IP6K gene discovery in plant mtDNA
CIBB'10 Proceedings of the 7th international conference on Computational intelligence methods for bioinformatics and biostatistics
L-SME: a system for mining loosely structured motifs
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Characterization and extraction of irredundant tandem motifs
SPIRE'12 Proceedings of the 19th international conference on String Processing and Information Retrieval
Information Sciences: an International Journal
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The discovery of information encoded in biological sequences is assuming a prominent role in identifying genetic diseases and in deciphering biological mechanisms. This information is usually encoded in patterns frequently occurring in the sequences, also called motifs. In fact, motif discovery has received much attention in the literature, and several algorithms have already been proposed, which are specifically tailored to deal with motifs exhibiting some kinds of "regular structure". Motivated by biological observations, this paper focuses on the mining of loosely structured motifs, i.e., of more general kinds of motif where several "exceptions" may be tolerated in pattern repetitions. To this end, an algorithm exploiting data structures conceived to efficiently handle pattern variabilities is presented and analyzed. Furthermore, a randomized variant with linear time and space complexity is introduced, and a theoretical guarantee on its performances is proven. Both algorithms have been implemented and tested on real data sets. Despite the ability of mining very complex kinds of pattern, performance results evidence a genome-wide applicability of the proposed techniques.