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Updated: Sep 3, 2025

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EpiVisR: exploratory data analysis and visualization in epigenome-wide association analyses.

Stefan Röder1, Gunda Herberth2, Ana C Zenclussen2,3

  • 1Department of Environmental Immunology, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany. stefan.roeder@ufz.de.

BMC Bioinformatics
|July 23, 2022
PubMed
Summary

EpiVisR simplifies epigenetic research by enabling public access to advanced visualization tools for analyzing differentially methylated probes and regions. This facilitates deeper insights into trait-methylation relationships and data integration.

Keywords:
DNAmEWASProfile plotShiny applicationVisualization

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Area of Science:

  • Epigenetics
  • Bioinformatics
  • Data Visualization

Background:

  • Microarray technology is widely used in epigenetic research.
  • Analyzing differentially methylated probes/regions requires expert knowledge.
  • Visualization is crucial for quality control and gaining insights from epigenetic data.

Purpose of the Study:

  • To develop an accessible visualization tool for epigenetic research.
  • To overcome the limitation of expert knowledge requirement for data analysis.
  • To make advanced visualization capabilities available to the public.

Main Methods:

  • Designed EpiVisR, a tool for selecting and visualizing trait and methylation data.
  • Implemented enriched Manhattan plots, enriched volcano plots, trait-methylation plots, and methylation profile plots.
  • Included correlation profile plots for identifying related probes and data export for network analysis.

Main Results:

  • EpiVisR allows visualization of trait concentrations and differentially methylated probes/regions.
  • The tool supports multiple enriched plot types for data exploration.
  • Data can be exported for external analyses like network analysis.

Conclusions:

  • EpiVisR's key advantage is data annotation and linking to external sources for integrated analysis.
  • The tool enables users to integrate trait data with epigenetic analyses within the same cohort.
  • Visualizing merged data from various sources enhances insights from existing epigenetic datasets.