Sequence and gene expression variability in cultivars of oat (Avena sativa L.)
Bibliographic record
Abstract
Many traits of economic importance in crop plants are quantitative, complicating the selection for desirable characteristics. Recent studies suggest a complex relationship between genotype and phenotype, with genetic variability often appearing as differences in gene expression rather than structural changes in proteins. In oat (Avena sativa L.), lipid and protein content are economically important traits. In the first of four studies reported here, partial sequences for eight genes involved in lipid or protein biosynthesis were obtained from ten oat cultivars with varying lipid and protein content. Phylogenetic analysis showed that these sequences clustered into families possibly corresponding to homeologous genes. Some cultivar- and family-specific polymorphisms were identified. In the second study, we surveyed differential gene expression between developing kernels of cultivars Kanota and Ogle by constructing reciprocal subtractive libraries. Of the 195 contig sequences obtained, only a minority had homology to characterized sequences. Grouping these sequences in categories based on gene ontology of their BLAST hits showed different profiles of expression for each cultivar. In the third study, we tested a method for transforming macroarray data consisting of dividing spot signal by the median array background. This reduced variation due to array exposure time. In the fourth study, gene expression levels were considered as quantitative traits in the Kanota x Ogle mapping population. Macroarrays featuring oat clones differentially expressed between both parents were hybridized with cDNA from the population lines. Among the 33 significant expression quantitative trait loci detected, most clustered to linkage group 29--43, a possible "hot-spot" of gene expression regulation.
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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 | 0.000 |
| 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".