Selection of reference genes for quantitative real-time PCR in <i>Cocos nucifera</i> during abiotic stress
Bibliographic record
Abstract
Reverse transcription quantitative real-time polymerase chain reaction (PCR) is a widely used and reproducible method for studying gene expression changes. However, its accuracy and reliability is highly dependent on the normalization step. Cocos nucifera L., a perennial palm with a long productive life span, is frequently exposed to soil and atmospheric drought and other stresses. In applying gene expression analysis to understand coconut stress responses, validation of suitable reference genes is an important first step. In this study, seven putative reference genes were identified from coconut transcriptome data. The stability of these putative reference genes was assessed in a diverse set of 18 coconut samples subject to cold, drought, and high-salinity treatment and representing different endosperm developmental stages. Using statistical algorithms geNorm, NormFinder, and BestKeeper, eEF1-α and UBC10 genes were identified as stable reference genes in all stress treatments and endosperm development stages, consistent with validated reference genes in rice (Oryza sativa L.), potato (Solanum tuberosum L.), and other species. Further validation of analyzed reference genes by normalization of a C-repeat binding factor (CBF)-like gene in cold-treatment samples indicated that the less stable reference genes produced different results from the more stable reference genes, with weaker variation or higher expression levels of a target gene. Our results will be beneficial for further research on molecular mechanisms of stress resistance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".