CTAG-Containing Cleavage Site Profiling to Delineate Salmonella into Natural Clusters
Bibliographic record
Abstract
BACKGROUND: The bacterial genus Salmonella contains thousands of serotypes that infect humans or other hosts, causing mild gastroenteritis to potentially fatal systemic infections in humans. Pathogenically distinct Salmonella serotypes have been classified as individual species or as serological variants of merely one or two species, causing considerable confusion in both research and clinical settings. This situation reflects a long unanswered question regarding whether the Salmonella serotypes exist as discrete genetic clusters (natural species) of organisms or as phenotypic (e.g. pathogenic) variants of a single (or two) natural species with a continuous spectrum of genetic divergence among them. Our recent work, based on genomic sequence divergence analysis, has demonstrated that genetic boundaries exist among Salmonella serotypes, circumscribing them into clear-cut genetic clusters of bacteria. METHODOLOGIES/PRINCIPAL FINDINGS: To further test the genetic boundary concept for delineating Salmonella into clearly defined natural lineages (e.g., species), we sampled a small subset of conserved genomic DNA sequences, i.e., the endonuclease cleavage sites that contain the highly conserved CTAG sequence such as TCTAGA for XbaI. We found that the CTAG-containing cleavage sequence profiles could be used to resolve the genetic boundaries as reliably and efficiently as whole genome sequence comparisons but with enormously reduced requirements for time and resources. CONCLUSIONS: Profiling of CTAG sequence subsets reflects genetic boundaries among Salmonella lineages and can delineate these bacteria into discrete natural clusters.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".