Predicting the phenotypic effects of non-synonymous single nucleotide polymorphisms based on support vector machines

Background: Human genetic variations primarily result from single nucleotide polymorphisms (SNPs) that occur approximately every 1000 bases in the overall human population. The nonsynonymous SNPs (nsSNPs) that lead to amino acid changes in the protein product may account for nearly half of the known genetic variations linked to inherited human diseases. One of the key problems of medical genetics today is to identify nsSNPs that underlie disease-related phenotypes in humans. As such, the development of computational tools that can identify such nsSNPs would enhance our understanding of genetic diseases and help predict the disease. Results: We propose a method, named Parepro (Predicting the amino acid replacement probability), to identify nsSNPs having either deleterious or neutral effects on the resulting protein function. Two independent datasets, HumVar and NewHumVar, taken from the PhD-SNP server, were applied to train the model and test the robustness of Parepro. Using a 20-fold ...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Jian Tian, Ningfeng Wu, Xuexia Guo, Jun Guo, Juhua Zhang, Yunliu Fan
• Keywords: Amino Acid, BMCBI 2007, Genetic Variations, Single Nucleotide Polymorphisms