A quantitative genomic imbalance gene expression assay in a hexaploid species: wheat (<i>Triticum aestivum</i>)
Bibliographic record
Abstract
Responses to allopolyploidy include unequal expression of duplicated genes, gene silencing, and sometimes genomic rearrangements. In plants, the relationship between allelic expression differences arising from changes in regulatory regions and the resulting phenotype is poorly understood because of the complexity of their genomes and lack of efficient methodology to identify regulatory variation. Identifying functionally important regulatory variation in crops such as hexaploid wheat (Triticum aestivum) is in its infancy. More knowledge is required about the contribution of participatory genomes to its transcriptome. In this paper, we demonstrate the use of allelic imbalance assays to quantify relative expression levels across tissues and growth regimes of homoeologous transcripts of the A, B, and D genomes. Polymorphisms in the type I thionins have been used as an example. We show that expression levels vary markedly and interactively over all factors. For this gene, the B genome is the smallest contributor to the transcriptome and the D genome the largest. As additional sequence information is accumulated across genomes, this assay will allow the simple study of relative expression across multiple homeologous loci.
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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 | 0.001 |
| 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".