Sequencing Data of North American SARS-CoV-2 Isolates Shows Widespread Complex Variants
Bibliographic record
Abstract
Several new variants of the SARS-CoV-2 have been isolated in the United States, Mexico, and Canada. Many of the variants contain single variants of functional significance (e.g. S: N501Y increases transmissibility). To study the occurrence and co-circulation of these variants, we have developed an easy-to-use dashboard at janieslab.github.io/sars-cov-2 . We created a multiple sequence alignment workflow and processing script to generate a variant dataset, which populates this dashboard. We then use the features of the dashboard, such as visualization of the single and complex nucleotide variants geospatially and in a color-coded matrix format. Users also interact with the dashboard to filter the underlying data to regions of interest and or variants of interest. The user can export reports based on the desired filters, which we intend to be used for regionally specific pandemic response. We find in Genbank, an isolate from Massachusetts containing [(S: Q677H), (ORF3a: Q57H), (M: A85S), (N: D377Y)] collected on September 11, 2020. Moreover, we find that many viral isolates bear a marker of increased transmissibility (S: N501Y) in linkage with at least one variant of concern isolated from Ohio also range across the Untied States and stretch from British Columbia, Canada to Mexico. When we analyze co-circulation of more complex variant constellations with (S: N501Y), we note that the Upper Midwest and Northeast United States contain these isolates. In summary, the viral variants that have raised concern in a few US States in recent reports are widespread. Based on the increase in the proportion of variant viruses being sampled and some empirical evidence in the United Kingdom, South Africa, and Ohio, these variants are likely to lead to increased transmission of SARS-CoV-2 across North America in the coming months.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".