Phytoplasma classification and phylogeny based on in silico and in vitro RFLP analysis of cpn60 universal target sequences
Bibliographic record
Abstract
Phytoplasmas are unculturable, phytopathogenic bacteria that cause economic losses worldwide. As unculturable micro-organisms, phytoplasma taxonomy has been based on the use of the 16S rRNA-encoding gene to establish 16Sr groups and subgroups based on the restriction fragment length polymorphism (RFLP) pattern resulting from the digestion of amplicon (in vitro) or sequence (in silico) with seventeen restriction enzymes. Problems such as heterogeneity of the ribosomal operon and the inability to differentiate closely related phytoplasma strains has motivated the search for additional markers capable of providing finer differentiation of phytoplasma strains. In this study we developed and validated a scheme to classify phytoplasmas based on the use of cpn60 universal target (cpn60 UT) sequences. Ninety-six cpn60 UT sequences from strains belonging to 19 16Sr subgroups were subjected to in silico RFLP using pDRAW32 software, resulting in 25 distinctive RFLP profiles. Based on these results we delineated cpn60 UT groups and subgroups, and established a threshold similarity coefficient for groups and subgroups classifying all the strains analysed in this study. The nucleotide identity among the reference strains, the correspondence between in vitro and in silico RFLP, and the phylogenetic relationships of phytoplasma strains based on cpn60 UT sequences are also discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".