Refinement of the <i>Brassica napus</i> NLRome using RenSeq
Bibliographic record
Abstract
ABSTRACT Canola ( Brassica napus L.), a valuable oilseed crop, faces significant challenges from diseases such as clubroot and blackleg, threatening global yields. To improve disease resistance, it’s crucial to accurately annotate nucleotide-binding leucine-rich repeat receptors (NLRs), which are key components of plant immune systems. This study focuses on refining the NLR repertoire (NLRome) of the Westar cultivar using Resistance gene enrichment sequencing (RenSeq). Initially, only 345 NLR genes were identified in the Westar genome annotation, a figure significantly lower than other canola cultivars. By employing RenSeq, we expanded the annotated NLR genes to 715, including 287 full NLRs and 428 partial NLRs, thus providing a more comprehensive understanding of the NLR diversity in this cultivar. Our findings reveal crucial genomic regions with previously misannotated NLR genes and identify key integrated domains that may contribute to enhanced disease resistance. This refined NLRome not only advances the fundamental understanding of B. napus genetics but also offers valuable insights for breeding programs and biotechnological applications aimed at improving crop resilience. The data generated in this study is available in public repositories for further research and application.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".