Abstract
BackgroundDNA 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.FindingsWe 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.ConclusionsCandiMeth (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.
Highlights
Epigenetics can be defined as stable, and most often heritable, changes to the chromatin that do not alter the DNA sequence itself but still affect gene expression and/or are required to maintain genomic stability [1]
DNA methylation microarrays are widely used in clinical epigenetics and are often processed using R packages such as ChAMP or RnBeads by trained bioinformaticians
The output file formats can be overwhelming and difficult to investigate further without experience in data analytics and bioinformatics. This situation is exacerbated by the typically higher number of samples in epidemiological or intervention studies where such arrays are commonly used. To help solve this predicament, we developed a Galaxy workflow known as CandiMeth, which takes the main output from such methylation analysis pipelines and pairs this with a list of features that the user may wish to investigate
Summary
Epigenetics can be defined as stable, and most often heritable, changes to the chromatin that do not alter the DNA sequence itself but still affect gene expression and/or are required to maintain genomic stability [1]. 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. 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
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