An open-source nanopore-only sequencing workflow for analysis of clonal outbreaks delivers short-read level accuracy.

UNLABELLED

In this work, we present an optimized nanopore long-read only sequencing workflow for epidemiologic analysis of clonal outbreaks built with open-source tools. A set of unrelated clinical isolates ( = 10) was chosen for workflow optimization, and sequencing libraries were prepared using a modified rapid barcoding strategy that incorporates temperature ramps to improve performance for high-GC content genomes. Sequencing data were used to benchmark the performance of the dorado suite (v0.9.1), including its basecaller, pre-assembly read error correction, and post-assembly polishing algorithms. All long-read assemblies and core genome multilocus sequence typing (cgMLST) were performed with Flye and pyMLST, respectively. Results were compared with a standard reference Illumina short-read approach, and discordant positions were determined at the core and whole-genome levels. Optimal performance was found with dorado sup@v5.0.0 basecalling with the inclusion of dorado error correction and dorado polish with its bacterial model. This workflow was then validated with four retrospective hospital outbreak isolate sets, including ( = 12), ( = 11), ( = 10), and ( = 10). The nanopore-only assemblies obtained from the optimized pipeline demonstrated fully concordant cgMLST-based minimum spanning trees compared to the Illumina short-read reference. At the whole-genome level, high concordance was also observed, with as few as two discordant positions per genome compared to short-read assemblies. This optimized library preparation and open-source computational workflow enables nanopore-only clonality and outbreak analysis with performance comparable to that of Illumina short-read sequencing and will contribute critically to hospital infection control.

IMPORTANCE

For the past decade, bacterial whole-genome sequencing has been performed using high-accuracy short-read sequencing. More recently, long-read sequencing with Oxford Nanopore Technologies (ONT) instruments has emerged as a potential alternative based on multiple advantages, including lower costs, portability, and speed. However, this platform has suffered from basecall error rates that were too high for many applications in clinical microbiology, including outbreak tracing. With the release of new flow cell chemistries and basecall algorithms, the accuracy has improved dramatically, making this approach feasible for outbreak investigations. In this work, we optimize a streamlined nanopore-only workflow for epidemiologic analysis of bacterial pathogens. The workflow was validated with isolates from four previously identified clinical outbreaks with varying GC content and demonstrated fully concordant cgMLST clustering as compared to short-read references. This workflow will facilitate the broader implementation of ONT-only genomes and cgMLST analysis to assist in hospital outbreaks worldwide.