Cataloging the potential functional diversity of Cacna1e splice variants using long-read sequencing
Bibliographic record
Abstract
ABSTRACT Voltage gated calcium channels (VGCCs) regulate the influx of calcium ions in many cell types, but our lack of knowledge about the plethora of VGCC splice variants remains a gap in our understanding of calcium channel function. A recent advance in profiling gene splice variation is to use long-read RNA-sequencing technology. We sequenced Cacna1e transcripts from the rat thalamus using Oxford Nanopore sequencing, yielding the full structure of 2,110 Cacna1e splice variants. However, we observed that only 154 Cacna1e splice variants were likely to encode for a functional VGCC based on predicted amino acid sequences. We then computationally prioritized these 154 splice variants using expression and evolutionary conservation and found that four splice variants are candidate functionally distinct splice isoforms. Our work not only provides long-read sequencing of Cacna1e for the first time, but also the first computational evaluation of which Cacna1e splice variants are the best candidates for future follow-up. SIGNIFICANCE STATEMENT Voltage gated calcium channels (Cacna1x genes) are implicated in many neurological disorders and their encoding genes are predicted to have complex patterns of alternative splicing. Previous approaches relied on short-read RNA-seq to characterize calcium channel splice variants. Here, we use long-read nanopore sequencing to establish a set of Cacna1e transcripts in the rat thalamus and use computational methods to prioritize four transcripts as functionally distinct splice isoforms. Our work to provide the field with prioritized transcripts will not only improve our understanding of Cacna1e function but its role in disease as well.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".