snoDB: an interactive database of human snoRNA sequences, abundance and interactions
Bibliographic record
Abstract
Small nucleolar RNAs (snoRNAs) are an abundant type of non-coding RNA with conserved functions in all known eukaryotes. Classified into two main families, the box C/D and H/ACA snoRNAs, they enact their most well characterized role of guiding site specific modifications in ribosomal RNA, through the formation of specific ribonucleoprotein complexes, with fundamental implications in ribosome biogenesis. However, it is becoming increasingly clear that the landscape of snoRNA cellular functionality is much broader than it once seemed with novel members, non-uniform expression patterns, new and diverse targets as well as several emerging non-canonical functions ranging from the modulation of alternative splicing to the regulation of chromatin architecture. In order to facilitate the further characterization of human snoRNAs in a holistic manner, we introduce an online interactive database tool: snoDB. Its purpose is to consolidate information on human snoRNAs from different sources such as sequence databases, target information, both canonical and non-canonical from the literature and from high-throughput RNA-RNA interaction datasets, as well as high-throughput sequencing data that can be visualized interactively.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.018 |
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".