Data from: Transcriptome comparative analysis of two Camellia species reveals lipid metabolism during mature seed natural drying
Bibliographic record
Abstract
AbstractCamellia seed oil has been used as high quality and healthy food for over two thousand years. Seed drying management effects oil quality and quantity. however, the molecular mechanisms of fatty acid biosynthesis and accumulation during the drying process remain unknown. In this study, the transcriptomes of Camellia meiocarpa and C. oleifera seed were characterized at five moisture content levels (10 - 50%) to identify the major processes and reveal genes affecting lipid metabolism in response to nature drying. We found a total of 111,156 unigenes by de novo assembled from RNA-Seq libraries of five moisture content levels during after-ripening of C. meiocarpa (74,016) and C. oleifera (76,374). Ten pathways were closely linked to changes in oil content and composition with 244 genes involved in fatty acid synthesis and accumulation. Gene Ontology enrichment of differentially expressed genes (DEGs) indicated that fatty acid synthesis and accumulation are essential in C. meiocarpa while fatty acid accumulation in C. oleifera during nature drying process. Comparative analyses of DEGs between any two consecutive moisture contents, identified six and three key unigenes in C. Meiocarpa and C. oleifera seeds, respectively, and one additional unigene responsible for the difference between the two species’ fatty acid synthesis and accumulation. Natural drying has improved the quality and quantity of the camellia seed oil. The study provided: a) global transcriptional profiles at five moisture content levels during seed nature drying, b) insights into highlighting transcripts putatively involved in the regulation of the gene expression program and in specific processes likely essential for lipid metabolism, and c) an opportunity to discovering genes associated with oil seed quantity and quality improvement for the studied two camellia species.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".