The human disease methylation database, DiseaseMeth version 2.0 is a web based resource focused on the aberrant methylomes of human diseases. Until recently, bulks of large-scale data are avaible and are increasingly grown, from which more information can be mined to gain further information towards human diseases. Our mission is to provide a curated set of methylation information datasets and tools in the human genome, to support and promote research in this area. Especially, we provide a genome-scale landscape to show human methylaton information in a scalable and flexible manner.
(1) The Search Engine was redesigned into three query entrance: DisMethSearch (query disease methylome or DNA methylation level for a specified gene/genomic position), DiseaseSearch (query DNA methylation level for disease associated genes by disease type) and DiseaseGeneSearch (query DNA methylation level across diseases for a specified gene/genomic position).
(2) For exploring the biological significance of DNA methylation across diseases or case-control samples through large amount of data, DiseaseMeth version 2.0 contains three new tools for the genetic analysis: cluster analysis (clustering the methylation profile of selected diseases samples within input genes and providing a heatmap view of the result to users), functional annotation (annotate the function of selected genes and obtain the functional cluster of these genes) and survival analysis (used to judge if a gene is related to the survival time of patients with a specific cancer).
(3) DisMethBrowser: A newly developed disease DNA methylation visualization tool, by showing disease associated differential methylation status, methylation level for different samples, ref-genome transcripts and transcriptional regulators in independent tracks.
The differential DNA methylation analysis tool is updated by integrating the method of Student's t-test and Shannon entropy to analyze the difference across two groups or more than two groups. And the significantly differential methylated genes are defined as the disease associated genes.
In DiseaseMeth version 2.0, the samples were increased to 17 024 samples with the methylation data from revolutionary hug international disease projects including The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA). The associations between diseases and methylation of genes was increased to a number of 638 563. The associations between diseases and methylation of genes were divided into three groups: 2345 experimental methylation-associated gene-disease associations collected from papers that have been verified by experiments, 216 415 inferred methylation-associated gene-disease associations that have been accumulated from research effort and 429 081 potential methylation-associated gene-disease associations that is published in DisGeNET (8) which integrates information on gene-disease associations from several public data sources and the literature.