Database Commons

a catalog of biological databases

e.g., animal; RNA; Methylation; China

Database Profile

General information

URL: http://bioinfo.hrbmu.edu.cn/diseasemeth/
Full name: Human Disease Methylation Database
Description: DiseaseMeth is a web based resource focused on the aberrant methylomes of human diseases.Currently, DiseaseMeth includes 175 datasets which are extracted from Methylation arrays and sequencing datasets and 14530 entries of scattered aberrant methylation information(72 diseases).
Year founded: 2011
Last update: 2016-08-14
Version: v2.0
Accessibility:
Manual:
Unaccessible
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Country/Region: China
Data type:
DNA
Data object:
Database category:
Major organism:
Keywords:

Contact information

University/Institution: Harbin Medical University
Address: College of Bioinformatics Science and Technology
City: Harbin
Province/State: Heilongjiang
Country/Region: China
Contact name (PI/Team): Yan Zhang
Contact email (PI/Helpdesk): yanyou1225@yahoo.com.cn

Publications

27899673
DiseaseMeth version 2.0: a major expansion and update of the human disease methylation database. [PMID: 27899673]
Xiong Y, Wei Y, Gu Y, Zhang S, Lyu J, Zhang B, Chen C, Zhu J, Wang Y, Liu H, Zhang Y.

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.

Nucleic Acids Res. 2017:45(D1) | 39 Citations (from Europe PMC, 2021-06-19)
22135302
DiseaseMeth: a human disease methylation database. [PMID: 22135302]
Lv J, Liu H, Su J, Wu X, Liu H, Li B, Xiao X, Wang F, Wu Q, Zhang Y.

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.

Nucleic Acids Res. 2012:40(Database issue) | 50 Citations (from Europe PMC, 2021-06-19)

Ranking

All databases:
856/5061 (83.106%)
Health and medicine:
178/1067 (83.411%)
856
Total Rank
89
Citations
9.889
z-index

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Record metadata

Created on: 2015-06-20
Curated by:
Shixiang Sun [2017-02-20]
Jian Sang [2016-04-04]
Jian Sang [2015-12-07]
Jian Sang [2015-06-26]