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Record W2171718532 · doi:10.1101/gr.105106.110

Phenotypic annotation of the mouse X chromosome

2010· article· en· W2171718532 on OpenAlexaff
Brian Cox, Marion Vollmer, Owen J. Tamplin, Mei Lu, Steffen Biechele, Marina Gertsenstein, Claude Van Campenhout, Thomas Floß, Ralf Kühn, Wolfgang Wurst, Heiko Lickert, Janet Rossant

Bibliographic record

VenueGenome Research · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicChromosomal and Genetic Variations
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalUniversity of TorontoToronto Centre for PhenogenomicsHospital for Sick Children
FundersDeutsche ForschungsgemeinschaftMarch of Dimes Foundation
KeywordsBiologyGeneticsAutosomeGenePhenotypeGenetic screenX chromosomeMutantChromosomeEmbryonic stem cellMutationGenome

Abstract

fetched live from OpenAlex

Mutational screens are an effective means used in the functional annotation of a genome. We present a method for a mutational screen of the mouse X chromosome using gene trap technologies. This method has the potential to screen all of the genes on the X chromosome without establishing mutant animals, as all gene-trapped embryonic stem (ES) cell lines are hemizygous null for mutations on the X chromosome. Based on this method, embryonic morphological phenotypes and expression patterns for 58 genes were assessed, approximately 10% of all human and mouse syntenic genes on the X chromosome. Of these, 17 are novel embryonic lethal mutations and nine are mutant mouse models of genes associated with genetic disease in humans, including BCOR and PORCN. The rate of lethal mutations is similar to previous mutagenic screens of the autosomes. Interestingly, some genes associated with X-linked mental retardation (XLMR) in humans show lethal phenotypes in mice, suggesting that null mutations cannot be responsible for all cases of XLMR. The entire data set is available via the publicly accessible website (http://xlinkedgenes.ibme.utoronto.ca/).

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 categoriesInsufficient payload (model declined to judge)
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 score0.872
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
Bibliometrics0.0000.001
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 head0.048
GPT teacher head0.293
Teacher spread0.244 · 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

Citations81
Published2010
Admission routes1
Has abstractyes

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