Development and Validation of Amplicon‐Based Protocol for Sequencing of Respiratory Syncytial Virus Genome
Year: 2025
Type: article
Abstract: ABSTRACT The most prevalent cause of severe respiratory infections in children is the human respiratory syncytial virus (RSV). The advent of next‐generation sequencing (NGS) has made it possible to incorporate this technology into pathogen monitoring and surveillance. Whole‐genome sequencing (WGS) of RSV has now become a relatively widely used method for tracking viral evolution. Here we report an improved high‐throughput RSV‐WGS assay performed directly on clinical samples that is suitable for short‐read sequencing platforms. A total of 100 RSV‐positive samples collected between November 2022 and March 2024 fulfilled the inclusion cycle quantification criteria and were randomly included in the validation process. The WGS protocol was designed to amplify three distinct amplicons to cover the entire RSV genome. The protocol described here can be successfully replicated in several instances (approximately 95%) in samples with a relatively low viral load, typically corresponding to cycle of quantification values of 27–32. The amplicon‐based protocol produced meaningful sequencing results in terms of median depth of coverage (more than 12000×) and median of mapped reads (> 1 × 10 6 reads). The sequences that had passed the filters showed a coverage of at least 98% across the entire genome, with cycle quantification values of 32. Based on the obtained data resulting in an easy‐to‐perform protocol helpful for the molecular epidemiology surveillance of RSV.
Source: Journal of Medical Virology
DOI: 10.1002/jmv.70571
Authors: Guglielmo Ferrari, Great Romano, Antonino Maria Guglielmo Pitrolo, Fausto Baldanti, Antonio Piralla
Institutions: Policlinico San Matteo Fondazione, University of Pavia
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,730
Funders: Ministero della Salute, NextGenerationEU
Award: G7984971330
OpenAlex ID: W4413339377
