MUSA

MUSA: a parameter free algorithm for the identification of biologically significant motifs. Results: We propose a new algorithm, MUSA (Motif finding using an UnSupervised Approach), that can be used either to autonomously find over-represented complex motifs or to estimate search parameters for modern motif finders. This method relies on a biclustering algorithm that operates on a matrix of co-occurrences of small motifs. The performance of this method is independent of the composite structure of the motifs being sought, making few assumptions about their characteristics. The MUSA algorithm was applied to two datasets involving the bacterium Pseudomonas putida KT2440. The first one was composed of 70 σ54-dependent promoter sequences and the second dataset included 54 promoter sequences of up-regulated genes in response to phenol, as suggested by quantitative proteomics. The results obtained indicate that this approach is very effective at identifying complex motifs of biological significance. Availability: The MUSA algorithm is available upon request from the authors, and will be made available via a Web based interface.

References in zbMATH (referenced in 2 articles )

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  1. Comin, Matteo; Verzotto, Davide: Filtering degenerate patterns with application to protein sequence analysis (2013)
  2. Mendes, Nuno D.; Casimiro, Ana C.; Santos, Pedro M.; Sá-Correia, Isabel; Oliveira, Arlindo L.; Freitas, Ana T.: Musa: A parameter free algorithm for the identification of biologically significant motifs (2006) ioport