MethylomeMiner: A novel tool for high-resolution analysis of bacterial methylation patterns from nanopore sequencing

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F65269705%3A_____%2F25%3A00082981" target="_blank" >RIV/65269705:_____/25:00082981 - isvavai.cz</a>
Alternative codes found
RIV/00216224:14110/25:00143816
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2001037025004507" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2001037025004507</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.csbj.2025.10.047" target="_blank" >10.1016/j.csbj.2025.10.047</a>

Result language
angličtina
Original language name
MethylomeMiner: A novel tool for high-resolution analysis of bacterial methylation patterns from nanopore sequencing
Original language description
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.
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10608 - Biochemistry and molecular biology

Project
<a href="/en/project/GA23-05845S" target="_blank" >GA23-05845S: Real-time determination of infection threats from raw nanopore signals using machine learning techniques</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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