High Resolution Melt Analysis as an SNP Genotyping Tool in Arabidopsis thaliana
Bibliographic record
Abstract
Single nucleotide polymorphisms (SNPs) are a common type of mutation in most species. An insertion/deletion mutation (in/del) is a type of SNP which denotes the addition or removal of nucleotides, and are genotyped using the same methodology as SNPs. SNPs are typically genotyped by using polymerase chain reaction (PCR) to amplify a specific fragment of the genome and use various sequencing methods to detect the SNPs present. High resolution melt analysis (HRM) is a technique used to precisely measure the annealing temperatures of the product DNA during quantitative PCR. Here, we report HRM analysis to screen for tt4-4, an in/del mutation of the TRANSPARENT TESTA (TT4, At5g 13930) gene in A.thaliana, in which there exists an extra C:G base pair. With HRM we can accurately determine the zygosity of each plant based on the slight differences in annealing temperatures between the amplicons produced by the wildtype and mutant alleles. HRM provides us with an inexpensive, high-throughput method of genotyping SNPs, which is used in a number of applications, including fast medical diagnostics, zygosity testing, and screening for DNA methylation patterns (Wojdacz & Dobrovic 2007, NAR 35(6):e41). * Indicates faculty mentor.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".