Unveiling pathogens and contaminants: refining metagenomics for clinical diagnostics

Shotgun metagenomic sequencing (mNGS), an untargeted approach that sequences all nucleic acids in a sample, has emerged as a powerful tool for pathogen detection and genome characterization. However, its implementation in clinical diagnostics remains limited due to technical challenges such as contamination and reduces sensitivity, especially in low-biomass samples.

We applied mNGS to 144 clinical samples representing chronic infections, acute infections, and respiratory co-infections. To address contamination, we established a framework integrating negative controls, lab-specific contaminant watchlists, and computational filtering. Viral detection performance and genome recovery were assessed across sample types and viral loads.

Viral load was shown to be the primary determinant of sensitivity, with reliable recovery achieved only at higher titers. Our framework substantially improved contamination management, reducing false-positive signals and enhancing viral genome recovery. mNGS enabled the detection of clinically relevant co-infections and refined viral classification beyond targeted diagnostics, while also revealing the substantial risk of spurious detections in the absence of contamination-aware workflows.

These findings define practical sensitivity thresholds for clinical mNGS and underscore the need for contamination-aware workflows, particularly for low-biomass samples, while providing an open-source contaminants watchlist that enhances reliability and utility of clinical metagenomics.