Classification of RNA sequences with pseudoknots using features based on partial sequences

Abstract

Classification on pseudoknots existence is a challenging and meaningful problem in Bioinformatics. As predicting RNA secondary structures with pseudoknots is NP-complete problem while predicting pseudoknot-free structures can be done in O(n 3 ) time, if a preliminary pseudoknots existence classification of RNA sequence can be done before the prediction, the classification result can enhance the efficiency of RNA secondary structure prediction. In this paper, a classification of the existence of pseudoknots in an RNA sequence is presented. A set of features have been chosen by partial sequence content and thousands of RNA sequences with validated structures are used to train the classifier. Using a validated testing dataset, this classification method is shown to achieve a very good performance that the best result get 87% accuracy in 10-fold cross validation and around 75% accuracy in testing data. Moreover it may reveal how partial sequence content can affect the formation of pseudoknots.