Statistical parser for RNA secondary structure prediction

Predicting the secondary structure of RNA molecules from the knowledge of the primary structure (the sequence of bases) is still a challenging task. This paper presents a novel efficient statistical parser to predict RNA secondary structure. The parser is based on Role Inverse algorithm which combines the advantages of both traditional Chart algorithm and LR algorithm, saving both space and time. The Role Inverse algorithm is revised into a probabilistic version to implement the parser. Then a PCFG grammar is established according to the base-pairing structure to predict RNA secondary structure.