Mapping barley<i>Ds</i>insertions using wheat deletion lines reveals high insertion frequencies in gene-rich regions with high to moderate recombination rates
Bibliographic record
Abstract
Gene distribution is highly uneven in the large genomes of barley and wheat; however, location, order, and gene density of gene-containing regions are very similar between the two genomes. Flanking sequences from 35 unique, single-copy, barley Ds insertion events were physically mapped using wheat nullisomic-tetrasomic, ditelosomic, and deletion lines. Of the 35 sequences, 23 (66%) detected 34 loci mapping on all 7 homoeologous wheat groups. Seven sequences were not mapped owing to lack of polymorphism and the remaining 5 (14%) were barley-specific. All 34 loci physically mapped to the previously identified gene-rich regions (GRRs) of wheat, making the contained genes candidates for targeted mutagenesis by remobilization. Transpositions occurred preferentially into GRRs with higher recombination rates. The GRRs containing 17 of the 23 Ds insertions accounted for 60%-89% of the respective arm's recombination. The remaining 6 (17%) insertions mapped to GRRs with <15% of the arm's recombination. Overall, kb/cM estimates for the Ds-containing GRRs were twofold higher than those for regions without insertions. These results suggest that all genes may be targeted by transposon-based gene cloning, although the transposition frequency for genes present in recombination-poor regions is significantly less than that present in highly recombinogenic regions.
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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 | 0.000 |
| 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 | 0.000 |
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".