Single nucleotide extension technology for quantitative site-specific evaluation of metC/C in GC-rich regions
Bibliographic record
Abstract
The development and use of high throughput technologies for detailed mapping of methylated cytosines (metC) is becoming of increasing importance for the expanding field of epigenetics. The single nucleotide primer extension reaction used for genotyping of single nucleotide polymorphisms has been recently adapted to interrogate the bisulfite modification induced 'quantitative' C/T polymorphism that corresponds to metC/C in the native DNA. In this study, we explored the opportunity to investigate C/T (and G/A) ratios using the Applied Biosystems (ABI) SNaPshot technology. The main effort of this study was dedicated to addressing the complexities in the analysis of DNA methylation in GC-rich regions where interrogation of the target cytosine can be confounded by variable degrees of methylation in other cytosines (resulting in variable C/T or G/A ratios after treatment with bisulfite) in the annealing site of the interrogating primer. In our studies, the mismatches of the SNaPshot primer with the target DNA sequence resulted in a biasing effect of up to 70% while these effects decreased as the location of the polymorphic site moved upstream of the target cytosine. We demonstrated that the biasing effect can be corrected with the SNaPshot primers containing degenerative C/T and G/A nucleotides. A series of experiments using various permutations of quantitative C/T and G/A polymorphisms at various positions of the target DNA sequence demonstrated that SNaPshot is able to accurately report cytosine methylation levels with <5% average SD from the true values. Given the relative simplicity of the method and the possibility to multiplex C/T and G/A interrogations, the SNaPshot approach may become a useful tool for large-scale mapping of metC.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".