Journal
HUMAN MUTATION
Volume 33, Issue 2, Pages 332-337Publisher
WILEY
DOI: 10.1002/humu.21642
Keywords
nsSNPs; missense mutation; pathogenic mutation; disease mutation; neutral mutation; support vector machine
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Funding
- Department of Biotechnology (DBT)
- CDFD
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Variations are mostly due to nonsynonymous single nucleotide polymorphisms (nsSNPs), some of which are associated with certain diseases. Phenotypic effects of a large number of nsSNPs have not been characterized. Although several methods have been developed to predict the effects of nsSNPs as disease or neutral, there is still a need for development of methods with improved prediction accuracies. We, therefore, developed a support vector machine (SVM) based method named Hansa which uses a novel set of discriminatory features to classify nsSNPs into disease (pathogenic) and benign (neutral) types. Validation studies on a benchmark dataset and further on an independent dataset of well-characterized known disease and neutral mutations show that Hansa outperforms the other known methods. For example, fivefold cross-validation studies using the benchmark HumVar dataset reveal that at the false positive rate (FPR) of 20% Hansa yields a true positive rate (TPR) of 82% that is about 10% higher than the best-known method. Hansa is available in the form of a web server at http://hansa.cdfd.org.in:8080. Hum Mutat 33: 332-337, 2012. (C) 2011 Wiley Periodicals, Inc.
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