Identification of discrete classes of small nucleolar RNA featuring different ends and RNA binding protein dependency
Bibliographic record
Abstract
Small nucleolar RNAs (snoRNAs) are among the first discovered and most extensively studied group of small non-coding RNA. However, most studies focused on a small subset of snoRNAs that guide the modification of ribosomal RNA. In this study, we annotated the expression pattern of all box C/D snoRNAs in normal and cancer cell lines independent of their functions. The results indicate that C/D snoRNAs are expressed as two distinct forms differing in their ends with respect to boxes C and D and in their terminal stem length. Both forms are overexpressed in cancer cell lines but display a conserved end distribution. Surprisingly, the long forms are more dependent than the short forms on the expression of the core snoRNP protein NOP58, thought to be essential for C/D snoRNA production. In contrast, a subset of short forms are dependent on the splicing factor RBFOX2. Analysis of the potential secondary structure of both forms indicates that the k-turn motif required for binding of NOP58 is less stable in short forms which are thus less likely to mature into a canonical snoRNP. Taken together the data suggest that C/D snoRNAs are divided into at least two groups with distinct maturation and functional preferences.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".