Identification and evaluation of suitable reference genes for gene expression analysis in rubber tree leaf
Bibliographic record
Abstract
Abstract Backgroud Gene expression profiling is increasingly applied to investigate molecular mechanisms for which, normalization with suitable reference genes is critical. Previously we have reported several suitable reference genes for laticifer samples from rubber, however, little is known about reference genes in leaf. Results The main objective of this current study was to identify some reference genes with stable expression patterns in leaf at various developmental stages, as well as during abiotic (temperature extremes) and biotic stresses. Gene expression profiling experiments in rubber tree leaf identified the ubiquitin-proteasome system as having excellent potential as reference genes. Among a total of 30 tested genes investigated, 24 new (including 11 genes involved in the ubiquitin-proteasome system), 4 previously identified and 2 specific genes, were further evaluated using quantitative real-time PCR. Our results indicated that the new genes had better stability of expression when compared with others. For instance, an ubiquitin conjugating enzyme (RG0099) and three ubiquitin-protein ligases (RG0928, RG2190 and RG0118) expressed stably in all samples, and were confirmed to be suitable reference genes in rubber tree leaf in four different conditions. Finally, we suggest that using more than one reference gene may be appropriate in gene expression studies when employing different software to normalize gene expression data. Conclusion Our findings have significant implications for the reliability of data obtained from genomics studies in rubber tree and perhaps in other 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".