Circbank: a comprehensive database for circRNA with standard nomenclature
Bibliographic record
Abstract
Circular RNAs (circRNAs) represent a new type of regulatory RNA which forms a covalently closed continuous loop from back-splicing events, a process in which the downstream 5′ splice site and the 3′ splice site are covalently linked. Emerging evidence indicates that circRNAs exert a new layer of transcriptional and post-transcriptional regulation of gene expression. However, there is no standard nomenclature of circRNA, although the study of circRNAs has exploded in the past few years. Here we present circbank (www.circbank.cn), a comprehensive database for human circRNAs, where a novel naming system of circRNAs based on the host genes of circRNAs was implemented. In addition to the new naming system, circbank collected other five features of circRNAs including the miRNA binding site, conservation of circRNAs, m6A modification of circRNAs, mutation of circRNAs and protein-coding potential of circRNAs. Circbank is publicly available and allows users to query, browse and download circRNAs with all six features we provided, based on different search criteria. The database may serve as a resource to facilitate the research of function and regulation of circRNAs.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.055 |
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".