Abstract
DNA methylation is an epigenetic mark involved in the regulation of gene expression, and patterns of DNA methylation anticorrelate with chromatin accessibility and transcription factor binding. DNA methylation can be profiled at the single cytosine resolution in the whole genome and has been performed in many cell types and conditions. Computational approaches are then essential to study DNA methylation patterns in a single condition or capture dynamic changes of DNA methylation levels across conditions. Toward this goal, we developed MethyLasso, a new approach to segment DNA methylation data. We use it as an all-in-one tool to perform the identification of low-methylated regions, unmethylated regions, DNA methylation valleys and partially methylated domains in a single condition as well as differentially methylated regions between two conditions. We performed a rigorous benchmarking comparing existing approaches by evaluating the agreement of the regions across tools, their number, size, level of DNA methylation, boundaries, cytosine-guanine content and coverage using several real datasets as well as the sensitivity and precision of the approaches using simulated data and show that MethyLasso performs best overall. MethyLasso is freely available at https://github.com/bardetlab/methylasso.