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Record W2075508564 · doi:10.1534/genetics.108.096487

Rapid High Resolution Single Nucleotide Polymorphism–Comparative Genome Hybridization Mapping in <i>Caenorhabditis elegans</i>

2008· article· en· W2075508564 on OpenAlexaff
Stéphane Flibotte, Mark L. Edgley, Jason S Maydan, Jon Taylor, Rick Zapf, R Waterston, Donald G. Moerman

Bibliographic record

VenueGenetics · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsUniversity of British ColumbiaCanada's Michael Smith Genome Sciences Centre
Fundersnot available
KeywordsBiologyGeneticsSingle-nucleotide polymorphismGenomeSNP arrayCaenorhabditis elegansMolecular Inversion ProbeRestriction fragment length polymorphismSNPGenotypeGene

Abstract

fetched live from OpenAlex

We have developed a significantly improved and simplified method for high-resolution mapping of phenotypic traits in Caenorhabditis elegans using a combination of single nucleotide polymorphisms (SNPs) and oligo array comparative genome hybridization (array CGH). We designed a custom oligonucleotide array using a subset of confirmed SNPs between the canonical wild-type Bristol strain N2 and the Hawaiian isolate CB4856, populated with densely overlapping 50-mer probes corresponding to both N2 and CB4856 SNP sequences. Using this method a mutation can be mapped to a resolution of approximately 200 kb in a single genetic cross. Six mutations representing each of the C. elegans chromosomes were detected unambiguously and at high resolution using genomic DNA from populations derived from as few as 100 homozygous mutant segregants of mutant N2/CB4856 heterozygotes. Our method completely dispenses with the PCR, restriction digest, and gel analysis of standard SNP mapping and should be easy to extend to any organism with interbreeding strains. This method will be particularly powerful when applied to difficult or hard-to-map low-penetrance phenotypes. It should also be possible to map polygenic traits using this method.

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 categoriesMeta-epidemiology (narrow)
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 score0.175
Threshold uncertainty score1.000

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 head0.027
GPT teacher head0.213
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.

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

Citations23
Published2008
Admission routes1
Has abstractyes

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