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DiseaseMeth version 3.0

The human disease DNA methylation database, DiseaseMeth version 3.0 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, based on the DMGs, we calculated correlations between genes in 162 diseases and provided the visualization results by network. Key genes can be characterized by DNA methylation correlation networks.

Architecture Design

• the blue trace

  (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),GeneFunctionSearch (query The biological function of genes 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 3.0 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 orange trace

  (1) 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.

  (2) Tools: A newly developed cancer prognosis and co-methylation visualization tool, by showing caner associated differential methylation level for different samples in differential clinical factors.

Extended Datasets in DiseaseMeth v3.0

In DiseaseMeth version 3.0, the number of disease types was increased from 88 to 162. We screened 4,708 methylation datasets in the GEO and TCGA database from Oct 1, 2015 to Jan 31, 2021, collecting a total of 247 high-throughput datasets related to human diseases from ChIP and 29 sequencing datasets. Total number of samples were increased from 32,701 to 49,949. Moreover, the associations between disease and gene from experiment were increased from 679,602 to 1,485,099 by manual literature search from Pubmed.

Search entrance

The Search Engine was redesigned into three query entrance: GeneSearch (query DNA methylation level for disease associated genes by disease type), DiseaseSearch (query DNA methylation level across diseases for a specified gene/genomic position), AdvanceSearch (query disease methylome or DNA methylation level for a specified gene/genomic position),.

• GeneSearch

  1.Gene symbol or Genomic position are used to search methylation level of the genomic region. Users can input gene symbol id or genomic position.

  2.The result will display a heatmap for each disease that is associated with the gene or genomic region.

  

• DiseaseSearch

  1.Disease option is used to select interested diseases for methylome displayed by a heatmap.

  2.Gene name/Transcript ID are used to set the genomic region. If not provided, the result will display all associated genes.

  3.The result will display a heatmap for each disease.

  4.Note:options marked with * are required.

  

• AdvanceSearch

  1.Gene name/Transcript ID or Genomic position are used to search methylation level of the genomic region. Users can input gene name/transcript id or genomic position.

  2.Disease option is used to select the interested diseases.

  3.Technology Experimental Platform option is used to select the datatype of methylome.

  4.The result will display all matched methylomes and methylation-associated disease genes.

  5.Note:options marked with * are required.

  6.Users can download, visualize and analyze the result of the differential methylation analysis.

  

  1.If Gene name/Transcript ID is provided, the option 'Select Region of Interest' is used to select 'promoter', 'refseq gene-related categories' or 'CGI-related categories'. Users can define the promoter by selecting or inputing. Either the Whole Promoter or the CpG Island In the defined Promoter can be analysed.

  2.The categories of 'refseq gene-related categories' include Up2kb, Gene Body, 5'UTR, Coding Exon, Intron, 3'UTR and Down2kb. The categories of 'CGI-related categories' include CpG Island, CpG Island Shelves and CpG Island Shore.

  

  The following result will display the matched genomic region with the search options. Then users can select and search.

  

• FunctionSearch

  1.Disease option is used to select interested diseases.

  2.Type option is used to select gene annotation sources.

  3.Gene name/Transcript ID are used to set the genomic region. If not provided, the result will display all associated genes.

  4.Note:options marked with * are required.

  

• DisMethBrowser

  For profiles of samples, horizontal red lines represent methylation level of 1.

  

  Users can add profiles to the DisMethBrowser by the following steps:

  

• Online Analysis

  Besides of updating the search engine and visualization, survival analysis and functional annotation transform from web tools to online analysis. The gene-gene relationship analysis and disease-disease relationship analysis can also be applied.

  1.Disease option is used to select the interested diseases.

  2.Gene name/Transcript ID/Genomic Interval are used to set interested genomic regions. Users can input multiple items.

  3.Technology Experimental Platform option is used to select the datatype of methylome.

  4.Control Type is used to select methylomes of controls that is normal tissues. The option of controls from the same tissue/cellline is used the select all controls from differential researches but the same tissue/cellline. The option of controls from the same research is to select controls in the same research with cases. In some researches, maybe there is no controls matched with cases.

  5.Method of Differential Analysis: select the method for case-control differential analysis. There are 4 methods including t-test, minfi, samr, edgeR. If t-test is selected, the differential analysis will be applied through Student's t test.

  6.Significant p-value is used to set the significant p-value for the analysis. The significant results will be marked with red color in the results panel.

  7.Absolute methylation difference: this option is used to filter the differentially methylated region together with the p-value.

  8.The result will display the outcome of differential analysis, disease-gene association and methlation profile of the given region in the selected diseases. Differential analysis will be applied in the TSS or genomic region by t-test between cases and controls. The Gene-Gene Relationship Analysis and Disease-Disease Relationship Analysis will display the Pearson correlation coefficient between genes or diseases.

  

  After click the analysis button, a dialog will be open to confirm the transcription of input genes or transcript.

  A Case for Analysis

  1. Select diseases of Rectum adenocarcinoma[READ], Stomach adenocarcinoma[STAD] and Colon adenocarcinoma[COAD]. Input genes including MIR663A, OR10K2, NKAPL, PRDM14 and OR2M3. Select the data platform of Illumina Infinium HumanMethylation450 Beadchip, control type from the same tissue/cellline and significant p-value as 0.01. Click the analysis button to start.

  2. Diff case-control, disease gene association and methylation profile can be viewed in the result panel.

     

  3. Gene-gene relationship analysis and disease-disease relationship analysis can also be viewed in the result panel.

     

Tools entrance

The Tools was redesigned into two query entrance: Cancer Prognosis And Co-methylation (analyze the DNA methylation level of genes in cancer from three aspects: clinical characteristics (TNM, stage, age), survival analysis and co-methylation module), Methylation Disease Correlation (analyze associations between two interested diseases).

• Cancer Prognosis And Co-methylation

  1.Cancer option is used to select the Concerned cancer.

  2.Focus option is used to select the factors related to cancer.

  3.Gene name are used to set interested genomic regions. Users can input multiple items.

  

• A Case for Analysis

  1. Select diseases of Colon adenocarcinoma[COAD]. Input single genes HRNBP3. When Focus chose 'T',The methylation profile of this gene in cancer Tumor stage-'T' can be viewed in the result pane.When focus selects different stages, the corresponding results will be displayed.

  2. Select diseases of Colon adenocarcinoma[COAD]. Input gene including HRNBP3,MEST, RUNX3, SEPT9, CBFA2T3 and HOXC4. When Focus chose 'T',The methylation profile of these gene in cancer Tumor stage-'T' can be viewed in the result pane.When focus selects different stages, the corresponding results will be displayed.

     

  3. Select diseases of Colon adenocarcinoma[COAD]. When Focus chose 'survival', only one gene can be entered.Input single gene HRNBP3,The methylation profile in Diff case-control and survival curve of this gene can be viewed in the result pane.

     

  4. Select diseases of Colon adenocarcinoma[COAD]. When Focus chose 'Module', no need to enter any genes.Mining modules through differential gene sets,the module contains the largest number of genes ,Only top 250 relationship pairs with the largest correlation coefficient are displayed in the result pane.

     

• Methylation Disease Correlation

  1.Two disease option is used to select the interested diseases.

  2.Two different disease names must be selected.

  

  3.If the Jaccard correlation coefficient of the two diseases is greater than 0.6,The result will display all one-step neighbors with Jaccard correlation coefficient greater than 0.6 and two-step neighbors with Jaccard correlation coefficient greater than 0.8 for the first disease in a network.

  

Why the Download can't be done sometimes?

A: The Download is achieved through Third-party network protocol,it may be that the internet speed is too slow or the browser does not allow cookies to be cached. Please try again later.

Why the gene information is unavailable sometimes?

A: The genomic region you choose is too wide, it would be too slow to show all of the RefSeq genes.

Why the heatmap can't be opened?

A: Maybe the plug-in for the pdf file is forbidden, please permit it in the web browser.