- Title Information
- Title
- Structure, Variation, and Reproducibility: Bayesian inference in problems arising from the study of RNA and an RNA-binding protein
- Name:
Personal
- Name Part
- Sugden, Lauren Alpert
- Role
- Role Term:
Text
- creator
- Origin Information
- Copyright Date
- 2014
- Physical Description
- Extent
- xv, 151 p.
- digitalOrigin
- born digital
- Note
- Thesis (Ph.D. -- Brown University (2014)
- Name:
Personal
- Name Part
- Lawrence, Charles
- Role
- Role Term:
Text
- Director
- Name:
Personal
- Name Part
- Thompson, William
- Role
- Role Term:
Text
- Reader
- Name:
Personal
- Name Part
- Reenan, Robert
- Role
- Role Term:
Text
- Reader
- Name:
Corporate
- Name Part
- Brown University. Applied Mathematics
- Role
- Role Term:
Text
- sponsor
- Genre (aat)
- theses
- Abstract
- Far from being solely a passive messenger between DNA and protein, RNA is a complex molecule involved in regulation at many levels. While the best-known RNAs, messenger RNAs (mRNA), do exist to carry information, there is also a large population of non-coding RNAs that can take on complicated structures that enable them to perform functions throughout the cell. Even mRNAs are not static molecules, however. Mechanisms for altering mRNA sequences result in transcripts that are no longer faithful representations of the information in the genome. One such mechanism is RNA editing by ADAR, which binds double-stranded RNA and targets a particular adenosine for conversion to inosine, which is recognized by the cell as guanosine. A major consequence of editing is amino-acid recoding, resulting in protein diversification.
In this thesis, we look at three problems motivated by the study of RNA and ADAR. First, we propose a method for assessing the reproducibility of genome-scale studies that make a large number of predictions, using the prediction of ADAR binding sites as a motivating example. We then address the problem of avoiding false positives when identifying subtle signals such as ADAR modifications in high-throughput sequencing data in the presence of genetic polymorphisms specific to laboratory populations. Finally, we turn to structural prediction of RNA, inferring the common structural and sequence characteristics of a set of related transcripts.
- Subject
- Topic
- Bayesian inference
- Subject
- Topic
- polymorphisms
- Subject
- Topic
- reproducibility
- Subject
- Topic
- secondary structure
- Subject (FAST)
(authorityURI="http://id.worldcat.org/fast", valueURI="http://id.worldcat.org/fast/1086255")
- Topic
- RNA editing
- Record Information
- Record Content Source (marcorg)
- RPB
- Record Creation Date
(encoding="iso8601")
- 20141006
- Language
- Language Term:
Code (ISO639-2B)
- eng
- Language Term:
Text
- English
- Identifier:
DOI
- 10.7301/Z0XS5SRN
- Access Condition:
rights statement
(href="http://rightsstatements.org/vocab/InC/1.0/")
- In Copyright
- Access Condition:
restriction on access
- Collection is open for research.
- Type of Resource (primo)
- dissertations