Genome complexity reduction for SNP genotyping analysis
Bibliographic record
Abstract
Efficient single nucleotide polymorphism (SNP) genotyping methods are necessary to accomplish many current gene discovery goals. A crucial element in large-scale SNP genotyping is the number of individual biochemical reactions that must be performed. An efficient method that can be used to simultaneously amplify a set of genetic loci across a genome with high reliability can provide a valuable tool for large-scale SNP genotyping studies. In this paper we describe and characterize a method that addresses this goal. We have developed a strategy for reducing genome complexity by using degenerate oligonucleotide primer (DOP)-PCR and applied this strategy to SNP genotyping in three complex eukaryotic genomes; human, mouse, and Arabidopsis thaliana. Using a single DOP-PCR primer, SNP loci spread throughout a genome can be amplified and accurately genotyped directly from a DOP-PCR product mixture. DOP-PCRs are extremely reproducible. The DOP-PCR method is transferable to many species of interest. Finally, we describe an in silico approach that can effectively predict the SNP loci amplified in a given DOP-PCR, permitting the design of an efficient set of reactions for large-scale, genome-wide SNP studies.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.002 |
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".