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Development of a specific PCR assay for the detection of Rhizoctonia solani AG 1-IB using SCAR primers

2006· article· en· W2002444194 on OpenAlexaff
Rita Grosch, J.H.M. Schneider, Andreas Peth, Astrid Waschke, Philipp Franken, A. Kofoet, Suha Jabaji

Bibliographic record

VenueJournal of Applied Microbiology · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Disease Resistance and Genetics
Canadian institutionsMcGill University
Fundersnot available
KeywordsRhizoctonia solaniPrimer (cosmetics)RAPDPolymerase chain reactionBiologyRibosomal DNAPhylogenetic treeSequence analysisGeneticsMolecular biologyInternal transcribed spacerDNABotanyGeneGenetic diversityChemistryPopulationMedicine

Abstract

fetched live from OpenAlex

AIMS: The aim of this study was to develop a specific and sensitive identification method for Rhizoctonia solani AG 1-IB isolates based on phylogenetic relationships of R. solani AG-1 subgroups using rDNA-internal transcribed spacer (rDNA-ITS) sequence analysis. METHODS AND RESULTS: A neighbour-joining tree analysis of 40 rDNA-ITS sequences demonstrated that R. solani AG-1 isolates cluster separately in six subgroups IA, IB, IC, ID, IE and IF. A molecular marker was generated from a random amplified polymorphic DNA fragment (RAPD). After conversion into a sequence-characterized amplified region (SCAR), a specific primer set for identification of subgroup AG 1-IB was designed for use in a polymerase chain reaction (PCR). The primer pair amplified a single DNA product of 324 bp. CONCLUSIONS: R. solani AG-1 subgroups were discriminated by sequence analysis of the ITS region. The designed SCAR primer pair allowed an unequivocal and rapid detection of R. solani AG 1-IB in plant and soil samples. SIGNIFICANCE AND IMPACT OF THE STUDY: Sequence analysis of the rDNA-ITS region can be used for differentiation of subgroups within AG-1. The use of the developed SCAR primer set allowed a reliable and fast identification of R. solani AG 1-IB and provides a powerful tool for disease diagnosis.

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 score0.049
Threshold uncertainty score0.120

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 head0.018
GPT teacher head0.207
Teacher spread0.189 · 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

Citations54
Published2006
Admission routes1
Has abstractyes

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