• nsSNPs;
  • missense mutation;
  • pathogenic mutation;
  • disease mutation;
  • neutral mutation;
  • support vector machine


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 Hum Mutat 33:332–337, 2012. © 2011 Wiley Periodicals, Inc.