Hansa: An automated method for discriminating disease and neutral human nsSNPs

✍ Scribed by Vishal Acharya; Hampapathalu A. Nagarajaram

• Publisher: John Wiley and Sons
• Year: 2011
• Tongue: English
• Weight: 154 KB
• Volume: 33
• Category: Article
• ISSN: 1059-7794
• DOI: 10.1002/humu.21642

✦ Synopsis

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 wellcharacterized 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.