Finding Gapped Motifs by a Novel Evolutionary Algorithm

Background: Identifying approximately repeated patterns, or motifs, in DNA sequences from a set of co-regulated genes is an important step towards deciphering the complex gene regulatory networks and understanding gene functions. Results: In this work, we develop a novel motif finding algorithm using a population-based stochastic optimization technique called Particle Swarm Optimization (PSO), which has been shown to be effective in optimizing difficult multidimensional problems in continuous domains. We propose a modification of the standard PSO algorithm to handle discrete values, such as characters in DNA sequences. The algorithm provides several features. First, we use both consensus and position-specific weight matrix representations in our algorithm, taking advantage of the efficiency of the former and the accuracy of the later. Furthermore, many real motifs contain gaps, but the existing methods usually ignore them or assume a user know their exact locations and lengths, wh...

• Type: Journal
• Year: 2010
• Where: EVOW
• Authors: Chengwei Lei, Jianhua Ruan
• Keywords: Algorithms, Artificial Intelligence, Dna Sequences, EVOW 2010, Standard Pso Algorithm