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

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...