Collective judgment predicts disease-associated single nucleotide variants.

ABSTRACT:

Background

In recent years the number of human genetic variants deposited into the publicly available databases has been increasing exponentially. The latest version of dbSNP, for example, contains ~50 million validated Single Nucleotide Variants (SNVs). SNVs make up most of human variation and are often the primary causes of disease. The non-synonymous SNVs (nsSNVs) result in single amino acid substitutions and may affect protein function, often causing disease. Although several methods for the detection of nsSNV effects have already been developed, the consistent increase in annotated data is offering the opportunity to improve prediction accuracy.

Results

Here we present a new approach for the detection of disease-associated nsSNVs (Meta-SNP) that integrates four existing

SUBMITTER: Capriotti E 

PROVIDER: S-EPMC3839641 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature