DNA Methylation (DM) data format and DMtools for efficient DNA methylation data storage and analysis

Abstract

DNA methylation is a critical epigenetic mechanism that plays a pivotal role in various biological processes. Currently, larger datasets from whole-genome bisulfite sequencing for DNA methylation pose challenges throughout the computational analysis pipeline, including storage and memory constraints. Unfortunately, storage formats and analysis tools have not kept pace with these increased resource demands. In this study, we present a new and efficient design for storing DNA methylation (DM) data after mapping in compressed binary indexed DM format. Our format significantly reduces storage space by 80%-95% compared to commonly used file formats for DNA methylation data after mapping. To enhance the processing of DNA methylation data in DM format, we have developed DMtools, a comprehensive toolkit that offers utilities such as rapid and random access, computation of DNA methylation profiles across genes, and analysis of differential DNA methylation. The analysis speed is improved by over 100 times compared to existing methods. Furthermore, we have created pyDMtools, a Python package that efficiently processes DM format files for Python users. The integration of the DM format and its associated tools represents significant progress in handling and exploring DNA methylation data, offering the potential to significantly reduce storage needs and improve downstream analysis capabilities.