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Record W2133048932 · doi:10.1139/g06-099

Length, orientation, and plant host influence the mutation frequency in microsatellites

2006· article· en· W2133048932 on OpenAlexafffundvenue
Aïda Azaiez, Éric F. Bouchard, Martine Jean, François Belzile

Bibliographic record

VenueGenome · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Disease Resistance and Genetics
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyMicrosatelliteGeneticsArabidopsisMutation frequencyTandem repeatMutationMutation rateGenomeMolecular biologyGeneMutantAllele

Abstract

fetched live from OpenAlex

Microsatellites are simple, tandem DNA repeats that represent unstable regions of the genome. They undergo frequent changes in tract length by base additions or deletions due to DNA polymerase slippage during replication. To characterize factors affecting the frequency of spontaneous mutations occurring in microsatellites in plants, a reporter system was used in Arabidopsis thaliana and tomato (Lycopersicon esculentum). The beta-glucuronidase (GUS) reporter system was used to measure the mutation frequency in various microsatellites (G(7), G(10), G(13), G(16), and C(16)) in somatic tissues. Our results indicate that this frequency increases with the number of repeats: a G(16) tract was almost 80-fold more mutable than a G(7) tract. Furthermore, the frequency of mutations depends on repeat orientation, as G(16) was 3-fold more mutable than C(16). The mutation rate was also found to differ markedly in Arabidopsis and tomato for an identical microsatellite. Indeed, Arabidopsis showed a 5-fold higher mutation frequency than tomato with the same G(7) reporter construct. Finally, mutation in a G(16) tract was frequent enough that mutations transmitted germinally to the next generation could be detected at a relatively high frequency.

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 score0.908
Threshold uncertainty score0.136

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 head0.006
GPT teacher head0.182
Teacher spread0.177 · 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

Citations43
Published2006
Admission routes3
Has abstractyes

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