Integrating multiple genomic data to predict disease-causing nonsynonymous single nucleotide variants in exome sequencing studies.

ABSTRACT: Exome sequencing has been widely used in detecting pathogenic nonsynonymous single nucleotide variants (SNVs) for human inherited diseases. However, traditional statistical genetics methods are ineffective in analyzing exome sequencing data, due to such facts as the large number of sequenced variants, the presence of non-negligible fraction of pathogenic rare variants or de novo mutations, and the limited size of affected and normal populations. Indeed, prevalent applications of exome sequencing have been appealing for an effective computational method for identifying causative nonsynonymous SNVs from a large number of sequenced variants. Here, we propose a bioinformatics approach called SPRING (Snv PRioritization via the INtegration of Genomic data) for identifying pathogenic nonsynonymou

SUBMITTER: Wu J 

PROVIDER: S-EPMC3961190 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature