De novo annotation of the wheat pan-genome reveals complexity and diversity of the hexaploid wheat pan-transcriptome
Bibliographic record
Abstract
Abstract Wheat is the most widely cultivated crop in the world with over 215 million hectares grown annually. However, to meet the demands of a growing global population, breeders face the challenge of increasing wheat production by approximately 60% within the next 40 years. The 10+ Wheat Genomes Project recently sequenced and assembled to chromosome level the genomes of nine wheat cultivars to develop our understanding of genetic diversity and selection within the pan-genome of wheat. Here, we provide a wheat pan-transcriptome with de novo annotation and differential expression analysis for these wheat cultivars, across multiple different tissues and whole seedlings sampled at dusk/dawn. Analysis of these de novo annotations facilitated the discovery of genes absent from the Chinese Spring reference, identified genes specific to particular cultivars and defined the core and dispensable genomes. Expression analysis across cultivars and tissues revealed conservation in expression between a large core set of homeologous genes, but also widespread changes in subgenome homeolog expression bias between cultivars. Co-expression network analysis revealed the impact of divergence of sub-genome homeolog expression and identified cultivar-specific expression profiles. In a case study utilising both the newly constructed wheat pan-genome and pan-transcriptome we demonstrate prevalent variation in the prolamin superfamily and immune-reactive proteins across the pan-cultivars.In summary, this work provides both a valuable resource for the wider wheat community and reveals diversity in gene content and expression patterns between global wheat cultivars.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".