Worldwide tracing of mutations and the evolutionary dynamics of SARS-CoV-2
Bibliographic record
Abstract
Abstract Understanding the mutational and evolutionary dynamics of SARS-CoV-2 is essential for treating COVID-19 and the development of a vaccine. Here, we analyzed publicly available 15,818 assembled SARS-CoV-2 genome sequences, along with 2,350 raw sequence datasets sampled worldwide. We investigated the distribution of inter-host single nucleotide polymorphisms (inter-host SNPs) and intra-host single nucleotide variations (iSNVs). Mutations have been observed at 35.6% (10,649/29,903) of the bases in the genome. The substitution rate in some protein coding regions is higher than the average in SARS-CoV-2 viruses, and the high substitution rate in some regions might be driven to escape immune recognition by diversifying selection. Both recurrent mutations and human-to-human transmission are mechanisms that generate fitness advantageous mutations. Furthermore, the frequency of three mutations (S protein, F400L; ORF3a protein, T164I; and ORF1a protein, Q6383H) has gradual increased over time on lineages, which provides new clues for the early detection of fitness advantageous mutations. Our study provides theoretical support for vaccine development and the optimization of treatment for COVID-19. We call researchers to submit raw sequence data to public databases.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".