Exploiting the Tetraploid Wheat Pangenome: a functional resource for the whole wheat community
Bibliographic record
Abstract
Durum wheat evolved from wild emmer through domesticated forms to modern cultivars. As donor of the A and B genomes of bread wheat, tetraploid wheats are key source of genetic diversity for modern breeding. Herein, we present preliminary results from the chromosome level assemblies of 40 tetraploid wheat genomes, selected from the Global Durum wheat Genomic Resource (GDGR, passport and genotypic data deposited in GrainGenes, https:// graingenes.org/GG3/global_durum_genomic_resources) to represent the full genetic spectrum of sequence variation across wild emmer, domesticated emmer landraces and cultivars for supporting evolutionary studies and pre-breeding efforts. As case studies, two loci related to disease resistance and spike development will be presented, providing insights into the effects of structural variation and allelic diversity and demonstrating the value of the pangenome in crop breeding. Short- and long-read RNA-sequencing data from multiple tissues of ten core accessions enabled pan-transcriptome analysis, classifying transcripts as core, shell or cloud. Early findings show genotype-specific expression in genes linked to metabolism, hormone signalling, and transcription. Ongoing co-expression network analysis aims to identify expression profiles tied to kernel development, with further investigation into links between transcript abundance, gene copy number and structural variation. Acknowledgements PANWHEATGRAIN (PRIN-2020), Agritech National Research Center, European Union Next-Generation EU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4), CEREALMED (PRIMA-2019), INNOVAR, WHEATSECURITY and PRO-GRACE (H2020), Canadian Tetraploid Pan Genomics, National Projects supporting the Svevo Durum Wheat Genome Sequencing Consortium, the Tetraploid Wheat Pangenome Consortium and the development of the GDGR.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".