Development and characterization of microsatellite markers from tropical forage <i>Stylosanthes</i> species and analysis of genetic variability and cross-species transferability
Bibliographic record
Abstract
A limited number of functional molecular markers has slowed the desired genetic improvement of Stylosanthes species. Hence, in an attempt to develop simple sequence repeat (SSR) markers, genomic libraries from Stylosanthes seabrana B.L. Maass & 't Mannetje (2n=2x=20) using 5' anchored degenerate microsatellite primers were constructed. Of the 76 new microsatellites, 21 functional primer pairs were designed. Because of the small number of primer pairs designed, 428 expressed sequence tag (EST) sequences from seven Stylosanthes species were also examined for SSR detection. Approximately 10% of sequences delivered functional primer pairs, and after redundancy elimination, 57 microsatellite repeats were selected. Tetranucleotides followed by trinucleotides were the major repeated sequences in Stylosanthes ESTs. In total, a robust set of 21 genomic-SSR (gSSR) and 20 EST-SSR (eSSR) markers were developed. These markers were analyzed for intraspecific diversity within 20 S. seabrana accessions and for their cross-species transferability. Mean expected (He) and observed (Ho) heterozygosity values with gSSR markers were 0.64 and 0.372, respectively, whereas with eSSR markers these were 0.297 and 0.214, respectively. Dendrograms having moderate bootstrap value (23%-94%) were able to distinguish all accessions of S. seabrana with gSSR markers, whereas eSSR markers showed 100% similarities between few accessions. The set of 21 gSSRs, from S. seabrana, and 20 eSSRs, from selected Stylosanthes species, with their high cross-species transferability (45% with gSSRs, 86% with eSSRs) will facilitate genetic improvement of Stylosanthes species globally.
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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".