A MBD-seq protocol for large-scale methylome-wide studies with (very) low amounts of DNA
Bibliographic record
Abstract
We recently showed that, after optimization, our methyl-CpG binding domain sequencing (MBD-seq) application approximates the methylome-wide coverage obtained with whole-genome bisulfite sequencing (WGB-seq), but at a cost that enables adequately powered large-scale association studies. A prior drawback of MBD-seq is the relatively large amount of genomic DNA (ideally >1 µg) required to obtain high-quality data. Biomaterials are typically expensive to collect, provide a finite amount of DNA, and may simply not yield sufficient starting material. The ability to use low amounts of DNA will increase the breadth and number of studies that can be conducted. Therefore, we further optimized the enrichment step. With this low starting material protocol, MBD-seq performed equally well, or better, than the protocol requiring ample starting material (>1 µg). Using only 15 ng of DNA as input, there is minimal loss in data quality, achieving 93% of the coverage of WGB-seq (with standard amounts of input DNA) at similar false/positive rates. Furthermore, across a large number of genomic features, the MBD-seq methylation profiles closely tracked those observed for WGB-seq with even slightly larger effect sizes. This suggests that MBD-seq provides similar information about the methylome and classifies methylation status somewhat more accurately. Performance decreases with <15 ng DNA as starting material but, even with as little as 5 ng, MBD-seq still achieves 90% of the coverage of WGB-seq with comparable genome-wide methylation profiles. Thus, the proposed protocol is an attractive option for adequately powered and cost-effective methylome-wide investigations using (very) low amounts of DNA.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.015 |
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".