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

文献类型： 外文期刊

第一作者： Tian, Jian

作者： Tian, Jian;Wu, Ningfeng;Guo, Jun;Fan, Yunliu;Guo, Xuexia;Zhang, Juhua

作者机构：

期刊名称：BMC BIOINFORMATICS （ 影响因子：3.169； 五年影响因子：3.629 ）

ISSN： 1471-2105

年卷期： 2007 年 8 卷

页码：

收录情况： SCI

摘要： Background: Human genetic variations primarily result from single nucleotide polymorphisms (SNPs) that occur approximately every 1000 bases in the overall human population. The non-synonymous 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 cross validation test on the HumVar dataset, Parepro achieved a Matthews correlation coefficient (MCC) of 50% and an overall accuracy (Q2) of 76%, both of which were higher than those predicted by the methods, such as PolyPhen, SIFT, and HydridMeth. Further analysis on an additional dataset (NewHumVar) using Parepro yielded similar results. Conclusion: The performance of Parepro indicates that it is a powerful tool for predicting the effect of nsSNPs on protein function and would be useful for large-scale analysis of genomic nsSNP data.

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