Application of high-throughput sequencing to whole rabies viral genome characterisation and its use for phylogenetic re-evaluation of a raccoon strain incursion into the province of Ontario
Bibliographic record
Abstract
Raccoon rabies remains a serious public health problem throughout much of the eastern seaboard of North America due to the urban nature of the reservoir host and the many challenges inherent in multi-jurisdictional efforts to administer co-ordinated and comprehensive wildlife rabies control programmes. Better understanding of the mechanisms of spread of rabies virus can play a significant role in guiding such control efforts. To facilitate a detailed molecular epidemiological study of raccoon rabies virus movements across eastern North America, we developed a methodology to efficiently determine whole genome sequences of hundreds of viral samples. The workflow combines the generation of a limited number of overlapping amplicons covering the complete viral genome and use of high throughput sequencing technology. The value of this approach is demonstrated through a retrospective phylogenetic analysis of an outbreak of raccoon rabies which occurred in the province of Ontario between 1999 and 2005. As demonstrated by the number of single nucleotide polymorphisms detected, whole genome sequence data were far more effective than single gene sequences in discriminating between samples and this facilitated the generation of more robust and informative phylogenies that yielded insights into the spatio-temporal pattern of viral spread. With minor modification this approach could be applied to other rabies virus variants thereby facilitating greatly improved phylogenetic inference and thus better understanding of the spread of this serious zoonotic disease. Such information will inform the most appropriate strategies for rabies control in wildlife reservoirs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".