MISCORE: Mismatch-Based Matrix Similarity Scores for DNA Motif Detection

• Main Authors: Wang, Dianhui, Lee, Nung Kion
• Other Authors: Köppen, Mario
• Format: Book Section
• Language: English
• Published: Springer Berlin/Heidelberg 2009
• Subjects: QA Mathematics; T Technology (General)
• Online Access: http://ir.unimas.my/id/eprint/11923/1/MISCORE_abstract.pdf; http://ir.unimas.my/id/eprint/11923/; http://download.springer.com/static/pdf/310/chp%253A10.1007%252F978-3-642-02490-0_59.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fchapter%2F10.1007%2F978-3-642-02490-0_59&token2=exp=1462501544~acl=%2Fstatic%2Fpdf%2F310%2Fchp%25253A10.1007%25252F978-3-64

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 mismatchbased 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.