EDNTOM: An Ensemble Learning and Weight Mechanism-Based Nanopore Methylation Detection Tool

DNA methylation is an epigenetic modification that plays a crucial role in genome stability and cellular specialization, essential for maintaining normal cellular function and development, also a manifestation indicator of some diseases. Various tools have been proposed for methylation detection, typically leveraging a third-generation sequencing technology called nanopore sequencing, which provides more accurate DNA sequencing data. However, existing tools have their own limitations and advantages in terms of computational resources and information processing, without achieving a good balance. In this situation, we developed EDNTOM (Ensemble Deep Network Tool Of Methylation), a DNA methylation detection tool based on deep learning technology. We employed ensemble learning techniques, integrating predictions from multiple pretrained single models, and introduced an attention weight mechanism to provide accurate and reliable detection, reducing the consumption of computational resources. Results demonstrate that EDNTOM outperforms individual models. Additionally, in cross-species transfer experiments, EDNTOM exhibits strong transfer learning capabilities. We hope this work can provide a more powerful and reliable solution for methylation detection, contributing to the fields of biological science and medicine. The project code is available at https://github.com/ViceMusic/EDNTOM.