MUSA: a parameter free algorithm for the identification of biologically significant motifs

• DOI: 10.1093/bioinformatics/btl537
• Publication Date: 2006-10-27T00:26:52Z
• Authors: Nuno D. Mendes; Ana C. Casimiro; Pedro M. Santos; Isabel Sá-Correia; Arlindo L. Oliveira; Ana T. Freitas
• Keywords: Motif (music); Structural motif; Biological data

Abstract Motivation: The ability to identify complex motifs, i.e. non-contiguous nucleotide sequences, is a key feature of modern motif finders. Addressing this problem extremely important, not only because these motifs can accurately model biological phenomena but its extraction highly dependent upon the appropriate selection numerous search parameters. Currently available combinatorial algorithms have proved be efficient in exhaustively enumerating (including motifs), which fulfill certain criteria. However, one major with methods large number parameters that need specified. Results: We propose new algorithm, MUSA (Motif finding using an UnSupervised Approach), used either autonomously find over-represented or estimate for This method relies on biclustering algorithm operates matrix co-occurrences small motifs. performance independent composite structure being sought, making few assumptions about their characteristics. was applied two datasets involving bacterium Pseudomonas putida KT2440. first composed 70 σ54-dependent promoter sequences and second dataset included 54 up-regulated genes response phenol, as suggested by quantitative proteomics. results obtained indicate approach very effective at identifying significance. Availability: request from authors, will made via Web based interface. Contact: [e-mail] Supplementary information: An appendix under ‘Papers on-line’.