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Manolis Kellis and Silvia Rouskin. Persad, Sitara C Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2018-12-18T19:47:23Z 2018-12-18T19:47:23Z 2018 2018 http://hdl.handle.net/1721.1/119725 1078639384 Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. 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 (pages 77-78). Beyond its function as a messenger molecule in protein formation, the linear sequence on RNA is capable of folding into higher order structures which may interact with other molecules and play key functional roles in the cell. Current methods in characterizing RNA structure via experimental probing are limited to the population average, which obscures structural heterogeneity. This thesis addresses the problem of inferring structural heterogeneity from dimethyl sulphate (DMS) probing data. First, we analysed sequence data to uncover experimental biases and developed simulations for sample structures. We proposed and evaluated machine learning methods in unsupervised learning to infer structural heterogeneity. Secondly, we designed and implemented runDMC, a web platform designed to facilitate the discovery of alternative RNA secondary structures, using in vivo chemical probing data and machine learning clustering methods. runDMC accepts experimental probing data and provides an intuitive, user-friendly interface for discovery of alternative structures. We anticipate that runDMC will facilitate the widespread use of DMS probing and analysis in the biological community, enabling the discovery of more RNA alternative structures. by Sitara C. Persad. M.Eng. 78 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. Detection of RNA structural heterogeneity : computational analysis and development of web-based tool Detection of ribonucleic acid structural heterogeneity : computational analysis and development of web-based tool Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="6f8184b9-2ad9-4354-b392-a53de498f093"> <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>Detection of RNA structural heterogeneity : computational analysis and development of web-based tool</Title> <Subtitle>Detection of ribonucleic acid structural heterogeneity : computational analysis and development of web-based tool</Subtitle> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <Authors> <Author> <DisplayName>Persad, Sitara C</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>Beyond its function as a messenger molecule in protein formation, the linear sequence on RNA is capable of folding into higher order structures which may interact with other molecules and play key functional roles in the cell. Current methods in characterizing RNA structure via experimental probing are limited to the population average, which obscures structural heterogeneity. This thesis addresses the problem of inferring structural heterogeneity from dimethyl sulphate (DMS) probing data. First, we analysed sequence data to uncover experimental biases and developed simulations for sample structures. We proposed and evaluated machine learning methods in unsupervised learning to infer structural heterogeneity. Secondly, we designed and implemented runDMC, a web platform designed to facilitate the discovery of alternative RNA secondary structures, using in vivo chemical probing data and machine learning clustering methods. runDMC accepts experimental probing data and provides an intuitive, user-friendly interface for discovery of alternative structures. We anticipate that runDMC will facilitate the widespread use of DMS probing and analysis in the biological community, enabling the discovery of more RNA alternative structures.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>