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Hairpin in a Haystack: Structure-Guided Search for Functional RNA Elements
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Hairpin in a Haystack: Structure-Guided Search for Functional RNA Elements. |
| 개인저자 | Ledda, Mirko Aurelio. |
| 단체저자명 | University of California, Davis. Integrative Genetics and Genomics. |
| 발행사항 | [S.l.]: University of California, Davis., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 200 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-04B. Dissertation Abstract International |
| ISBN | 9781085795722 |
| 학위논문주기 | Thesis (Ph.D.)--University of California, Davis, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Advisor: Aviran, Sharon. |
| 이용제한사항 | This item must not be sold to any third party vendors.This item must not be added to any third party search indexes. |
| 요약 | RNA plays a central role in biology and is involved in a plethora of biological processes. An RNA's function directly derives from its ability to fold into specific structures. Yet, establishing a direct link between its structure and biological function remains one the greatest challenges in RNA biology. Historically, RNA structures have been determined using Nuclear Magnetic Resonance (NMR) and X-ray crystallography. However those methods are too labor-intensive and costly for high-throughput applications. On the other hand, computational models for RNA structure prediction suffer from poor accuracies. The emergence of chemical and enzymatic experiments, called structure probing, have revolutionized the field by enabling the simultaneous quantification of structures across the entire transcriptome and in vivo. Despite these advances and the start of a Big Data era for RNA structures, these data have been largely underutilized due to a large variety of probes and experimental approaches, combined with a lack of universal, fast and accurate methods to readily interpret and leverage these data.This dissertation focuses on the development of novel methods to interpret and use structure probing data to understand RNA biology. I started by developing optimal methods to characterize the statistical properties of structure probing data their probabilistic interpretation in term of structure. I applied this work to better understand and improve the performance of an algorithm for data-driven thermodynamic RNA secondary structure prediction. I then leveraged these findings to develop an unsupervised machine learning algorithm that can rapidly mine structural elements from any type of SP data and at any experimental scale, from a handful of RNAs to entire structuromes. One of the strength of this algorithm is that it does not require known reference structures and it does not rely on thermodynamic models, which are often inadequate for complex experimental conditions, for structure inference. I validated the accuracy of the algorithm on an in vitro dataset of RNAs with known secondary structures. I then proved the utility of the algorithm on several datasets and across a variety of biological questions, and proved that this method can readily detect functional structural elements in both small- and large-scale datasets. |
| 일반주제명 | Bioinformatics. Physiology. Molecular biology. Genetics. Health sciences. Medical imaging. Virology. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
