Detection of revertant mosaicism in epidermolysis bullosa through Cas9‐targeted long‐read sequencing
Bibliographic record
Abstract
Revertant mosaicism (RM) is a phenomenon in which inherited mutations are spontaneously corrected in somatic cells. RM occurs in some congenital skin diseases, but genetic validation of RM in clinically revertant skin has been challenging, especially when homologous recombination (HR) is responsible for RM. Here, we introduce nanopore Cas9-targeted sequencing (nCATS) for identifying HR in clinically revertant skin. We took advantage of compound heterozygous COL7A1 mutations in a patient with recessive dystrophic epidermolysis bullosa who showed revertant skin spots. Cas9-mediated enrichment of genomic DNA (gDNA) covering the two mutation sites (>8 kb) in COL7A1 and subsequent MinION sequencing successfully detected intragenic crossover in the epidermis of the clinically revertant skin. This method enables the discernment of haplotypes of up to a few tens of kilobases of gDNA. Moreover, it is devoid of polymerase chain reaction amplification, which can technically induce recombination. We, therefore, propose that nCATS is a powerful tool for understanding complicated gene modifications, including RM.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".