Distinct regulatory functions and biological roles of lncRNA splice variants
Bibliographic record
Abstract
Alternative splicing (AS) of RNA molecules is a key contributor to transcriptome diversity. In humans, 90%-95% of multi-exon genes produce alternatively spliced RNA transcripts. Therefore, every single gene has the opportunity of producing multiple splice variants, including long non-coding RNA (lncRNA) genes that undergo RNA maturation steps such as conventional and alternative splicing. Emerging evidence suggests significant roles for these lncRNA splice variants in many aspects of cell biology. Differential changes in expression of specific lncRNA splice variants have also been associated with many diseases including cancer. This review covers the current knowledge on this emerging topic of investigation. We provide exclusive insights on the AS landscape of lncRNAs and also describe at the molecular level the functional relevance of lncRNA splice variants, i.e., RNA-based differential functions, production of micropeptides, and generation of circular RNAs. Finally, we discuss exciting perspectives for this emerging field and outline the work required to further develop research endeavors in this field.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".