Soft Computing based Model for Identification of Pseudoknots in RNA Sequence using Learning Grammar

RNA structure prediction is one of the major topics in bioinformatics. Among the various RNA structures, pseudoknots are the most complex and unique structure. Various methods have been used for modeling RNA pseudoknotted secondary structure. In this paper a new model for prediction of RNA pseudoknot structure has been proposed. In this model, features of two existing techniques, i. e. neural network and grammar are combined. The advantage of grammar, identification based on rules is combined with the strength of a neural network to learn. An Elman neural network is used to learn the context free grammar that represents a pseudoknot. This Learning grammar network further identifies if the RNA sequence contains pseudoknot or not. Learning grammar helps in reducing the drawbacks of both neural network and grammar thus increasing the overall power of identifying sequences with pseudoknots.