A hybrid method for splice site prediction based on Markov model and codon information. (2016)

• Record Type: Journal Article
• Authors: Wei, Dan; Peng, Yin; Wei, Yanjie; Jiang, Qingshan; Fang, Jinglong
• Is Part Of: International journal of data mining and bioinformatics. Volume 16:Number 4(2016)
• Page Start: 345
• Page End: 362
• Publication Date: 2016
• Subjects: splice site prediction -- support vector machines -- SVM -- Markov models -- codon bias -- splice sites -- gene identification -- bioinformatics -- feature selection -- gene sequences; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

Predicting splice sites is very important for gene identification. In this paper, we propose a hybrid splice site prediction method, SVM with Markov model and Codon usage (MC-SVM). The sequence features used for MC-SVM contain the codon bias information and the Markov probabilistic dependence information between adjacent nucleotides. Feature selection is performed using an F-score-based method, and then MC-SVM employs SVM to predict splice sites for both the acceptor and the donor sites. The test on the HS3D data set shows MC-SVM performs well for human gene sequences. The prediction accuracy of MC-SVM is 94.0% for donor splice sites, and 91.5% for acceptor splice sites on the data set with an equal amount of true and false splice site sequences. Compared with many other methods, MC-SVM achieved an improved prediction performance.