MISCORE: mismatch-based matrix similarity scores for DNA motif detection

Authors:
Dianhui Wang;Nung Kion Lee
Affiliations:
Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Victoria, Australia;Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Victoria, Australia
Venue:
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Year:
2008

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Abstract

To detect or discover motifs in DNA sequences, two important concepts related to existing computational approaches are motif model and similarity score. One of motif models, represented by a position frequency matrix (PFM), has been widely employed to search for putative motifs. Detection and discovery of motifs can be done by comparing kmers with a motif model, or clustering kmers according to some criteria. In the past, information content based similarity scores have been widely used in searching tools. In this paper, we present a mismatch-based matrix similarity score (namely, MISCORE) for motif searching and discovering purpose. The proposed MISCORE can be biologically interpreted as an evolutionary metric for predicting a kmer as a motif member or not. Weighting factors, which are meaningful for biological data mining practice, are introduced in the MISCORE. The effectiveness of the MISCORE is investigated through exploring its separability, recognizability and robustness. Three well-known information contentbased matrix similarity scores are compared, and results show that our MISCORE works well.