10.6084/m9.figshare.c.3851767_D16.v1
Elena Campa
Elena
Campa
NatĂ lia Padilla
NatĂ lia
Padilla
Xavier Cruz
Xavier
Cruz
Additional file 8: Figure S3. of Development of pathogenicity predictors specific for variants that do not comply with clinical guidelines for the use of computational evidence
Springer Nature
2017
In silico pathogenicity predictors
Protein sequence variants
Molecular diagnostics
Missense variants
Next-generation sequencing
2017-08-11 05:00:00
Figure
https://springernature.figshare.com/articles/figure/Additional_file_8_Figure_S3_of_Development_of_pathogenicity_predictors_specific_for_variants_that_do_not_comply_with_clinical_guidelines_for_the_use_of_computational_evidence/5305870
In the coincidence rule (see main text) computational information is accepted as supporting evidence in clinical settings only when the pathogenicity predictions of different methods agree. Here we describe how the success rate of this rule depends on the chosen in silico predictors. (A) Violin plots for the Accuracy grouped by method. Each violin plot corresponds to all possible combinations of reference predictors that include the method shown at the bottom. For example, the first plot to the left represents all combinations of five reference predictors (SIFT, PolyPhen-2, PON-P2, CADD and Mutation Taster2) that include MutationTaster2. (B) Dependence of Accuracy values on the number of predictors used to implement the coincidence rule. (PNG 135 kb)