DoubleRecViz: a web-based tool for visualizing transcript–gene–species tree reconciliation
Bibliographic record
Abstract
MOTIVATION: A phylogenetic tree reconciliation is a mapping of one phylogenetic tree onto another which represents the co-evolution of two sets of taxa (e.g. parasite-host co-evolution, gene-species co-evolution). The reconciliation framework was extended to allow modeling the co-evolution of three sets of taxa such as transcript-gene-species co-evolutions. Several web-based tools have been developed for the display and manipulation of phylogenetic trees and co-phylogenetic trees involving two trees, but there currently exists no tool for visualizing the joint reconciliation between three phylogenetic trees. RESULTS: Here, we present DoubleRecViz, a web-based tool for visualizing double reconciliations between phylogenetic trees at three levels: transcript, gene and species. DoubleRecViz extends the RecPhyloXML model-developed for gene-species tree reconciliation-to represent joint transcript-gene and gene-species tree reconciliations. It is implemented using the Dash library, which is a toolbox that provides dynamic visualization functionalities for web data visualization in Python. AVAILABILITY AND IMPLEMENTATION: DoubleRecViz is available through a web server at https://doublerecviz.cobius.usherbrooke.ca. The source code and information about installation procedures are also available at https://github.com/UdeS-CoBIUS/DoubleRecViz. 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 0.016 |
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".