Development of a comparative genomic fingerprinting assay for rapid and high resolution genotyping of Arcobacter butzleri
Bibliographic record
Abstract
BACKGROUND: Molecular typing methods are critical for epidemiological investigations, facilitating disease outbreak detection and source identification. Study of the epidemiology of the emerging human pathogen Arcobacter butzleri is currently hampered by the lack of a subtyping method that is easily deployable in the context of routine epidemiological surveillance. In this study we describe a comparative genomic fingerprinting (CGF) method for high-resolution and high-throughput subtyping of A. butzleri. Comparative analysis of the genome sequences of eleven A. butzleri strains, including eight strains newly sequenced as part of this project, was employed to identify accessory genes suitable for generating unique genetic fingerprints for high-resolution subtyping based on gene presence or absence within a strain. RESULTS: A set of eighty-three accessory genes was used to examine the population structure of a dataset comprised of isolates from various sources, including human and non-human animals, sewage, and river water (n=156). A streamlined assay (CGF40) based on a subset of 40 genes was subsequently developed through marker optimization. High levels of profile diversity (121 distinct profiles) were observed among the 156 isolates in the dataset, and a high Simpson's Index of Diversity (ID) observed (ID > 0.969) indicate that the CGF40 assay possesses high discriminatory power. At the same time, our observation that 115 isolates in this dataset could be assigned to 29 clades with a profile similarity of 90% or greater indicates that the method can be used to identify clades comprised of genetically similar isolates. CONCLUSIONS: The CGF40 assay described herein combines high resolution and repeatability with high throughput for the rapid characterization of A. butzleri strains. This assay will facilitate the study of the population structure and epidemiology of A. butzleri.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".