Efficient sparse self-similarity matrix construction for repeating sequence detection
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
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There has been a growing interest in discovery of significant patterns in biological sequences that correspond to some structural and/ or functional feature of the bio-molecule known as motifs. It has important application in determining regulatory sites and drug target identification. Identification of motif is a challenging problem because motifs exist in different sequences in various mutated forms. In this paper, we consider the problem of finding the position of a given a motif of length l with up to d number of mismatches in a given set of DNA sequences. We represent the given set of sequences as a generalized suffix tree (GST) and obtain new data structures based on this tree. We develop a tree based algorithm to find motifs with allowable number of mismatches. Our algorithm aims to rank the search space, there by ensuring the highest ranking space contains the motif with allowable number of mismatches. Our hypothesis that the subtree under the highest ranking node is the most probable search space to contain the motif is explained through examples and implementation.