Detecting DNA Methylation using the Oxford Nanopore Technologies MinION sequencer
Bibliographic record
Abstract
Abstract Nanopore sequencing instruments measure the change in electric current caused by DNA transiting through the pore. In experimental and prototype nanopore sequencing devices it has been shown that the electrolytic current signals are sensitive to base modifications, such as 5-methylcytosine. Here we quantify the strength of this effect for the Oxford Nanopore Technologies MinION sequencer. Using synthetically methylated DNA we are able to train a hidden Markov model to distinguish 5-methylcytosine from unmethylated cytosine in DNA. We demonstrate by sequencing natural human DNA, without any special library preparation, that global patterns of methylation can be detected from low-coverage sequencing and that the methylation status of CpG islands can be reliably predicted from single MinION reads. Our trained model and prediction software is open source and freely available to the community under the MIT license.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".