A generic motif discovery algorithm for sequential data

• Published in: Bioinformatics Vol. 22; no. 1; pp. 21 - 28
• Main Authors: Jensen, Kyle L., Styczynski, Mark P., Rigoutsos, Isidore, Stephanopoulos, Gregory N.
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01.01.2006; Oxford Publishing Limited (England)
• Subjects: Algorithms; Amino Acid Motifs; Amino Acid Sequence; Amino acids; 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; Composition; Similarity; Regular expression; Discoveries; Algorithm; Protein; Clusterin; Original document; Computer program; Length; Web site; Bioinformatics; Deterministic approach
• ISSN: 1367-4803; 1367-4811; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/bti745

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