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We developed a machine learning strategy to link the effects of VHL missense mutations to phenotype.
We use predictions from in silico tools to link the functional effects of missense VHL mutations to phenotype.
Predictions both link the functional effects of missense VHL mutations to phenotype and classify ccRCC risk with high sensitivity and specificity.
Furthermore, to evaluate the contribution of identified mutations to phenotype, we introduced these mutations into the genome of the parent strain of strain F and then quantified the change in ethanol tolerance.
Recombination mapping in the backcross using markers based on identified homozygous EMS-induced mutations was used to determine linkage of identified mutations to phenotype in AR241 and AR211 (Supporting Information, Table S1).
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Therefore, we invented the gametic mutation box (GMB) to link DNA alteration by mutation to phenotype change.
The differing phenotypes of HCM and DCM resulting from TNNT2 mutations suggests that divergent mechanisms lead from different mutations to either phenotype.
Our data demonstrate the major contribution of exonic mutations to the phenotypes [ 3] and the effectiveness of combining WES with a haploid-based ENU mutagenesis approach.
The mechanism leading from GNE mutations to the myopathy phenotype is not yet understood.
Data produced can be also useful to prioritize mutations to reveal a phenotype.
The contribution of spontaneous mutations to a resistant phenotype during the 100 days of treatment was considered negligible [ 8].
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