Oxford Nanopore enhanced accuracy of long-read amplicons applied to microbial whole-genome sequencing.

The development of long-read sequencing technologies has enabled the analysis of extended nucleic acid sequences. These methods have proven their strength through their capacity to generate long reads, facilitating the analysis of complex genomic regions and rearrangements. Oxford Nanopore Technologies (ONT) offers a rapid and portable system that brings sequencing to the field. Although this is a great advantage for clinical settings, applications of long-read sequencing in this context have been limited by the high error rates reported for these methods. Here, we report an adaptation of an amplicon sequencing approach combined with unique molecular identifiers. We applied this method to whole-genome sequencing using mock community samples and human blood cultures spiked with common bloodstream infection pathogens. Our results showed a total error rate of <0.1% with V9 chemistry, which was further reduced by <0.05% when using the V14 chemistry. Our results also highlight the improvements of the V14 chemistry on the standard ONT ligation protocol and the importance of the basecalling tool for sequencing accuracy.IMPORTANCERecent advances in genome sequencing have greatly improved our ability to study microbes and detect infections. One such technology, Oxford Nanopore Technologies (ONT), can read long stretches of nucleic acids. ONT is also portable and can sequence in real time, making it useful in clinical settings. However, ONT accuracy is known to be lower than traditional short-read methods, limiting its widespread use. Fortunately, many strategies have emerged to overcome this limitation: better ONT chemistry, better basecaller, and hybrid approaches combining ONT with highly accurate short reads. Another promising method uses molecular barcodes or "Unique Molecular Identifiers" (UMIs) to make long reads at high accuracy, reaching accuracy levels similar to the existing short-read technologies. In our study, we optimized this UMI-based method and successfully applied it to human blood samples spiked with common infection-causing bacteria. The results showed a significant drop in ONT error rate, suggesting that this approach could make ONT a reliable tool for diagnosing infections and analyzing microbial DNA in clinical samples.