Omics for Improving Seed Quality and Yield
Bibliographic record
Abstract
Seed-related traits such as seed size, germination, vigour, dormancy, biochemical composition, and stress resistance are critical to ensuring agricultural productivity and global food security, particularly in current scenarios of climate change and environmental unpredictability. This review examines the transformative potential of omics technologies, encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics, in enhancing our understanding of seed biology and its applications in crop improvement. Genomics and transcriptomics are key technologies in future plant breeding and gene editing to optimise seed yield and quality. We reviewed the role of metabolomic approaches in uncovering the molecular mechanisms behind seed germination, vigour, dormancy, and the proteomic advances to elucidate markers of seed quality, combining these omic technologies to decipher DOG1 as a marker of dormancy. Both biotic and abiotic stress resistance in seeds were reviewed from a multi-omics perspective to determine the best avenues for improving the resilience of seeds against drought, salinity and pathogens. Moreover, omics approaches have been reviewed to optimise plant–microbe interactions, particularly in enhancing symbiotic relationships within the soil microbiome.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".