Short- and long-read hybrid-capture viral metagenomics reach high sensitivity and specificity with data-driven cut-offs compared with routine diagnostics in CSF and BAL specimens
- 1Laboratory of Clinical and Epidemiological Virology, Rega Institute, KU Leuven
Abstract
Background Hybrid-capture viral metagenomics improves sensitivity over untargeted metagenomics, but its performance across sequencing platforms and the definition of data-driven analysis cut-offs using real clinical samples have not been systematically evaluated. This study benchmarks the hybrid-capture workflow on mock and clinical specimens across short- and long-read platforms while introducing a new, reproducible approach for setting pathogen-calling cut-offs. Methods The Twist Comprehensive Viral Research Panel was used with both AVITI short-read and Oxford Nanopore Technologies (ONT) long-read sequencing on 12 mock cerebrospinal fluid (CSF) external quality assessment (EQA) panel samples and on 14 CSF and 21 bronchoalveolar lavage (BAL) clinical specimens. Data-driven cut-offs for pathogen calling were derived using Latin hypercube sampling across genome coverage, abundance, and bleed-ratio parameters, then validated on the independent EQA datasets. Results After cut-off application, both platforms detected 22 of 23 expected viral targets in EQA samples (95.7% detection-level sensitivity) with 100% specificity in negative controls. In clinical specimens, AVITI corroborated 26 of 30 (86.7%) and ONT 25 of 30 (83.3%) routine viral detections while maintaining 100% diagnostic specificity. Sensitivity was 100% for CSF but lower in BAL, particularly for low viral load targets, and all detections below a Cq value of 32 were identified by both platforms. Metagenomics also identified a potentially clinically relevant pathogen not tested by routine testing in two CSF samples. Conclusion Hybrid-capture viral metagenomics performed comparably on AVITI and ONT for routine viral targets, with AVITI showing slightly higher genome recovery. In addition, the newly developed data-driven cut-off selection strategy offers a practical, reproducible method other laboratories can apply to their own workflows.
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BibTeX
@misc{karatas2026short,
title = {{Short- and long-read hybrid-capture viral metagenomics reach high sensitivity and specificity with data-driven cut-offs compared with routine diagnostics in CSF and BAL specimens}},
author = {M. Karatas and L. Cuypers and L. Laenen and M. Bloemen and A. Rector and J. Maes and M. Vanrenterghem and M. Van Ranst and E. Wollants and S. Desmet and J. Matthijnssens},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.27.26366582},
url = {https://www.medrxiv.org/content/10.64898/2026.10.27.26366582v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Short- and long-read hybrid-capture viral metagenomics reach high sensitivity and specificity with data-driven cut-offs compared with routine diagnostics in CSF and BAL specimens
AU - M. Karatas
AU - L. Cuypers
AU - L. Laenen
AU - M. Bloemen
AU - A. Rector
AU - J. Maes
AU - M. Vanrenterghem
AU - M. Van Ranst
AU - E. Wollants
AU - S. Desmet
AU - J. Matthijnssens
PY - 2026
DA - 2026/10/27
PB - medRxiv
DO - 10.64898/2026.10.27.26366582
UR - https://www.medrxiv.org/content/10.64898/2026.10.27.26366582v1
AB - Background Hybrid-capture viral metagenomics improves sensitivity over untargeted metagenomics, but its performance across sequencing platforms and the definition of data-driven analysis cut-offs using real clinical samples have not been systematically evaluated. This study benchmarks the hybrid-capture workflow on mock and clinical specimens across short- and long-read platforms while introducing a new, reproducible approach for setting pathogen-calling cut-offs. Methods The Twist Comprehensive Viral Research Panel was used with both AVITI short-read and Oxford Nanopore Technologies (ONT) long-read sequencing on 12 mock cerebrospinal fluid (CSF) external quality assessment (EQA) panel samples and on 14 CSF and 21 bronchoalveolar lavage (BAL) clinical specimens. Data-driven cut-offs for pathogen calling were derived using Latin hypercube sampling across genome coverage, abundance, and bleed-ratio parameters, then validated on the independent EQA datasets. Results After cut-off application, both platforms detected 22 of 23 expected viral targets in EQA samples (95.7% detection-level sensitivity) with 100% specificity in negative controls. In clinical specimens, AVITI corroborated 26 of 30 (86.7%) and ONT 25 of 30 (83.3%) routine viral detections while maintaining 100% diagnostic specificity. Sensitivity was 100% for CSF but lower in BAL, particularly for low viral load targets, and all detections below a Cq value of 32 were identified by both platforms. Metagenomics also identified a potentially clinically relevant pathogen not tested by routine testing in two CSF samples. Conclusion Hybrid-capture viral metagenomics performed comparably on AVITI and ONT for routine viral targets, with AVITI showing slightly higher genome recovery. In addition, the newly developed data-driven cut-off selection strategy offers a practical, reproducible method other laboratories can apply to their own workflows.
ER -