Sequence-based prioritization of nonsynonymous single-nucleotide polymorphisms for the study of disease mutations.

ABSTRACT: The increasing demand for the identification of genetic variation responsible for common diseases has translated into a need for sophisticated methods for effectively prioritizing mutations occurring in disease-associated genetic regions. In this article, we prioritize candidate nonsynonymous single-nucleotide polymorphisms (nsSNPs) through a bioinformatics approach that takes advantages of a set of improved numeric features derived from protein-sequence information and a new statistical learning model called "multiple selection rule voting" (MSRV). The sequence-based features can maximize the scope of applications of our approach, and the MSRV model can capture subtle characteristics of individual mutations. Systematic validation of the approach demonstrates that this approach is capable

SUBMITTER: Jiang R 

PROVIDER: S-EPMC1950793 | biostudies-literature | 2007 Aug

REPOSITORIES: biostudies-literature