Abstract
DNA-methylation has a strong influence on gene expression such that differences in methylation are associated with a wide range of diseases. Array-based approaches like the Illumina 450 K or 850 K EPIC chips have been used in a wide range of studies mostly comparing a disease group with healthy control, but also to correlate with survival times, for instance. Processing, normalization, and analysis of raw data require extensive knowledge in statistics and programming languages such as R. Here we introduce DiMmer, an easy-to-use Java tool for the analysis of EWAS. A graphical user interface guides the user through preprocessing, normalization, testing for differentially methylated CpGs, and finally the discovery of differentially methylated regions (DMRs). The software performs randomization tests to compute empirical P-values, corrects for multiple testing, and requires no prior knowledge in programming. All computed results are provided as plots or tables and can be easily exported. DiMmer is thus a powerful one-stop-shop for EWAS data analysis.
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Acknowledgements
Jan Baumbach and Tobias Frisch are grateful for financial support from the VILLUM foundation (Young Investigator Grant nr. 13154).
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Frisch, T., Gøttcke, J., Röttger, R., Tan, Q., Baumbach, J. (2018). DiMmer: Discovery of Differentially Methylated Regions in Epigenome-Wide Association Study (EWAS) Data. In: Mamitsuka, H. (eds) Data Mining for Systems Biology. Methods in Molecular Biology, vol 1807. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8561-6_5
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DOI: https://doi.org/10.1007/978-1-4939-8561-6_5
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