Computational Study of Small Noncoding RNAs and Their Functions

Date of Award

Summer 8-15-2012

Author's Department

Computer Science & Engineering

Degree Name

Doctor of Philosophy (PhD)

Degree Type



Post-transcriptional gene regulation at the RNA level has been recently shown to be more widespread and important than previously assumed. While various regulatory RNA molecules have been reported in animals and plants, two prominent types of regulatory small RNAs are microRNAs (miRNAs) and endogenous short interfering RNAs (siRNAs). Because of their importance, their nature and the difficulties in studying them, research in miRNAs has been an active research topic with

many computational challenges.

First, computational strategies for miRNA identification have been developed to overcome the technical hurdles for experimental methods based on expression screening. We propose and develop a novel ranking algorithm based on random walks to computationally predict novel miRNAs from genomes, which have a few known miRNAs, may be poorly annotation and even not completely assembled. We also develop meta-feature based classification method to identify miRNAs from high-throughput sequencing data of small RNAs. In addition, we devise a pipeline to analyze nat-siRNA in high-throughput sequencing data.

Secondly, we formulate the problem of promoter prediction based on multiple instance learning scheme, and propose an effective promoter identification algorithm, called CoVote. We apply CoVote to predict microRNA core promoters. We investigate core promoter regions of microRNA genes in Caenorhabditis elegans, Homo sapiens, Arabidopsis thaliana and Oryza sativa, and further analyze sequence motifs in the putative core promoters which may be involved in the transcription of microRNA genes.

Furthermore, with characterized promoters of miRNA genes, we apply data mining approaches to model the transcriptome of miRNAs under particular conditions. Finally, by integrating miRNA target genes, we further analyze the miRNA-mediate regulatory networks and computationally identify network motifs. Since miRNAs and their targets can be formulated as a natural bipartite network(graph), we propose and develop a tool to study modules in the miRNA-regulatory network.


English (en)


Weixiong Zhang

Committee Members

Barbara N Kunkel, Gary D Stormo


Permanent URL: https://doi.org/10.7936/K7GT5K4K

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