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Automated and quantitative phenotyping of C. elegans genetic screens from high-throughput image data
Descriptive
TitleInfo
Title
Automated and quantitative phenotyping of C. elegans genetic screens from high-throughput image data
Name
(type = personal)
NamePart
(type = family)
White
NamePart
(type = given)
Amelia
NamePart
(type = date)
1977-
DisplayForm
Amelia White
Role
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(authority = RULIB)
author
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(type = personal)
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(type = family)
Sontag
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(type = given)
Eduardo
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Eduardo Sontag
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Advisory Committee
Role
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chair
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(type = personal)
NamePart
(type = family)
Driscoll
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(type = given)
Monica
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Monica Driscoll
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Advisory Committee
Role
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(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Chen
NamePart
(type = given)
Kevin
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Kevin Chen
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Morozov
NamePart
(type = given)
Alexandre
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Alexandre Morozov
Affiliation
Advisory Committee
Role
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(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Piano
NamePart
(type = given)
Fabio
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Fabio Piano
Affiliation
Advisory Committee
Role
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(authority = RULIB)
outside member
Name
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NamePart
Rutgers University
Role
RoleTerm
(authority = RULIB)
degree grantor
Name
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NamePart
Graduate School - New Brunswick
Role
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(authority = RULIB)
school
TypeOfResource
Text
Genre
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theses
OriginInfo
DateCreated
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2013
DateOther
(qualifier = exact);
(type = degree)
2013-10
Place
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(type = code)
xx
Language
LanguageTerm
(authority = ISO639-2b);
(type = code)
eng
Abstract
(type = abstract)
High throughput screens producing image data, are becoming increasingly easy to perform. Nevertheless, manual evaluation of image data is impractical because it is not only time intensive, but also prone to error. The model organism C. elegans is frequently used to study fundamental questions in development and behavior and is particularly amenable to high throughput screening due to its small size and ability to be cultured in a well of a 96 well plate. In this thesis I will describe an automated image analysis system (DevStaR) for quantitative phenotyping of embryonic lethality and sterility from populations of C. elegans in 96 well plates. This image analysis system counts each developmental stage in an image of a C. elegans population, allowing efficient high throughput calculation of C. elegans viability phenotypes. DevStaR is an object recognition machine comprising several hierarchical layers that build successively more sophisticated representations of the objects (developmental stages) to be classified. The algorithm segments the objects, decomposes the objects into parts, extracts features from these parts, and classifies them using a Support Vector Machine (SVM) and global shape information. This enables correct classifications in the presence of complicated occlusions and deformations of the animals. Features of the classified objects are then used to obtain a count of each developmental stage. I have used this system to analyze phenotypic data from approximately 50 C. elegans genome wide genetic interaction screens, as well as a genome wide RNAi screen in high replication (~30 replicates per RNAi clone). Using the quantitative phenotype output by DevStaR I have examined features of the high-throughput screen data, such as variability and penetrance, which have not been examined in detail previously due to a lack of automated and quantitative scoring methods available for high-throughput image data. DevStaR overcomes a previous bottleneck in image analysis by achieving near real-time scoring of image data in a fully automated manner. Moreover, DevStaR reduces the need for human evaluation of images and provides rapid quantitative output that is not feasible at high throughput by manual scoring.
Subject
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Topic
Computational Biology and Molecular Biophysics
RelatedItem
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
(type = RULIB)
ETD
Identifier
ETD_4902
PhysicalDescription
Form
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electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
ix, 110 p. : ill.
Note
(type = degree)
Ph.D.
Note
(type = bibliography)
Includes bibliographical references
Note
(type = statement of responsibility)
by Amelia White
Subject
(authority = ETD-LCSH)
Topic
Computational biology
Subject
(authority = ETD-LCSH)
Topic
Diagnostic imaging
Subject
(authority = ETD-LCSH)
Topic
Caenorhabditis elegans--Research
RelatedItem
(type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
Location
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(displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier
(type = doi)
doi:10.7282/T3N29V0F
Genre
(authority = ExL-Esploro)
ETD doctoral
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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
White
GivenName
Amelia
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
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(qualifier = exact);
(point = start)
2013-07-09 13:57:15
AssociatedEntity
Name
Amelia White
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
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ContentModel
ETD
OperatingSystem
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windows xp
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Statistical Profile