CRISPR/Cas9 Genome Editing in Legumes: Opportunities for Functional Genomics and Breeding
Bibliographic record
Abstract
Legumes play a crucial role in global agriculture and food security, yet they face significant challenges in breeding for improved traits. This study explores the potential of CRISPR/Cas9 genome editing as a transformative tool in legume functional genomics and breeding. It begins by outlining the importance of legumes and the limitations of traditional breeding methods. The study then delves into the mechanisms and advantages of CRISPR/Cas9, highlighting its application in functional genomics, such as gene knockout and activation studies. A case study on drought tolerance in soybeans demonstrates the practical application of CRISPR/Cas9 in identifying and enhancing key traits. Furthermore, the study discusses the broad applications of this technology in improving biotic and abiotic stress resistance, enhancing quality traits, and accelerating the breeding process, including a detailed case study on disease resistance in chickpeas. The study also addresses the challenges and ethical considerations associated with CRISPR/Cas9, such as off-target effects and regulatory issues. Looking forward, the study explores future innovations and the integration of CRISPR/Cas9 into legume breeding programs, emphasizing its potential for sustainable agriculture and global food security. This study underscores the vast opportunities that CRISPR/Cas9 presents for advancing legume breeding and anticipates its growing impact on agricultural practices.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".