High precision Neisseria gonorrhoeae variant and antimicrobial resistance calling from metagenomic Nanopore sequencing

Nicholas D Sanderson, Jeremy Swann, Leanne Barker, James Kavanagh, Sarah Hoosdally, Derrick Crook, GonFast Investigators Group, Teresa L Street, David W Eyre, Nicholas D Sanderson, Jeremy Swann, Leanne Barker, James Kavanagh, Sarah Hoosdally, Derrick Crook, GonFast Investigators Group, Teresa L Street, David W Eyre

Abstract

The rise of antimicrobial-resistant Neisseria gonorrhoeae is a significant public health concern. Against this background, rapid culture-independent diagnostics may allow targeted treatment and prevent onward transmission. We have previously shown metagenomic sequencing of urine samples from men with urethral gonorrhea can recover near-complete N. gonorrhoeae genomes. However, disentangling the N. gonorrhoeae genome from metagenomic samples and robustly identifying antimicrobial resistance determinants from error-prone Nanopore sequencing is a substantial bioinformatics challenge. Here, we show an N. gonorrhoeae diagnostic workflow for analysis of metagenomic sequencing data obtained from clinical samples using R9.4.1 Nanopore sequencing. We compared results from simulated and clinical infections with data from known reference strains and Illumina sequencing of isolates cultured from the same patients. We evaluated three Nanopore variant callers and developed a random forest classifier to filter called SNPs. Clair was the most suitable variant caller after SNP filtering. A minimum depth of 20× reads was required to confidently identify resistant determinants over the entire genome. Our findings show that metagenomic Nanopore sequencing can provide reliable diagnostic information in N. gonorrhoeae infection.

© 2020 Sanderson et al.; Published by Cold Spring Harbor Laboratory Press.

Figures

Figure 1.
Figure 1.
Detection of SNPs using QUAL scores alone. Swarm plots of true (orange) and false SNPs (blue) detected by Clair (top), Nanopolish (middle), and Medaka (bottom). Each column is a different sequence. Each row has different y-axis values.
Figure 2.
Figure 2.
Random forest–based variant filtering using Nanopolish, Medaka, and Clair. (A) Receiver operating characteristic (ROC) curve for random forest classifier using different features including Quality (QUAL only, dashed line) and a composite selection of input features (Composite, solid line) for Nanopolish (green), Medaka (orange), and Clair (blue). AUC for each variant caller: Nanopolish 0.86 to 0.98, Medaka 0.93 to 0.97, Clair 0.84 to 0.97, using QUAL and Composite features, respectively. (B) Bar chart of feature importance for composite selection of features used to train the classifier.
Figure 3.
Figure 3.
Effect of read coverage depth on SNP calling for each strain and variant caller. (A) SNP recall by median depth of coverage. (B) False positive SNPs (FP) by median depth of coverage. Color represents different sequences, shapes represent variant callers, circles are Clair, crosses are Medaka, and squares are Nanopolish. Insets show upper and lower regions of the y-axis in more detail for A and B, respectively.
Figure 4.
Figure 4.
Recombination-corrected maximum likelihood tree of metagenomic Nanopore and paired Illumina isolate sequences. All Nanopore consensus sequences were generated from metagenomic sequencing with the exception of H18-208 and WHO Q, which were sequenced from isolates.

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