# Benchmarking edgeR and methylKit for the Detection of Differential DNA Methylation: A Methodological Evaluation

## TL;DR

This study compares two tools, edgeR and methylKit, for detecting DNA methylation changes in mouse embryonic stem cells after morphine exposure.

## Contribution

The study evaluates and compares the performance of edgeR and methylKit in detecting differential DNA methylation in a morphine exposure model.

## Key findings

- Both edgeR and methylKit detected global hypomethylation patterns despite differences in the number of DMCs identified.
- DMCs were predominantly found in intergenic, intronic, and open sea genomic regions.
- High concordance at the gene level (~90%) suggests overlapping sets of differentially methylated genes despite tool differences.

## Abstract

Despite the improvements in tool development for DNA methylation analysis, there is a lack of a consensus on computational and statistical models used for differentially methylated cytosine (DMC) identification. This variability complicates the interpretation of findings and raises concerns about the reproducibility and biological significance of the detected results. In this regard, here we conducted a comparative evaluation of edgeR and methylKit tools to assess their performance, concordance, and biological relevance in detecting DMCs following a morphine exposure model in mouse embryonic stem cells (mESCs). Both pipelines were applied to the same WGBS dataset (GEO accession number: GSE292082), and concordance was calculated at both single-base and gene levels. Although the total number of DMCs identified differed between tools, both pipelines detected a global hypomethylation pattern. Genomic distribution analysis revealed that DMCs predominantly localized to intergenic and intronic regions, as well as to open sea regions. Despite differences in sensitivity, both pipelines demonstrated moderate concordance at the DMC level (~56%) and high concordance at the gene level (~90%), identifying largely overlapping sets of differentially methylated genes (DMGs). Comparative assessments further showed that the choice of statistical metric can influence the perceived magnitude of biological effects. Sensitivity analyses indicated that threshold selection and normalization methods influence DMC detection, whereas aggregation at gene level reduces discrepancies. Overall, our findings underscore the complementary strengths of methylKit and edgeR and highlight the importance of careful tool selection for epigenetic studies. As a conclusion, we recommend integrating both pipelines to ensure a balanced interpretation of effect sizes, particularly in studies with complex experimental designs.