Development of EST–SSR markers in castor bean (<i>Ricinus communis</i>L.) and their utilization for genetic purity testing of hybrids
Bibliographic record
Abstract
Expressed sequence tag (EST) databases offer opportunity for the rapid development of simple sequence repeat (SSR) markers in crops. Sequence assembly and clustering of 57 895 ESTs of castor bean resulted in the identification of 10 960 unigenes (6459 singletons and 4501 contigs) having 7429 SSRs. On an average, the unigenes contained 1 SSR for every 1.23 kb of unigene sequence. The identified SSRs mostly consisted of dinucleotide (62.4%) and trinucleotide (33.5%) repeats. The AG class was the most common among the dinucleotide motifs (68.9%), whereas the AAG class (25.9%) was predominant among the trinucleotide motifs. A total of 611 primer pairs were designed for the SSRs, having repeat length more than or equal to 20 nucleotides, of which a set of 130 markers were tested and 92 of these yielding robust amplicons were analyzed for their utility in genetic purity assessment of castor bean hybrids. Nine markers were able to detect polymorphism between the parental lines of nine commercial castor bean hybrids (DCH-32, DCH-177, DCH-519, GCH-2, GCH-4, GCH-5, GCH-6, GCH-7, and RHC-1), and their utility in genetic purity testing was demonstrated. These novel EST-SSR markers would be a valuable addition to the growing molecular marker resources that could be used in genetic improvement programmes of castor bean.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".