ecceTERA: comprehensive gene tree-species tree reconciliation using parsimony
Bibliographic record
Abstract
UNLABELLED: : A gene tree-species tree reconciliation explains the evolution of a gene tree within the species tree given a model of gene-family evolution. We describe ecceTERA, a program that implements a generic parsimony reconciliation algorithm, which accounts for gene duplication, loss and transfer (DTL) as well as speciation, involving sampled and unsampled lineages, within undated, fully dated or partially dated species trees. The ecceTERA reconciliation model and algorithm generalize or improve upon most published DTL parsimony algorithms for binary species trees and binary gene trees. Moreover, ecceTERA can estimate accurate species-tree aware gene trees using amalgamation. AVAILABILITY AND IMPLEMENTATION: ecceTERA is freely available under http://mbb.univ-montp2.fr/MBB/download_sources/16__ecceTERA and can be run online at http://mbb.univ-montp2.fr/MBB/subsection/softExec.php?soft=eccetera CONTACT: celine.scornavacca@umontpellier.fr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.013 |
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".