Omics-directed Reverse Genetics Enables the Creation of New Productivity Traits for the Vegetable Oil Crop Canola
Bibliographic record
Abstract
Bayer CropScience is a leader in the oilseed rape seeds business with a 2013 market share of 50% in Canada, based on the creation and use of a unique hybridization system enabling the development of high-yielding canola (B.napus) InVigor® hybrids. For the European markets, Bayer is developing non-transgenic hybrids that will be complemented with differentiating traits. To this end, a highly effective mutagenesis-based and omics-directed reverse genetics platform was established which enables the creation of novel productivity traits in canola.The reverse genetics process involves three major steps described in the following figure. The selection of relevant homoeologs is facilitated by Bayer's B.napus genome sequence and transcript atlas. The genome sequence allows the in silico identification of functional homoeologs and the transcript atlas enables to prioritize on homoeologs that are highly expressed in the right tissues. A new trait is created by stacking relevant mutant alleles in a single line. Bayer CropScience is using its canola reverse genetics platform to improve certain canola characteristics including pod shattering, grain yield and oil composition and to develop traits such as herbicide tolerance. Pod shatter reduction was the first trait developed with the platform. A first pod shatter-reduced InVigor hybrid, L140P, was commercially grown in Canada during the 2014 summer season. Bayer CropScience has established and is successfully using a this biotech-based platform for the improvement of the productivity of canola. The resulting traits do not require regulation and can be deployed in all continents. The major limitation of reverse genetics is that the scope of modification is limited to the crops’ own gene content and the expression levels of these genes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".