Novel splicing and open reading frames revealed by long-read direct RNA sequencing of adenovirus transcripts
Bibliographic record
Abstract
Abstract Adenovirus is a common human pathogen that relies on host cell processes for production and processing of viral RNA. Although adenoviral promoters, splice junctions, and cleavage and polyadenylation sites have been characterized using low-throughput biochemical techniques or short read cDNA-based sequencing, these technologies do not fully capture the complexity of the adenoviral transcriptome. By combining Illumina short-read and nanopore long-read direct RNA sequencing approaches, we mapped transcription start sites and cleavage and polyadenylation sites across the adenovirus genome. The canonical viral early and late RNA cassettes were confirmed, but analysis of splice junctions within long RNA reads revealed an additional 20 novel viral transcripts. These RNAs include seven new splice junctions which lead to expression of canonical open reading frames (ORF), as well as 13 transcripts encoding for messages that potentially alter protein functions through truncations or the fusion of canonical ORFs. In addition, we also detect RNAs that bypass canonical cleavage sites and generate potential chimeric proteins by linking separate gene transcription units. Our work highlights how long-read sequencing technologies can reveal further complexity within viral transcriptomes.
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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 | 0.000 | 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".