Abstract
We develop, analyze and experiment with a new tool, called madmx, which extracts frequent motifs, possibly including don’t care characters, from biological sequences. We introduce density, a simple and flexible measure for bounding the number of don’t cares in a motif, defined as the ratio of solid (i.e., different from don’t care) characters to the total length of the motif. By extracting only maximal dense motifs, madmx reduces the output size and improves performance, while enhancing the quality of the discoveries. The efficiency of our approach relies on a newly defined combining operation, dubbed fusion, which allows for the construction of maximal dense motifs in a bottom-up fashion, while avoiding the generation of nonmaximal ones. We provide experimental evidence of the efficiency and the quality of the motifs returned by madmx.
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Grossi, R., Pietracaprina, A., Pisanti, N., Pucci, G., Upfal, E., Vandin, F. (2009). MADMX: A Novel Strategy for Maximal Dense Motif Extraction. In: Salzberg, S.L., Warnow, T. (eds) Algorithms in Bioinformatics. WABI 2009. Lecture Notes in Computer Science(), vol 5724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04241-6_30
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DOI: https://doi.org/10.1007/978-3-642-04241-6_30
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