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High precision Neisseria gonorrhoeae variant and antimicrobial resistance calling from metagenomic Nanopore sequencing
Author(s) -
Nicholas Sanderson,
Jeremy Swann,
Leanne Barker,
James Kavanagh,
Sarah Hoosdally,
Derrick W. Crook,
Teresa Street,
David W. Eyre
Publication year - 2020
Publication title -
genome research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 9.556
H-Index - 297
eISSN - 1549-5469
pISSN - 1088-9051
DOI - 10.1101/gr.262865.120
Subject(s) - metagenomics , neisseria gonorrhoeae , biology , dna sequencing , nanopore sequencing , genome , computational biology , deep sequencing , whole genome sequencing , genetics , antibiotic resistance , gene , bacteria
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.

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