Phenotype-optimized sequence ensembles substantially improve prediction of disease-causing mutation in cystic fibrosis.

ABSTRACT: Cystic fibrosis transmembrane conductance regulator (CFTR) mutation is associated with a phenotypic spectrum that includes cystic fibrosis (CF). The disease liability of some common CFTR mutations is known, but rare mutations are seen in too few patients to categorize unequivocally, making genetic diagnosis difficult. Computational methods can predict the impact of mutation, but prediction specificity is often below that required for clinical utility. Here, we present a novel supervised learning approach for predicting CF from CFTR missense mutation. The algorithm begins by constructing custom multiple sequence alignments called phenotype-optimized sequence ensembles (POSEs). POSEs are constructed iteratively, by selecting sequences that optimize predictive performance on a training set of

SUBMITTER: Masica DL 

PROVIDER: S-EPMC4364283 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature