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

Song Yao Zhang, Shao Wu Zhang, Xiao Nan Fan, Jia Meng, Yidong Chen, Shou Jiang Gao, Yufei Huang

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

N6-methyladenosine (m 6 A) is the most abundant methylation, existing in >25% of human mRNAs. Exciting recent discoveries indicate the close involvement of m 6 A in regulating many different aspects of mRNA metabolism and diseases like cancer. However, our current knowledge about how m 6 A levels are controlled and whether and how regulation of m 6 A 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 m 6 A-regulated genes and m 6 A-associated disease, which includes Deep-m 6 A, the first model for detecting condition-specific m 6 A sites from MeRIP-Seq data with a single base resolution using deep learning and Hot-m 6 A, a new network-based pipeline that prioritizes functional significant m 6 A genes and its associated diseases using the Protein-Protein Interaction (PPI) and gene-disease heterogeneous networks. We applied Deep-m 6 A and this pipeline to 75 MeRIP-seq human samples, which produced a compact set of 709 functionally significant m 6 A-regulated genes and nine functionally enriched subnetworks. The functional enrichment analysis of these genes and networks reveal that m 6 A 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 m 6 A-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 m 6 A regulatory functions and its roles in diseases.

Original languageEnglish (US)
Article numbere1006663
JournalPLoS computational biology
Volume15
Issue number1
DOIs
StatePublished - 2019

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

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