Increasing cotton genome coverage with polymorphic SSRs as revealed by SSCP
Bibliographic record
Abstract
Simple sequence repeat (SSR) markers are widely used in plant genetics and breeding. However, there are many SSR markers that do not reveal polymorphism in cotton. Traditional SSR genotyping methods only provide information on product sizes. This leaves many marker polymorphism undetected, thus, lowering the utility of SSRs. In the present study, monomorphic SSRs between two mapping parents, 'Emian22' and 3-79, were subjected to single-strand conformation polymorphism (SSCP) analysis to reveal polymorphism. Of the 4194 monomorphic SSR primer pairs, 158 pairs (3.77%) showed polymorphism and revealed 174 polymorphic loci. Sequence analysis showed that the differences in PCR products between the mapping parents were solely due to base transition or transversion, which was in agreement with SSCP principles. SSCP also revealed SSRs with motifs of AT/TA and GAA/CTT were more polymorphic in dinucleotides and trinucleotides, respectively. Genetic mapping integrated 160 loci into our interspecific BC(1) linkage map, 5 of which associated with QTLs related to cotton fiber quality. The technique discussed in the present study enables us to detect polymorphism of monomorphic SSRs, and increase the utilization efficiency of the existing SSR primers.
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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) | 0.002 | 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".