Modeling and identification of alternative folding in regulatory RNAs using context-sensitive HMMS

Recent research on gene regulation has revealed that many non-coding RNAs (ncRNAs) are actively involved in controlling various gene-regulatory networks. For such ncRNAs, their secondary structures play crucial roles in carrying out their functions. Interestingly enough, many regulatory RNAs can choose from two alternative structures based on external factors, which enables the RNAs to regulate the expression of certain genes in an environment-dependent manner. The existence of alternative structures give rise to complex correlations in the primary sequence of the RNA. In this paper, we propose an efficient method for modeling alternative secondary structures in regulatory RNAs. The proposed method can be applied to the prediction of novel regulatory RNAs in genome sequences.