Gnom<sup>Cmp</sup>: a quantitative approach for comparative analysis of closely related genomes of bacterial pathogens
Bibliographic record
Abstract
Comparative genome analysis is a powerful approach to understanding the biology of infectious bacterial pathogens. In this study, a quantitative approach, referred to as Gnom(Cmp), was developed to study the microevolution of bacterial pathogens. Although much more time-consuming than existing tools, this procedure provides a much higher resolution. Gnom(Cmp) accomplishes this by establishing genome-wide heterogeneity genotypes, which are then quantified and comparatively analyzed. The heterogeneity genotypes are defined as chromosomal base positions that have multiple variants within particular genomes, resulted from DNA duplications and subsequent mutations. To prove the concept, the procedure was applied on the genomes of 15 Staphylococcus aureus strains, focusing extensively on two pairs of hVISA/VISA strains. hVISA refers to heteroresistant vancomycin-intermediate S. aureus strains and VISA is their VISA mutants. hVISA/VISA displays some remarkable properties. hVISA is susceptible to vancomycin, but VISA mutants emerge soon after a short period of vancomycin therapy, therefore making the pathogen a great model organism for fast-evolving bacterial pathogens. The analysis indicated that Gnom(Cmp) could reveal variants within the genomes, which can be analyzed within the global genome context. Gnom(Cmp) discovered evolutionary hotspots and their dynamics among many closely related, even isogenic genomes. The analysis thus allows the exploration of the molecular mechanisms behind hVISA/VISA evolution, providing a working hypotheses for experimental testing and validation.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".