Whole-genome-based taxonomy as the most accurate approach to identify <i>Flavobacterium</i> species
Bibliographic record
Abstract
The genus Flavobacterium comprises a diversity of species, including fish pathogens. Multiple techniques have been used to identify isolates of this genus, such as phenotyping, polymerase chain reaction genotyping, and in silico whole-genome taxonomy. In this study, we demonstrate that whole-genome-based taxonomy, using average nucleotide identity and molecular phylogeny, is the most accurate approach for Flavobacterium species. We obtained various isolated strains from official collections; these strains had been previously characterized by a third party using various identification methodologies. We analyzed isolates by PCR genotyping using previously published primers targeting gyrB and gyrA genes, which are supposedly specific to the genus Flavobacterium and Flavobacterium psychrophilum, respectively. After genomic analysis, nearly half of the isolates had their identities re-evaluated: around a quarter of them were re-assigned to other genera and two isolates are new species of flavobacteria. In retrospect, the phenotyping method was the least accurate. While gyrB genotyping was accurate with the isolates included in this study, bioinformatics analysis suggests that only 70% of the Flavobacterium species could be appropriately identified using this approach. We propose that whole-genome taxonomy should be used for accurate Flavobacterium identification, and we encourage bacterial collections to review the identification of isolates identified by phenotyping.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".