Investigating the evolutionary dynamics and mutational pattern of SARS-CoV-2 spike gene on selected SARS-CoV-2 variants
Bibliographic record
Abstract
The continuous evolution of SARS-CoV-2 has led to the emergence of several variants representing significant challenges for public health. Many studies highlight the relevance of phylogenetic inference or mutational pattern analysis to understand the evolutionary relatedness of viral variants and to estimate the potential effect of new mutations on viral transmission, virulence and antigenicity. Here we describe an evolutionary investigation approach combined with mutational analyses of SARS-CoV-2 Spike gene to annotate and potentially track important amino acid site variation of specific functional domain relevant for viral survival. This approach was applied on XBB*, EG* and BA* and their sub-lineages (see materials and methods) available from GISAID. In addition, we considered the major variants of concern (Alpha, Delta, Omicron) and Wuhan-Hu-1 strain as references. Maximum likelihood phylogenetic tree was constructed from the complete dataset while selection pressure and mutational analyses were conducted on single variants separately. The obtained phylogenetic tree of Spike amino acid gene sequence showed a clear separation of viral variants as well as their expected appearance order. This result supported the significance of selection pressure analyses outcomes combined with amino acid mutational frequencies where in many cases they showed a linear and parallel trend. This allowed also to hypothesize the potential importance of low-frequency mutations in new potential virus variants. This study constitutes an asset of important insights to be considered in regular monitoring programs. In addition, the analysis framework described here introduces a starting point for further standardization, optimization and application on different data types and in large-scale studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".