Inferring sequence regions under functional divergence in duplicate genes
Bibliographic record
Abstract
MOTIVATION: A number of statistical phylogenetic methods have been proposed to identify type-I functional divergence in duplicate genes by detecting heterogeneous substitution rates in phylogenetic trees. A common disadvantage of the existing methods is that autocorrelation of substitution rates along sequences is not modeled. This reduces the power of existing methods to identify regions under functional divergence. RESULTS: We design a phylogenetic hidden Markov model to identify protein regions relevant to type-I functional divergence. A C++ program, HMMDiverge, has been developed to estimate model parameters and to identify regions under type-I functional divergence. Simulations demonstrate that HMMDiverge can successfully identify protein regions under type-I functional divergence unless the discrepancy of substitution rates between subfamilies is very limited or the regions under functional divergence are very short. Applying HMMDiverge to G protein α subunits in animals, we identify a candidate region longer than 20 amino acids, which overlaps with the α-4 helix and the α4-β6 loop in the GTPase domain with divergent rates of substitutions. These sites are different from those reported by an existing program, DIVERGE2. Interestingly, previous biochemical studies suggest the α-4 helix and the α4-β6 loop are important to the specificity of the receptor-G protein interaction. Therefore, the candidate region reported by HMMDiverge highlights that the type-I functional divergence in G protein α subunits may be relevant to the change of receptor-G protein specificity after gene duplication. From these results, we conclude that HMMDiverge is a useful tool to identify regions under type-I functional divergence after gene duplication. AVAILABILITY: C++ source codes of HMMDiverge and simulation programs used in this study, as well as example datasets, are available at http://info.mcmaster.ca/yifei/software/HMMDiverge.html.
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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".