First the seed: Genomic advances in seed science for improved crop productivity and food security
Bibliographic record
Abstract
Abstract Seeds are valuable sources of carbohydrates, lipids, proteins, fibers, minerals, and vitamins. They provide energy and nutrition to germinating seedlings, food to humans, feed to livestock, and feedstocks to industry. High‐throughput analyses of gene expression in crops has identified many candidate genes associated with seed dormancy, longevity, germination, and vigor. In this review, we cover the latest research focusing on such key seed traits. Transcriptome analyses of time courses of seed filling have identified sets of genes expressed at different stages of this process. The potential role of epigenetics (including seed–endosperm imprinted genes) in regulating seed development and chemistry is highlighted herein. We also discuss how advances in genomics and seed biology are facilitating the unravelling of associations between seed traits with gene bank accessions and gene sequences, including how functional research can accelerate the discovery of allelic variants. Such knowledge of functional effects relating to gene variants is necessary for more efficient and cost‐effective management of genetic resources or for redesigning crops with specific seed characteristics. For instance, gene bank curators may assess seed viability by monitoring changes in gene expression of biomarker genes in dry seed samples to decide germplasm regeneration and assess genetic integrity of collections by monitoring changes in diversity and allele frequencies between samples of same accession stored in gene banks. We highlight that resistance to preharvest sprouting can be enhanced through genomics‐assisted breeding in otherwise nondormant rice (Oryza sativa L.) and wheat (Triticum aestivum L.) cultivars, while pimt, another valuable marker for seed longevity, may be deployed to enhance seed vigor in crops.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".