Selection and Evaluation of Reference Genes for ddPCR-Based Transcript Abundance Studies in Oidiodendron Maius Across Varying Carbon Sources
Bibliographic record
Abstract
Abstract Background When identifying transcript abundance in response to treatment, accurate quantification is critical, especially when examining subtle differences in expression. In particular, data normalization is necessary to account for differences among samples including those associated with RNA quantity and quality. Due to the capacity of droplet digital PCR to absolutely quantify the copy number of the target gene in a given sample, normalization, such as the use of an internal control gene, has not customarily been considered obligatory. Decades of quantitative PCR research have shown, however, that the use of endogenous controls undoubtedly aid in correcting sample variability. With our limited knowledge of gene function in many fungi, typical ‘housekeeping genes’ commonly used as internal references may not be relevant in these organisms. This study aimed to identify and validate suitable reference genes for transcript abundance studies in Oidiodendron maius, a globally distributed, model ericoid mycorrhizal fungus. Results A shortlist of 251 non-differentially expressed genes was generated from RNA-Seq analyses of O. maius grown on three different carbon sources or in symbiosis with Vaccinium myrtillus. Subsequently, a set of criteria (stable expression, valid annotation and relatively high expression) was applied to select three candidate reference genes. These three genes were validated across a further eleven carbon sources using ddPCR and the application of geNorm and NormFinder stability analysis algorithms. Expression stability analysis of three genes - EfTu, vma, and sar - confirmed their reliability as internal references; the geometric mean of their expression values demonstrated the highest stability as a normalization factor.Conclusions We propose the use of the geometric mean of O. maius genes EfTu, vma and sar as a reference tool to normalize RNA expression in ddPCR assays. These newly selected and validated reference genes will increase reliability and reproducibility when studying transcriptional responses of O. maius at different developmental stages and/or under a range of physiological conditions. In addition, the list of 251 non-differentially expressed genes can serve as a valuable resource for selecting reference genes for related experiments and enhances the limited information available on O. maius.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".