Analytical Validation of the Tag-It High-Throughput Microsphere-Based Universal Array Genotyping Platform: Application to the Multiplex Detection of a Panel of Thrombophilia-Associated Single-Nucleotide Polymorphisms
Bibliographic record
Abstract
BACKGROUND: We have developed a novel, microsphere-based universal array platform referred to as the Tag-It platform. This platform is suitable for high-throughput clinical genotyping applications and was used for multiplex analysis of a panel of thrombophilia-associated single-nucleotide polymorphisms (SNPs). METHODS: Genomic DNA from 132 patients was amplified by multiplex PCR using 6 primer sets, followed by multiplex allele-specific primer extension using 12 universally tagged genotyping primers. The products were then sorted on the Tag-It array and detected by use of the Luminex xMAP system. Genotypes were also determined by sequencing. RESULTS: Empirical validation of the universal array showed that the highest nonspecific signal was 3.7% of the specific signal. Patient genotypes showed 100% concordance with direct DNA sequencing data for 736 SNP determinations. CONCLUSIONS: The Tag-It microsphere-based universal array platform is a highly accurate, multiplexed, high-throughput SNP-detection platform.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".