An analysis of exome sequencing for diagnostic testing of the genes associated with muscle disease and spastic paraplegia
Bibliographic record
Abstract
In this study, we assess exome sequencing (ES) as a diagnostic alternative for genetically heterogeneous disorders. Because ES readily identified a previously reported homozygous mutation in the CAPN3 gene for an individual with an undiagnosed limb girdle muscular dystrophy, we evaluated ES as a generalizable clinical diagnostic tool by assessing the targeting efficiency and sequencing coverage of 88 genes associated with muscle disease (MD) and spastic paraplegia (SPG). We used three exome-capture kits on 125 individuals. Exons constituting each gene were defined using the UCSC and CCDS databases. The three exome-capture kits targeted 47-92% of bases within the UCSC-defined exons and 97-99% of bases within the CCDS-defined exons. An average of 61.2-99.5% and 19.1-99.5% of targeted bases per gene were sequenced to 20X coverage within the CCDS-defined MD and SPG coding exons, respectively. Greater than 95-99% of targeted known mutation positions were sequenced to ≥1X coverage and 55-87% to ≥20X coverage in every exome. We conclude, therefore, that ES is a rapid and efficient first-tier method to screen for mutations, particularly within the CCDS annotated exons, although its application requires disclosure of the extent of coverage for each targeted gene and supplementation with second-tier Sanger sequencing for full coverage.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".