Inferring the Evolutionary History of Gene Clusters from Phylogenetic and Gene Order Data
Bibliographic record
Abstract
Gene duplication is frequent within gene clusters and plays a fundamental role in evolution by providing a source of new genetic material upon which natural selection can act. Although classical phylogenetic inference methods provide some insight into the evolutionary history of a gene cluster, they are not sufficient alone to differentiate single- from multiple gene duplication events and to answer other questions regarding the nature and size of evolutionary events. In this paper, we present an algorithm allowing to infer a set of optimal evolutionary histories for a gene cluster in a single species, according to a general cost model involving variable length duplications (in tandem or inverted), deletions, and inversions. We applied our algorithm to the human olfactory receptor and protocadherin gene clusters, showing that the duplication size distribution differs significantly between the two gene families. The algorithm is available through a web interface at http://www-lbit.iro.umontreal.ca/DILTAG/.
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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".