m7GHub V2.0: an updated database for decoding the N7-methylguanosine (m7G) epitranscriptome

Xuan Wang, Yuxin Zhang, Kunqi Chen, Zhanmin Liang, Jiongming Ma, Rong Xia, João Pedro de Magalhães, Daniel J Rigden, Jia Meng, Bowen Song*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

With recent progress in mapping N7-methylguanosine (m7G) RNA methylation sites, tens of thousands of experimentally validated m7G sites have been discovered in various species, shedding light on the significant role of m7G modification in regulating numerous biological processes including disease pathogenesis. An integrated resource that enables the sharing, annotation and customized analysis of m7G data will greatly facilitate m7G studies under various physiological contexts. We previously developed the m7GHub database to host mRNA m7G sites identified in the human transcriptome. Here, we present m7GHub v.2.0, an updated resource for a comprehensive collection of m7G modifications in various types of RNA across multiple species: an m7GDB database containing 430 898 putative m7Gsites identified in 23 species, collected from both widely applied next-generation sequencing (NGS) and the emerging Oxford Nanopore direct RNA sequencing (ONT) techniques; an m7GDiseaseDB hosting 156 206 m7G-associated variants (involving addition or removal of an m7G site), including 3238 disease-relevant m7G-SNPs that may function through epitranscriptome disturbance; and two enhanced analysis modules to perform interactive analyses on the collections of m7G sites (m7GFinder) and functional variants (m7GSNPer). We expect that m7Ghub v.2.0 should serve as a valuable centralized resource for studying m7G modification. It is freely accessible at: www.rnamd.org/m7GHub2.
Original languageEnglish
Pages (from-to)D203-D212
Number of pages10
JournalNucleic Acids Research
Volume52
Issue numberD1
Early online date9 Oct 2023
DOIs
Publication statusPublished - 5 Jan 2024

Bibliographical note

Funding:
National Natural Science Foundation of China [32100519 and 31671373]; XJTLU Key Program Special Fund [KSF-E-51 and KSF-P-02]. Scientific Research Foundation of Nanjing University of Chinese Medicine (Grant No. 013038030001). This work is supported by the Supercomputing Platform of Xi’an Jiaotong-Liverpool University.

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