L-SME: A System for Mining Loosely Structured Motifs

  • Fabio Fassetti
  • Gianluigi Greco
  • Giorgio Terracina
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6913)


We present L-SME, a system to efficiently identify loosely structured motifs in genome-wide applications. L-SME is innovative in three aspects. Firstly, it handles wider classes of motifs than earlier motif discovery systems, by supporting boxes swaps and skips in the motifs structure as well as various kinds of similarity functions. Secondly, in addition to the standard exact search, it supports search via randomization in which guarantees on the quality of the results can be given a-priori based on user-definable resource (time and space) constraints. Finally, L-SME comes equipped with an intuitive graphical interface through which the structure for the motifs of interest can be defined, the discovery method can be selected, and results can be visualized. The tool is flexible and scalable, by allowing genome-wide searches for very complex motifs and is freely accessible at A detailed description of the algorithms underlying L-SME is available in [1].


Complex Motif Motif Discovery Model Template Pattern Instance Levenshtein Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Fabio Fassetti
    • 1
  • Gianluigi Greco
    • 2
  • Giorgio Terracina
    • 2
  1. 1.ICAR-CNRItaly
  2. 2.Dep. of MathematicsRendeItaly

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