<i>cpn</i> 60 barcode sequences accurately identify newly defined genera within the <i>Lactobacillaceae</i>
Bibliographic record
Abstract
The cpn60 barcode sequence has been established as an informative target for microbial species identification. Applications of cpn60 barcode sequencing are supported by the availability of “universal” PCR primers for amplification and a curated reference database of cpn60 sequences, cpnDB. A recent reclassification of lactobacilli involving the definition of 23 new genera provided an opportunity to update cpnDB and to determine if the cpn60 barcode could be used for accurate identification of species consistent with the new framework. Analysis of 275 cpn60 sequences representing 258/269 of the validly named species in Lactobacillus, Paralactobacillus, and the 23 newer genera showed that cpn60-based sequence relationships were generally consistent with whole-genome-based phylogeny. Aligning or mapping full-length barcode sequences or a 150 bp subsequence resulted in accurate and unambiguous species identification in almost all cases. Taken together, our results show that the combination of available reference sequence data, “universal” barcode amplification primers, and the inherent sequence diversity within the cpn60 barcode makes it a useful target for the detection and identification of lactobacilli, as defined by the latest taxonomic framework.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".