An Improved Likelihood Ratio Test for Detecting Site-Specific Functional Divergence among Clades of Protein-Coding Genes
Bibliographic record
Abstract
Maximum likelihood codon substitution models have proven useful for studying when and how protein function evolves, but they have recently been criticized on a number of fronts. The strengths and weaknesses of such methods must therefore be identified and improved upon. Here, using simulations, we show that the Clade model C versus M1a test for functional divergence among clades is prone to false positives under simple evolutionary conditions. We then propose a new null model (M2a_rel) that better accounts for among-site variation in selective constraint. We show that the revised test has an improved false-positive rate and good power. Applying this test to previously analyzed data sets of primate ribonucleases and mammalian rhodopsins reveals that some conclusions may have been misled by the original method. The improved test should prove useful for identifying patterns of divergence in selective constraint among paralogous gene families and among orthologs from ecologically divergent species.
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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 | 0.001 | 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".