Transcriptome dissection of candidate genes associated with lentil seed quality traits
Bibliographic record
Abstract
Lentils provide a rich plant-based protein source and staple food in many parts of the world. Despite numerous nutritional benefits, lentil seeds also possess undesirable elements, such as anti-nutritional factors. Understanding the genetic networks of seed metabolism is of great importance for improving the seed nutritional profile. We applied RNA sequencing analysis to survey the transcriptome of developing lentil seeds and compared this with that of the pod shells and leaves. In total, we identified 2622 genes differentially expressed among the tissues examined. Genes preferentially expressed in seeds were enriched in the Gene Ontology (GO) terms associated with development, nitrogen and carbon (N/C) metabolism and lipid synthesis. We further categorized seed preferentially expressed genes based on their involvement in storage protein production, starch accumulation, lipid and suberin metabolism, phytate, saponin and phenylpropanoid biosynthesis. The availability of transcript profile datasets on lentil seed metabolism and a roadmap of candidate genes presented here will be of great value for breeding strategies towards further improvement of lentil seed quality traits.
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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".