An NGS approach for the identification of precise homoeologous recombination sites between A and C genomes in <i>Brassica</i> genus
Bibliographic record
Abstract
The introgression of heterologous genomes through interspecific hybridization offers a great opportunity to expand the gene pool of crops, thereby broadening the traits that can be targeted for improvement. The introgression of C genomic regions carrying desirable traits from Brassica napus (AACC) into the diploid B. rapa (AA) via homoeologous recombination (HR) has been commonly used. However, the precise identification of HR sites remains a significant challenge, limiting the practical application of genome introgression via HR in breeding programs. Here, we developed an indicator named ‘Dosage-score’ from the coverage depth of next-generation sequencing reads. Then, Dosage-score analysis applied to both in BC1F1 individuals obtained by backcrossing B. rapa to F1 progeny (B. rapa × B. napus) and in the parental lines, and successfully identified the precise HR sites resulting from F1 meiosis as well as those that were native in the parental B. napus genome. Additionally, we introgressed the C6 segment from HR identified by Dosage-score analysis into B. rapa genome background, revealing gene expression on the added segment without noticeable phenotypic change. The identification of HR by Dosage-score analysis will contribute to the expansion of the gene pool for breeding by introgression of heterologous genomes in Brassica crops.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".