title: Predicting the phenotypic effects of non-synonymous single nucleotide polymorphisms based on support vector machines.
abstract: 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China. tianjian3721@163.com
authors.forename: Jian
authors.initials: J
authors.lastname: Tian
authors.forename: Ningfeng
authors.initials: N
authors.lastname: Wu
authors.forename: Xuexia
authors.initials: X
authors.lastname: Guo
authors.forename: Jun
authors.initials: J
authors.lastname: Guo
authors.forename: Juhua
authors.initials: J
authors.lastname: Zhang
authors.forename: Yunliu
authors.initials: Y
authors.lastname: Fan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001483:Base Sequence; D004252:DNA Mutational Analysis / Q000379:methods*; D014644:Genetic Variation / Q000235:genetics; D006801:Humans; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*