Clinical validation of a single NGS targeted panel pipeline using the KAPA HyperChoice system for detection of germline, somatic and mitochondrial sequence and copy number variants
Bibliographic record
Abstract
BACKGROUND: Comprehensive molecular diagnostics are highly dependent on the technical performance of next-generation sequencing (NGS) pipelines, which are assessed by data quality, cost, turnaround time, and accuracy of detecting a range of sequence and copy number variants. METHODS: A dataset of 285 clinically validated cases (205 retrospective and 80 prospective), carrying complex sequence and copy number variants and thousands of genetic polymorphisms underwent a clinical validation of the KAPA HyperChoice target enrichment system with parallel sample fidelity assessment across a number of NGS panels. The analysis included assessment of peripheral blood, urine, muscle and FFPE tissues. RESULTS: High-quality and exceptionally uniform data with 100% coverage of all targeted panels were obtained, resulting in complete sensitivity and specificity for all variant types across nearly all panels and tissue types. Overall reduction in cost and turnaround times was obtained with the implementation of a parallel genotyping sample fidelity system. CONCLUSION: Results of the laboratory quality improvement study focused on a single NGS pipeline that includes both nuclear and mitochondrial genomes demonstrated utility in the clinical setting to assess a range of referral reasons, necessary due to the complex molecular etiology of human genetic disorders, while reducing costs and turnaround times.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".