Melt Analysis of Mismatch Amplification Mutation Assays (Melt-MAMA): A Functional Study of a Cost-Effective SNP Genotyping Assay in Bacterial Models
Bibliographic record
Abstract
Single nucleotide polymorphisms (SNPs) are abundant in genomes of all species and biologically informative markers extensively used across broad scientific disciplines. Newly identified SNP markers are publicly available at an ever-increasing rate due to advancements in sequencing technologies. Efficient, cost-effective SNP genotyping methods to screen sample populations are in great demand in well-equipped laboratories, but also in developing world situations. Dual Probe TaqMan assays are robust but can be cost-prohibitive and require specialized equipment. The Mismatch Amplification Mutation Assay, coupled with melt analysis (Melt-MAMA), is flexible, efficient and cost-effective. However, Melt-MAMA traditionally suffers from high rates of assay design failures and knowledge gaps on assay robustness and sensitivity. In this study, we identified strategies that improved the success of Melt-MAMA. We examined the performance of 185 Melt-MAMAs across eight different pathogens using various optimization parameters. We evaluated the effects of genome size and %GC content on assay development. When used collectively, specific strategies markedly improved the rate of successful assays at the first design attempt from ~50% to ~80%. We observed that Melt-MAMA accurately genotypes across a broad DNA range (~100 ng to ~0.1 pg). Genomic size and %GC content influence the rate of successful assay design in an independent manner. Finally, we demonstrated the versatility of these assays by the creation of a duplex Melt-MAMA real-time PCR (two SNPs) and conversion to a size-based genotyping system, which uses agarose gel electrophoresis. Melt-MAMA is comparable to Dual Probe TaqMan assays in terms of design success rate and accuracy. Although sensitivity is less robust than Dual Probe TaqMan assays, Melt-MAMA is superior in terms of cost-effectiveness, speed of development and versatility. We detail the parameters most important for the successful application of Melt-MAMA, which should prove useful to the wider scientific community.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".