PlasmidFlow: A Web-based Platform for Interactive Visualization of Plasmid-Driven Traits
Bibliographic record
Abstract
Abstract Plasmids are central mediators of horizontal gene transfer (HGT), facilitating the spread of antimicrobial resistance, virulence determinants, and adaptive functions across microbial communities. Despite the availability of extensive plasmid sequencing data, visualization of plasmid-mediated traits remains hindered by reliance on static figures or coding-intensive pipelines, limiting accessibility for microbiologists and epidemiologists. We present PlasmidFlow ( https://plasmidflow.metabopotential.site ), an interactive, web-based platform for the exploration of plasmid–host–trait associations across ecological and clinical contexts. The system integrates structured data parsing, relational storage, and modular visualization to support real-time exploration without requiring programming expertise. Users can upload plasmid–host–trait datasets, apply environment-or trait-specific filters, and generate publication-ready Sankey diagrams, plasmid-sharing network graphs, and binary trait heatmaps with full customization of visual properties. High-resolution export in SVG, PDF, and PNG formats, along with reproducible session storage, ensures compatibility with both research and publication workflows. By combining interactivity, scalability, and reproducibility, PlasmidFlow addresses a critical gap in microbial genomics, providing a user-friendly and extensible resource for investigating plasmid-driven traits in the context of antimicrobial resistance surveillance, microbial ecology, and evolutionary biology.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.011 |
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".