A local-stem-search algorithm to predict the RNA secondary structure

RNA secondary structure predicting is a classical problem in bioinformatics and the optimal algorithms based on minimal free energy (MFE) criterion are the widely used methods. However, pseudoknots render the problem of computing the RNA MFE structure with pseudoknot becomes a NP-hard problem. A heuristic algorithm-StemFind to predict RNA secondary structure with pseudoknot was presented. The algorithm regard stem as the basic search unit, adopting heuristic search strategy, and search the most possible RNA secondary structure in stem combination space. The StemFind algorithm to a large number of test sets was applied. Performance evaluation demonstrates that StemFind not only outperforms the well-known optimal and heuristic algorithms in overall sensitivity and specificity but also requires significantly less time than the optimal algorithm.