In silico analysis identifies novel restriction enzyme combinations that expand reduced representation bisulfite sequencing CpG coverage
Bibliographic record
Abstract
BACKGROUND: Epigenetics is the study of gene expression changes that are not caused by changes in the deoxyribonucleic acid (DNA) sequence. DNA methylation is an epigenetic mark occurring in C-phosphate-G sites (CpGs) that leads to local or regional gene expression changes. Reduced-representation bisulfite sequencing (RRBS) is a technique that is used to ascertain the DNA methylation of millions of CpGs at single-nucleotide resolution. The genomic coverage of RRBS is given by the restriction enzyme combination used during the library preparation and the throughput capacity of the next-generation sequencer, which is used to read the generated libraries. The four-nucleotide cutters, MspI and TaqαI, are restriction enzymes commonly used in RRBS that, when combined, achieve ~12% genomic coverage. The increase in throughput of next-generation sequencers allows for novel combinations of restriction enzymes that provide higher CpG coverage. RESULTS: We performed a near-neighbor analysis of the four nucleotide sequences most frequently found within 50 nt of all genomic CpGs. This resulted in the identification of seven methylation-insensitive restriction enzymes (AluI, BfaI, HaeIII, HpyCH4V, MluCI, MseI, and MspI) that shared similar restriction conditions suitable for RRBS library preparation. We report that the use of two or three enzyme combinations increases the theoretical epigenome coverage to almost half of the human genome. CONCLUSIONS: We provide the enzyme combinations that are more likely to increase the CpG coverage in human, rat, and mouse genomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".