Whole genome sequencing and phylogenetic classification accelerate the implementation of respiratory syncytial virus genomic surveillance in Canada: a pilot study
Bibliographic record
Abstract
ABSTRACT Whole genome sequencing (WGS) has emerged as a powerful tool to facilitate the study of existing and emerging infectious diseases. WGS-based genomic surveillance provides information on the genetic diversity and tracks the evolution of important viral pathogens, including respiratory syncytial virus (RSV). Multiplex tiling polymerase chain reaction (PCR) assays have been used to facilitate sequencing of a variety of pathogens in support of genomics-based surveillance initiatives. We developed, optimized, and implemented multiplex tiling PCR assays for RSVA and RSVB capable of generating near-complete genomes in the majority of contemporaneous specimens tested. A pilot data set comprising 52 RSVA and 37 RSVB genomes derived from Canadian clinical specimens during the 2022–2023 respiratory virus season was used to perform phylogenetic analyses using both near-complete genome and glycoprotein (G) sequences. Overall, the RSV phylogenetic tree built with whole genomes showed identical lineage clusters as compared to the G gene but was more discriminatory. Moreover, the availability of complete genomes enables the identification of a broader range of mutations. For instance, mutations identified in the fusion protein among Canadian isolates tested here, including S377N, K272M, S276N, S211N, S206I, and S209Q, could affect the efficacy of current vaccines or antiviral-based therapeutics. In conclusion, our work reinforces other recent studies demonstrating the utility of multiplex tiling PCR assays to facilitate high-throughput WGS of RSV, which is capable of supporting enhanced genomic surveillance initiatives, as well as the more comprehensive genomic analyses required to inform public health strategies for the development and usage of vaccines and antiviral drugs. IMPORTANCE We present assays to efficiently sequence genomes of RSVA and RSVB. This enables researchers and public health agencies to acquire high-quality genomic data using rapid and cost-effective approaches. Genomic data-based comparative analysis can be used to conduct surveillance and monitor circulating isolates for efficacy of vaccines and antiviral therapeutics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".