Use of a large-scale Triticeae expressed sequence tag resource to reveal gene expression profiles in hexaploid wheat (<i>Triticum aestivum</i>L.)
Bibliographic record
Abstract
The US Wheat Genome Project, funded by the National Science Foundation, developed the first large public Triticeae expressed sequence tag (EST) resource. Altogether, 116,272 ESTs were produced, comprising 100,674 5' ESTs and 15 598 3' ESTs. These ESTs were derived from 42 cDNA libraries, which were created from hexaploid bread wheat (Triticum aestivum L.) and its close relatives, including diploid wheat (T. monococcum L. and Aegilops speltoides L.), tetraploid wheat (T. turgidum L.), and rye (Secale cereale L.), using tissues collected from various stages of plant growth and development and under diverse regimes of abiotic and biotic stress treatments. ESTs were assembled into 18,876 contigs and 23,034 singletons, or 41,910 wheat unigenes. Over 90% of the contigs contained fewer than 10 EST members, implying that the ESTs represented a diverse selection of genes and that genes expressed at low and moderate to high levels were well sampled. Statistical methods were used to study the correlation of gene expression patterns, based on the ESTs clustered in the 1536 contigs that contained at least 10 5' EST members and thus representing the most abundant genes expressed in wheat. Analysis further identified genes in wheat that were significantly upregulated (p < 0.05) in tissues under various abiotic stresses when compared with control tissues. Though the function annotation cannot be assigned for many of these genes, it is likely that they play a role associated with the stress response. This study predicted the possible functionality for 4% of total wheat unigenes, which leaves the remaining 96% with their functional roles and expression patterns largely unknown. Nonetheless, the EST data generated in this project provide a diverse and rich source for gene discovery in 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".