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Record W2095919764 · doi:10.1534/genetics.112.145573

Genetic Variation in<i>Saccharomyces cerevisiae</i>: Circuit Diversification in a Signal Transduction Network

2012· article· en· W2095919764 on OpenAlexafffund
Brian Chin, Owen Ryan, F. Lewitter, Charles Boone, Gerald R. Fink

Bibliographic record

VenueGenetics · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFungal and yeast genetics research
Canadian institutionsUniversity of Toronto
FundersNational Institute of General Medical SciencesNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthHoward Hughes Medical Institute
KeywordsBiologyGeneticsGeneAllelePhenotypeSaccharomyces cerevisiaeTranscription (linguistics)Transcription factorSignal transduction

Abstract

fetched live from OpenAlex

The connection between genotype and phenotype was assessed by determining the adhesion phenotype for the same mutation in two closely related yeast strains, S288c and Sigma, using two identical deletion libraries. Previous studies, all in Sigma, had shown that the adhesion phenotype was controlled by the filamentation mitogen-activated kinase (fMAPK) pathway, which activates a set of transcription factors required for the transcription of the structural gene FLO11. Unexpectedly, the fMAPK pathway is not required for FLO11 transcription in S288c despite the fact that the fMAPK genes are present and active in other pathways. Using transformation and a sensitized reporter, it was possible to isolate RPI1, one of the modifiers that permits the bypass of the fMAPK pathway in S288c. RPI1 encodes a transcription factor with allelic differences between the two strains: The RPI1 allele from S288c but not the one from Sigma can confer fMAPK pathway-independent transcription of FLO11. Biochemical analysis reveals differences in phosphorylation between the alleles. At the nucleotide level the two alleles differ in the number of tandem repeats in the ORF. A comparison of genomes between the two strains shows that many genes differ in size due to variation in repeat length.

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 score0.665
Threshold uncertainty score0.712

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 head0.023
GPT teacher head0.252
Teacher spread0.229 · 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

Citations40
Published2012
Admission routes2
Has abstractyes

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