Material Detail

This video was recorded at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Athens 2011. 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 http://siloe.deis.unical.it/l-sme. A detailed description of the algorithms underlying L-SME is available in [1].

Disciplines:

• Science and Technology  / Computer Science  / Programming & Programming Languages