A systematic approach to classify and characterize genomic islands driven by conjugative mobility using protein signatures
Bibliographic record
Abstract
Genomic islands (GIs) play a crucial role in the spread of antibiotic resistance, virulence factors and antiviral defense systems in a broad range of bacterial species. However, the characterization and classification of GIs are challenging due to their relatively small size and considerable genetic diversity. Predicting their intercellular mobility is of utmost importance in the context of the emerging crisis of multidrug resistance. Here, we propose a large-scale classification method to categorize GIs according to their mobility profile and, subsequently, analyze their gene cargo. We based our classification decision scheme on a collection of mobility protein motif definitions available in publicly accessible databases. Our results show that the size distribution of GI classes correlates with their respective structure and complexity. Self-transmissible GIs are usually the largest, except in Bacillota and Actinomycetota, accumulate antibiotic and phage resistance genes, and favour the use of a tyrosine recombinase to insert into a host's replicon. Non-mobilizable GIs tend to use a DDE transposase instead. Finally, although tRNA genes are more frequently targeted as insertion sites by GIs encoding a tyrosine recombinase, most GIs insert in a protein-encoding gene. This study is a stepping stone toward a better characterization of mobile GIs in bacterial genomes and their mechanism of mobility.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".