ramr: an R/Bioconductor package for detection of rare aberrantly methylated regions

Abstract

Motivation 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. Results 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. Availability The R package is freely available at https://github.com/BBCG/ramr and https://bioconductor.org/packages/ramr. Supplementary information Supplementary data are available at Bioinformatics online.

epialleleR: an R/Bioconductor package for sensitive allele-specific methylation analysis in NGS data

“…While this approach is suitable for addressing large differences in DNA methylation profiles between two sets of case and control samples, it lacks sensitivity for low-level mosaic methylation detection, as the detection is hindered by sometimes much more common biological variation (11, 12) or technical artefacts (13, 14). Moreover, the lack of haplotype linkage makes such analysis difficult in tiling array-based data sets, and therefore requires nontrivial approaches (15). In contrast, analysis of NGS-based data can provide much higher sensitivity when the base-resolution methylation data is combined with information on allelic belongingness (epihaplotype linkage).…”