Identification of Base Pairs in Single-Nucleotide Polymorphisms by MutS Protein-Mediated Capillary Electrophoresis
Bibliographic record
Abstract
Single-nucleotide polymorphisms (SNPs) are widespread genomic variations, which are associated with serious health disorders and drug resistance. Multiple clinical applications and studies of global population genetics require fast and informative analysis of SNPs. Most of conventional methods sense the presence of the SNP but cannot identify the base pair in it. Here we report simple identification of base pairs in SNPs without DNA sequencing. Our approach is based on the unique ability of MutS protein to bind different single-nucleotide mismatches in DNA with different affinities. Conceptually, the DNA in question is mixed with reference DNA, melted, and reannealed. If the DNA in question has an SNP, the products of reannealing will have two different single-nucleotide mismatches, which provide a base-pair-specific signature of the SNP. The products of reannealing are mixed with MutS, equilibrated, and separated by equilibrium capillary electrophoresis of equilibrium mixtures with MutS in the run buffer. The pattern of migration times of DNAs with mismatches is used for unequivocal identification of the base pair in the SNP. In addition to its ability to identify base pairs in SNPs, the new analytical approach is fast, simple, highly sensitive, and requires no quantitation. It will find applications in studies of heterogeneity of base pairs in known SNPs in large human populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".