Supplementary data --The mutational landscape of SARS-CoV-2 variants diversifies T cell targets in an HLA supertype-dependent manner
Bibliographic record
Abstract
Supplementary data for the Cell Systems manuscript 'The mutational landscape of SARS-CoV-2 variants diversifies T cell targets in an HLA supertype-dependent manner', by Hamelin et al. GISAID_Authors_Recognition_2021-01-19. Metadata concerning the SARS-CoV-2 viral sequences analysed, and generously provided by GISAID. Table S1. SARS-CoV-2 mutations identified from 330,246 GISAID entries (December 31st 2020), Related to Figure 1 and Figure 2. SARS-CoV-2 mutations at the nucleic and amino acid level are indicated. Number of genomes carrying mutation show the frequency of individual mutations among all SARS-CoV-2 variants. Table S2. SARS-CoV-2 prevalent mutations identified from 330,246 GISAID entries (December 31st 2020) and detected in at least 100 individuals, Related to Figure 2. Table S3. Previously validated SARS-CoV-2 CD8+ T cell epitopes and their matching mutated forms identified in this study, Related to Figure 1 and Figure 2. Table S4. List of previously validated SARS-CoV-2 CD8+ T cell epitopes. Epitopes were downloaded from https://www.mckayspcb.com/SARS2TcellEpitopes/ (as of January 2021). This database has effectively catalogued all SARS-CoV-2 CD8+ epitopes validated by 18 separate studies. See Quadeer et al. 2021 for details, Related to Figure 1 and Figure 2.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.814 | 0.388 |
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".