A High-Resolution Melting Approach for Analyzing Allelic Expression Dynamics
Bibliographic record
Abstract
Single nucleotide polymorphisms (SNPs) are single base pair mutations that provide new approaches to studies of allele transcript abundances. High-resolution DNA melting curve (HRM) analysis using a LightScanner (Hi-Res Melting system with Idaho's LC Green) provides post-PCR detection of mutations and SNPs in genomic DNA. This study investigated whether the HRM analysis can distinguish alleles among potato (Solanum tuberosum) transcript abundances. Transcript properties of genes encoding seven carbohydrate metabolism enzymes/proteins in various tissues and cold storage durations were studied. The HRM assay measured differential expression of alleles between different organs, between different storage treatments and stages of tubers from the same variety, and between different varieties with the same treatment. The RT-PCR amplicons were directly sequenced to assist the interpretation of HRM data. The cDNA HRM curves correlated well with the nucleotide polymorphisms of the cDNA sequences and the transcript abundance of alleles and therefore can serve as functional allele activity (FAA) markers. By combining the allelic specificity of HRM with simple PCR design, this technology can be applied to rapidly determine the most active allele of a gene among the cells analyzed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".