Identification and characterization of genes on a single subgenome in the hexaploid wheat (<i>Triticum aestivum</i> L.) genotype ‘Chinese Spring’
Bibliographic record
Abstract
Gene loss during the formation of hexaploid bread wheat has been repeatedly reported. However, our knowledge on genome-wide analysis of the genes present on a single subgenome (SSG) in bread wheat is still limited. In this study, by analysing the 'Chinese Spring' chromosome arm shotgun sequences together with high-confidence gene models, we detected 433 genes on a SSG. Greater gene loss was observed in A and D subgenomes compared with B subgenome. More than 79% of the orthologs for these SSG genes were detected in diploid and tetraploid relatives of hexaploid wheat. Unexpectedly, no bias in expression breadth or in the distribution patterns of GO (gene ontology) terms for these genes was detected among the high-confidence genes. Further, network and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analyses indicated that most of these genes were not functionally related to each other. Interestingly, 30.7% of these SSG genes were most highly expressed in root, showing biased distribution given the distribution of the whole high-confidence genes. Collectively, these results facilitate our understanding of the loss of the genes that were retained in a SSG during the formation of hexaploid wheat.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".