Evaluation of a new fluorimetric DNA–DNA hybridization method
Bibliographic record
Abstract
Standardized procedures must be followed when characterizing, officially describing, and validly naming novel bacteria. For species descriptions, DNA-DNA hybridization still is needed for whole-genome comparisons between close relatives, but many established hybridization methods have drawbacks, such as requiring labeled or large amounts of DNA. We evaluated a new technique based on the spectrophotometric method in which renaturation rates are used for calculating the degree of binding, which estimates relatedness. In this new approach, DNA is denatured and reassociated in a real-time PCR thermal cycler and the process monitored fluorimetrically using SYBR Green I dye that selectively binds to double-stranded DNA. We investigated the effects of different parameters on the renaturation rates, such as the quantities of DNA and SYBR Green I used. Then using this technique, we calculated the percent binding for pairs of selected bacterial species representing different taxonomic groups and compared our results with published values. We demonstrated that the SYBR Green I method is useful for describing new species and as a screening tool to quickly identify the relatedness of uncharacterized isolates with similar 16S rRNA gene sequences.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".