2026-09-19T17:02:30Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/330812022-01-13T07:53:45Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Marth L. Bulyk.
He, Fangxue
Harvard University--MIT Division of Health Sciences and Technology.
Harvard University--MIT Division of Health Sciences and Technology
2006-06-19T17:38:52Z
2006-06-19T17:38:52Z
2005
2005
http://hdl.handle.net/1721.1/33081
62171687
Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2005.
Includes bibliographical references (leaves 55-57).
Regulation of gene expression occurs largely through the binding of sequence- specific transcription factors (TFs) to genomic DNA binding sites (BSs). This thesis presents a rigorous scoring scheme, implemented as a C program termed "ModuleFinder", that evaluates the likelihood that a given genomic region is a cis regulatory module (CRM) for an input set of TFs according to its degree of: (1) homotypic site clustering; (2) heterotypic site clustering; and (3) evolutionary conservation across multiple genomes. Importantly, ModuleFinder obtains all parameters needed to appropriately weight the relative contributions of these sequence features directly from the input sequences and TFBS motifs, and does not need to first be trained. Using two previously described collections of experimentally verified CRMs in mammals as validation datasets, we show that ModuleFinder is able to identify CRMs with great sensitivity and specificity. We also evaluated ModuleFinder on a set of DNA binding site data for the human TFs Hepatocyte Nuclear Factor HNF1 [alpha], HNF4 [alpha] and HNF6 and compared its performance with logistic regression and neural network models.
by Fangxue He.
S.M.
57 leaves
2423925 bytes
2425194 bytes
application/pdf
application/pdf
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
Harvard University--MIT Division of Health Sciences and Technology.
ModuleFinder : a computational model for the identification of Cis regulatory modules
Module Finder : a computational model for the identification of Cis regulatory modules
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="2ceda1b7-19d4-4b61-9e26-a803e7f8b4b2">
<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>ModuleFinder : a computational model for the identification of Cis regulatory modules</Title>
<Subtitle>Module Finder : a computational model for the identification of Cis regulatory modules</Subtitle>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2005</PublicationDate>
<Authors>
<Author>
<DisplayName>He, Fangxue</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>Harvard University--MIT Division of Health Sciences and Technology.</Keyword>
<Abstract>Regulation of gene expression occurs largely through the binding of sequence- specific transcription factors (TFs) to genomic DNA binding sites (BSs). This thesis presents a rigorous scoring scheme, implemented as a C program termed "ModuleFinder", that evaluates the likelihood that a given genomic region is a cis regulatory module (CRM) for an input set of TFs according to its degree of: (1) homotypic site clustering; (2) heterotypic site clustering; and (3) evolutionary conservation across multiple genomes. Importantly, ModuleFinder obtains all parameters needed to appropriately weight the relative contributions of these sequence features directly from the input sequences and TFBS motifs, and does not need to first be trained. Using two previously described collections of experimentally verified CRMs in mammals as validation datasets, we show that ModuleFinder is able to identify CRMs with great sensitivity and specificity. We also evaluated ModuleFinder on a set of DNA binding site data for the human TFs Hepatocyte Nuclear Factor HNF1 [alpha], HNF4 [alpha] and HNF6 and compared its performance with logistic regression and neural network models.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>