Assessment of Oxford Nanopore whole genome sequencing for large-scale genomic characterisation of Staphylococcus aureus

Whole-genome sequencing (WGS) is increasingly being utilised in microbial diagnostics, surveillance, and research. In this paper we assess the performance of one leading long-read sequencing technology, Oxford Nanopore Technology (ONT), on 836 Staphylococcus aureus bacteraemia isolates. We compare the results to that of a leading short-read sequencing technology, Illumina. All isolates were sequenced using ONT MinION Mk1B and Illumina HiSeq or MiSeq. Libraries were prepared according to manufacturers’ instructions. Preprocessing and downstream bioinformatic analyses were performed using a combination of in-house pipelines and publicly available software tools. The average base substitution error rate in ONT assemblies was low but varied between sequence types, possibly due to lineage-specific methylation patterns. Multi locus sequence typing was similar between the technologies, while ONT assemblies allowed for better spa typing than Illumina assemblies. The reported detection rate was similar between ONT and Illumina assemblies for most virulence- and AMR-associated genes and variants. For 42 (22.2%) of 189 genes/variants, the two technologies disagreed in gene detection in 5 isolates or more, and in 39 (20.6.%) of these the highest detection rate was found with ONT. Discrepancies were mainly associated with low GC content, multiple repetitive segments, and small plasmids. Polishing of ONT data resulted in minor changes in gene/variant calling. Our study supports the use of ONT WGS for bacterial population genomic studies on a large collection of S. aureus isolates. While assembly of ONT reads may be affected by its own methodological limitations, it was superior to Illumina assemblies in detection of potentially clinically relevant genes and variants at a low read error rate. Understanding the advantages and limitations of WGS technologies is essential before undertaking studies involving such methods on large sets of bacteria.