Hairpin: a tool for predicting structural non-coding RNA genes ab initio

BackgroundStructural RNA genes play important and various roles in gene expression and its regulation. Finding such RNA genes in a genome poses a challenge, which in most cases is solved by homology approaches. Ab intio methods for prediction exist, but are not that much explored.\n\nResultsWe introduce hairpin which identify potential structural RNA genes only based on the sequence. We use the algorithm to predict RNA genes in Escherichia coli K-12. When looking at very short regions of the genome, we do not get results differing very much from a random shuffling of the genome. However, at longer stretches it is a clear biological signal. It turns out that none of the regions predicted to code for RNA genes have such an annotation in literature.\n\nConclusionsArbitrary DNA sequences seem to give rise to transcripts with secondary structures similar to real ncRNA. We therefore conclude that exclusively looking at secondary structure base-parings is in general a futile approach.