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DNA methylation is a fundamental epigenetic mechanism, and its significant changes (i.e., differential methylation) regulate gene expression, cell-type specification, and disease progression without altering the underlying DNA sequence. Differential methylation has traditionally been detected by statistical comparison of two groups using existing tools and has supported a wide range of downstream analyses in human disease studies. However, few toolboxes efficiently integrate robust detection, annotation, and visualization of differential methylation, and the reproducibility of detected signals across tools remains limited. Moreover, existing methods have rarely been evaluated on long-read methylomes, despite their increasing availability. Here, we present DiffMethylTools, an end-to-end framework designed to address analytical and computational challenges in differential methylation studies. DiffMethylTools generates robust differential methylation detection with comprehensive annotation and visualization modules to streamline analysis workflows. Benchmarking across six datasets, including three long-read methylomes, shows that DiffMethylTools outperforms four widely used tools in detecting differential methylation, with improved overall performance and reproducibility. In addition, DiffMethylTools offers compatibility with upstream methylation-calling pipelines, and facilitates downstream biological interpretation through flexible annotation and visualization capabilities.
Derbel et al. (Mon,) studied this question.