Performance of in silico prediction tools for the classification of rare BRCA1/2 missense variants in clinical diagnostics.

ABSTRACT:

Background

The use of next-generation sequencing approaches in clinical diagnostics has led to a tremendous increase in data and a vast number of variants of uncertain significance that require interpretation. Therefore, prediction of the effects of missense mutations using in silico tools has become a frequently used approach. Aim of this study was to assess the reliability of in silico prediction as a basis for clinical decision making in the context of hereditary breast and/or ovarian cancer.

Methods

We tested the performance of four prediction tools (Align-GVGD, SIFT, PolyPhen-2, MutationTaster2) using a set of 236 BRCA1/2 missense variants that had previously been classified by expert committees. However, a major pitfall in the creation of a reliable evaluation set for

SUBMITTER: Ernst C 

PROVIDER: S-EPMC5870501 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature