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Identification of splice sites plays a key role in the  annotation of genes. Consequently, improvement of computational prediction of  splice sites would be very useful. We examined the effect of the window size  and the number and position of the consensus bases with a chi-square test, and  then extracted the sequence multi-scale component features and the position and  adjacent position relationship features of consensus sites. Then, we  constructed a novel classification model using a support vector machine with  the previously selected features and applied it to the Homo sapiens splice  site dataset. This method greatly improved cross-validation accuracies for  training sets with true and spurious splice sites of both equal and different  proportions. This method was also applied to the NN269 dataset for further  evaluation and independent testing. The results were superior to those obtained  with previous methods, and demonstrate the stability and superiority of this  method for prediction of splice sites.

High-accuracy splice site prediction based on sequence component and position features