In silico analysis of missense substitutions using sequence-alignment based methods.

ABSTRACT: Genetic testing for mutations in high-risk cancer susceptibility genes often reveals missense substitutions that are not easily classified as pathogenic or neutral. Among the methods that can help in their classification are computational analyses. Predictions of pathogenic vs. neutral, or the probability that a variant is pathogenic, can be made based on: 1) inferences from evolutionary conservation using protein multiple sequence alignments (PMSAs) of the gene of interest for almost any missense sequence variant; and 2) for many variants, structural features of wild-type and variant proteins. These in silico methods have improved considerably in recent years. In this work, we review and/or make suggestions with respect to: 1) the rationale for using in silico methods to help predict the

SUBMITTER: Tavtigian SV 

PROVIDER: S-EPMC3431198 | biostudies-literature | 2008 Nov

REPOSITORIES: biostudies-literature