Persistent DNA Contamination in Competitive RT-PCR Using cRNA Internal Standards: Identity, Quantity, and Control
Bibliographic record
Abstract
Accurate quantification of mRNA by competitive RT-PCR demands that the quality of the cRNA internal standard be strictly controlled and that at least two criteria should be satisfied. First, genomic DNA should be removed from the total RNA being analyzed; second, template DNA should be removed from the cRNA internal standard following in vitro transcription. We observed that the routine use of RNase-free DNase I is insufficient for removing template DNA from cRNA samples and can degrade cRNA. Furthermore, reducing the template DNA before digestion, selectively extracting template DNA, and gel fractionation are all ineffective at completely eliminating template DNA contamination in cRNA standards. A strategy was developed ("inverted" competitive RT-PCR) to quantify template DNA contamination in cRNA standards. Regardless of treatment method, a small percentage of DNA contamination remained in the products of in vitro transcription. Without correction, the number of mRNA copies calculated from competitive RT-PCR is systematically overestimated. The number of template DNAs contaminating the cRNA samples was remarkably large, though as a percentage of the total cRNA, DNA contamination was small and could be easily corrected.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".