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.

Department of Biomedical Engineering Brno University of Technology Technicka 12 Brno 61200 Czechia

Division of Clinical Microbiology and Immunology Department of Laboratory Medicine University Hospital Brno and Faculty of Medicine of Masaryk University Jihlavska 20 Brno 62500 Czechia

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Kumar S., Mohapatra T. Dynamics of DNA methylation and its functions in plant growth and development. Front Plant Sci. 2021;12 doi: 10.3389/fpls.2021.596236. PubMed DOI PMC

Robertson K.D. Dna methylation and human disease. Nat Rev Genet. 2005;6:597–610. doi: 10.1038/nrg1655. PubMed DOI

Ehrlich M., Lacey M. Dna methylation and differentiation: silencing, upregulation and modulation of gene expression. Epigenomics. 2013;5:553–568. doi: 10.2217/epi.13.43. PubMed DOI PMC

Beaulaurier J., Schadt E.E., Fang G. Deciphering bacterial epigenomes using modern sequencing technologies. Nat Rev Genet. 2019;20:157–172. doi: 10.1038/s41576-018-0081-3. PubMed DOI PMC

Gao Q., Lu S., Wang Y., He L., Wang M., Jia R., Chen S., Zhu D., Liu M., Zhao X., Yang Q., Wu Y., Zhang S., Huang J., Mao S., Ou X., Sun D., Tian B., Cheng A. Bacterial DNA methyltransferase: a key to the epigenetic world with lessons learned from Proteobacteria. Frontiers In Microbiology. 2023;14 doi: 10.3389/fmicb.2023.1129437. PubMed DOI PMC

Dai Q., Chen H., Yi W.-J., Zhao J.-N., Zhang W., He P.-A., Liu X.-Q., Zheng Y.-F., Shi Z.-X. Precision DNA methylation typing via hierarchical clustering of nanopore current signals and attention-based neural network. Brief Bioinform. 2024;25 doi: 10.1093/bib/bbae596. PubMed DOI PMC

Oxford Nanopore Technologies Dorado documentation: Basecaller mods. 2024. https://dorado-docs.readthedocs.io/en/latest/basecaller/mods/

Loman N.J., Quick J., Simpson J.T. A complete bacterial genome assembled de novo using only nanopore sequencing data. Nat Methods. 2015;12:733–735. doi: 10.1038/nmeth.3444. PubMed DOI

Stoiber M, Quick J, Egan R, Eun Lee J, Celniker S, Neely RK, Loman N, Pennacchio LA, Brown J. De novo identification of DNA modifications enabled by genome-guided nanopore signal processing. 2016. 10.1101/094672

Rand A.C., Jain M., Eizenga J.M., Musselman-Brown A., Olsen H.E., Akeson M., Paten B. Mapping DNA methylation with high-throughput nanopore sequencing. Nat Methods. 2017;14:411–413. doi: 10.1038/nmeth.4189. PubMed DOI PMC

McIntyre A.B.R., Alexander N., Grigorev K., Bezdan D., Sichtig H., Chiu C.Y., Mason C.E. Single-molecule sequencing detection of N6-methyladenine in microbial reference materials. Nature Commun. 2019;10:579. doi: 10.1038/s41467-019-08289-9. PubMed DOI PMC

Liu Q., Georgieva D.C., Egli D., Wang K. Nanomod: a computational tool to detect DNA modifications using nanopore long-read sequencing data. BMC Genomics. 2019;20:78. doi: 10.1186/s12864-018-5372-8. PubMed DOI PMC

Liu Q., Fang L., Yu G., Wang D., Xiao C.-L., Wang K. Detection of DNA base modifications by deep recurrent neural network on Oxford Nanopore sequencing data. Nature Commun. 2019;10:2449. doi: 10.1038/s41467-019-10168-2. PubMed DOI PMC

Tourancheau A., Mead E.A., Zhang X.-S., Fang G. Discovering multiple types of DNA methylation from bacteria and microbiome using nanopore sequencing. Nat Methods. 2021;18:491–498. doi: 10.1038/s41592-021-01109-3. PubMed DOI PMC

A signal processing and deep learning framework for methylation detection using Oxford Nanopore sequencing. Nature Communications 2024;15:1448. PubMed PMC

Tidwell A.K., Faust E., Eckert C.A., Guss A.M., Alexander W.G. Discovering methylated DNA motifs in bacterial nanopore sequencing data with MIJAMP. J Ind Microbiol Biotechnol. 2024;52 doi: 10.1093/jimb/kuaf022. PubMed DOI PMC

Chen Z., Ni P., Wang J. Identifying DNA methylation types and methylated base positions from bacteria using nanopore sequencing with multi-scale neural network. Bioinformatics. 2025;41 doi: 10.1093/bioinformatics/btaf397. PubMed DOI PMC

Meyer D, Barth E, Wiehle L, Marz M. Diffont: predicting methylation-specific PCR biomarkers based on nanopore sequencing data for clinical application. 2025. 10.1101/2025.02.17.638597

Snajder R., Leger A., Stegle O., Bonder M.J. Pycometh: a toolbox for differential methylation testing from nanopore methylation calls. Genome Biol. 2023;24:83. doi: 10.1186/s13059-023-02917-w. PubMed DOI PMC

Zbudilová M., Jakubíčková M., Vítková H. Proceedings of the 11th World Congress on electrical Engineering and Computer Systems and Sciences (EECSS’25) Avestia Publishing; 2025. Novel computational pipeline for comparative whole-genome methylation profiling in bacteria. DOI

The pandas development team. Pandas-dev/pandas: pandas. 2024. 10.5281/zenodo.13819579

Cock P.J.A., Antao T., Chang J.T., Chapman B.A., Cox C.J., Dalke A., Friedberg I., Hamelryck T., Kauff F., Wilczynski B., de Hoon M.J.L. Biopython: freely available Python tools for computational molecular Biology and Bioinformatics. Bioinformatics. 2009;25:1422–1423. doi: 10.1093/bioinformatics/btp163. PubMed DOI PMC

Waskom M.L. Seaborn: statistical data visualization. J Open Source Softw. 2021;6:3021. doi: 10.21105/joss.03021. DOI

Virtanen P., Gommers R., Oliphant T.E., Haberland M., Reddy T., Cournapeau D., Burovski E., Peterson P., Weckesser W., Bright J., van der Walt S.J., Brett M., Wilson J., Millman K.J., Mayorov N., Nelson A.R.J., Jones E., Kern R., Larson E., Carey C.J., Polat I., Feng Y., Moore E.W., VanderPlas J., Laxalde D., Perktold J., Cimrman R., Henriksen I., Quintero E.A., Harris C.R., Archibald A.M., Ribeiro A.H., Pedregosa F., van Mulbregt P., SciPy 1.0 Contributors SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020;17:261–272. doi: 10.1038/s41592-019-0686-2. PubMed DOI PMC

Müllner D. Fastcluster: fast hierarchical, agglomerative clustering routines for R and Python. J Stat Softw. 2013;53:1–18. doi: 10.18637/jss.v053.i09. DOI

Jakubíčková M, Sabatova K, Zbudilova M, Bezdicek M, Lengerova M, Vitkova H. Methylomeminer: a novel tool for high-resolution analysis of bacterial methylation patterns from nanopore sequencing. 2025. 10.5281/zenodo.16942046

Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics. 2018;34:3094–3100. doi: 10.1093/bioinformatics/bty191. PubMed DOI PMC

Danecek P., Bonfield J.K., Liddle J., Marshall J., Ohan V., Pollard M.O., Whitwham A., Keane T., McCarthy S.A., Davies R.M., Li H. Twelve years of SAMtools and BCFtools. GigaScience. 2021;10 doi: 10.1093/gigascience/giab008. PubMed DOI PMC

Kolmogorov M., Yuan J., Lin Y., Pevzner P.A. Assembly of long, error-prone reads using repeat graphs. Nat Biotechnol. 2019;37:540–546. doi: 10.1038/s41587-019-0072-8. PubMed DOI

Tanizawa Y., Fujisawa T., Nakamura Y. Dfast: a flexible prokaryotic genome annotation pipeline for faster genome publication. Bioinformatics. 2018;34:1037–1039. doi: 10.1093/bioinformatics/btx713. PubMed DOI PMC

Page A.J., Cummins C.A., Hunt M., Wong V.K., Reuter S., Holden M.T., Fookes M., Falush D., Keane J.A., Parkhill J. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics. 2015;31:3691–3693. doi: 10.1093/bioinformatics/btv421. PubMed DOI PMC

Luo G.-Z., Blanco M.A., Greer E.L., He C., Shi Y. Dna n6-methyladenine: a new epigenetic mark in eukaryotes? Nat Rev Mol Cell Biol. 2015;16:705–710. doi: 10.1038/nrm4076. PubMed DOI PMC

Zhao H., Ma J., Tang Y., Ma X., Li J., Li H., Liu Z. Genome-wide DNA N6-methyladenosine in Aeromonas veronii and Helicobacter pylori. BMC Genomics. 2024;25:161. doi: 10.1186/s12864-024-10074-y. PubMed DOI PMC

Altschul S. Gapped BLAST and Psi-blast: a new generation of protein database search programs. Nucleic Acids Res. 1997;25:3389–3402. doi: 10.1093/nar/25.17.3389. PubMed DOI PMC

Wang F., Wang D., Hou W., Jin Q., Feng J., Zhou D. Evolutionary diversity of prophage DNA in Klebsiella pneumoniae chromosomes. Frontiers In Microbiology. 2019;10 doi: 10.3389/fmicb.2019.02840. PubMed DOI PMC

Kang F., Chai Z., Li B., Hu M., Yang Z., Wang X., Liu W., Ren H., Jin Y., Yue J. Characterization and diversity of klebsiella pneumoniae prophages. Int J Mol Sci. 2023;24:9116. doi: 10.3390/ijms24119116. PubMed DOI PMC

Nykrynova M., Bezdicek M., Lengerova M., Skutkova H. 2023 IEEE conference on computational intelligence in Bioinformatics and computational Biology (CIBCB) IEEE; 2023. Bacterial phenotype prediction based on methylation site profiles; pp. 1–6. DOI

Matson S.W., Ragonese H. The F-plasmid TraI protein contains three functional domains required for conjugative DNA strand transfer. J Bacteriol. 2005;187:697–706. doi: 10.1128/JB.187.2.697-706.2005. PubMed DOI PMC

Lin J.T., Goldman B.S., Stewart V. Structures of genes NASA and nasB, encoding assimilatory nitrate and nitrite reductases in Klebsiella pneumoniae M5aL. J Bacteriol. 1993;175:2370–2378. doi: 10.1128/jb.175.8.2370-2378.1993. PubMed DOI PMC

Pollock V.V., Barber M.J. Biotin sulfoxide reductase. J Biol Chem. 1997;272:3355–3362. doi: 10.1074/jbc.272.6.3355. PubMed DOI