Improved, ACMG-compliant, in silico prediction of pathogenicity for missense substitutions encoded by TP53 variants.

ABSTRACT: Clinical interpretation of germline missense variants represents a major challenge, including those in the TP53 Li-Fraumeni syndrome gene. Bioinformatic prediction is a key part of variant classification strategies. We aimed to optimize the performance of the Align-GVGD tool used for p53 missense variant prediction, and compare its performance to other bioinformatic tools (SIFT, PolyPhen-2) and ensemble methods (REVEL, BayesDel). Reference sets of assumed pathogenic and assumed benign variants were defined using functional and/or clinical data. Area under the curve and Matthews correlation coefficient (MCC) values were used as objective functions to select an optimized protein multisequence alignment with best performance for Align-GVGD. MCC comparison of tools using binary categories show

SUBMITTER: Fortuno C 

PROVIDER: S-EPMC6043381 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature