An Integrated Database for Exploring Alternative Promoters in Animals
Bibliographic record
Abstract
Alternative promoter (AP) events, as a major pre-transcriptional mechanism, can initiate different transcription start sites to generate distinct mRNA isoforms and regulate their expression. At present, hundreds of thousands of APs have been identified across human tissues, and a considerable number of APs have been demonstrated to be associated with complex traits and diseases. Recent researches have also proven important effects of APs on animals. However, the landscape of APs in animals has not been fully recognized. In this study, 102,349 AP profiles from 23,077 samples across 12 species were systematically characterized. We further identified tissue-specific APs and investigated trait-related promoters among various species. In addition, we analyzed the associations between APs and enhancer RNAs (eRNA)/transcription factors (TF) as a means of identifying potential regulatory factors. Integrating these findings, we finally developed Animal-APdb, a database for the searching, browsing, and downloading of information related to Animal APs. Animal-APdb is expected to serve as a valuable resource for exploring the functions and mechanisms of APs in animals.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".