Analytical validity of nanopore sequencing for rapid SARS-CoV-2 genome analysis

Abstract

Viral whole-genome sequencing (WGS) provides critical insight into the transmission and evolution of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Long-read sequencing devices from Oxford Nanopore Technologies (ONT) promise significant improvements in turnaround time, portability and cost, compared to established short-read sequencing platforms for viral WGS (e.g., Illumina). However, adoption of ONT sequencing for SARS-CoV-2 surveillance has been limited due to common concerns around sequencing accuracy. To address this, here we perform viral WGS with ONT and Illumina platforms on 157 matched SARS-CoV-2-positive patient specimens and synthetic RNA controls, enabling rigorous evaluation of analytical performance. We report that, despite the elevated error rates observed in ONT sequencing reads, highly accurate consensus-level sequence determination was achieved, with single nucleotide variants (SNVs) detected at >99% sensitivity and >99% precision above a minimum ~60-fold coverage depth, thereby ensuring suitability for SARS-CoV-2 genome analysis. ONT sequencing also identified a surprising diversity of structural variation within SARS-CoV-2 specimens that were supported by evidence from short-read sequencing on matched samples. However, ONT sequencing failed to accurately detect short indels and variants at low read-count frequencies. This systematic evaluation of analytical performance for SARS-CoV-2 WGS will facilitate widespread adoption of ONT sequencing within local, national and international COVID-19 public health initiatives.

Fig. 1. Variant detection performance for whole-genome ONT sequencing of SARS-CoV-2.

(a; upper) Sensitivity with which Illumina comparison SNVs at consensus-level variant frequencies (80–100%) were detected via ONT sequencing on matched SARS-CoV-2 specimens (n = 157). Bars show mean ± range. (a; lower) Fraction of specimens tested in which SNVs were detected with perfect sensitivity (sn). Data are plotted separately for genome-wide variant detection (gold) and variant detection with error-prone ‘blacklist’ sites excluded (red). b Same as in a but Jaccard similarity (jac) scores for all variant candidates are plotted instead of SNV sn. c Correlation of variant frequencies observed for SNV candidates detected at sub-consensus frequencies (20–80%) with Illumina and ONT sequencing. Candidates detected with ONT but not Illumina were considered to be false-positives (FP; red) and candidates detected with Illumina but not ONT were considered to be false-negatives (FP; pink). d Sensitivity (blue) and precision (green) of SNV detection with ONT sequencing at sub-consensus variant frequencies (20–80%). Data are plotted separately for high (60–80%), intermediate (40–60%) and low (20–40%) frequencies. Error bars show 95% confidence intervals (Clopper-Pearson) calculated over all specimens (n = 157). Source data are provided as Source Data file.