LbCML38 and LbRH52, two reference genes derived from RNA-Seq data suitable for assessing gene expression in Lycium barbarum L.
Bibliographic record
Abstract
For quantitative real-time PCR (qRT-PCR) analysis, the key prerequisite that determines result accuracy is the selection of appropriate reference gene(s). Goji (Lycium barbarum L.) is a multi-branched shrub belonging to the Solanaceae family. To date, no systematic screening or evaluation of reference gene(s) in Goji has been performed. In this work, we identified 18 candidate reference genes from the transcriptomic sequencing data of 14 samples of Goji at different developmental stages and under drought stress condition. The expression stability of these candidate genes was rigorously analyzed using qRT-PCR and four different statistical algorithms: geNorm, BestKeeper, NormFinder and RefFinder. Two novel reference genes LbCML38 and LbRH52 showed the most stable expression, whereas the traditionally used reference genes such as LbGAPDH, LbHSP90 and LbTUB showed unstable expression in the tested samples. Expression of a target gene LbMYB1 was also tested and compared using optimal reference genes LbCML38 and LbRH52, mediocre reference gene LbActin7, and poor reference gene LbHSP90 as normalization standards, respectively. As expected, calculation of the target gene expression by normalization against LbCML38, LbActin7 or LbHSP90 showed significant differences. Our findings suggest that LbCML38 and LbRH52 can be used as reference genes for gene expression analysis in Goji.
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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 | 0.001 | 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".