Wheat Sequencing: The Pan-Genome and Opportunities for Accelerating Breeding
Bibliographic record
Abstract
Abstract Wheat is a crucial crop globally, with widespread cultivation and significant economic importance. To ensure food security amidst the increasing human population and new production challenges, such as climate change, it is imperative to develop novel wheat varieties that exhibit better quality, higher yield, and enhanced resistance to biotic and abiotic stress. To achieve this, leveraging comprehensive genomic resources from global breeding programs can aid in identifying within-species allelic diversity and selecting optimal allele combinations for superior cultivars. While previous single-reference genome assemblies have facilitated gene discovery and whole-genome level genotype–phenotype relationship modeling, recent research on variations within the pan-genome of all individuals in a plant species underscores their significance for crop breeding. We summarize the different approaches and techniques used for sequencing the large and intricate wheat genome, while highlighting the challenge of generating high-quality reference assemblies. We discuss the computational methods for building the pan-genome and research efforts that are aimed at utilizing the wheat pan-genome in wheat breeding programs.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".