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Record W1964072567 · doi:10.1139/g09-002

Mutation scanning and genotyping by high-resolution DNA melting analysis in olive germplasm

2009· article· en· W1964072567 on OpenAlexvenueno aff
R. Muleo, Maria Chiara Colao, Dario Miano, Marco Cirilli, Maria Intrieri, Luciana Baldoni, E. Rugini

Bibliographic record

VenueGenome · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsnot available
FundersMinistero dell'Economia e delle Finanze
KeywordsHigh Resolution MeltBiologyGenotypingMelting curve analysisAmpliconMicrosatelliteGeneticsGermplasmSingle-nucleotide polymorphismMolecular biologyGenotypeGenePolymerase chain reactionBotanyAllele

Abstract

fetched live from OpenAlex

The application of high-resolution melting (HRM) analysis of DNA is reported for scanning and genotyping Olea europaea germplasm. To test the sensitivity of the method, a functional gene marker, phytochrome A (phyA), was used, since this gene is correlated with important traits for the ecology of the species. We have designed a set of oligos able to produce amplicons of 307 bp to scan for the presence of single polymorphic mutations in a specific phyA fragment encompassing the chromophore attachment site (Cys323). The presence of mutations for substitution, either homozygous or heterozygous, was easily detected by melting curve analysis in a high-resolution melter. It has been established that the sensitivity of the HRM analysis can be significantly improved designing specific primers very close to the mutation sites. All SNPs found were confirmed by sequence analyses and ARMS-PCR. The method has also been confirmed to be very powerful for the visualization of microsatellite (SSR) length polymorphisms. HRM analysis has a very high reproducibility and sensitivity for detecting SNPs and SSRs, allowing olive cultivar genotyping and resulting in an informative, easy, and low-cost method able to greatly reduce the operating time.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.977
Threshold uncertainty score0.364

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 head0.006
GPT teacher head0.217
Teacher spread0.211 · 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 designObservational
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

Citations76
Published2009
Admission routes1
Has abstractyes

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