A novel computational method for the identification of plant alternative splice sites

Abstract

Alternative splicing (AS) increases protein diversity by generating multiple transcript isoforms from a single gene in higher eukaryotes. Up to 48% of plant genes exhibit alternative splicing, which has proven to be involved in some important plant functions such as the stress response. A hybrid feature extraction approach which combing the position weight matrix (PWM) with the increment of diversity (ID) was proposed to represent the base conservative level (BCL) near splice sites and the similarity level of two datasets, respectively. Using the extracted features, the support vector machine (SVM) was applied to classify alternative and constitutive splice sites. By the proposed algorithm, 80.8% of donor sites and 85.4% of acceptor sites were correctly classified. It is anticipated that the novel computational method is promising for the identification of AS sites in plants.