NcRNA Homology Search Using Hamming Distance Seeds

NcRNAs play important roles in many biological processes. Existing genome-scale ncRNA homology search tools identify ncRNAs in local sequence alignments generated by conventional sequence comparison methods. However, some types of ncRNA lack strong sequence conservation and tend to be missed by conventional sequence comparison methods. In this paper, we propose an ncRNA identification framework that is complementary to existing sequence comparison tools. By integrating a filtration step based on Hamming distance and a local structural alignment program such as FOLDALIGN, we can identify ncRNAs that lack strong sequence conservation. We introduce a coding method by which the Hamming-distance based filtration can easily distinguish transition from transversion, which show different frequency in functional ncRNAs. Our experiments demonstrate that the carefully designed Hamming distance seed can achieve better sensitivity in searching for poorly conserved ncRNAs than conventional sequence comparison tools