A systematic search for new mammalian noncoding RNAs indicates little conserved intergenic transcription
Bibliographic record
Abstract
BACKGROUND: Systematic identification and functional characterization of novel types of noncoding (nc)RNA in genomes is more difficult than it is for protein coding mRNAs, since ncRNAs typically do not possess sequence features such as splicing or translation signals, or long open reading frames. Recent "tiling" microarray studies have reported that a surprisingly larger proportion of mammalian genomes is transcribed than was previously anticipated. However, these non-genic transcripts often appear to be low in abundance, and their functional significance is not known. RESULTS: To systematically search for functional ncRNAs, we designed microarrays to detect 3,478 intergenic and intronic sequences that are conserved between the human, mouse, and rat genomes, and that score highly by other criteria that characterize ncRNAs. We probed these arrays with total RNA isolated from 16 wild-type mouse tissues. Among 55 candidates for highly-expressed novel ncRNAs tested by northern blotting, eight were confirmed as small, highly-and ubiquitously-expressed RNAs in mouse. Of the eight, five were also detected in rat tissues, but none were detected at appreciable levels in human tissues or cultured cells. CONCLUSION: Since the sequence and expression of most known coding transcripts and functional ncRNAs is conserved between human and mouse, the lack of northern-detectable expression in human cells and tissues of the novel mouse and rat ncRNAs that we identified suggests that they are not functional or possibly have rodent-specific functions. Our results confirm that relatively little of the intergenic sequence conserved between human, mouse and rat is transcribed at high levels in mammalian tissues, possibly suggesting a limited role for transcribed intergenic and intronic sequences as independent functional elements.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".