title: A novel method for in silico identification of regulatory SNPs in human genome.
abstract: Regulatory single nucleotide polymorphisms (rSNPs), kind of functional noncoding genetic variants, can affect gene expression in a regulatory way, and they are thought to be associated with increased susceptibilities to complex diseases. Here a novel computational approach to identify potential rSNPs is presented. Different from most other rSNPs finding methods which based on hypothesis that SNPs causing large allele-specific changes in transcription factor binding affinities are more likely to play regulatory functions, we use a set of documented experimentally verified rSNPs and nonfunctional background SNPs to train classifiers, so the discriminating features are found. To characterize variants, an extensive range of characteristics, such as sequence context, DNA structure and evolutionary conservation etc. are analyzed. Support vector machine is adopted to build the classifier model together with an ensemble method to deal with unbalanced data. 10-fold cross-validation result shows that our method can achieve accuracy with sensitivity of ~78% and specificity of ~82%. Furthermore, our method performances better than some other algorithms based on aforementioned hypothesis in handling false positives. The original data and the source matlab codes involved are available at https://sourceforge.net/projects/rsnppredict/.
authors.affiliation: Ministry of Education Key Lab for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an 710049, PR China.
authors.forename: Rong
authors.initials: R
authors.lastname: Li
authors.affiliation: Ministry of Education Key Lab for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an 710049, PR China. Electronic address: bell@mail.xjtu.edu.cn.
authors.forename: Dexing
authors.initials: D
authors.lastname: Zhong
authors.affiliation: Ministry of Education Key Lab for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an 710049, PR China.
authors.forename: Ruiling
authors.initials: R
authors.lastname: Liu
authors.affiliation: Ministry of Education Key Lab for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an 710049, PR China.
authors.forename: Hongqiang
authors.initials: H
authors.lastname: Lv
authors.affiliation: Ministry of Education Key Lab for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an 710049, PR China.
authors.forename: Xinman
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Ministry of Education Key Lab for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an 710049, PR China; School of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, PR China.
authors.forename: Jun
authors.initials: J
authors.lastname: Liu
authors.affiliation: Ministry of Education Key Lab for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an 710049, PR China.
authors.forename: Jiuqiang
authors.initials: J
authors.lastname: Han
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D005786:Gene Expression Regulation; D015894:Genome, Human; D006801:Humans; D008722:Methods; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D012680:Sensitivity and Specificity; D000069553:Supervised Machine Learning
keywords: Hydroxyl radical cleavage patterns; Imbalanced data; Position weight matrix; Support vector machine