MethylomeMiner: A novel tool for high-resolution analysis of bacterial methylation patterns from nanopore sequencing
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Date
2025-10-24
Authors
Jakubíčková, Markéta
Šabatová, Kateřina
Zbudilová, Michaela
Bezdíček, Matěj
Lengerová, Martina
Vítková, Helena
0000-0002-0205-6935Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
Altmetrics
Abstract
DNA methylation plays a key role in gene regulation, genome stability, bacterial adaptation, and many other essential cellular processes. Thanks to nanopore sequencing technology, it is now possible to detect these modifications during sequencing without any prior chemical treatment. However, methylation data processing and their interpretation in a biological context remain challenging as there are no convenient and easy-to-use tools available for this purpose. Therefore, here, we present a simple Python-based tool, MethylomeMiner, to process methylation calls from nanopore sequencing. The tool allows high-confidence methylation sites to be selected based on coverage and methylation rate and assigned to coding or non-coding regions using genome annotation. In addition, the tool supports population-level analysis using pangenome data to compare methylation patterns across multiple bacterial genomes. Altogether, MethylomeMiner provides a straightforward and reproducible workflow that can be easily integrated into existing analyses and helps uncover the functional roles of DNA methylation in bacterial genomes.
DNA methylation plays a key role in gene regulation, genome stability, bacterial adaptation, and many other essential cellular processes. Thanks to nanopore sequencing technology, it is now possible to detect these modifications during sequencing without any prior chemical treatment. However, methylation data processing and their interpretation in a biological context remain challenging as there are no convenient and easy-to-use tools available for this purpose. Therefore, here, we present a simple Python-based tool, MethylomeMiner, to process methylation calls from nanopore sequencing. The tool allows high-confidence methylation sites to be selected based on coverage and methylation rate and assigned to coding or non-coding regions using genome annotation. In addition, the tool supports population-level analysis using pangenome data to compare methylation patterns across multiple bacterial genomes. Altogether, MethylomeMiner provides a straightforward and reproducible workflow that can be easily integrated into existing analyses and helps uncover the functional roles of DNA methylation in bacterial genomes.
DNA methylation plays a key role in gene regulation, genome stability, bacterial adaptation, and many other essential cellular processes. Thanks to nanopore sequencing technology, it is now possible to detect these modifications during sequencing without any prior chemical treatment. However, methylation data processing and their interpretation in a biological context remain challenging as there are no convenient and easy-to-use tools available for this purpose. Therefore, here, we present a simple Python-based tool, MethylomeMiner, to process methylation calls from nanopore sequencing. The tool allows high-confidence methylation sites to be selected based on coverage and methylation rate and assigned to coding or non-coding regions using genome annotation. In addition, the tool supports population-level analysis using pangenome data to compare methylation patterns across multiple bacterial genomes. Altogether, MethylomeMiner provides a straightforward and reproducible workflow that can be easily integrated into existing analyses and helps uncover the functional roles of DNA methylation in bacterial genomes.
Description
Citation
Computational and Structural Biotechnology Journal. 2025, vol. 27, issue October, p. 4753-4759.
https://www.sciencedirect.com/science/article/pii/S2001037025004507
https://www.sciencedirect.com/science/article/pii/S2001037025004507
Document type
Peer-reviewed
Document version
Published version
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Language of document
en