CisBP-RNA: a web resource for eukaryotic RNA-binding proteins and their motifs
Bibliographic record
Abstract
RNA-binding proteins (RBPs) are key mediators of post-transcriptional gene regulation, including splicing, transport, stability, and other facets of RNA metabolism. Many RBPs exert their function through sequence-specific protein-RNA interactions. RBP RNA-binding specificity models, or motifs, are thus essential for understanding post-transcriptional gene regulatory mechanisms. Here, we present CisBP-RNA (Catalog of inferred sequence Binding Preferences of RNA-binding proteins), a freely available web-based database that provides centralized access to eukaryotic RBP motif data. A key feature of CisBP-RNA is the availability of both experimentally determined motifs and motifs that are computationally predicted via our homology-based approaches. The current version of CisBP-RNA catalogs >148 000 sequence-specific RBPs across 690 eukaryotic species. Motifs are currently available for >34 000 of these RBPs. Motif data are available for download in a variety of formats for downstream computational analyses. In addition, CisBP-RNA provides user-friendly web-hosted tools to scan for predicted RBP binding sites, predicts motifs for a protein of interest, and compares a motif to motifs contained in the database. The CisBP-RNA database can be accessed through www.cisbp.org/rna/.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 0.078 |
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".