Inferring Ancestral Gene Orders for a Family of Tandemly Arrayed Genes
Bibliographic record
Abstract
Tandemly arrayed genes (TAG) constitute a large fraction of most genomes and play important biological roles. They evolve through unequal recombination, which places duplicated genes next to the original ones (tandem duplications). Many algorithms have been proposed to infer a tandem duplication history for a TAG cluster. However, the presence of different transcriptional orientations in many clusters highlights the fact that processes such as inversions also contribute to their evolution. Moreover, existing algorithms are restricted to the study of TAGs evolution in a single species (only paralogous genes are considered). To circumvent these limitations, we consider an evolutionary model for TAGs involving duplication, gene loss, inversion, and speciation events. A general framework to infer ancestral gene orders that minimize the number of inversions in the whole evolutionary history is presented. At the methodological level, this paper integrates three approaches to genome evolution: the duplication tree reconstruction, the gene tree/species tree reconciliation theory, and the concept of inversion median used in order-based phylogeny reconstruction. An application on a cluster of olfactory receptor genes in four mammals is presented.
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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".