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Record W201794229 · doi:10.21775/9781912530069.01

A High-Resolution Melting Approach for Analyzing Allelic Expression Dynamics

2009· article· en· W201794229 on OpenAlexaff
Jiazheng Yuan, Muhammad Haroon, David A. Lightfoot, Yvan Pelletier, Qiang Liu, Xiu‐Qing Li

Bibliographic record

VenueCaister Academic Press eBooks · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsHigh Resolution MeltAmpliconBiologyAlleleSingle-nucleotide polymorphismComplementary DNAMelting curve analysisGeneGeneticsgenomic DNAMolecular biologyPolymerase chain reactionGenotype

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.898
Threshold uncertainty score0.726

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.292
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2009
Admission routes1
Has abstractyes

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