2026-09-19T21:33:53Zhttps://dspace.mit.edu/server/oai/request

oai:dspace.mit.edu:1721.1/1245732026-06-06T00:56:06Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023

Manolis Kellis. Herr, Taylor(Taylor J.) Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2020-04-13T18:22:23Z 2020-04-13T18:22:23Z 2017 2019 https://hdl.handle.net/1721.1/124573 1149038812 This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 Cataloged from student-submitted PDF version of thesis. "June 2019." Includes bibliographical references (pages 89-91). Disease-associated nucleotides lie primarily in non-coding regions, increasing the urgency of understanding how gene-regulatory circuitry impacts human disease. Here, we use the increasing availability of functional genomics datasets and models elucidating how regulatory proteins control genes, to evaluate the impact of genetic variants on the activity of diverse regulators. First, we generate a comprehensive compendium of predicted binding intensities across the entire genome for over 500 transcription factors. Second, we create a novel dataset to connect how these binding intensities change in the context of disease datasets. Third, we develop a statistical framework to integrate these two datasets using dimensionality reduction, latent cluster discovery, and topic modeling. We use these techniques to show that regulatory proteins with analogous biological functions share similar global changes in binding due to genome-wide genetic variation. We also use our framework to discover a latent set of topics behind all genomic locations in chromosome 1, to link the locations in each of the topic clusters with a class of related diseases, and to show that relevant biological processes are statistically enriched in the genomic locations most related to each cluster. by Taylor Herr. M.Eng. M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 91 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Dissecting the gene-regulatory circuitry of disease-associated genetic variants Thesis application/pdf 2020-04-13T18:21:54Z Publication Master EECS unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="e0c80390-b006-4c9a-b518-458a1943f3d5"> <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>Dissecting the gene-regulatory circuitry of disease-associated genetic variants</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2019</PublicationDate> <Authors> <Author> <DisplayName>Herr, Taylor(Taylor J.)</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>Disease-associated nucleotides lie primarily in non-coding regions, increasing the urgency of understanding how gene-regulatory circuitry impacts human disease. Here, we use the increasing availability of functional genomics datasets and models elucidating how regulatory proteins control genes, to evaluate the impact of genetic variants on the activity of diverse regulators. First, we generate a comprehensive compendium of predicted binding intensities across the entire genome for over 500 transcription factors. Second, we create a novel dataset to connect how these binding intensities change in the context of disease datasets. Third, we develop a statistical framework to integrate these two datasets using dimensionality reduction, latent cluster discovery, and topic modeling. We use these techniques to show that regulatory proteins with analogous biological functions share similar global changes in binding due to genome-wide genetic variation. We also use our framework to discover a latent set of topics behind all genomic locations in chromosome 1, to link the locations in each of the topic clusters with a class of related diseases, and to show that relevant biological processes are statistically enriched in the genomic locations most related to each cluster.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>