DRUM: Inference of Disease-Associated m(6)A RNA Methylation Sites From a Multi-Layer Heterogeneous Network



Tang, Yujiao, Chen, Kunqi, Wu, Xiangyu, Wei, Zhen, Zhang, Song-Yao, Song, Bowen, Zhang, Shao-Wu, Huang, Yufei and Meng, Jia ORCID: 0000-0003-3455-205X
(2019) DRUM: Inference of Disease-Associated m(6)A RNA Methylation Sites From a Multi-Layer Heterogeneous Network. FRONTIERS IN GENETICS, 10 (APR). 266-.

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Abstract

Recent studies have revealed that the RNA <i>N</i> <sup>6</sup>-methyladenosine (m<sup>6</sup>A) modification plays a critical role in a variety of biological processes and associated with multiple diseases including cancers. Till this day, transcriptome-wide m<sup>6</sup>A RNA methylation sites have been identified by high-throughput sequencing technique combined with computational methods, and the information is publicly available in a few bioinformatics databases; however, the association between individual m<sup>6</sup>A sites and various diseases are still largely unknown. There are yet computational approaches developed for investigating potential association between individual m<sup>6</sup>A sites and diseases, which represents a major challenge in the epitranscriptome analysis. Thus, to infer the disease-related m<sup>6</sup>A sites, we implemented a novel multi-layer heterogeneous network-based approach, which incorporates the associations among diseases, genes and m<sup>6</sup>A RNA methylation sites from gene expression, RNA methylation and disease similarities data with the Random Walk with Restart (RWR) algorithm. To evaluate the performance of the proposed approach, a ten-fold cross validation is performed, in which our approach achieved a reasonable good performance (overall AUC: 0.827, average AUC 0.867), higher than a hypergeometric test-based approach (overall AUC: 0.7333 and average AUC: 0.723) and a random predictor (overall AUC: 0.550 and average AUC: 0.486). Additionally, we show that a number of predicted cancer-associated m<sup>6</sup>A sites are supported by existing literatures, suggesting that the proposed approach can effectively uncover the underlying epitranscriptome circuits of disease mechanisms. An online database DRUM, which stands for <b>d</b>isease-associated <b>r</b>ibon<b>u</b>cleic acid <b>m</b>ethylation, was built to support the query of disease-associated RNA m<sup>6</sup>A methylation sites, and is freely available at: www.xjtlu.edu.cn/biologicalsciences/drum.

Item Type: Article
Uncontrolled Keywords: disease, RWR, random walk with restart, m6A modification, Co-expression, network analysis
Depositing User: Symplectic Admin
Date Deposited: 14 May 2019 09:08
Last Modified: 19 Jan 2023 00:46
DOI: 10.3389/fgene.2019.00266
Open Access URL: https://doi.org/10.3389/fgene.2019.00266
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3041203