Abstract
BACKGROUND: Robust identification of pathogens is essential for managing patients with symptomatic infection, yet conventional diagnostic methods focus on a subset of the most prevalent pathogens and genes. Metagenomic next-generation sequencing (mNGS) is a powerful technology that can comprehensively and simultaneously assess a broader range of pathogens and genes in a sample. This study evaluates the clinical (22 targets), analytical (19 targets), and in silico (176 targets) performance of a faecal mNGS assay on clinically relevant bacterial, eukaryotic, viral, virulence factor (VF) and antimicrobial resistance (AMR) genes. METHODS: Diagnostic performance was evaluated relative to conventional pathology testing using 510 clinical faecal samples from patients presenting with gastrointestinal symptoms. Contrived samples were used to assess analytical performance and establish the assay's limit of detection by adding cells to a faecal matrix. In silico faecal samples containing targets reflecting the limit of detection of the assay were used to evaluate performance across all 176 targets. RESULTS: Clinical specificity was ≥96% (≥99% for all but Adenovirus F), and median pathogen sensitivity was 91%. VF and AMR gene detection was less sensitive (median 58.7%). The assay was highly reproducible in biological triplicates (27,656/27,808 calls concordant; 99.5%). Importantly, broad mNGS coverage increased diagnostic yield, with 256/510 (50.2%) samples containing one or more additional targets not reported by standard care, and 181/510 (35.5%) containing AMR genes, including carbapenemases. In silico benchmarking showed strong performance for all 176 targets down to analytically defined detection limits. CONCLUSIONS: The faecal mNGS assay performed competitively with existing diagnostic techniques while substantially expanding actionable detection in a single assay. These results support stool mNGS as a high-yield second-line or syndromic test for gastrointestinal infection, enabling improved recognition of rare pathogens, co-infections, and resistance determinants.