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Manolis Kellis. Lin, Michael F. (Michael Fong-Jay) Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2007-03-12T17:55:35Z 2007-03-12T17:55:35Z 2006 2006 http://hdl.handle.net/1721.1/36807 80777803 Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006. Includes bibliographical references (leaves 55-56). An important step in genome interpretation is the accurate identification of protein-coding genes. One approach to gene identification is comparative analysis of the genomes of several related species, to find genes that have been conserved by natural selection over millions of years of evolution. I develop general computational methods that combine statistical analysis of genome sequence alignments with classification algorithms in order to detect the distinctive signatures of protein-coding DNA sequence evolution. I implement these methods as a software system, which I then use to identify previously unknown genes, and cast doubt on some existing gene annotations, in the genomes of the fungi Saccharomyces cerevisiae and Candida albicans, the fruit fly Drosophila melanogaster, and the human. These methods perform competitively with the best existing de novo gene identification systems, and are practically applicable to the goal of improving existing gene annotations through comparative genomics. by Michael F. Lin. M.Eng. 56 leaves 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. Comparative gene identification in mammalian, fly, and fungal genomes Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="f910a87a-962a-4f4c-a838-070632155afe"> <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>Comparative gene identification in mammalian, fly, and fungal genomes</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2006</PublicationDate> <Authors> <Author> <DisplayName>Lin, Michael F. (Michael Fong-Jay)</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>An important step in genome interpretation is the accurate identification of protein-coding genes. One approach to gene identification is comparative analysis of the genomes of several related species, to find genes that have been conserved by natural selection over millions of years of evolution. I develop general computational methods that combine statistical analysis of genome sequence alignments with classification algorithms in order to detect the distinctive signatures of protein-coding DNA sequence evolution. I implement these methods as a software system, which I then use to identify previously unknown genes, and cast doubt on some existing gene annotations, in the genomes of the fungi Saccharomyces cerevisiae and Candida albicans, the fruit fly Drosophila melanogaster, and the human. These methods perform competitively with the best existing de novo gene identification systems, and are practically applicable to the goal of improving existing gene annotations through comparative genomics.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>