Systematic analysis of the codon usage patterns of African swine fever virus genome coding sequences reveals its host adaptation phenotype
Bibliographic record
Abstract
African swine fever (ASF) is a severe haemorrhagic disease caused by the African swine fever virus (ASFV), transmitted by ticks, resulting in high mortality among domestic pigs and wild boars. The global spread of ASFV poses significant economic threats to the swine industry. This study employs diverse analytical methods to explore ASFV’s evolution and host adaptation, focusing on codon usage patterns and associated factors. Utilizing phylogenetic analysis methods including neighbour-joining and maximum-likelihood, 64 ASFV strains were categorized into four clades. Codon usage bias (CUB) is modest in ASFV coding sequences. This research identifies multiple factors – such as nucleotide composition, mutational pressures, natural selection and geographical diversity – contributing to the formation of CUB in ASFV. Analysis of relative synonymous codon usage reveals CUB variations within clades and among ASFVs and their hosts. Both Codon Adaptation Index and Similarity Index analyses confirm that ASFV strains are highly adapted to soft ticks (Ornithodoros moubata) but less so to domestic pigs, which could be a result of the long-term co-evolution of ASFV with ticks. This study sheds light on the factors influencing ASFV’s codon usage and fitness dynamics, enriching our understanding of its evolution, adaptation and host interactions.
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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 | 0.001 |
| 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) | 0.000 | 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".