MétaCan
Menu
Back to cohort
Record W3159831454

Phylogenetic analysis of 48 early sars-cov-2 genomes

2021· article· en· W3159831454 on OpenAlexvenueaboutno aff
Minfeng Xiao, Daniel G. Whitney

Bibliographic record

VenueUniversity of Toronto Medical Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsnot available
Fundersnot available
KeywordsPhylogenetic treeGenomeBiologyPandemicGeneticsPopulationSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Evolutionary biologyMost recent common ancestorCoronavirusCoronavirus disease 2019 (COVID-19)Computational biologyGeneMedicineDiseaseInfectious disease (medical specialty)
DOInot available

Abstract

fetched live from OpenAlex

Background: First isolated in December 2019, SARS-CoV-2 is the agent responsible for the ongoing breakout of COVID-19. Method: We curated an assembly of the first 48 full-length SARS-CoV-2 genomes isolated and sequenced across the world and performed a phylogenetic network analysis to monitor the emergence of genomic divergence in the global SARS-CoV-2 population. Results: We identified regions of the genome that have accumulated mutations producing non-synonymous changes at the protein level, suggesting ongoing adaptation of SARS-CoV-2 to its novel human host. We identified a strong L84S mutational signal in ORF8 (present in 29.16% of genomes) together with 12 variable sites in the region encoding non-structural protein Nsp3 that represent the strongest putative regions under selection in our dataset. We did not detect mutations in the coronavirus spike protein, which is reassuring for the vaccines currently available or are ongoing large-scale clinical trials. Conclusion: Our analysis provides a snapshot in time of a rapidly evolving pandemic based on available data. Our results are in line with previous findings that point to a common ancestor isolated in Wuhan that is likely to have circulated and spread worldwide. © 2021, University of Toronto. All rights reserved.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.028
GPT teacher head0.309
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueUniversity of Toronto Medical JournalSame topicSARS-CoV-2 and COVID-19 ResearchFrench-language works237,207