The research of RNA secondary structure prediction based on extended NSSEL labels

RNA secondary structures are important in many biological processes and the efficient structure prediction can give vital directions for experimental investigations. A new representation of the RNA structure information that can express pseudoknots is introduced in this paper, which is called ldquoextened NSSEL labelsrdquo. An SVM model is presented to predict RNA secondary structures based on this labeled method. In this algorithm, test sequences can be converted to label sequences which can be revert to corresponding RNA secondary structures. Experiment shows that this model not only can solve too long computational time problem of traditional algorithms, but also can predict long RNA sequences which are difficult with traditional folding algorithms.