Comparison of R9.4.1/Kit10 and R10/Kit12 Oxford Nanopore flowcells and chemistries in bacterial genome reconstruction
Authors: Sanderson Nicholas , Kapel Natalia , Rodger Gillian , Webster Hermione , Lipworth Samuel , street Teresa , Peto Tim , Crook Derrick , Stoesser Nicole 
Published: May 2022

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Complete, accurate, cost-effective, and high-throughput reconstruction of bacterial genomes for large-scale genomic epidemiological studies is currently only possible with hybrid assembly, combining long- (typically using nanopore sequencing) and short-read (Illumina) datasets. Being able to utilise nanopore-only data would be a significant advance. Oxford Nanopore Technologies (ONT) have recently released a new flowcell (R10.4) and chemistry (Kit12), which reportedly generate per-read accuracies rivalling those of Illumina data. To evaluate this, we sequenced DNA extracts from four commonly studied bacterial pathogens, namely Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa and Staphylococcus aureus, using Illumina and ONT’s R9.4.1/Kit10, R10.3/Kit12, R10.4/Kit12 flowcells/chemistries. We compared raw read accuracy and assembly accuracy for each modality, considering the impact of different nanopore basecalling models, commonly used assemblers, sequencing depth, and the use of duplex versus simplex reads. “Super accuracy” (sup) basecalled R10.4 reads - in particular duplex reads - have high per-read accuracies and could be used to robustly reconstruct bacterial genomes without the use of Illumina data. However, the per-run yield of duplex reads generated in our hands with standard sequencing protocols was low (typically <10%), with substantial implications for cost and throughput if relying on nanopore data only to enable bacterial genome reconstruction. In addition, recovery of small plasmids with the best-performing long-read assembler (Flye) was inconsistent. R10.4/Kit12 combined with sup basecalling holds promise as a singular sequencing technology in the reconstruction of commonly studied bacterial genomes, but hybrid assembly (Illumina+R9.4.1 hac) currently remains the highest throughput, most robust, and cost-effective approach to fully reconstruct these bacterial genomes.

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Impact statement

Our understanding of microbes has been greatly enhanced by the capacity to evaluate their genetic make-up using a technology known as whole genome sequencing. Sequencers represent microbial genomes as stretches of shorter sequence known as ‘reads’, which are then assembled using computational algorithms. Different types of sequencing approach have advantages and disadvantages with respect to the accuracy and length of the reads they generate; this in turn affects how reliably genomes can be assembled.

Currently, to completely reconstruct bacterial genomes in a high-throughput and cost-effective manner, researchers tend to use two different types of sequencing data, namely Illumina (short-read) and nanopore (long-read) data. Illumina data are highly accurate; nanopore data are much longer, and this combination facilitates accurate and complete bacterial genomes in a so-called “hybrid assembly”. However, new developments in nanopore sequencing have reportedly greatly improved the accuracy of nanopore data, hinting at the possibility of requiring only a single sequencing approach for bacterial genomics.

Here we evaluate these improvements in nanopore sequencing in the reconstruction of four bacterial reference strains, where the true sequence is already known. We show that although these improvements are extremely promising, for high-throughput, low-cost complete reconstruction of bacterial genomes hybrid assembly currently remains the optimal approach.

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Data summary

The authors confirm all supporting data, code and protocols have been provided within the article, through supplementary data files, or in publicly accessible repositories.

Nanopore fast5 and fastq data are available in the ENA under project accession: PRJEB51164.

Assemblies have been made available at: https://figshare.com/articles/online_resource/q20_comparison_genome_assemblies/196838 67.

Code and analysis outputs are available at: https://gitlab.com/ModernisingMedicalMicrobiology/assembly_comparison_analysis/-/tree/main (tagged version v0.5.5).