Use of tall fescue EST-SSR markers in phylogenetic analysis of cool-season forage grasses
Bibliographic record
Abstract
Microsatellites or simple sequence repeats (SSRs) are highly useful molecular markers for plant improvement. Expressed sequence tag (EST)-SSR markers have a higher rate of transferability across species than genomic SSR markers and are thus well suited for application in cross-species phylogenetic studies. Our objectives were to examine the amplification of tall fescue EST-SSR markers in 12 grass species representing 8 genera of 4 tribes from 2 subfamilies of Poaceae and the applicability of these markers for phylogenetic analysis of grass species. About 43% of the 145 EST-SSR primer pairs produced PCR bands in all 12 grass species and had high levels of polymorphism in all forage grasses studied. Thus, these markers will be useful in a variety of forage grass species, including the ones tested in this study. SSR marker data were useful in grouping genotypes within each species. Lolium temulentum, a potential model species for cool-season forage grasses, showed a close relation with the major Festuca-Lolium species in the study. Tall wheat grass was found to be closely related to hexaploid wheat, thereby confirming the known taxonomic relations between these species. While clustering of closely related species was found, the effectiveness of such data in evaluating distantly related species needs further investigations. The phylogenetic trees based on DNA sequences of selected SSR bands were in agreement with the phylogenetic relations based on length polymorphism of SSRs markers. Tall fescue EST-SSR markers depicted phylogenetic relations among a wide range of cool-season forage grass species and thus are an important resource for researchers working with such grass species.
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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 | 0.001 |
| 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) | 0.000 | 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".