Predicting the functional, molecular, and phenotypic consequences of amino acid substitutions using hidden Markov models.

ABSTRACT: The rate at which nonsynonymous single nucleotide polymorphisms (nsSNPs) are being identified in the human genome is increasing dramatically owing to advances in whole-genome/whole-exome sequencing technologies. Automated methods capable of accurately and reliably distinguishing between pathogenic and functionally neutral nsSNPs are therefore assuming ever-increasing importance. Here, we describe the Functional Analysis Through Hidden Markov Models (FATHMM) software and server: a species-independent method with optional species-specific weightings for the prediction of the functional effects of protein missense variants. Using a model weighted for human mutations, we obtained performance accuracies that outperformed traditional prediction methods (i.e., SIFT, PolyPhen, and PANTHER) on two

SUBMITTER: Shihab HA 

PROVIDER: S-EPMC3558800 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature