This data set contains all the necessary data sets (biologically-relevant simulated data sets, preprocessed public data sets) used to evaluate performance and obtain results using ramr (https://github.com/BBCG/ramr, http://www.bioconductor.org/packages/ramr/) - a new method for identification of aberrantly methylated regions (AMRs). All the necessary R scripts that were used for preparation, testing and analysis of data sets are also provided. For additional information please check ramr package README.md file, vignettes or reference citation.

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Abstract With recent advances in the field of epigenetics, the focus is widening from large and frequent disease- or phenotype-related methylation signatures to rare alterations transmitted mitotically or transgenerationally (constitutional epimutations). Merging evidence indicate that such constitutional alterations, albeit occurring at a low mosaic level, may confer risk of disease later in life. Given their inherently low incidence rate and mosaic nature, there is a need for bioinformatic tools specifically designed to analyse such events. We have developed a method (ramr) to identify aberrantly methylated DNA regions (AMRs). ramr can be applied to methylation data obtained by array or next-generation sequencing techniques to discover AMRs being associated with elevated risk of cancer as well as other diseases. We assessed accuracy and performance metrics of ramr and confirmed its applicability for analysis of large public data sets. Using ramr we identified aberrantly methylated regions that are known or may potentially be associated with development of colorectal cancer and provided functional annotation of AMRs that arise at early developmental stages.

R, 3.6.3

comb-p, 0.50.3

ramr, 1.1.2