A Benchmark of methods for SARS-CoV-2 whole genome sequencing and development of a more sensitive method
Bibliographic record
Abstract
The raging COVID-19 pandemic caused by SARS-CoV-2 has so far claimed the lives of 7 million people and continues to infect many more. Further, virus evolution has caused mutations that have compromised public health interventions like vaccination regimes and monoclonal antibody and convalescent sera treatments. In response, unprecedented large-scale whole genome viral surveillance approaches have been devised to keep track of the evolution and transmission patterns of the virus within and across populations. Here, we aimed to compare efficiencies of SARS-CoV-2 whole genome sequencing approaches using synthetic SARS-CoV-2 genome and six cell culture SARS-CoV-2 variants titrated to represent samples at high, medium, and low viral load. We found that the ARTIC protocols performed best in terms of PCR amplicon yield returning 67% more amplicons than Entebbe protocol which was the second highest PCR amplicon yielding protocol. ARTIC v4.1 protocol yields were only slightly better than ARTIC v3. Despite yielding the lowest PCR amplicons, the SNAP protocol showed the highest genome completeness using a synthetic genome at high viral titre followed by ARTIC protocols. However, the ARTIC protocols showed highest genome completeness with cell culture SARS-CoV-2 variants across high, medium and low viral titres. ARTIC protocol also performed best in calling the correct lineage among cell culture SARS-CoV-2 variants across different viral titres. We also designed a new method termed ARTIC-Amp which leverages ARTIC protocol and performs a rolling circle amplification to increase yield of amplicons. In a proof-of-principle experiment, this method showed 100% coverage in all four targeted genes across three replicates unlike the ARTIC protocol missed one gene in two of the three replicates. Our results demonstrate the robustness of the ARTIC protocol and propose an improved method that could be useful for samples that routinely have limited SARS-CoV-2 RNA such as wastewater samples.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".