IslandViewer update: improved genomic island discovery and visualization
Bibliographic record
Abstract
IslandViewer (http://pathogenomics.sfu.ca/islandviewer) is a web-accessible application for the computational prediction and analysis of genomic islands (GIs) in bacterial and archaeal genomes. GIs are clusters of genes of probable horizontal origin and are of high interest because they disproportionately encode virulence factors and other adaptations of medical, environmental and industrial interest. Many computational tools exist for the prediction of GIs, but three of the most accurate methods are available in integrated form via IslandViewer: IslandPath-DIMOB, SIGI-HMM and IslandPick. IslandViewer GI predictions are precomputed for all complete microbial genomes from National Center for Biotechnology Information, with an option to upload other genomes and/or perform customized analyses using different settings. Here, we report recent changes to the IslandViewer framework that have vastly improved its efficiency in handling an increasing number of users, plus better facilitate custom genome analyses. Users may also now overlay additional annotations such as virulence factors, antibiotic resistance genes and pathogen-associated genes on top of current GI predictions. Comparisons of GIs between user-selected genomes are now facilitated through a highly requested side-by-side viewer. IslandViewer improvements aim to provide a more flexible interface, coupled with additional highly relevant annotation information, to aid analysis of GIs in diverse microbial species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".