Improving RNA secondary structure prediction using direct coupling analysis

Secondary structures of RNAs are the basis of understanding their tertiary structures and functions and so their predictions are widely needed due to increasing discovery of noncoding RNAs. In the last decades, a lot of methods have been proposed to predict RNA secondary structures but their accuracies encountered bottleneck. Here we present a method for RNA secondary structure prediction using direct coupling analysis and a remove-and-expand algorithm that shows better performance than four existing popular multiple-sequence methods. We further show that the results can also be used to improve the prediction accuracy of the single-sequence methods.