Wu, Xiangyu
(2021)
Large-scale functional annotation of individual RNA methylation sites by mining complex biological networks.
PhD thesis, University of Liverpool.
|
Text
PhD Thesis_Xiangyu Wu_201026564.pdf - Unspecified Download (2MB) | Preview |
Abstract
Increasing evidences suggest that post-transcriptional RNA modifications regulate essential biomolecular functions and are related to the pathogenesis of various diseases. To date, the study of epitranscriptome layer gene regulation is mostly focused on the function of mediator proteins of RNA methylation limited by laborious experimental procedures, i.e., the readers, writers and erasers. However, there is limited investigation of the functional relevance of individual m6A RNA methylation sites. To address this, we annotated human m6A sites in large-scale based on the guilt-by-association principle from complex biological networks. In the first chapter, the network was constructed based on public human MeRIP-Seq datasets profiling the m6A epitranscriptome under independent experimental conditions. By systematically examining the network characteristics obtained from the RNA methylation profiles, a total of 339,158 putative gene ontology functions associated with 1446 human m6A sites were identified. These are biological functions that may be regulated at epitranscriptome layer via reversible m6A RNA methylation. The results were further validated on a soft benchmark by comparing to a random predictor. In the second chapter, another approach was applied to annotate the individual human m6A sites by integrating the methylation profile, gene expression profile and protein-protein interaction network with guilt-by-association principle. The consensus signals on sites were amplified by multiplying the co-methylation network and the methylation-expression network. The PPI network smoothed the correlation for a query site to gene expression for furthering GSEA functional annotation. In the third chapter, we functionally annotated 18,886 m6A sites that are conserved between human and mouse from a larger epitranscriptome datasets using method previously described. Besides, we also completed two side projects related to SARS-CoV-2 viral m6A site prediction and m6A site prediction from Nanopore sequencing technology.
| Item Type: | Thesis (PhD) |
|---|---|
| Uncontrolled Keywords: | m6A, functional annotation, co-methyaltion network, guilt-by-association principle, gene ontology |
| Divisions: | Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 15 Feb 2021 14:11 |
| Last Modified: | 18 Jan 2023 23:03 |
| DOI: | 10.17638/03113369 |
| Supervisors: |
|
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3113369 |
Dimensions
Dimensions
