A generic motif discovery algorithm for sequential data

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...

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Bibliographic Details
Published in:Bioinformatics 2006-01, Vol.22 (1), p.21-28
Main Authors: Jensen, Kyle L., Styczynski, Mark P., Rigoutsos, Isidore, Stephanopoulos, Gregory N.
Format: Article
Language:English
Subjects:
Algorithms
Amino Acid Motifs
Amino Acid Sequence
Biological and medical sciences
Cluster Analysis
Computational Biology - methods
Conserved Sequence
Fundamental and applied biological sciences. Psychology
Gene Expression Profiling - methods
General aspects
Humans
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Models, Molecular
Molecular Sequence Data
Pattern Recognition, Automated
Protein Conformation
Protein Structure, Secondary
Sequence Alignment
Sequence Analysis, DNA
Sequence Homology, Amino Acid
Software
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Summary: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 Contact: gregstep@mit.edu Supplementary Information: Available at
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/bti745