Analytical Performance of a Novel Nanopore Sequencing for SARS-CoV-2 Genomic Surveillance

Abstract

The genomic analysis of SARS-CoV-2 has served as a crucial tool for generating invaluable data that fulfils both epidemiological and clinical necessities. Long-read sequencing technology (e.g., ONT) has been widely used, providing a real-time and faster response when necessitated. A novel nanopore-based long-read sequencing platform named QNome nanopore has been successfully used for bacterial genome sequencing and assembly; however, its performance in the SARS-CoV-2 genomic surveillance is still lacking. Synthetic SARS-CoV-2 controls and 120 nasopharyngeal swab (NPS) samples that tested positive by real-time polymerase chain reaction were sequenced on both QNome and MGI platforms in parallel. The analytical performance of QNome was compared to the short-read sequencing on MGI. For the synthetic SARS-CoV-2 controls, despite the increased error rates observed in QNome nanopore sequencing reads, accurate consensus-level sequence determination was achieved with an average mapping quality score of approximately 60 (i.e., a mapping accuracy of 99.9999%). For the NPS samples, the average genomic coverage was 89.35% on the QNome nanopore platform compared with 90.39% for MGI. In addition, fewer consensus genomes from QNome were determined to be good by Nextclade compare with MGI (p < 0.05). A total of 9004 mutations were identified using QNome sequencing, with substitutions being the most prevalent, in contrast, 10 997 mutations were detected on MGI (p < 0.05). Furthermore, 23 large deletions (i.e., deletions≥ 10 bp) were identified by QNome while 19/23 were supported by evidence from short-read sequencing. Phylogenetic analysis revealed that the Pango lineage of consensus genomes for SARS-CoV-2 sequenced by QNome concorded 83.04% with MGI. QNome nanopore sequencing, though challenged by read quality and accuracy compared to MGI, is overcoming these issues through bioinformatics and computational advances. The advantage of structure variation (SV) detection capabilities and real-time data analysis renders it a promising alternative nanopore platform for the surveillance of the SARS-CoV-2.

Keywords: QNome; SARS‐CoV‐2; accuracy; genome surveillance; nanopore sequencing.

Figure 1

Consensus genome quality of 120 NPS obtained from the QNome and MGI sequencing and analyzed on Nextclade. (A) Comparison of sequencing scores of 120 NPS that were broken down into three groups: good, mediocre, and bad on two platforms; (B) Comparison of genome coverage of 120 NPS sequenced on two platforms; (C) Comparison of genome coverage of 11 genes in 120 NPS sequenced on two platforms; (D) Comparison of QNome and MGI sequencing on sequencing depth of 11 genome depth in 120 NPS sequenced on two platforms; statistical significance (Mann–Whitney U test) is represented by “*” (*: p < 0.05, **: p < 0.01, ***: p < 0.001). ns, nonsignificant. Sequencing scores ranging between 0 and 29 are classified as good, 30‐99 are classified as mediocre, while 100 and above are classified as bad.

Figure 2

Comparison of mutations in 120 NPS sequenced on the QNome and MGI. (A) Comparison of the number of mutations in 120 NPS on two platforms. (B) Comparison of the types of mutations in120 NPS on two platforms; statistical significance (Mann–Whitney U test) is represented by “*” (**: p < 0.01, ***: p < 0.001).ns, nonsignificant.

Figure 3

Phylogenetic comparison between identical samples sequenced using both the QNome and MGI. A phylogenetic tree was created using IQTREE and visualized using FigTree for samples sequenced on both the QNome and the MGI but classified in different clades by Nextclade. Two of the 14 samples, represented in blue, clustered on the same branch.