Bioinformatical approaches to RNA structure prediction

Motivation: The importance of non-coding RNAs is becoming increasingly evident, andoften the functionof thesemoleculesdepends on the structure. It is common to use alignments of related RNA sequences to deduce the consensus secondary structure by detecting patterns of co-evolution. A central part of such an analysis is to measure covariation between two positions in an alignment. Here, we rank various measures ranging from simple mutual information to more advanced covariation measures. Results: Mutual information is still used for secondary structure prediction, but the results of this study indicate which measures are useful. Incorporating more structural information by considering e.g. indels and stacking improves accuracy, suggesting that physically realistic measures yield improved predictions. This can be used to improve both current and future programs for secondary structure prediction. The best measure tested is the RNAalifold covariation measure modified to include stacking. Availability:Scripts, data and supplementary material can be found at http://www.binf.ku.dk/Stinus_covariation Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.