Next generation sequencing is a highly reliable method to analyze exon 7 deletion of survival motor neuron 1 (SMN1) gene
Bibliographic record
Abstract
Spinal muscular atrophy (SMA) is one of the most common and severe genetic diseases. SMA carrier screening is an effective way to identify couples at risk of having affected children. Next-generation sequencing (NGS)-based expanded carrier screening could detect SMN1 gene copy number without extra experiment and with high cost performance. However, its performance has not been fully evaluated. Here we conducted a systematic comparative study to evaluate the performance of three common methods. 478 samples were analyzed with multiplex ligation probe amplification (MLPA), real-time quantitative polymerase chain reaction (qPCR) and NGS, simultaneously. Taking MLPA-based results as the reference, for 0 copy, 1 copy and ≥ 2 copy SMN1 analysis with NGS, the sensitivity, specificity and precision were all 100%. Using qPCR method, the sensitivity was 100%, 97.52% and 94.30%, respectively; 98.63%, 95.48% and 100% for specificity; and 72.72%, 88.72% and 100% for precision. NGS repeatability was higher than that of qPCR. Moreover, among three methods, NGS had the lowest retest rate. Thus, NGS is a relatively more reliable method for SMN1 gene copy number detection. In expanded carrier screening, compared with the combination of multiple methods, NGS method could reduce the test cost and simplify the screening process.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".