Reproducibility and Accuracy of Nanopore-Based Methylome Profiling of Streptococcus dysgalactiae subspecies equisimilis Strains from Cancer Patients

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Abstract Background DNA methylation influences bacterial gene regulation, virulence, and restriction-modification (RM) systems. Advances by Oxford Nanopore Technologies (ONT) now enable direct methylome profiling from nanopore sequencing using the Dorado basecaller. However, the comparative performance of ONT-only versus hybrid-assembly reference-based methylation calling, particularly regarding genomic DNA quality and inter-operator variability, remains understudied. Methods Six operators independently prepared fifteen sequencing libraries each for nanopore (MinION R10.4.1 flow cells, Mk1D) and Illumina MiniSeq platforms for two Streptococcus dysgalactiae subsp. equisimilis strains (UT9728, 12 replicates; UT10237, 3 replicates). MicrobeMod v1.0.3 was used to identify methylation and motif profiles using Illumina-corrected hybrid reference assemblies (HRAs) and ONT-only reference assemblies (ORAs). Reproducibility and accuracy were compared using a custom genome annotation feature-enabled modular analysis that mapped and counted methylation site calls to CDS, rRNA and tRNA coordinates. Results Strain UT9728 predominantly exhibited N6-methyladenine (6mA) at GATC motifs, whereas strain UT10237 displayed dual methylation patterns: C5-methylcytosine (5mC) at CCWGG motifs and 6mA at GAGNNNNNTAA motifs. Both strains contained Type I and Type II RM systems; UT10237 uniquely harbored a Type IIG RM system with combined restriction and methylation activities. Motif identification concordance using HRAs and ORAs exceeded 99.9%. Reproducibility for methylation calls was high across independent replicates for both HRA (Pearson’s r >0.989) and ORA (Pearson’s r >0.993) methylation calls in GATC and CCWGG motifs but lower in the GAGNNNNNTAA motif (Pearson’s r (HRA) = 0.80; r (ORA) = 0.78). ORA-based methylation site calls for all motifs showed excellent precision and recall compared to HRA-based calls (F1-score >99.999%). Conclusion Our findings support the accuracy, robustness, and utility of ONT-only data based methylome profiling for bacterial epigenetic characterization. Our analytical framework facilitates detailed evaluations of reproducibility and accuracy. Data Summary and Availability Unprocessed_data_files : DOI: 10.5281/zenodo.15555625 Raw paired-end FastQ Files (Illumina). Raw FastQ Files (ONT; unmodified basecalls). Raw uBAM Files (ONT; 6mA_5MC_modified_basecalls). Genome_assemblies_and_annotation_files : DOI: 10.5281/zenodo.15558488 Genome_Assemblies reconstucted with ONT reads and polished with Illumina Reads (Hybrid assemblies);(strain_name_ _Hyb.gbk) Genome_Assemblies reconstructed with ONT reads and polished with ONT reads (ONT-only assemblies); (strain_name_ _ONT.fasta) GenBank flatfiles from Hybrid assemblies; (strain_name_ _Hyb.gbk) Genbank flatfiles from ONT-only assemblies. (strain_name_ _ONT.gbk) Data_Tables and Python_code for modular analysis: DOI: doi.org/10.5281/zenodo.15579791 9728_10237_ORA+HRA_all_reps_microbemod_output.zip Methylation calls mapped to parsed genbank features & feature count matrix files. - 9728_HRA_reproducibility_analysis_tables.zip - 9728_ORA_reproducibility_analysis_tables.zip - 10237_HRA_5mC_6mA_reproducibility_analysis_tables.zip - 10237_ORA_reproducibility_analysis_tables.zip Python code for annotation feature-based modular analysis - methylation_feature_hash_assert_FILTER_9728.py - methylation_feature_hash_NOFILTER_9728.py -

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