An Improved Model for Circular RNA Overexpression: Using the Actin Intron Reveals High Circularization Efficiency
Bibliographic record
Abstract
gene is used to generate a foreign circular sequence. However, the T4 system has been shown to be fairly inefficient in expressing circular RNA (circRNA). Here, a new method is developed to express circular sequences with high circularization efficiency to strengthen the confidence for future circRNA functional studies. CircRNA expression plasmids, constructed with different lengths derived from the actin intron (15-nt, 30-nt, 60-nt, 100-nt, 180-nt) and T4 intron, are introduced into human and mouse cell lines 293T and B16. Junction detection and sequencing are used to determine successful circularization of introns and their expression efficiencies. An actin intron with a medium length (60-nt-100-nt) shows significantly increased efficiency of circularization, whereas intron-100-nt shows the best efficiency in most conditions. RNA pull-down assays are designed to precipitate the splicing factors that are bound to the introns and intron/exon junction. The precipitated proteins are analyzed by mass spectrometry (MS), aiming to identify the possible underlying mechanism behind the high circularization efficiency. This expression system has been validated using different circRNAs, and such method shows potential in contributing to the expanding field of circRNA studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".