Prediction and verification of mouse tRNA gene families
Bibliographic record
Abstract
BACKGROUND: Transfer RNA (tRNA) gene predictions are complicated by challenges such as structural variation, limited sequence conservation and the presence of highly reiterated short interspersed sequences (SINEs) that originally derived from tRNA genes or tRNA-like transcription units. Annotation of "tRNA genes" in sequenced genomes generally have not been accompanied by experimental verification of the expression status of predicted sequences. RESULTS: To address this for mouse tRNA genes, we have employed two programs, tRNAScan-SE and ARAGORN, to predict the tRNA genes in the nuclear genome, resulting in diverse but overlapping predicted gene sets. From these, we removed known SINE repeats and sorted the genes into predicted families and single-copy genes. In particular, four families of intron-containing tRNA genes were predicted for the first time in mouse, with introns in positions and structures similar to the well characterized intron-containing tRNA genes in yeast. We verified the expression of the predicted tRNA genes by microarray analysis. We then confirmed the expression of appropriately sized RNA for the four intron-containing tRNA gene families, as well as the other 30 tRNA gene families creating an index of expression-verified mouse tRNAs. CONCLUSIONS: These confirmed tRNA genes represent all anticodons and all known mammalian tRNA structural groups, as well as a variety of predicted "rogue" tRNA genes within families with altered anticodon identities.
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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 | 0.000 |
| 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) | 0.002 | 0.001 |
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".