Authors: ZongXiao Sun LingJie Sang LiNing Ju HuaiQiu Zhu
Publish Date: 2008/10/29
Volume: 53, Issue: 21, Pages: 3331-
Abstract
It is of significance for splice site prediction to develop novel algorithms that combine the sequence patterns of regulatory elements such as enhancers and silencers with the patterns of splicing signals In this paper a statistical model of splicing signals was built based on the entropy density profile EDP method weight array method WAM and κ test moreover the model of splicing regulatory elements was developed by an unsupervised selflearning method to detect motifs associated with regulatory elements With two models incorporated a multilevel support vector machine SVM system was devised to perform ab initio prediction for splice sites originating from DNA sequence in eukaryotic genome Results of large scale tests on human genomic splice sites show that the new method achieves a comparative high performance in splice site prediction The method is demonstrated to be with at least the same level of performance and usually better performance than the existing SpliceScan method based on modeling regulatory elements and shown to have higher accuracies than the traditional methods with modeling splicing signals such as the GeneSplicer In particular the method has evident advantage over splice site prediction for the genes with lower GC content
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