A generic motif discovery algorithm for sequential data.
BACKGROUND
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.


BACKGROUND
Gemoda is freely available at http://web.mit.edu/bamel/gemoda
Jensen
Kyle L
KL
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Styczynski
Mark P
MP
Rigoutsos
Isidore
I
Stephanopoulos
Gregory N
GN
D000465:Algorithms; D020816:Amino Acid Motifs; D000595:Amino Acid Sequence; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D017124:Conserved Sequence; D020869:Gene Expression Profiling / Q000379:methods; D006801:Humans; D008958:Models, Molecular; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated; D011487:Protein Conformation; D017433:Protein Structure, Secondary; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA; D017386:Sequence Homology, Amino Acid; D012984:Software