Global analysis of N6-methyladenosine functions and its disease association using deep learning and network-based methods

Zhang, Song-Yao, Zhang, Shao-Wu, Fan, Xiao-Nan, Meng, Jia ORCID: 0000-0003-3455-205X, Chen, Yidong, Gao, Shou-Jiang and Huang, Yufei (2019) Global analysis of N6-methyladenosine functions and its disease association using deep learning and network-based methods. PLOS COMPUTATIONAL BIOLOGY, 15 (1). e1006663-.

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Official URL: http://dx.doi.org/10.1371/journal.pcbi.1006663

Abstract

N6-methyladenosine (m6A) is the most abundant methylation, existing in >25% of human mRNAs. Exciting recent discoveries indicate the close involvement of m6A in regulating many different aspects of mRNA metabolism and diseases like cancer. However, our current knowledge about how m6A levels are controlled and whether and how regulation of m6A levels of a specific gene can play a role in cancer and other diseases is mostly elusive. We propose in this paper a computational scheme for predicting m6A-regulated genes and m6A-associated disease, which includes Deep-m6A, the first model for detecting condition-specific m6A sites from MeRIP-Seq data with a single base resolution using deep learning and Hot-m6A, a new network-based pipeline that prioritizes functional significant m6A genes and its associated diseases using the Protein-Protein Interaction (PPI) and gene-disease heterogeneous networks. We applied Deep-m6A and this pipeline to 75 MeRIP-seq human samples, which produced a compact set of 709 functionally significant m6A-regulated genes and nine functionally enriched subnetworks. The functional enrichment analysis of these genes and networks reveal that m6A targets key genes of many critical biological processes including transcription, cell organization and transport, and cell proliferation and cancer-related pathways such as Wnt pathway. The m6A-associated disease analysis prioritized five significantly associated diseases including leukemia and renal cell carcinoma. These results demonstrate the power of our proposed computational scheme and provide new leads for understanding m6A regulatory functions and its roles in diseases.

Item Type:	Article
Uncontrolled Keywords:	Humans, Neoplasms, Adenosine, Genetic Markers, Computational Biology, Algorithms, Software, Protein Interaction Maps, Deep Learning
Depositing User:	Symplectic Admin
Date Deposited:	14 May 2019 10:51
Last Modified:	19 Jan 2023 00:45
DOI:	10.1371/journal.pcbi.1006663
Open Access URL:	http://doi.org/10.1371/journal.pcbi.1006663
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3041254