Clinical Feasibility of Long‐Read <scp>WGS</scp> for <scp>DNA</scp> Methylation Signature Analysis
Bibliographic record
Abstract
DNA methylation (DNAm) signatures have emerged as valuable diagnostic biomarkers for rare genetic disorders. To date, the most widely used approach for establishing and validating these signatures has relied on array-based technologies. However, in clinical diagnostics, there is a growing shift from short-read sequencing (SRS) toward long-read sequencing (LRS) technologies. Recent advances in platforms such as Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) enable direct assessment of DNAm from native DNA, offering improved resolution and reduced technical bias compared to array-based technologies. In this study, we compared DNAm profiles generated by LRS with those obtained from DNAm arrays. DNAm profiles of two individuals with pathogenic KMT2D variants were analyzed using DNAm arrays, LRS using PacBio and ONT, and ONT multiplexed sequencing with adaptive sampling. A support vector machine (SVM) classifier trained on array data, as well as the public classification platform EpigenCentral, yielded correct predictions for all LRS samples, underscoring the potential of LRS platforms in DNAm-based diagnostics. Our results suggest that DNAm profiles generated by LRS align well with DNAm signatures established using DNAm arrays, supporting their feasibility in clinical and research applications with the added benefit of simultaneous methylation and sequence analysis.
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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".