Primer Design and Marker Clustering for Multiplex SNP-IT Primer Extension Genotyping Assay using Statistical Modeling
Bibliographic record
Abstract
MOTIVATION: The optimization of the primer design is critical for the development of high-throughput SNP genotyping methods. Recently developed statistical models of the SNP-IT primer extension genotyping reaction allow further improvement of primer quality for the assay. RESULTS: Here we describe how the statistical models can be used to improve primer design for the assay. We also show how to optimize clustering of the SNP markers into multiplex panels using statistical model for multiplex SNP-IT. The primer set failure probability calculated by a model is used as a minimization function for both primer selection and primers clustering. Three clustering algorithms for the multiplex genotyping SNP-IT assay are described and their relative performance is evaluated. We also describe the approaches to improve the speed of primer design and clustering calculations when using the statistical models. Our clustering decreases the average failure probability of the marker set by 7-25%. The experimental marker failure rate in the multiplex reaction was reduced dramatically and success rate can be achieved as high as 96%. AVAILABILITY: The primer design using statistical models is freely available from www.autoprimer.com.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".