2026-09-20T12:08:36Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/284932022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
David K. Gifford.
Danford, Timothy W. (Timothy William), 1979-
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2005-09-26T20:45:03Z
2005-09-26T20:45:03Z
2004
2004
http://hdl.handle.net/1721.1/28493
57303928
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (leaves 94-99).
We formalize a probabilistic model of inter-species sequence conservation for motif discovery, and demonstrate that adding large-scale genomic conservation data to an existing motif discovery procedure improves the quality of that procedure's results. Existing motif discovery algorithms reveal binding motifs that are statistically over-represented in small sets of promoter regions. To the extent that binding motifs form a reliable part of a cell's regulatory apparatus, and that apparatus is preserved across closely related species, these binding motifs should also be conserved in the corresponding genomes. Previous studies have tried to assess levels of conservation in genomic fragments of several yeast species. Our approach computes the conditional probability of inter-species sequences, and uses this probability measure to maximize the likelihood of the data from different species with a motif model.
by Timothy W. Danford.
S.M.
99 leaves
4648981 bytes
4660362 bytes
application/pdf
application/pdf
en_US
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.
Integrating genomic conservation data with motif discovery
Thesis
application/pdf
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="2f8e08cb-e1be-4035-aea7-16d0df5cc0e8">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>en_US</Language>
<Title>Integrating genomic conservation data with motif discovery</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2004</PublicationDate>
<Authors>
<Author>
<DisplayName>Danford, Timothy W. (Timothy William), 1979-</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>We formalize a probabilistic model of inter-species sequence conservation for motif discovery, and demonstrate that adding large-scale genomic conservation data to an existing motif discovery procedure improves the quality of that procedure's results. Existing motif discovery algorithms reveal binding motifs that are statistically over-represented in small sets of promoter regions. To the extent that binding motifs form a reliable part of a cell's regulatory apparatus, and that apparatus is preserved across closely related species, these binding motifs should also be conserved in the corresponding genomes. Previous studies have tried to assess levels of conservation in genomic fragments of several yeast species. Our approach computes the conditional probability of inter-species sequences, and uses this probability measure to maximize the likelihood of the data from different species with a motif model.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>