Estimating the proportion of nonsense variants undergoing the newly described phenomenon of manufactured splice rescue
Bibliographic record
Abstract
Abstract A recent report described a nonsense variant simultaneously creating a donor splice site, resulting in a truncated but functional protein. To explore the generalizability of this unique mechanism, we analyzed > 115,000 nonsense variants from different databases using SpliceAI annotations. Between 0.61% (donor gain delta score > 0.8, for high precision) and 2.57% (> 0.2, for high sensitivity) of nonsense variants were predicted to create new donor splice sites at or upstream of the stop codon. These variants were less likely than all remaining nonsense variants in the same genes to be pathogenic in ClinVar (p < 0.001). Up to 1 in 40 nonsense variants may create new donor splice sites, potentially allowing for loss-of-function evasion through “manufactured splice rescue”. We urge caution when interpreting nonsense variants where manufactured splice rescue is a strong possibility and correlation with phenotype is challenging, as will often be the case with secondary findings and newborn genomic screening programs.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".