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Record W2071202701 · doi:10.1139/g99-107

Expression and genome organization of resistance gene analogs in soybean

2000· article· en· W2071202701 on OpenAlexvenueno aff
Michelle A. Graham, Laura F. Marek, David Lohnes, Perry B. Cregan, Randy C. Shoemaker

Bibliographic record

VenueGenome · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoybean genetics and cultivation
Canadian institutionsnot available
FundersU.S. Department of Agriculture
KeywordsBiologyGeneticsContigGeneComplementary DNAGene familyGenomeGene clusterPlant disease resistanceUniGeneR genecDNA libraryExpressed sequence tag

Abstract

fetched live from OpenAlex

Sequence analysis of cloned plant disease-resistance genes reveals a number of conserved domains. Researchers have used these domains to amplify analogous sequences, resistance gene analogs (RGAs), from soybean and other crops. Many of these RGAs map in close proximity to known resistance genes. While this technique is useful in identifying potential disease resistance loci, identifying the functional resistance gene from a cluster of homologs requires sequence information from outside of these conserved domains. To study RGA expression and to determine the extent of their similarity to other plant resistance genes, two soybean cDNA libraries (root and epicotyl) were screened by hybridization with RGA class-specific probes. cDNAs hybridizing to RGA probes were detected in each library. Two types of cDNAs were identified. One type was full-length and contained several disease-resistance gene (R-gene) signatures. The other type contained several deletions within these signatures. Sequence analyses of the cDNA clones placed them in the Toll-Interleukin-1 receptor, nucleotide binding domain, and leucine-rich repeat family of disease-resistance genes. Using clone-specific primers from within the 3' end of the LRRs, we were able to map two cDNA clones (LM6 and MG13) to a BAC contig that is known to span a cluster of disease-resistance genes.

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 score0.973
Threshold uncertainty score0.735

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)0.0010.000

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 head0.010
GPT teacher head0.184
Teacher spread0.175 · 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

Citations52
Published2000
Admission routes1
Has abstractyes

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