Whole Genome Amplification of Sodium Bisulfite-Treated DNA Allows the Accurate Estimate of Methylated Cytosine Density in Limited DNA Resources
Bibliographic record
Abstract
Sodium bisulfite modification-based fine mapping of methylated cytosines represents the gold standard technique for DNA methylation studies. A major problem with this approach, however is that it results in considerable DNA degradation, and large quantities of genomic DNA material are needed if numerous genomic regions are to be profiled. In this study, we examined whether whole genome amplification (WGA) techniques can be applied to sodium bisulfite-treated DNA and whether WGA would bias DNA methylation results. Sodium bisulfite-treated DNA was amplified using a standard WGA method: optimized primer-extension preamplification (PEP) with degenerate primers. Following the PCR of bisulfite-treated DNA, the DNA methylation profiles of specific DNA fragments were assessed using three approaches: (i) direct sequencing of the overall product; (ii) the sequencing of cloned PCR products; and (iii) methylation-sensitive single nucleotide primer extension (MS-SNuPE)--and compared with those obtained from bisulfite-treated DNA not subjected to WGA. Our data indicates that the DNA methylation profiles obtained from WGA of sodium bisulfite-treated DNA are consistent with those obtained from non-WGA DNA. The average difference in methylation percentage calculated from the two sets of template using MS-SNuPE was 4%. If our results are replicated on other genomic loci, WGA may become a useful technique in DNA methylation studies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".