Reconstructing protein and gene phylogenies using reconciliation and soft-clustering
Bibliographic record
Abstract
The architecture of eukaryotic coding genes allows the production of several different protein isoforms by genes. Current gene phylogeny reconstruction methods make use of a single protein product per gene, ignoring information on alternative protein isoforms. These methods often lead to inaccurate gene tree reconstructions that require to be corrected before phylogenetic analyses. Here, we propose a new approach for the reconstruction of gene trees and protein trees accounting for alternative protein isoforms. We extend the concept of reconciliation to protein trees, and we define a new reconciliation problem called MinDRGT that consists in finding a gene tree that minimizes a double reconciliation cost with a given protein tree and a given species tree. We define a second problem called MinDRPGT that consists in finding a protein supertree and a gene tree minimizing a double reconciliation cost, given a species tree and a set of protein subtrees. We propose a shift from the traditional view of protein ortholog groups as hard-clusters to soft-clusters and we study the MinDRPGT problem under this assumption. We provide algorithmic exact and heuristic solutions for versions of the problems, and we present the results of applications on protein and gene trees from the Ensembl database. The implementations of the methods are available at https://github.com/UdeS-CoBIUS/Protein2GeneTree and https://github.com/UdeS-CoBIUS/SuperProteinTree .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".