The emerging landscape of small nucleolar <scp>RNAs</scp> in cell biology
Bibliographic record
Abstract
Small nucleolar RNAs (snoRNAs) are a large class of small noncoding RNAs present in all eukaryotes sequenced thus far. As a family, they have been well characterized as playing a central role in ribosome biogenesis, guiding either the sequence-specific chemical modification of pre-rRNA (ribosomal RNA) or its processing. However, in higher eukaryotes, numerous orphan snoRNAs were described over a decade ago, with no known target or ascribed function, suggesting the possibility of alternative cellular functionality. In recent years, thanks in great part to advances in sequencing methodologies, we have seen many examples of the diversity that exists in the snoRNA family on multiple levels. In this review, we discuss the identification of novel snoRNA members, of unexpected binding partners, as well as the clarification and extension of the snoRNA target space and the characterization of diverse new noncanonical functions, painting a new and extended picture of the snoRNA landscape. Under the deluge of novel features and functions that have recently come to light, snoRNAs emerge as a central, dynamic, and highly versatile group of small regulatory RNAs.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".