An analysis of exome sequencing for diagnostic testing of the genes associated with muscle disease and spastic paraplegia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.005 |
| 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 | 0.000 | 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 it