MPore: database-driven identification of active methyltransferases in prokaryotic genomes from nanopore sequencing

In prokaryotic genomes, methylation is an important epigenetic modification that regulates the uptake of foreign DNA; it can also contribute to replication or virulence. We present MPore, a novel method for the database-driven detection of active methyltransferases and their associated target site recognition motifs from Nanopore R10 sequencing data of prokaryotic isolates. In contrast to existing methods, which typically start with the de novo identification of differentially methylated sequence motifs, MPore starts by identifying potential methyltransferase genes by homology search against REBASE; activity is then assessed through a regularized logistic regression model of observed genome-wide methylation patterns, integrating motif and genomic sequence context information.

On two benchmarking datasets, 10 bacterial monocultures and two Helicobacter pylori genomes with complex methylation patterns, MPore achieved a combined recall of 93% and a combined PPV of 96%, outperforming Nanomotif (81%/91%), Modkit (66%/4%), and Snappy (89%/50%). Further validation on a well-characterized dataset of Mycoplasma hominis isolates showed perfect agreement with wet-lab-based validation results and demonstrated that MPore could complement REBASE information by disambiguating the specific methylated base in a motif with multiple potential methylation sites. MPore automatically produces integrated visualizations of the identified methyltransferases and observed methylation patterns; the tool is implemented as a user-friendly Snakemake pipeline.

MPore is freely available under the MIT license at https://github.com/DiltheyLab/MPore.