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Statistical Methods and Software for Comparative Analysis of RNA Structurome Pro詮걄ing Data
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Statistical Methods and Software for Comparative Analysis of RNA Structurome Pro詮걄ing Data. |
| 개인저자 | Choudhary, Krishna. |
| 단체저자명 | University of California, Davis. Biomedical Engineering. |
| 발행사항 | [S.l.]: University of California, Davis., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 225 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-02B. Dissertation Abstract International |
| ISBN | 9781085592253 |
| 학위논문주기 | Thesis (Ph.D.)--University of California, Davis, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-02, Section: B.
Advisor: Aviran, Sharon. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | RNA is one of the most important classes of biopolymers and drives a myriad of cellular processes. Unlike DNA, RNA molecules generally exist as single strands of nucleotides, which fold upon themselves to take on diverse and highly intricate molecular structures. The rich tapestry of RNAs' stereochemistry dictated by their structural landscapes and intermolecular interactions enables their vital functions. Hence, it is essential to understand their stereochemical characteristics in order to understand their functions. Traditionally, RNA structure studies have relied on techniques such as nuclear magnetic resonance or X-ray crystallography. However, these are labor-intensive and technologically limited. Thermodynamic models to predict structure algorithmically from sequence alone suffer from poor accuracy. A class of methods called structure pro詮걄ing has existed for several decades. These utilize reagents that modify RNA nucleotides in structure-sensitive reactions. The intensities of reactions encode the local stereochemical characteristics and are captured in a cDNA library. Until recently, structure pro詮걄ing was limited in scope due to its reliance on electrophoresis for reading the cDNA library. With the advances in next-generation sequencing and library preparation technologies, this limitation has been overcome. RNA structure studies have entered the age of "omics" and studies increasingly concern with RNA structuromes. To fulfill the promise of this new era, statistical methods and software tools are needed to enable analysis of large-scale structuromic data.The most common analysis goals include recovering structural information, ensuring its quality and comparing the structural information for different physiological conditions. This dissertation focuses on developing methods for performing such analyses. I have derived closed-form and intuitive expressions to estimate structural parameters, developed measures to perform quality control and developed a statistical approach to differential analysis. The method for parameter estimation builds upon prior work in the 詮갻ld and the latter two methods are the 詮걊st of their kind. These methods are broadly applicable, are integrated in easy-to-use software tools and are validated with data from diverse structurome pro詮걄ing technologies and a range of organisms. I have applied them to mechanistic studies in collaborations with experimentalists to demonstrate their utility and facilitate their transfer to the RNA community. |
| 일반주제명 | Biostatistics. Bioinformatics. |
| 언어 | 영어 |
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