Coding potential and transcript analysis of fowl adenovirus 4: insight into upstream ORFs as common sequence features in adenoviral transcripts
Bibliographic record
Abstract
Recombinant fowl adenoviruses (FAdVs) have been successfully used as veterinary vaccine vectors. However, insufficient definitions of the protein-coding and non-coding regions and an incomplete understanding of virus-host interactions limit the progress of next-generation vectors. FAdVs are known to cause several diseases of poultry. Certain isolates of species FAdV-C are the aetiological agent of inclusion body hepatitis/hydropericardium syndrome (IBH/HPS). In this study, we report the complete 45667 bp genome sequence of FAdV-4 of species FAdV-C. Assessment of the protein-coding potential of FAdV-4 was carried out with the Bio-Dictionary-based Gene Finder together with an evaluation of sequence conservation among species FAdV-A and FAdV-D. On this basis, 46 potentially protein-coding ORFs were identified. Of these, 33 and 13 ORFs were assigned high and low protein-coding potential, respectively. Homologues of the ancestral adenoviral genes were, with few exceptions, assigned high protein-coding potential. ORFs that were unique to the FAdVs were differentiated into high and low protein-coding potential groups. Notable putative genes with high protein-coding capacity included the previously unreported fiber 1, hypothetical 10.3K and hypothetical 10.5K genes. Transcript analysis revealed that several of the small ORFs less than 300 nt in length that were assigned low coding potential contributed to upstream ORFs (uORFs) in important mRNAs, including the ORF22 mRNA. Subsequent analysis of the previously reported transcripts of FAdV-1, FAdV-9, human adenovirus 2 and bovine adenovirus 3 identified widespread uORFs in AdV mRNAs that have the potential to act as important translational regulatory elements.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".