DNMIVD: DNA methylation interactive visualization database

Author:

Ding Wubin1ORCID,Chen Jiwei1,Feng Guoshuang23,Chen Geng1,Wu Jun1,Guo Yongli23,Ni Xin23,Shi Tieliu124

Affiliation:

1. Center for Bioinformatics and Computational Biology, and the Institute of Biomedical Sciences, School of Life Sciences, East China Normal University, Shanghai 200241, China

2. Big Data and Engineering Research Center, Beijing Key Laboratory for Pediatric Diseases of Otolaryngology, Head and Neck Surgery, the Ministry of Education Key Laboratory of Major Diseases in Children, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China

3. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University & Capital Medical University, Beijing 100083, China

4. Biological Targeting Diagnosis and Therapy Research Center, Guangxi Medical University, Nanning 530021, China

Abstract

Abstract Aberrant DNA methylation plays an important role in cancer progression. However, no resource has been available that comprehensively provides DNA methylation-based diagnostic and prognostic models, expression–methylation quantitative trait loci (emQTL), pathway activity-methylation quantitative trait loci (pathway-meQTL), differentially variable and differentially methylated CpGs, and survival analysis, as well as functional epigenetic modules for different cancers. These provide valuable information for researchers to explore DNA methylation profiles from different aspects in cancer. To this end, we constructed a user-friendly database named DNA Methylation Interactive Visualization Database (DNMIVD), which comprehensively provides the following important resources: (i) diagnostic and prognostic models based on DNA methylation for multiple cancer types of The Cancer Genome Atlas (TCGA); (ii) meQTL, emQTL and pathway-meQTL for diverse cancers; (iii) Functional Epigenetic Modules (FEM) constructed from Protein-Protein Interactions (PPI) and Co-Occurrence and Mutual Exclusive (COME) network by integrating DNA methylation and gene expression data of TCGA cancers; (iv) differentially variable and differentially methylated CpGs and differentially methylated genes as well as related enhancer information; (v) correlations between methylation of gene promoter and corresponding gene expression and (vi) patient survival-associated CpGs and genes with different endpoints. DNMIVD is freely available at http://www.unimd.org/dnmivd/. We believe that DNMIVD can facilitate research of diverse cancers.

Funder

China Human Proteome Project

National Science Foundation of China

Beihang University & Capital Medical University Advanced Innovation Center for Big Data-Based Precision Medicine Plan

111 Project

Publisher

Oxford University Press (OUP)

Subject

Genetics

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