A miRNA-tRNA mix-up: tRNA origin of proposed miRNA.
Bibliographic record
Abstract
The rapid release of new data from DNA genome sequencing projects has led to a variety of misannotations in public databases. Our results suggest that next generation sequencing approaches are particularly prone to such misannotations. Two related miRNA candidates did recently enter the miRBase database, miR-1274b and miR-1274a, but they share identical 18-nucleotide stretches with tRNA (Lys3) and tRNA (Lys5) , respectively. The possibility that the small RNA fragments that led to the description of these two miRNAs originated from the two tRNAs was examined. The ratio of the miR-1274b:miR-1274a fragments does closely resemble the known tRNA lys3:lys5 ratio in the cell. Furthermore, the proposed miRNA hairpins have a very low prediction score and the proposed miRNA genes are in fact endogenous retroviral elements. We searched for other miRNA-mimics in the human genome and found more examples of tRNA-miRNA mimicry. We propose that the corresponding miRNAs should be validated in more detail, as the small RNA fragments that led to their description are likely derived from tRNA processing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".