Methods in and Applications of the Sequencing of Short Non-Coding RNAs

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Doctor of Philosophy (PhD)
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Genomics & Computational Biology
Alzheimer's disease
machine learning
non-coding RNA
RNA modification
Molecular Biology
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Short non-coding RNAs are important for all domains of life. With the advent of modern molecular biology their applicability to medicine has become apparent in settings ranging from diagonistic biomarkers to therapeutics and fields ranging from oncology to neurology. In addition, a critical, recent technological development is high-throughput sequencing of nucleic acids. The convergence of modern biotechnology with developments in RNA biology presents opportunities in both basic research and medical settings. Here I present two novel methods for leveraging high-throughput sequencing in the study of short non-coding RNAs, as well as a study in which they are applied to Alzheimer's Disease (AD). The computational methods presented here include High-throughput Annotation of Modified Ribonucleotides (HAMR), which enables researchers to detect post-transcriptional covalent modifications to RNAs in a high-throughput manner. In addition, I describe Classification of RNAs by Analysis of Length (CoRAL), a computational method that allows researchers to characterize the pathways responsible for short non-coding RNA biogenesis. Lastly, I present an application of the study of non-coding RNAs to Alzheimer's disease. When applied to the study of AD, it is apparent that several classes of non-coding RNAs, particularly tRNAs and tRNA fragments, show striking changes in the dorsolateral prefrontal cortex of affected human brains. Interestingly, the nature of these changes differs between mitochondrial and nuclear tRNAs, implicating an association between Alzheimer's disease and perturbation of mitochondrial function. In addition, by combining known genetic factors of AD with genes that are differentially expressed and targets of regulatory RNAs that are differentially expressed, I construct a network of genes that are potentially relevant to the pathogenesis of the disease. By combining genetics data with novel results from the study of non-coding RNAs, we can further elucidate the molecular mechanisms that underly Alzheimer's disease pathogenesis.

Li-San Wang
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