Predicting RNA secondary structures: one-grammar-fits-all solution

Abstract

RNA secondary structures are known to be important in many biological processes. Many available programs have been developed for RNA secondary structure prediction. Based on our knowledge, however, there still exist secondary structures of known RNA sequences which cannot be covered by these algorithms. In this paper, we provide an efficient algorithm that can handle all RNA secondary structures found in Rfam database. We designed a new stochastic context-free grammar named Rectangle Tree Grammar (RTG) which significantly expands the classes of structures that can be modelled. Our algorithm runs in O(n 6) time and the accuracy is reasonably high, with average PPV and sensitivity over 75%. In addition, the structures that RTG predicts are very similar to the real ones.