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Record W2904298751 · doi:10.5539/jas.v11n1p11

Kompetitive Allele Specific PCR (KASP): A Singleplex Genotyping Platform and Its Application

2018· article· en· W2904298751 on OpenAlexvenueno aff
Uzma Majeed, Essam Darwish, Shoaib Ur Rehman, Xueyong Zhang

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsnot available
FundersNational Key Research and Development Program of China
KeywordsGenotypingSNP genotypingVariants of PCRBiologyComputational biologyMolecular Inversion ProbeMultiplexSingle-nucleotide polymorphismSNPGeneticsMultiplex polymerase chain reactionPolymerase chain reactionGenotypeGene

Abstract

fetched live from OpenAlex

Single nucleotide polymorphism (SNP) can be detected by mining sequence databases or by using different singleplex or multiplex SNP genotyping platforms. Development of high-throughput genotyping molecular markers can be instrumental towards maximizing genetic gain. In this review we provide an overview of Kompetitive Allele Specific PCR (KASP) genotyping platform requirements and its application that might be helpful in KASP marker development. This literature further illustrates the possibilities to design KASP primers. Several research institutes routinely using KASP platform, producing in excess of humungous data points yearly for breeding cultivars and as well as for medical and commercial purposes. KASP genotyping technology offers cost effectiveness and high throughput molecular marker development platform. Conventional molecular markers can be converted into more robust and high throughput KASP markers. More than 2000 published references clearly show the popularity of KASP technology among the researchers.

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: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.215

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 head0.012
GPT teacher head0.254
Teacher spread0.242 · 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

Citations45
Published2018
Admission routes1
Has abstractyes

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