A Mutant Brassica napus (Canola) Population for the Identification of New Genetic Diversity via TILLING and Next Generation Sequencing
Bibliographic record
Abstract
We have generated a Brassica napus (canola) population of 3,158 EMS-mutagenised lines and used TILLING to demonstrate that the population has a high enough mutation density that it will be useful for identification of mutations in genes of interest in this important crop species. TILLING is a reverse genetics technique that has been successfully used in many plant and animal species. Classical TILLING involves the generation of a mutagenised population, followed by screening of DNA samples using a mismatch-specific endonuclease that cleaves only those PCR products that carry a mutation. Polyacrylamide gel detection is then used to visualise the mutations in any gene of interest. We have used this TILLING technique to identify 432 unique mutations in 26 different genes in B. napus (canola cv. DH12075). This reflects a mutation density ranging from 1/56 kb to 1/308 kb (depending on the locus) with an average of 1/109 kb. We have also successfully verified the utility of next generation sequencing technology as a powerful approach for the identification of rare mutations in a population of plants, even in polyploid species such as B. napus. Most of the mutants we have identified are publically available.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".