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David K. Gifford. Edwards, Matthew Douglas Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2012-01-11T20:17:25Z 2012-01-11T20:17:25Z 2011 2011 http://hdl.handle.net/1721.1/68180 770662454 Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student submitted PDF version of thesis. Includes bibliographical references (p. 61-65). Advances in high-throughput DNA sequencing have created new avenues of attack for classical genetics problems. This thesis develops and applies principled methods for analyzing DNA sequencing data from multiple pools of individual genomes. Theoretical expectations under several genetic models are used to inform specific experimental designs and guide the allocation of experimental resources. A computational framework is developed for analyzing and accurately extracting informative data from DNA sequencing reads obtained from pools of individuals. A series of statistical tests are proposed in order to detect nonrandom associations in pooled data, including a novel approach based on hidden Markov models that optimally shares data across genomic locations. The methods are applied to new and existing datasets and improve on the resolution of published methods, frequently obtaining single-gene accuracy. by Matthew Douglas Edwards. S.M. 65 p. eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Computational methods for high-throughput pooled genetic experiments Thesis application/pdf Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="5b1007ff-ba5a-4e13-9a40-830f18d6f17a"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Computational methods for high-throughput pooled genetic experiments</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2011</PublicationDate> <Authors> <Author> <DisplayName>Edwards, Matthew Douglas</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Advances in high-throughput DNA sequencing have created new avenues of attack for classical genetics problems. This thesis develops and applies principled methods for analyzing DNA sequencing data from multiple pools of individual genomes. Theoretical expectations under several genetic models are used to inform specific experimental designs and guide the allocation of experimental resources. A computational framework is developed for analyzing and accurately extracting informative data from DNA sequencing reads obtained from pools of individuals. A series of statistical tests are proposed in order to detect nonrandom associations in pooled data, including a novel approach based on hidden Markov models that optimally shares data across genomic locations. The methods are applied to new and existing datasets and improve on the resolution of published methods, frequently obtaining single-gene accuracy.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>