Predicting the phenotypic effects of non-synonymous single nucleotide polymorphisms based on support vector machinesReport as inadecuate

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BMC Bioinformatics

, 8:450

First Online: 16 November 2007Received: 26 April 2007Accepted: 16 November 2007


BackgroundHuman 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.

ResultsWe propose a method, named Parepro P redicting the a mino acid re placement pro bability, 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.

ConclusionThe 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.

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2105-8-450 contains supplementary material, which is available to authorized users.

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Author: Jian Tian - Ningfeng Wu - Xuexia Guo - Jun Guo - Juhua Zhang - Yunliu Fan


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