Thesis: Ph. D. in Bioinformatics and Integrative Genomics, Harvard-MIT Program in Health Sciences and Technology, 2018. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged student-submitted from PDF version of thesis. / Includes bibliographical references (pages 141-156). / Comparative genomics is a powerful method for identifying functional genetic elements by their evolutionary patterns across species. However, current studies largely focus on analysis of genome sequences. The recent development of RNA-sequencing reveals dimensions of regulatory information previously inaccessible to us by sequence alone. The comparison of RNA-sequencing data across mammals has great potential for addressing two open problems in biology: identifying the regulatory mechanisms crucial to mammalian physiology, and deciphering how gene regulation contributes to the diversity of mammalian phenotypes. For my thesis, I developed two methodologies for interrogating comparative transcriptomic data for biological inference. First, I developed a framework for quantifying the evolutionary forces acting on gene expression and inferring evolutionarily optimal expression levels. I demonstrate how to use this framework to identify expression pathways underlying conserved, adaptive, and disease states of mammalian biology. Second, I developed novel metrics of transcriptional evolution to evaluate the conservation of long noncoding RNAs. These metrics further reveal that long noncoding RNAs harbor distinct evolutionary signatures, suggesting that they are not a homogenous class of molecules but rather a mixture of multiple functional classes with distinct biological roles. My thesis work provides fundamental quantitative tools for asking biological questions about transcriptome evolution. These tools provide a pivotal framework for interpreting transcriptional data across species and pave the way for deciphering the regulatory changes that lead to mammalian phenotypic variation. / by Jenny Chen. / Ph. D. in Bioinformatics and Integrative Genomics
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/117792 |
| Date | January 2018 |
| Creators | Chen, Jenny (Jennifer) |
| Contributors | Aviv Regev., Harvard--MIT Program in Health Sciences and Technology., Harvard--MIT Program in Health Sciences and Technology. |
| Publisher | Massachusetts Institute of Technology |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 156 pages, application/pdf |
| Rights | 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 |
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