Evaluation of a Quantitative DNA Methylation Analysis Technique using Methylation-Sensitive/Dependent Restriction Enzymes and Real-Time PCR
Bibliographic record
Abstract
DNA methylation in mammals has been shown to play many important roles in diverse biological phenomena. Several methods have been developed for the measurement of region-specific levels of DNA methylation. We sought a technique that could be used to quantitatively evaluate multiple independent loci in several tissues in a quick and cost-effective manner. Recently, a few quantitative techniques have been developed by employing the use of real-time PCR, though they require the additional step of sodium bisulfite conversion. Here we evaluate a technique that involves the digestion of non-sodium bisulfite-treated genomic DNA using methylation-sensitive and methylation-dependent restriction enzymes followed by real-time PCR. The utility of this method is tested by analyzing seventeen genomic regions of known tissue-specific levels of DNA methylation including three imprinted genes. We find that this approach generates rapid, reproducible and accurate results (range = +/-5%) without the additional time required for bisulfite conversion. This approach is also adaptable for use with smaller amounts of starting material. We propose this method as a rapid, quantitative method for the analysis of DNA methylation at single sites or within small regions of DNA.
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".