Evolution of conserved secondary structures and their function in transcriptional regulation networks
Bibliographic record
Abstract
BACKGROUND: Many conserved secondary structures have been identified within conserved elements in the human genome, but only a small fraction of them are known to be functional RNAs. The evolutionary variations of these conserved secondary structures in human populations and their biological functions have not been fully studied. RESULTS: We searched for polymorphisms within conserved secondary structures and identified a number of SNPs within these elements even though they are highly conserved among species. The density of SNPs in conserved secondary structures is about 65% of that of their flanking, non-conserved, sequences. Classification of sites as stems or as loops/bulges revealed that the density of SNPs in stems is about 62% of that found in loops/bulges. Analysis of derived allele frequency data indicates that sites in stems are under stronger evolutionary constraint than sites in loops/bulges. Intergenic conserved secondary structures tend to associate with transcription factor-encoding genes with genetic distance being the measure of regulator-gene associations. A substantial fraction of intergenic conserved secondary structures overlap characterized binding sites for multiple transcription factors. CONCLUSION: Strong purifying selection implies that secondary structures are probably important carriers of biological functions for conserved sequences. The overlap between intergenic conserved secondary structures and transcription factor binding sites further suggests that intergenic conserved secondary structures have essential roles in directing gene expression in transcriptional regulation networks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".