A bioinformatics workflow for the evaluation of RT-qPCR primer specificity: Application for the assessment of gene expression data reliability in toxicological studies
Bibliographic record
Abstract
The reliability of Reverse Transcription quantitative real-time PCR (RT-qPCR) gene expression data depends on proper primer design and RNA quality controls. Despite freely available genomic databases and bioinformatics tools, primer design deficiencies can be found across life science publications. In order to assess the prevalence of such deficiencies in the toxicological literature, 504 primer sets extracted from a random selection of 70 recent rat toxicological studies were evaluated. The specificity of each primer set was systematically analysed using a bioinformatics workflow developed from publicly available resources (NCBI Primer BLAST, in silico PCR in UCSC genome browser, Ensembl DNA database). Potential mismatches (9%), cross-matches (13.5%), co-amplification of multiple gene splice variants (9%) and sub-optimal amplicon sizes (25%) were identified for a significant proportion of the primer sets assessed in silico. Quality controls for gDNA contamination of RNA samples were infrequently reported in the surveyed manuscripts. Hence, the impacts of gDNA contamination on RT-qPCR data were further investigated, revealing that lowly expressed genes presented higher susceptibility to contaminating gDNA. In addition to the retrospective identification of potential primer design issues presented in this study, the described bioinformatics workflow can also be used prospectively to select candidate primer sets for experimental validation.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.019 |
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".