Detecting non-coding selective pressure in coding regions
Bibliographic record
Abstract
BACKGROUND: Comparative genomics approaches, where orthologous DNA regions are compared and inter-species conserved regions are identified, have proven extremely powerful for identifying non-coding regulatory regions located in intergenic or intronic regions. However, non-coding functional elements can also be located within coding region, as is common for exonic splicing enhancers, some transcription factor binding sites, and RNA secondary structure elements affecting mRNA stability, localization, or translation. Since these functional elements are located in regions that are themselves highly conserved because they are coding for a protein, they generally escaped detection by comparative genomics approaches. RESULTS: We introduce a comparative genomics approach for detecting non-coding functional elements located within coding regions. Codon evolution is modeled as a mixture of codon substitution models, where each component of the mixture describes the evolution of codons under a specific type of coding selective pressure. We show how to compute the posterior distribution of the entropy and parsimony scores under this null model of codon evolution. The method is applied to a set of growth hormone 1 orthologous mRNA sequences and a known exonic splicing elements is detected. The analysis of a set of CORTBP2 orthologous genes reveals a region of several hundred base pairs under strong non-coding selective pressure whose function remains unknown. CONCLUSION: Non-coding functional elements, in particular those involved in post-transcriptional regulation, are likely to be much more prevalent than is currently known. With the numerous genome sequencing projects underway, comparative genomics approaches like that proposed here are likely to become increasingly powerful at detecting such 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 | 0.001 | 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 | 0.000 | 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".