Homogeneous Assays for Single-Nucleotide Polymorphism Typing Using AlphaScreen
Bibliographic record
Abstract
AlphaScreen technology allows the development of high-throughput homogeneous proximity assays. In these assays, signal is generated when 680 nm laser light irradiates a donor bead in close proximity to an acceptor bead. For the detection of nucleic acids, donor and acceptor beads are brought into proximity by two bridging probes that hybridize simultaneously to a common target and to the generic oligonucleotides attached covalently to the beads. This method allows the detection of as little as 10 amole of a single-stranded DNA target. The combination of AlphaScreen with allele-specific amplification (ASA) and allele-specific hybridization (ASH) has allowed the development of two homogenous single-nucleotide polymorphism (SNP) genotyping platforms. Both types of assay are very robust, routinely giving accurate genotyping results with < 2 ng of genomic DNA per genotype. An AlphaScreen validation study was performed for 12 SNPs by using ASA assays and seven SNPs by using ASH assays. More than 580 samples were genotyped with accuracy >99%. The two assays are remarkably simple, requiring no post-PCR manipulations. Genotyping has been performed successfully in 96- and 384-well formats with volumes as small as 2 μL, allowing a considerable reduction in the amount of reagents and genomic DNA necessary for genotyping. These results show that the AlphaScreen technology can be successfully adapted to high-throughput genotyping.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".