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Global analysis of N6-methyladenosine functions and its disease association using deep learning and network-based methods.
- Source :
- PLoS Computational Biology; 1/2/2019, Vol. 15 Issue 01, p1-24, 24p, 3 Diagrams, 2 Charts, 2 Graphs
- Publication Year :
- 2019
-
Abstract
- N6-methyladenosine (m<superscript>6</superscript>A) is the most abundant methylation, existing in >25% of human mRNAs. Exciting recent discoveries indicate the close involvement of m<superscript>6</superscript>A in regulating many different aspects of mRNA metabolism and diseases like cancer. However, our current knowledge about how m<superscript>6</superscript>A levels are controlled and whether and how regulation of m<superscript>6</superscript>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<superscript>6</superscript>A-regulated genes and m<superscript>6</superscript>A-associated disease, which includes Deep-m<superscript>6</superscript>A, the first model for detecting condition-specific m<superscript>6</superscript>A sites from MeRIP-Seq data with a single base resolution using deep learning and Hot-m<superscript>6</superscript>A, a new network-based pipeline that prioritizes functional significant m<superscript>6</superscript>A genes and its associated diseases using the Protein-Protein Interaction (PPI) and gene-disease heterogeneous networks. We applied Deep-m<superscript>6</superscript>A and this pipeline to 75 MeRIP-seq human samples, which produced a compact set of 709 functionally significant m<superscript>6</superscript>A-regulated genes and nine functionally enriched subnetworks. The functional enrichment analysis of these genes and networks reveal that m<superscript>6</superscript>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<superscript>6</superscript>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<superscript>6</superscript>A regulatory functions and its roles in diseases. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1553734X
- Volume :
- 15
- Issue :
- 01
- Database :
- Complementary Index
- Journal :
- PLoS Computational Biology
- Publication Type :
- Academic Journal
- Accession number :
- 133816759
- Full Text :
- https://doi.org/10.1371/journal.pcbi.1006663