Abstract
Epigenome mapping consortia are generating resources of tremendous value for studying epigenetic regulation. To maximize their utility and impact, new tools are needed that facilitate interactive analysis of epigenome datasets. Here we describe EpiExplorer, a web tool for exploring genome and epigenome data on a genomic scale. We demonstrate EpiExplorer's utility by describing a hypothesis-generating analysis of DNA hydroxymethylation in relation to public reference maps of the human epigenome. All EpiExplorer analyses are performed dynamically within seconds, using an efficient and versatile text indexing scheme that we introduce to bioinformatics. EpiExplorer is available at http://epiexplorer.mpi-inf.mpg.de.
Highlights
Epigenome mapping consortia are generating resources of tremendous value for studying epigenetic regulation
In contrast to genome browsers, which implement browse-and-zoom navigation similar to that of map viewing software, EpiExplorer was inspired by the interactive filter-and-refine workflow of web search engines: Most web searches start broadly and are refined iteratively until relevant websites show up among the top hits
EpiExplorer supports the same kind of exploratory search by making it easy to dynamically filter genomic region sets and by providing instant feedback in the form of graphical results summaries
Summary
Toward the goal of interactively exploring large epigenomic datasets, EpiExplorer borrows key concepts from interactive web search. The interactive nature of such analyses depends on fast response times, as any delay tends to stifle the creative act of live data exploration For this reason, we designed and optimized EpiExplorer to complete complex genome-wide analyses in seconds, rather than the minutes or hours that are the norm for existing genome analysis toolkits (for example, Galaxy [18], Genomic HyperBrowser [20] and EpiGRAPH [22]). We illustrated EpiExplorer’s utility for interactive data exploration by a case study of hydroxymethylation in relation to public reference epigenome datasets, which recreates and extends results from a recently published paper [35] in ten minutes of analysis time (detailed tutorials are available from the supplementary website [27]) With this example in mind, we are optimistic that EpiExplorer will help bridge the ‘digital divide’ in biomedical research and constitute a step toward making large-scale epigenome datasets more useful and readily explorable for researchers with little or no bioinformatic experience
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