Gene expression is an essential mechanism for physical and mental development of human. Aberrant regulation of gene expression creates abnormality in human body than can lead to complicated diseases. Gene expression can be regulated at any stage from the chromatin unfolding stage to post-translation stage of protein. In this study, we focused on two important factors of gene expression regulation that participate in the gene expression process at the transcription and the post-transcriptional stages; enhancer-promoter interactions and miRNA-mRNA interactions. The enhancer-promoter interactions are difficult to detect due to the large distance between the enhancer and promoter region and cell-specific activity of the interactions. The cell-specific interactions have not been well studied due to inconsistent feature availability in different cells. We designed a tool that considers a large variety of enhancer-promoter interaction features in different cell lines, can deal with missing features, and can predict cell-specific interactions with better accuracy than the available tools. By analyzing the cell-specific interactions from different sources we also found that enhancers-promoter interactions are shared in groups. MiRNA-mRNA interactions are more complicated in human than other organism because of the imperfectness of the interactions and the smaller size and complex target choosing strategy of the miRNA. Available miRNA target prediction tools, designed on canonical features, often suffer from low accuracy with the new experimentally supported datasets. These tools do not consider the position-wise binding preference and relationship between adjacent positions and regions of the miRNA sequence. Here, we designed a Markov-model based feature to capture this position wise information from experimental data sets, which can be incorporated with any prediction tool to improve the performance of the tool.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-1569 |
| Date | 01 January 2021 |
| Creators | Talukder, Amlan |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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