Original paper
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...
Paper Details
Title
Improved, ACMG-compliant, in silico prediction of pathogenicity for missense substitutions encoded by TP53 variants
Published Date
Jun 5, 2018
Journal
Volume
39
Issue
8
Pages
1061 - 1069
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Notes
History