The Potential of Genome Editing for Improving Seed Oil Content and Fatty Acid Composition in Oilseed Crops
Bibliographic record
Abstract
A continuous rise in demand for vegetable oils, which comprise mainly the storage lipid triacylglycerol, is fueling a surge in research efforts to increase seed oil content and improve fatty acid composition in oilseed crops. Progress in this area has been achieved using both conventional breeding and transgenic approaches to date. However, further advancements using traditional breeding methods will be complicated by the polyploid nature of many oilseed crops and associated time constraints, while public perception and the prohibitive cost of regulatory processes hinders the commercialization of transgenic oilseed crops. As such, genome editing using CRISPR/Cas is emerging as a breakthrough breeding tool that could provide a platform to keep pace with escalating demand while potentially minimizing regulatory burden. In this review, we discuss the technology itself and progress that has been made thus far with respect to its use in oilseed crops to improve seed oil content and quality. Furthermore, we examine a number of genes that may provide ideal targets for genome editing in this context, as well as new CRISPR-related tools that have the potential to be applied to oilseed plants and may allow additional gains to be made in the future.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".