A novel approach for simultaneous detection of structural and single-nucleotide variants based on a combination of chromosome conformation capture and exome sequencing
Bibliographic record
Abstract
Abstract Effective molecular diagnosis of congenital diseases hinges on comprehensive genomic analysis, traditionally reliant on various methodologies specific to each variant type—whole exome or genome sequencing for single nucleotide variants (SNVs), array CGH for copy-number variants (CNVs), and microscopy for structural variants (SVs). We introduce a novel, integrative approach combining exome sequencing with chromosome conformation capture, termed Exo-C. This method enables the concurrent identification of SNVs in clinically relevant genes and SVs across the genome and allows analysis of heterozygous and mosaic carriers. Enhanced with targeted long-read sequencing, Exo-C evolves into a cost-efficient solution capable of resolving complex SVs at base-pair accuracy. Through several case studies, we demonstrate how Exo-C’s multifaceted application can effectively uncover diverse causative variants and elucidate disease mechanisms in patients with rare disorders.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".