This data set contains necessary data sets (simulated next-generation sequencing data, preprocessed public next-generation sequencing data, precomputed analysis results) used to evaluate performance of epialleleR (https://github.com/BBCG/epialleleR, http://www.bioconductor.org/packages/epialleleR/) - a computational framework for sensitive detection, quantification and visualisation of low-frequency, mosaic epimutations in methylation sequencing data. All the supplementary R scripts that were used for preparation, testing and analysis of data sets are also provided. For additional information please check epialleleR package README.md file, vignettes or reference citation.

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Abstract Constitutional epigenetic silencing of tumour suppressor genes has been detected in a small number of cancer patients. Recent finding have indicated that low-level mosaic methylation of the BRCA1 gene promoter occurs in 5-10% of healthy individuals and is associated with a significantly elevated risk of breast and ovarian cancer. This further suggests that similar mosaic constitutional methylation may occur in other tumour suppressor genes as well, potentially being a significant contributor to cancer burden. However, detection of low-level mosaic epigenetic events requires highly sensitive and robust methodology for methylation analysis. We here present epialleleR, a computational framework for sensitive detection, quantification and visualisation of low-frequency, mosaic epimutations in methylation sequencing data. We provide in-depth analysis of epialleleR performance using simulated and real data sets, as compared to the other three commonly applied tools for methylation assessment, and conclude that linkage to epihaplotype data allows very sensitive detection of low-frequency methylation events.

R, 4.1.2

epialleleR, 1.3.5

Sherman Bisulfite FastQ Read Simulator, 0.1.8

Illumina DRAGEN Bio-IT Platform, 3.9.5

methclone, 1.0