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dc.contributor.advisorChristopher B. Burge.en_US
dc.contributor.authorAlexis, Maria Sarah.en_US
dc.contributor.otherMassachusetts Institute of Technology. Computational and Systems Biology Program.en_US
dc.date.accessioned2019-11-04T20:20:57Z
dc.date.available2019-11-04T20:20:57Z
dc.date.copyright2019en_US
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/122719
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2019en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 125-146).en_US
dc.description.abstractRNA-binding proteins (RBPs) regulate all aspects of RNA metabolism, such as splicing, localization, translation, and degradation. RNA processing is a critical component of gene expression regulation, and adaptive changes in RNA processing underlie many phenotypic differences between species. RBPs regulate RNA processing by recognizing RNA sequence elements (motifs) within RNAs. Studying the determinants of these RBP:RNA interactions is therefore key to understanding how RBPs select their targets, and how RNA processing evolves over time. This thesis presents three chapters revolving around these questions. First, I present a large-scale analysis of the evolution of gene expression across tissues, species, and studies. This study differs from previous studies in its usage of inter-sample distances to model gene expression divergence, a method that allowed us to reconcile disparate findings in the field.en_US
dc.description.abstractSecond, I present a comprehensive study of the affinity landscapes of 78 human RBPs using RNA Bind-N-Seq (RBNS), an unbiased assay that determines the sequence, structure, and context preferences of RBPs. Integrated analysis of all 78 motifs reveals an unexpectedly low diversity of RNA motifs, implying frequent convergence of binding specificity towards a relatively small set of RNA motifs, many with low compositional complexity. Offsetting this trend, RBPs show extensive preferences for contextual features distinct from short linear motifs, including spaced "bipartite" motifs, biased flanking nucleotide composition, and bias away from or toward RNA structure. These results emphasize the importance of contextual features in RNA recognition, which likely enable targeting of distinct subsets of transcripts by different RBPs that recognize the same linear motif.en_US
dc.description.abstractLastly, I compile a catalog of all known RBP specificities and examine their conservation patterns, in vivo binding patterns, and evolutionary trajectories across species. This work demonstrates that RNA regulation can be well conserved despite rapid evolution of RBP binding sites, and highlights mechanisms that may contribute to this robustness. This phenomenon is well-characterized for transcriptional regulation at promoters, but has not well been described for RNA regulation. Taken together, these studies advance our understanding of RBP target selection and how it evolves over time, thereby furthering our understanding of the basic mechanisms that govern gene regulation.en_US
dc.description.statementofresponsibilityby Maria Sarah Alexis.en_US
dc.format.extent146 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT 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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectComputational and Systems Biology Program.en_US
dc.titleRegulation by RNA-binding proteins : sequence determinants and evolutionary dynamicsen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computational and Systems Biology Programen_US
dc.identifier.oclc1124073959en_US
dc.description.collectionPh.D. Massachusetts Institute of Technology, Computational and Systems Biology Programen_US
dspace.imported2019-11-04T20:20:56Zen_US
mit.thesis.degreeDoctoralen_US
mit.thesis.departmentCSBen_US


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