HIGEDA: a hierarchical gene-set genetics based algorithm for finding subtle motifs in biological sequences

• Published in: Bioinformatics Vol. 26; no. 3; pp. 302 - 309
• Main Authors: Le, Thanh, Altman, Tom, Gardiner, Katheleen
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01.02.2010
• Subjects: Algorithms; Biological and medical sciences; Databases, Genetic; Databases, Protein; DNA - chemistry; Fundamental and applied biological sciences. Psychology; General aspects; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Proteins - chemistry; Sequence Analysis, DNA - methods; Sequence Analysis, Protein - methods; Software; Genetics; Hierarchical system; Gene; Genetic algorithm
• ISSN: 1367-4803; 1367-4811; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btp676

Motivation: Identification of motifs in biological sequences is a challenging problem because such motifs are often short, degenerate, and may contain gaps. Most algorithms that have been developed for motif-finding use the expectation-maximization (EM) algorithm iteratively. Although EM algorithms can converge quickly, they depend strongly on initialization parameters and can converge to local sub-optimal solutions. In addition, they cannot generate gapped motifs. The effectiveness of EM algorithms in motif finding can be improved by incorporating methods that choose different sets of initial parameters to enable escape from local optima, and that allow gapped alignments within motif models. Results: We have developed HIGEDA, an algorithm that uses the hierarchical gene-set genetic algorithm (HGA) with EM to initiate and search for the best parameters for the motif model. In addition, HIGEDA can identify gapped motifs using a position weight matrix and dynamic programming to generate an optimal gapped alignment of the motif model with sequences from the dataset. We show that HIGEDA outperforms MEME and other motif-finding algorithms on both DNA and protein sequences. Availability and implementation: Source code and test datasets are available for download at http://ouray.cudenver.edu/∼tnle/, implemented in C++ and supported on Linux and MS Windows. Contact: katheleen.gardiner@ucdenver.edu