Performance evaluation of pathogenicity-computation methods for missense variants.

ABSTRACT: With expanding applications of next-generation sequencing in medical genetics, increasing computational methods are being developed to predict the pathogenicity of missense variants. Selecting optimal methods can accelerate the identification of candidate genes. However, the performances of different computational methods under various conditions have not been completely evaluated. Here, we compared 12 performance measures of 23 methods based on three independent benchmark datasets: (i) clinical variants from the ClinVar database related to genetic diseases, (ii) somatic variants from the IARC TP53 and ICGC databases related to human cancers and (iii) experimentally evaluated PPARG variants. Some methods showed different performances under different conditions, suggesting that they were no

SUBMITTER: Li J 

PROVIDER: S-EPMC6125674 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature