ARTIC RSV amplicon sequencing reveals global RSV genotype dynamics
Bibliographic record
Abstract
Abstract Respiratory syncytial virus (RSV) is a leading cause of lower respiratory tract infections (LTRIs) in young children and adults over 65, contributing significantly to global healthcare burdens. With the recent approval of multiple pharmacological interventions for RSV, there is an increased demand for efficient, high-throughput sequencing methods to monitor RSV genetic diversity and any potential impact these interventions may have. Here we introduce two novel amplicon-based sequencing schemes designed for RSV A and B, optimised for integration with widespread existing ARTIC sequencing workflows. We demonstrate that these primer schemes can produce high quality genomes from RSV samples across the globe, with eight laboratories in five countries generating complete genomes on both Nanopore and Illumina sequencing platforms. The ability to effectively multiplex these RSV A and B primer schemes, enables streamlined, high-throughput sequencing without prior subtyping. Furthermore, these results provide a snapshot of the circulating diversity of RSV. Phylogenetic analysis of the 882 samples sequenced for this study suggests only minimal geographic clustering of RSV sequences, underscoring the global nature of RSV spread. It also highlights the distinct lineage dynamics seen between RSV A and B. This study represents an advancement in RSV genomics, providing robust tools for global sequencing efforts aimed at tracking RSV evolution and assessing the efficacy of new therapeutic interventions both rapidly and at scale.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".