Taxonomic colouring of phylogenetic trees of protein sequences
Bibliographic record
Abstract
BACKGROUND: Phylogenetic analyses of protein families are used to define the evolutionary relationships between homologous proteins. The interpretation of protein-sequence phylogenetic trees requires the examination of the taxonomic properties of the species associated to those sequences. However, there is no online tool to facilitate this interpretation, for example, by automatically attaching taxonomic information to the nodes of a tree, or by interactively colouring the branches of a tree according to any combination of taxonomic divisions. This is especially problematic if the tree contains on the order of hundreds of sequences, which, given the accelerated increase in the size of the protein sequence databases, is a situation that is becoming common. RESULTS: We have developed PhyloView, a web based tool for colouring phylogenetic trees upon arbitrary taxonomic properties of the species represented in a protein sequence phylogenetic tree. Provided that the tree contains SwissProt, SpTrembl, or GenBank protein identifiers, the tool retrieves the taxonomic information from the corresponding database. A colour picker displays a summary of the findings and allows the user to associate colours to the leaves of the tree according to any number of taxonomic partitions. Then, the colours are propagated to the branches of the tree. CONCLUSION: PhyloView can be used at http://www.ogic.ca/projects/phyloview/. A tutorial, the software with documentation, and GPL licensed source code, can be accessed at the same web address.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".