Large-scale development of functional markers in<i>Brassica</i>species
Bibliographic record
Abstract
Numerous quantitative trait loci (QTL) have been detected in Brassica species, but fine-mapping of major QTL has advanced slowly. The development of functional markers can overcome this barrier. We used publicly available PlantGDB-assembled unique transcripts (PUTs) from Brassica species to design 7836 functional simple sequence repeat (SSR) primer pairs. Functional annotation of the PUTs containing SSRs was done by Blast2GO. The PUTs harbouring SSRs were mainly involved with nucleotide or protein binding and enzyme activity, and preferentially functioned in membranes and cytoplasm. Totally, 210 PUT primer pairs were selected to test their polymorphism, stability, and PCR quality. Approximately 70% (147) of the primer pairs resulted in successful amplification with an average polymorphic information content (PIC) value of 0.49. The highest level of polymorphism was dinucleotide repeat SSRs, followed by tri- and mononucleotide repeats. Approximately 60% of the primer pairs showed good transferability among Brassica species. These results show that the development of markers from PUTs is a feasible and simple approach to develop functional SSR markers on a large scale across Brassica species. In addition, these markers can provide a novel alternative that is a putative approach for rapid determination of candidate genes, genetic mapping, genetic diversity analysis, and comparative mapping in Brassica 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".