Enabling genomic island prediction and comparison in multiple genomes to investigate bacterial evolution and outbreaks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Outbreaks of virulent and/or drug-resistant bacteria have a significant impact on human health and major economic consequences. Genomic islands (GIs; defined as clusters of genes of probable horizontal origin) are of high interest because they disproportionately encode virulence factors, some antimicrobial-resistance (AMR) genes, and other adaptations of medical or environmental interest. While microbial genome sequencing has become rapid and inexpensive, current computational methods for GI analysis are not amenable for rapid, accurate, user-friendly and scalable comparative analysis of sets of related genomes. To help fill this gap, we have developed IslandCompare, an open-source computational pipeline for GI prediction and comparison across several to hundreds of bacterial genomes. A dynamic and interactive visualization strategy displays a bacterial core-genome phylogeny, with bacterial genomes linearly displayed at the phylogenetic tree leaves. Genomes are overlaid with GI predictions and AMR determinants from the Comprehensive Antibiotic Resistance Database (CARD), and regions of similarity between the genomes are also displayed. GI predictions are performed using Sigi-HMM and IslandPath-DIMOB, the two most precise GI prediction tools based on nucleotide composition biases, as well as a novel blast-based consistency step to improve cross-genome prediction consistency. GIs across genomes sharing sequence similarity are grouped into clusters, further aiding comparative analysis and visualization of acquisition and loss of mobile GIs in specific sub-clades. IslandCompare is an open-source software that is containerized for local use, plus available via a user-friendly, web-based interface to allow direct use by bioinformaticians, biologists and clinicians (at https://islandcompare.ca).
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.
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 | 0.001 |
| 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 it