NanoMethCluster: A comprehensive tool for DNA methylation analysis using Oxford Nanopore sequencing data

Abstract

Oxford Nanopore sequencing technology, capable of analyzing native DNA, has significantly advanced DNA methylation studies by enabling simultaneous nucleotide sequencing and methylation detection without bisulfite conversion. This advancement addresses the need to study cytosine methylation, a key epigenetic mechanism influencing gene regulation, development, and disease. To fully utilize this technology, there is a growing demand for tools capable of performing a comprehensive analysis of methylation patterns directly from raw sequencing reads. Here, we present NanoMethCluster, a versatile software tool for extracting, normalizing, and clustering DNA methylation data, with advanced, user-friendly visualization capabilities. The tool includes modules for read-level methylation data processing, dimensionality reduction, and clustering, enabling researchers to uncover epigenetic patterns across diverse biological contexts. The high-resolution capability of NanoMethCluster was demonstrated through the analysis of tumor and normal cell samples, as well as mixed-species data. In this proof-of-concept study, these applications demonstrate their effectiveness in resolving complex methylation profiles and in providing useful information about biological variability and species-specific methylation patterns, while highlighting the need for validation in larger patient cohorts and additional species to fully assess their robustness and generalizability.