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Insights Into evolution and adaptation using computational methods and next generation sequencing

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Title
Insights Into evolution and adaptation using computational methods and next generation sequencing
Name (type = personal)
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Shanku
NamePart (type = given)
Alexander G.
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1979-
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Alexander G. Shanku
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author
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Andrew D
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Andrew D Kern
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Advisory Committee
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chair
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Chen
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Kevin
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Kevin Chen
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Advisory Committee
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internal member
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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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internal member
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Edery
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Isaac
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Isaac Edery
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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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Graduate School - New Brunswick
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school
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theses
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2016
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2016-05
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2016
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xx
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eng
Abstract (type = abstract)
Historically, much of the research in evolutionary biology and population genetics has involved analysis at the level of either a single locus or a few number thereof. However, Next Generation sequencing technology has opened the floodgates with respect to both the sheer volume and quality of sequence data that researchers have long needed to address and answer long-standing questions in their fields. Scientists are now, by and large, no longer hampered in their efforts by technological hurdles to obtain data, but are in fact facing the problem of how best to use the vast amount of data that are accumulating at an ever-increasing rate. This is a good problem to have. The following research described in this dissertation is an attempt to derive answers to questions in the fields of population genetics and evolutionary biology that, until recently, have been either intractable or, at best, extremely difficult to address. In the first chapter I provide an introduction and a brief historical look at the research efforts that have proceeded my own. In the second chapter I describe how modern sequencing methods and computational analysis can be used to study, analyze, and answer evolutionary questions about the non-model organism, Enallagma hageni, in order to 1) determine this organism's phylogenetic position within Arthropoda, 2) provide answers and insight into the evolutionary history of the protein-encoding genes in the Enallagma transcriptome, and 3) give functional annotation to these expressed proteins. In the third chapter I examine how natural selection acts on the genome and derive a method that can accurately determine the evolutionary cause of nucleotide fixations, having occurred either through positive selection or neutral processes. I then apply the methodology to North American populations of Drosophila melanogaster, providing further evidence as to how adaptive evolution proceeds in a newly established population. This is an important question, for though there have been multiple approaches devised to determine the targets and modes of evolution in the genome, to date there has not emerged a definitive method which can determine both the location and type of a selective process, and as a result, the picture of how and where adaptive evolution proceeds in the genome has remained opaque. In the forth chapter I examine how levels of natural selection within the genome have the potential to inhibit the ability to accurately learn population demographic history. Using a number of modern algorithms and extensive simulations, I first examine whether or not demographic histories that are learned under simple biological assumptions will yield accurate results when the actual data itself does not adhere to these assumptions. Further, I go on to examine more complicated models of demographic history, looking specifically at how positive selection biases inference, which directions these biases occur, and at what levels of selection do inference methods fail to be robust. Finally, I describe potential evolutionary scenarios where these inference methods may be more prone to fail, as well as methods which might mitigate positive selection's effects, thus allowing for more accurate histories to be inferred. The work contained in this dissertation, at the broadest scale, is an effort to marry state-of-the-art techniques in statistics, computer science, and machine learning algorithms to the technological advances of next generation sequencing; the potent combination of these technologies has provided a means with which to derive answers to multiple, long-standing questions in population genetics and evolutionary biology.
Subject (authority = RUETD)
Topic
Computational Biology and Molecular Biophysics
Subject (authority = ETD-LCSH)
Topic
Evolution (Biology)--Mathematical models
Subject (authority = ETD-LCSH)
Topic
Genomes--Analysis
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electronic resource
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Supplementary File: Table_S3.xlsx
Extent
1 online resource (xxxiv, 244 p. : ill.)
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Ph.D.
Note (type = bibliography)
Includes bibliographical references
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by Alexander G. Shanku
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Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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Identifier (type = doi)
doi:10.7282/T3M61NDC
Genre (authority = ExL-Esploro)
ETD doctoral
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The author owns the copyright to this work.
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Name
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Shanku
GivenName
Alexander
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G.
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Permission or license
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2016-04-10 08:41:10
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Alexander Shanku
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Rutgers University. Graduate School - New Brunswick
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Author Agreement License
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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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2016-05-31
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2016-11-30
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Access to this PDF has been restricted at the author's request. It will be publicly available after November 30th, 2016.
Copyright
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Copyright protected
Availability
Status
Open
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Permission or license
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