CandiMeth: Powerful yet simple visualization and quantification of DNA methylation at candidate genes. Issue 6 (22nd June 2020)

• Record Type: Journal Article
• Authors: Thursby, Sara-Jayne; Lobo, Darin K; Pentieva, Kristina; Zhang, Shu-Dong; Irwin, Rachelle E; Walsh, Colum P
• Is Part Of: GigaScience. Volume 9:Issue 6(2020)
• Publication Date: 2020-06-22
• Subjects: galaxy -- methylation -- workflow -- DNA methylation -- arrays -- epigenetics -- EWAS; Information storage and retrieval systems -- Research -- Periodicals; Biology -- Research -- Periodicals; Medical sciences -- Research -- Periodicals; Database management -- Periodicals; 570.285
• DOI: 10.1093/gigascience/giaa066
• Languages: English
• ISSNs: 2047-217X

Abstract

Abstract: Background: DNA methylation microarrays are widely used in clinical epigenetics and are often processed using R packages such as ChAMP or RnBeads by trained bioinformaticians. However, looking at specific genes requires bespoke coding for which wet-lab biologists or clinicians are not trained. This leads to high demands on bioinformaticians, who may lack insight into the specific biological problem. To bridge this gap, we developed a tool for mapping and quantification of methylation differences at candidate genomic features of interest, without using coding. Findings: We generated the workflow "CandiMeth" (Candidate Methylation) in the web-based environment Galaxy. CandiMeth takes as input any table listing differences in methylation generated by either ChAMP or RnBeads and maps these to the human genome. A simple interface then allows the user to query the data using lists of gene names. CandiMeth generates (i) tracks in the popular UCSC Genome Browser with an intuitive visual indicator of where differences in methylation occur between samples or groups of samples and (ii) tables containing quantitative data on the candidate regions, allowing interpretation of significance. In addition to genes and promoters, CandiMeth can analyse methylation differences at long and short interspersed nuclear elements. Cross-comparison to other open-resource genomic data at UCSC facilitates interpretation of the biological significance of the data and the design of wet-lab assays to further explore methylation changes and their consequences for the candidate genes. Conclusions: CandiMeth (RRID:SCR_017974 ; Biotools: CandiMeth) allows rapid, quantitative analysis of methylation at user-specified features without the need for coding and is freely available at https://github.com/sjthursby/CandiMeth .