Mepylome: A Point‐of‐Care Tumor Diagnostic Toolkit for Tumor DNA Methylation and Copy Number Analysis

DNA methylation and copy number variation (CNV) profiling are essential for diagnostic tumor classification. Current methods are complex and require considerable bioinformatics expertise, limiting clinical implementation. Mepylome ( https://mepylome.readthedocs.io ), an efficient open‐source Python‐based toolkit for microarray‐based DNA methylation and CNV analysis, resolves these challenges. It enables, through artificial intelligence, local tumor methylome comparison with customizable reference sets. Mepylome provides an intuitive graphical interface, supports supervised and unsupervised learning techniques, and processes various Illumina methylation array types. Users can examine their own data, add references from any source, perform analyses including dimension reduction, individual and multisample CNV plotting, and build supervised learning models to augment diagnostic classification. Tested on Ubuntu Linux and Windows with WSL, Mepylome runs on most modern hospital computers, including macOS. Performance is demonstrated on previously published datasets, including 363 salivary gland tumors, 1077 soft tissue tumor specimens, and a multisource collection of 1644 squamous cell carcinomas, reproducing classification accuracies and visualizations. The trained soft tissue classifier is validated on an independent in‐house set. Mepylome runs up to 65 times faster than comparable tools, enabling direct point‐of‐care application and substantially simplifying and accelerating DNA methylation and CNV analysis in clinical settings.