Redirecting splicing with bifunctional oligonucleotides
Bibliographic record
Abstract
Ectopic modulators of alternative splicing are important tools to study the function of splice variants and for correcting mis-splicing events that cause human diseases. Such modulators can be bifunctional oligonucleotides made of an antisense portion that determines target specificity, and a non-hybridizing tail that recruits proteins or RNA/protein complexes that affect splice site selection (TOSS and TOES, respectively, for targeted oligonucleotide silencer of splicing and targeted oligonucleotide enhancer of splicing). The use of TOSS and TOES has been restricted to a handful of targets. To generalize the applicability and demonstrate the robustness of TOSS, we have tested this approach on more than 50 alternative splicing events. Moreover, we have developed an algorithm that can design active TOSS with a success rate of 80%. To produce bifunctional oligonucleotides capable of stimulating splicing, we built on the observation that binding sites for TDP-43 can stimulate splicing and improve U1 snRNP binding when inserted downstream from 5' splice sites. A TOES designed to recruit TDP-43 improved exon 7 inclusion in SMN2. Overall, our study shows that bifunctional oligonucleotides can redirect splicing on a variety of genes, justifying their inclusion in the molecular arsenal that aims to alter the production of splice variants.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".