Transferability of cereal EST-SSR markers to ryegrass
Bibliographic record
Abstract
A large number of expressed sequence tags (ESTs) in public databases have provided an opportunity for the systematic development of simple sequence repeat (SSR) markers. EST-SSRs derived from conserved coding sequences show considerable cross-species transferability in related species. In the present study, we assessed the utility of cereal EST-SSRs in ryegrass (Lolium spp.). A total of 165 cereal EST-SSRs were tested; a high rate of transferability (57%) and polymorphism (67% of functional EST-SSRs) was demonstrated between cereals and ryegrass. A total of 46 segregating loci derived from 37 EST-SSRs were mapped on an existing ryegrass genetic map. The mapped loci were uniformly distributed across all seven linkage groups without significant clustering at the distal regions of linkage groups. Sequences of ryegrass amplicons generated by randomly selected 16 EST-SSRs were aligned with reference sequences of cereal EST-SSRs. The SSR motifs and repeat lengths of the cereal EST-SSR markers were different from the majority of ryegrass amplicons. Furthermore, a majority of EST-SSRs amplified different flanking sequences of SSRs in ryegrass than the original cereal sequences. Our results suggest that the high degree of cereal EST-SSR transferability to ryegrass can be a useful enhancement to the molecular database of PCR-based markers but sequence analysis is essential before transferring genetic information using comparative mapping.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
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".