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Record W1977153307 · doi:10.1139/g01-098

Expression, mapping, and genetic variability of<i>Brassica napus</i>disease resistance gene analogues

2001· article· en· W1977153307 on OpenAlexvenueno aff
M. Fourmann, Florence Charlot, Nicole Froger, Régine Delourme, Dominique Brunel

Bibliographic record

VenueGenome · 2001
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant-Microbe Interactions and Immunity
Canadian institutionsnot available
FundersInstitut National de la Recherche Agronomique
KeywordsBrassicaBiologyBrassica rapaGeneticsBrassica oleraceaGeneGenomeArabidopsis thalianaR genePlant disease resistanceBotanyMutant

Abstract

fetched live from OpenAlex

Numerous sequences analogous to resistance (R) genes exist in plant genomes and could be involved in resistance traits. The aim of this study was to identify a large number of Brassica napus sequences related to R genes and also to test the adequacy of specific PCR-based tools for studying them. Different consensus primers were compared for their efficiency in amplifying resistance-gene analogues (RGAs) related to the nucleotide-binding-site subgroup of R genes. Specific primers were subsequently designed to fine-study the different RGAs and we tested their efficiency in three species related to B. napus: Brassica oleracea, Brassica rapa, and Arabidopsis thaliana. Forty-four B. napus RGAs were identified. Among 29 examined, at least one-third were expressed. Eighteen RGAs were mapped on 10 of the 19 B. napus linkage groups. The high variability within these sequences permitted discrimination of each genotype within a B. napus collection. The RGA-specific primers amplified RGAs in the B. oleracea and B. rapa genomes, but the sequences appear to be poorly conserved in A. thaliana. Specific RGA primers are a precise tool for studying known-sequence RGAs. These sequences represent interesting markers that could be correlated with resistance traits in B. napus or related Brassica genomes.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.980
Threshold uncertainty score0.311

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 head0.016
GPT teacher head0.203
Teacher spread0.187 · 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

Citations48
Published2001
Admission routes1
Has abstractyes

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