The cellular landscape of mid‐size noncoding RNA
Bibliographic record
Abstract
Noncoding RNA plays an important role in all aspects of the cellular life cycle, from the very basic process of protein synthesis to specialized roles in cell development and differentiation. However, many noncoding RNAs remain uncharacterized and the function of most of them remains unknown. Mid-size noncoding RNAs (mncRNAs), which range in length from 50 to 400 nucleotides, have diverse regulatory functions but share many fundamental characteristics. Most mncRNAs are produced from independent promoters although others are produced from the introns of other genes. Many are found in multiple copies in genomes. mncRNAs are highly structured and carry many posttranscriptional modifications. Both of these facets dictate their RNA-binding protein partners and ultimately their function. mncRNAs have already been implicated in translation, catalysis, as guides for RNA modification, as spliceosome components and regulatory RNA. However, recent studies are adding new mncRNA functions including regulation of gene expression and alternative splicing. In this review, we describe the different classes, characteristics and emerging functions of mncRNAs and their relative expression patterns. Finally, we provide a portrait of the challenges facing their detection and annotation in databases. This article is categorized under: Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs RNA Structure and Dynamics > RNA Structure, Dynamics, and Chemistry RNA Structure and Dynamics > Influence of RNA Structure in Biological Systems RNA Evolution and Genomics > RNA and Ribonucleoprotein Evolution.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".