Derbel Houssemeddine, Kinnear Evan, Wong Justin J-L, Liu Qian
Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas, 4505 S Maryland Pkwy, Las Vegas, NV 89154, USA.
Epigenetics and RNA Biology Laboratory, School of Medical Sciences, The University of Sydney, Camperdown, NSW 2050, Australia.
bioRxiv. 2025 Jul 5:2025.07.01.662655. doi: 10.1101/2025.07.01.662655.
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 was usually detected via existing statistical tools by comparing two groups of methyomes (i.e. whole-genome methylation profiles) and has wide applications of various downstream investigations for human disease studies. However, few toolboxes were available to efficiently streamline methylation investigation by integrating robust detection, annotation and visualization of differential methylation. Also, differential methylation detected via tools has poor reproducibility and no tools were tested on the increasing volume of 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 of detection performance on six datasets including three long-read methylomes demonstrated that DiffMethylTools achieved overall better performance of detecting 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.
DNA甲基化是一种基本的表观遗传机制,其显著变化(即差异甲基化)可在不改变基础DNA序列的情况下调节基因表达、细胞类型特异性和疾病进展。差异甲基化通常通过现有的统计工具,通过比较两组甲基化组(即全基因组甲基化图谱)来检测,并且在人类疾病研究的各种下游研究中具有广泛应用。然而,很少有工具包能够通过整合强大的差异甲基化检测、注释和可视化功能来有效地简化甲基化研究。此外,通过工具检测到的差异甲基化具有较差的可重复性,并且没有工具在不断增加的长读甲基化组上进行测试。为了解决这些问题,我们引入了DiffMethylTools,这是一种端到端的解决方案,可消除差异甲基化剖析中的分析和计算困难。在包括三个长读甲基化组在内的六个数据集上对检测性能进行比较表明,DiffMethylTools在检测差异甲基化方面比MethylKit、DSS、MethylSig和bsseq等现有工具具有更好的整体性能。此外,DiffMethylTools支持多种输入格式,以便与上游甲基化检测工具无缝过渡,并提供各种注释和可视化功能以促进下游研究。因此,DiffMethylTools为差异甲基化研究提供了一种强大、可解释且用户友好的解决方案,有助于剖析甲基化在人类疾病研究中的作用。