Predicting Conserved Hairpin Motifs in Unaligned RNA Sequences

Several experiments and observations have revealed the fact that small local distinct structural features in RNA molecules are correlated with their biological function, for example in post-transcriptional regulation of gene expression. Thus, finding similar structural features in a set of RNA sequences known to play the same biological function could provide substantial information concerning which parts of the sequences are responsible for the function itself. The main difficulty lies in the fact that in nearly all the cases the structure of the molecules is unknown, has to be somehow predicted, and that sequences with little or no similarity can fold into similar structures. The algorithm we present searches for regions of the sequences that, according to base pairing rules, can fold into similar structures, where the degree of similarity can be defined by the user. Any information concerning sequence similarity in the motifs can be used either as a search constraint, or a posteriori, by post- processing the output. The search for the regions sharing structural similarity is implemented with the affix tree, a novel text- indexing structure that significantly accelerates the search for patterns having a symmetric layout, like those forming RNA hairpins. Tests based on experimentally known structures have shown that the algorithm is able to identify functional motifs in the secondary structure of non coding RNA, such as Iron Responsive Elements (IRE) in the untranslated regions of ferritin mRNA, and the domain IV stem-loop structure in SRP RNA.