Whole genome sequencing of SARS-CoV-2 for Canada's COVID-19 genomic surveillance
Bibliographic record
Abstract
At the onset of the COVID-19 pandemic, researchers around the globe joined forces to study the evolutionary biology of SARS-CoV-2 and identify approaches to minimize its spread in the population. Whole genome sequencing (WGS) quickly became the gold standard for monitoring SARS-CoV-2 viral evolution and how it may impact disease severity, transmission, and vaccine efficacy. As a result, researchers demonstrated concerted efforts to develop, optimize, and validate methods for WGS of the novel virus. This research herein optimized wet-lab sequencing protocols developed by the international research group using reverse transcription PCR-tiling and nanopore sequencing technologies to sequence the whole genome of SARS-CoV-2. As a part of the Canadian COVID Genomics Network (CanCOGeN), the work presented in this thesis outlines materials, methods, and results that contributed to the optimization and validation of a Canadian-specific SARS-CoV-2 WGS approach. To achieve this goal, the investigation included identifying the most economical reverse transcriptase, DNA polymerase, PCR primer schemes, library preparation conditions, and comparison of Nanopore and Illumina sequencing platforms. The optimized WGS protocol was shared with the CanCOGeN partners across Canada to increase Canada’s SARS-CoV-2 sequencing capacity towards a sustainable national genomic surveillance program. Throughout this project, the WGS protocol was validated to ensure that emerging variants of concern were detectable. In summary, the current research contributed to operationalizing the first national genomic surveillance program for viral pathogens in Canada. The developed protocol remains in use across Canadian partners and contributes data to support evidence-based decisions for implementing public health measures. Thanks to our work through the CanCOGeN project and its network of expertise, Canada is well-positioned and prepared to perform genomic surveillance of emerging and re-emerging pathogens should a new public health threat arise.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".