Database Commons

a catalog of worldwide biological databases

DNA methylation has a growing potential for use as a biomarker because of its involvement in disease. DNA methylation data have also substantially grown in volume during the past 5 years. To facilitate access to these fragmented data, we proposed DiseaseMeth version 3.0 based on DiseaseMeth version 2.0, in which the number of diseases including increased from 88 to 162 and High-throughput profiles samples increased from 32 701 to 49 949. Experimentally confirmed associations added 448 pairs obtained by manual literature mining from 1472 papers in PubMed. The search, analyze and tools sections were updated to increase performance. In particular, the FunctionSearch now provides for the functional enrichment of genes from localized GO and KEGG annotation. We have also developed a unified analysis pipeline for identifying differentially DNA methylated genes (DMGs) from the original data stored in the database. 22 718 DMGs were found in 99 diseases. These DMGs offer application in disease evaluation using two self-developed online tools, Methylation Disease Correlation and Cancer Prognosis & Co-Methylation. All query results can be downloaded and can also be displayed through a box plot, heatmap or network module according to whichever search section is used. DiseaseMeth version 3.0 is freely available at http://diseasemeth.edbc.org/.

The human disease methylation database (DiseaseMeth, http://bioinfo.hrbmu.edu.cn/diseasemeth/) is an interactive database that aims to present the most complete collection and annotation of aberrant DNA methylation in human diseases, especially various cancers. Recently, the high-throughput microarray and sequencing technologies have promoted the production of methylome data that contain comprehensive knowledge of human diseases. In this DiseaseMeth update, we have increased the number of samples from 3610 to 32 701, the number of diseases from 72 to 88 and the disease-gene associations from 216 201 to 679 602. DiseaseMeth version 2.0 provides an expanded comprehensive list of disease-gene associations based on manual curation from experimental studies and computational identification from high-throughput methylome data. Besides the data expansion, we also updated the search engine and visualization tools. In particular, we enhanced the differential analysis tools, which now enable online automated identification of DNA methylation abnormalities in human disease in a case-control or disease-disease manner. To facilitate further mining of the disease methylome, three new web tools were developed for cluster analysis, functional annotation and survival analysis. DiseaseMeth version 2.0 should be a useful resource platform for further understanding the molecular mechanisms of human diseases. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

DNA methylation is an important epigenetic modification for genomic regulation in higher organisms that plays a crucial role in the initiation and progression of diseases. The integration and mining of DNA methylation data by methylation-specific PCR and genome-wide profiling technology could greatly assist the discovery of novel candidate disease biomarkers. However, this is difficult without a comprehensive DNA methylation repository of human diseases. Therefore, we have developed DiseaseMeth, a human disease methylation database (http://bioinfo.hrbmu.edu.cn/diseasemeth). Its focus is the efficient storage and statistical analysis of DNA methylation data sets from various diseases. Experimental information from over 14,000 entries and 175 high-throughput data sets from a wide number of sources have been collected and incorporated into DiseaseMeth. The latest release incorporates the gene-centric methylation data of 72 human diseases from a variety of technologies and platforms. To facilitate data extraction, DiseaseMeth supports multiple search options such as gene ID and disease name. DiseaseMeth provides integrated gene methylation data based on cross-data set analysis for disease and normal samples. These can be used for in-depth identification of differentially methylated genes and the investigation of gene-disease relationship.