Nucleotide diversity based on phaseolin and iron reductase genes in common bean accessions of different geographical origins
Bibliographic record
Abstract
Discriminating genotypes within plant collections is imperative, and DNA sequence approaches for detecting single nucleotide polymorphisms (SNPs) have proved essential in any modern analysis of germplasm. By sequencing the α-Phs and PvFRO1 genes that, respectively, encode phaseolin and an iron reductase, we prospected for SNPs in exonic and intronic regions of both genes in a sample of 31 accessions of Phaseolus vulgaris from Mesoamerican and Andean gene pools, and one accession of Phaseolus lunatus, chosen as an outgroup. Sequence alignment showed 95 SNPs in α-Phs and 83 in PvFRO1, but diversity along the nucleotide sequences was not evenly distributed in both genes. Accessions from the same gene pool showed greater similarity than those from different gene pools, and the cluster patterns obtained in this study were consistent with the hierarchical organization into two P. vulgaris gene pools. The polymorphisms detected in the α-Phs gene allowed better discrimination among the accessions within each cluster than the PvFRO1 polymorphisms. Furthermore, some variations within exons changes amino acids in both predicted protein sequences. In an unprecedented result, the phaseolin-predicted amino acid variation allowed most of the accessions to be typified.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".