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

<h4>Motivation</h4>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 <i>de novo</i> 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.<h4>Results</h4>On two benchmarking datasets, 10 bacterial monocultures and two <i>Helicobacter pylori</i> 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 <i>Mycoplasma hominis</i> 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.<h4>Availability and implementation</h4>MPore is freely available under the MIT license at https://github.com/DiltheyLab/MPore.