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020 ▼a 9781085795722
035 ▼a (MiAaPQ)AAI13862165
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 247004
0820 ▼a 576.6
1001 ▼a Ledda, Mirko Aurelio.
24510 ▼a Hairpin in a Haystack: Structure-Guided Search for Functional RNA Elements.
260 ▼a [S.l.]: ▼b University of California, Davis., ▼c 2019.
260 1 ▼a Ann Arbor: ▼b ProQuest Dissertations & Theses, ▼c 2019.
300 ▼a 200 p.
500 ▼a Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
500 ▼a Advisor: Aviran, Sharon.
5021 ▼a Thesis (Ph.D.)--University of California, Davis, 2019.
506 ▼a This item must not be sold to any third party vendors.
506 ▼a This item must not be added to any third party search indexes.
520 ▼a 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.
590 ▼a School code: 0029.
650 4 ▼a Bioinformatics.
650 4 ▼a Physiology.
650 4 ▼a Molecular biology.
650 4 ▼a Genetics.
650 4 ▼a Health sciences.
650 4 ▼a Medical imaging.
650 4 ▼a Virology.
690 ▼a 0715
690 ▼a 0566
690 ▼a 0720
690 ▼a 0574
690 ▼a 0369
690 ▼a 0307
690 ▼a 0719
71020 ▼a University of California, Davis. ▼b Integrative Genetics and Genomics.
7730 ▼t Dissertations Abstracts International ▼g 81-04B.
773 ▼t Dissertation Abstract International
790 ▼a 0029
791 ▼a Ph.D.
792 ▼a 2019
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T15490960 ▼n KERIS ▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다.
980 ▼a 202002 ▼f 2020
990 ▼a ***1816162
991 ▼a E-BOOK