Incorporating evolutionary information and functional domains for identifying RNA splicing factors in XXXX .

@entity1 @entity535
Regulation of pre-mRNA splicing is achieved through the interaction of RNA sequence elements and a variety of RNA-splicing related proteins (splicing factors). The splicing machinery in @entity1 is not yet fully elucidated, partly because splicing factors in @entity1 have not been exhaustively identified. Furthermore, experimental methods for splicing factor identification are time-consuming and lab-intensive. Although many computational methods have been proposed for the identification of RNA-binding proteins, there exists no development that focuses on the identification of RNA-splicing related proteins so far. Therefore, we are motivated to design a method that focuses on the identification of @entity1 splicing factors using experimentally verified splicing factors. The investigation of @entity535 composition reveals that there are remarkable differences between splicing factors and non-splicing proteins. A support vector machine (SVM) is utilized to construct a predictive model, and the five-fold cross-validation evaluation indicates that the SVM model trained with @entity535 composition could provide a promising accuracy (80.22%). Another basic feature, @entity535 dipeptide composition, is also examined to yield a similar predictive performance to @entity535 composition. In addition, this work presents that the incorporation of evolutionary information and domain information could improve the predictive performance. The constructed models have been demonstrated to effectively classify (73.65% accuracy) an independent data set of @entity1 splicing factors. The result of independent testing indicates that in silico identification could be a feasible means of conducting preliminary analyses of splicing factors and significantly reducing the number of potential targets that require further in vivo or in vitro confirmation.
