Date on Master's Thesis/Doctoral Dissertation


Document Type

Doctoral Dissertation

Degree Name

Ph. D.


Computer Engineering and Computer Science

Degree Program

Computer Science and Engineering, PhD

Committee Chair

Park, Juw Won

Committee Co-Chair (if applicable)

Rouchka, Eric

Committee Member

Rouchka, Eric

Committee Member

Nasraoui, Olfa

Committee Member

Altiparmak, Nihat

Committee Member

Zhange, Huang-Ge

Author's Keywords

bioinformatics; microRNAs; functional analysis; pattern matching


Genes are DNA sequences that encode the information needed to synthesize molecules necessary for the function of the cell. Some genes are called protein-coding genes because they have the code required to manufacture proteins. The expression of a certain gene means its product (protein) is produced. Although some genes are not protein-coding, they regulate the gene expression of other protein-coding genes. Of these, microRNAs (miRNAs) are small RNA molecules that inhibit the expression of other genes by binding to their mRNA transcripts. miRNAs have been shown to be linked to several biological processes like development and diseases like cancer. Recently, researchers have hypothesized that miRNAs are involved in the regulation of the expression of genes from other species. Although tools to predict miRNA target genes are available in the case when miRNAs and target genes belong to the same species, to our knowledge there are no available tools to predict inter-kingdom miRNA target genes (miRNA and target genes belong to two different kingdoms). To address this limitation, we developed an efficient tool to predict potential gut bacterial genes targeted by miRNAs from edible plants. We successfully predicted ginger miRNAs that target two genes from a gut bacterial strain called Lactobacillus rhamnosus GG. To maintain the efficiency of our tool while using a larger number of miRNAs and bacterial strains, we used a hash-table to index the sequences of bacterial genes. To predict the function of a miRNA, we start by compiling the list of direct target genes (ones with binding sites) and then we search for biological process in which these genes are enriched. This approach does not include other genes affected by the miRNA but do not necessarily have a physical binding site (indirect targets). An example of an indirect target is the gene that doesn’t have a binding site and is regulated through other direct targets like transcription factors. To overcome this limitation, we developed miRinGO an interactive web application to include these indirect targets in the functional analysis. Our approach showed better performance compared to the existing approach in predicting biological processes known to be targeted by certain miRNAs.