DomainViz: intuitive visualization of consensus domain distributions across groups of proteins
Bibliographic record
Abstract
The prediction of functional domains is typically among the first steps towards understanding the function of new proteins and protein families. There are numerous databases of annotated protein domains that permit researchers to identify domains on individual proteins of interest. However, it is necessary to perform high-throughput domain searches to gain evolutionary insight into the functions of proteins and protein families. Unfortunately, at present, it is difficult to search for, and visualize domain conservation across multiple proteins and/or multiple groups of proteins in an intuitive manner. Here we present DomainViz, a new web-server that streamlines the identification and visualization of domains across multiple protein sequences. Currently, DomainViz uses the well-established PFAM and Prosite databases for domain searching and assembles intuitive, publication-ready 'monument valley' plots (mv-plots) that display the extent of domain conservation along two dimensions: positionality and frequency of occurrence in the input protein sequences. In addition, DomainViz produces a conventional domain-ordering figure. DomainViz can be used to explore the conservation of domains within a single protein family, across multiple families, and across families from different species to support studies into protein function and evolution. The web-server is publicly available at: https://uhrigprotools.biology.ualberta.ca/domainviz.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.036 | 0.005 |
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".