Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Trie-based apriori motif discovery approach
ISBRA'12 Proceedings of the 8th international conference on Bioinformatics Research and Applications
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Consensus and sequence pattern analysis on family alignments are extensively used to identify new family members and to determine functionally and structurally important identities. Since these common approaches emphasize dominant characteristics of the family and assume residue identities are independent at each position, there is no way to describe residue preferences outside of the family consensus. In this study, we propose a novel approach to detect motifs outside the consensus of a protein family alignment via an information theoretic approach. We implemented an algorithm that discovers frequent residue motifs that are high in information content and outside of the family consensus, called informative motifs, inspired by the classic Apriori algorithm. We observed that these informative motifs are mostly spatially localized and present distinctive features of various members of the family.