Development of EST-derived simple sequence repeat markers for wheat leaf rust fungus,<i>Puccinia triticina</i>Eriks
Bibliographic record
Abstract
Gene-associated simple sequence repeat (SSR) markers were developed for Puccinia triticina through the data mining of existing EST libraries. Analysis of 7134 expressed sequence tags (ESTs) from cDNA libraries of P. triticina detected 204 EST-SSRs with a minimum of 12 repeating nucleotides. The majority of EST-SSRs contained short di- or tri-nucleotide repeats. These EST-SSRs were evaluated on 35 P. triticina isolates collected in Canada and 21 EST-SSRs were polymorphic and informative in determining intraspecific genetic diversity. A comparison of virulence and EST-SSR genotypes showed a strong correlation between virulence to Lr2a, Lr2c and Lr17a and EST-SSRs genotypes. The differentiation of the P. triticina population based on EST-SSR genotypes was comparable to that obtained with genomic SSRs, despite differences between two types of SSR markers. Eight of the 21 EST-SSRs produced the cross amplification in Puccinia coronata and Puccinia graminis, suggesting that EST-SSRs are more applicable than genomic SSRs for interspecific analysis. In summary, our study suggests that the data mining of EST databases is a feasible way to generate informative molecular markers for genetic studies of P. triticina.
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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) | 0.001 | 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".