Predicting the phenotypic effects of non-synonymous single nucleotide polymorphisms based on support vector machines
1 Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China
2 Agricultural By-Products Processing Research Institute, Academy of Planning and Designing of the Ministry of Agriculture, Beijing 100026, China
3 Department of Biomedical Engineering, Beijing Institute of Technology, Beijing 100081, China
BMC Bioinformatics 2007, 8:450 doi:10.1186/1471-2105-8-450Published: 16 November 2007
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.
We propose a method, named Parepro (
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.