oRNAment: a database of putative RNA binding protein target sites in the transcriptomes of model species
Bibliographic record
Abstract
Protein-RNA interactions are essential for controlling most aspects of RNA metabolism, including synthesis, processing, trafficking, stability and degradation. In vitro selection methods, such as RNAcompete and RNA Bind-n-Seq, have defined the consensus target motifs of hundreds of RNA-binding proteins (RBPs). However, readily available information about the distribution features of these motifs across full transcriptomes was hitherto lacking. Here, we introduce oRNAment (o RNA motifs enrichment in transcriptomes), a database that catalogues the putative motif instances of 223 RBPs, encompassing 453 motifs, in a transcriptome-wide fashion. The database covers 525 718 complete coding and non-coding RNA species across the transcriptomes of human and four prominent model organisms: Caenorhabditis elegans, Danio rerio, Drosophila melanogaster and Mus musculus. The unique features of oRNAment include: (i) hosting of the most comprehensive mapping of RBP motif instances to date, with 421 133 612 putative binding sites described across five species; (ii) options for the user to filter the data according to a specific threshold; (iii) a user-friendly interface and efficient back-end allowing the rapid querying of the data through multiple angles (i.e. transcript, RBP, or sequence attributes) and (iv) generation of several interactive data visualization charts describing the results of user queries. oRNAment is freely available at http://rnabiology.ircm.qc.ca/oRNAment/.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".