Cataloging the potential functional diversity of Cacna1e splice variants using long-read sequencing
Bibliographic record
Abstract
Abstract Background The degree to which alternative RNA splicing influences the function and structure of voltage gated calcium channel (VGCC) splice variants is poorly understood. Here we used long-read RNA-sequencing to catalog rat Cacna1e (Cav2.3) splice variants, and computationally prioritize which are likely to impact channel function. Result We sequenced Cacna1e transcripts from rat thalamus using Oxford Nanopore sequencing yielding the structure of 2,110 Cacna1e splice variants. Of these, up to 154 had the potential encode for a functional channel based on predicted amino acid sequences. Our analysis revealed a total of 31 cassette splicing events (in various combinations) potentially affecting channel function, with three cassette exons appreciably expressed and conserved. Conclusion Our work both provides the first long-read sequencing of Cacna1e and the first computational evaluation of Cacna1e splice variants for future follow-up. This overall strategy to provide the field with prioritized transcripts will improve our understanding of Cacna1e function, its role in disease pathophysiology, and serve as a general approach to evaluate splice variant function across multiple ion channel types.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".