Repitools

Repitools introduces BayMeth, an empirical Bayes methodology for analyzing DNA methylation data obtained through affinity capture combined with high-throughput sequencing. This innovative approach addresses the limitations of whole genome bisulfite sequencing's high costs and methylation arrays' low coverage by transforming observed read counts into accurate estimates of regional methylation levels using a fully methylated control sample. BayMeth stands out by allowing for the explicit differentiation between inefficient capture and genuinely low methylation levels, a distinction not readily available in other methods.

BayMeth incorporates the ability to explicitly model copy number variations, offering a more nuanced analysis that accounts for genomic alterations that may impact methylation measurement. In addition to its methodological advancements, BayMeth provides computationally efficient estimators for both analytical mean and variance, enhancing the speed and accuracy of methylation analysis.