm
Md Pavel Mahmud
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Schliep
NamePart (type = given)
Alexander
DisplayForm
Alexander Schliep
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Chen
NamePart (type = given)
Kevin
DisplayForm
Kevin Chen
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Farach-Colton
NamePart (type = given)
Martin
DisplayForm
Martin Farach-Colton
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Freudenberg
NamePart (type = given)
Jan
DisplayForm
Jan Freudenberg
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2014
DateOther (qualifier = exact); (type = degree)
2014-10
CopyrightDate (encoding = w3cdtf)
2014
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
Since the genomics era has started in the ’70s, microarray technologies have been extensively used for biological applications such as gene expression profiling, copy number variation (CNV) or Single Neucleotide Polymorphism (SNP) detection. To analyze microarray data, numerous statistical and algorithmic techniques have been developed over the last two decades; specially, for detecting CNV from array comparative genomic hybridization (arrayCGH) data, Hidden Markov Models (HMMs) have been successfully used. Still, due to computational reasons, the benefits of using Bayesian HMMs have been overlooked, and their use has been, at best, minimal in practice. The large demand for computational resources has also affected the analysis of high throughput sequencing (HTS) data, which, over the last few years, has started to revolutionize the field of computational biology. For example, the most sensitive tools for mapping HTS data to reference genomes are generally ignored in favor of fast, less accurate ones. In this dissertation, we strive for reduced representations of biological data which enable us to perform efficient computations on large datasets. Since biological datasets often contain repetitive, sometimes redundant, elements, it is a natural idea to identify groups of similar elements and directly perform computations on these groups. Usually,the relevant type of similarity is specific to the type of data and application in hand. Specifically, we make the following four contributions in this thesis. First, we show that, by exploiting repetition in discrete sequences, Markov Chain Monte Carlo (MCMC) simulations of Bayesian HMM can be accelerated, which can then be applied to the DNA segmentation problem [1]. Second, in case of Gaussian observations representing copy number ratio data, we show that, through precomputing similar, contiguous observations into blocks, MCMC for Bayesian HMM can be well-approximated [2]. Third, by representing sequences to multi-dimensional vectors, we introduce a nearest neighbor based novel technique for mapping HTS data to reference genome [3]. Finally, we present a highly efficient clustering approach for HTS data, which allows us to speed-up computationally demanding, sensitive tools for mapping HTS data [4].
Subject (authority = RUETD)
Topic
Computer Science
Subject (authority = ETD-LCSH)
Topic
Genomes--Analysis
Subject (authority = ETD-LCSH)
Topic
Markov processes--Mathematical models
Subject (authority = ETD-LCSH)
Topic
Bayesian statistical decision theory
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_5700
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xi, 123 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Md Pavel Mahmud
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/T3VM49ZV
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Mahmud
GivenName
Md
MiddleName
Pavel
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2014-05-27 12:08:57
AssociatedEntity
Name
Md Mahmud
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
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 (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
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