Identification of Common Structural Motifs in RNA Sequences Using Artificial Bee Colony Algorithm for Optimization

RNA molecules folded into secondary structure are found to have structure related functionalities. Efficient computational techniques are required for common structural motif identification due to its relevance in the study of various functional aspects. In this work we focus on finding the most frequent descriptor motif inherent in given set of RNA sequences. Our approach uses an efficient computational method incorporating Nature inspired optimization algorithm. The motif skeletons are obtained by applying context free grammar defined for the descriptor motif. Then swarm intelligence based Artificial Bee Colony optimization algorithm is applied to derive the common motif with minimum and maximum length values of each motif element. Optimization process is done based on the objective function defined with the frequency of occurrence as major criterion. This method is able to generate correct motif structures in Signal Recognition Particle data set. The resultant motif is compared with the common motifs generated by other evolutionary methods.