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Bioinformatics Advance Access originally published online on October 27, 2005
Bioinformatics 2006 22(1):21-28; doi:10.1093/bioinformatics/bti745
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

A generic motif discovery algorithm for sequential data

Kyle L. Jensen 1, Mark P. Styczynski 1, Isidore Rigoutsos 1,2 and Gregory N. Stephanopoulos 1,*

1Department of Chemical Engineering, Massachusetts Institute of Technology Cambridge, MA 02139, USA
2IBM Research Division, Thomas J. Watson Research Center Yorktown Heights, NY 10598, USA

*To whom correspondence should be addressed.

Motivation: Motif discovery in sequential data is a problem of great interest and with many applications. However, previous methods have been unable to combine exhaustive search with complex motif representations and are each typically only applicable to a certain class of problems.

Results: Here we present a generic motif discovery algorithm (Gemoda) for sequential data. Gemoda can be applied to any dataset with a sequential character, including both categorical and real-valued data. As we show, Gemoda deterministically discovers motifs that are maximal in composition and length. As well, the algorithm allows any choice of similarity metric for finding motifs. Finally, Gemoda's output motifs are representation-agnostic: they can be represented using regular expressions, position weight matrices or any number of other models for any type of sequential data. We demonstrate a number of applications of the algorithm, including the discovery of motifs in amino acids sequences, a new solution to the (l,d)-motif problem in DNA sequences and the discovery of conserved protein substructures.

Availability: Gemoda is freely available at http://web.mit.edu/bamel/gemoda

Contact: gregstep{at}mit.edu

Supplementary Information: Available at http://web.mit.edu/bamel/gemoda

Received on August 4, 2005; revised on October 14, 2005; accepted on October 24, 2005

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[Abstract] [Full Text] [PDF]


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