Publication Date


Document Type

Doctoral Dissertation

Academic Program

Interdisciplinary Graduate Program


Program in Bioinformatics & Integrative Biology

First Thesis Advisor

Jeffrey D. Jensen,

Second Thesis Advisor

Fen-Biao Gao, PhD


Binding Sites, Molecular Evolution, Population Genetics, Genome, MicroRNAs, Small Interfering RNA, Small Untranslated RNA


Dissertations, UMMS; Binding Sites; Evolution, Molecular; Genetics, Population; Genome; MicroRNAs; RNA, Small Interfering; RNA, Small Untranslated


Short noncoding RNAs play roles in regulating nearly every biological process, in nearly every organism, yet the exact function and importance of these molecules remains a subject of some debate. In order to gain a better understanding of the contexts in which these regulators have evolved, I have undertaken a variety of approaches to study the evolutionary history of the components that make up these pathways, in the form of two main research efforts. In the first chapter, I have used a combination of population genetics and molecular evolution techniques to show that proteins involved in the piRNA pathway are rapidly evolving, and that different components of the pathway seem to be evolving rapidly on different timescales. These rapidly evolving piRNA pathway proteins can be loosely separated into two groups. The first group appears to evolve quickly at the species level, perhaps in response to transposons that invade across species lines, while the second group appears to evolve quickly at the level of individual populations, perhaps in response to transposons that are paternally present yet novel to the maternal genome. In the second chapter of my research, I have used molecular evolution techniques and carefully devised controls to show that the binding sites of well-conserved miRNAs are among the most slowly changing short motifs in the genome, consistent with a conserved function for these short RNAs in regulatory pathways that are ancient and extremely slow to change. I have additionally discovered a major flaw in an existing approach to motif turnover calculations, which may lead to systematic biases in the published literature toward the false inference of increased regulatory complexity over time. I have implemented a revised approach to motif turnover that addresses this flaw.



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