DNA methylation is a compulsory and 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 biomarkers were widely used as inputs for various downstream investigations, and differential methylation could be detected via existing statistical tools by comparing two groups of methyomes (i.e. whole-genome methylation profiles). However, few toolboxes were available to integrate robust detection, annotation and visualization of differential methylation to efficiently streamline methylation investigation. Also, differential methylation detected via tools has poor reproducibility and no tools were tested on long-read methylomes. To address these issues, we introduced DiffMethylTools, an end-to-end solution to eliminate analytical and computational difficulties for differential methylation dissection. Comparison on six datasets including three long-read methylomes demonstrated that DiffMethylTools achieved overall better detection performance of differential methylation than existing tools like MethylKit, DSS, MethylSig, and bsseq. Besides, DiffMethylTools supported versatile input formats for seamless transition from upstream methylation detection tools, and offered diverse annotations and visualizations to facilitate downstream investigations. DiffMethylTools therefore offered a robust, interpretable, and user-friendly solution for differential methylation investigation, benefiting the dissection of methylation\'s roles in human disease studies.