Finding gapped motifs by a novel evolutionary algorithm

Chengwei Lei, Jianhua Ruan

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Scopus citations


    Identifying approximately repeated patterns, or motifs, in biological sequences from a set of co-regulated genes is an important step towards deciphering the complex gene regulatory networks and understanding gene functions. In this work, we develop a novel motif finding algorithm based on 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. Our algorithm also provides several unique 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, which is usually impractical for real applications. In comparison, our method models gaps explicitly, and provides an easy solution to find gapped motifs without any detailed knowledge of gaps. Our method also allows some input sequences to contain zero or multiple binding sites. Experimental results on synthetic challenge problems as well as real biological sequences show that our method is both more efficient and more accurate than several existing algorithms, especially when gaps are present in the motifs.

    Original languageEnglish (US)
    Title of host publicationEvolutionary Computation, Machine Learning and Data Mining in Bioinformatics - 8th European Conference, EvoBIO 2010, Proceedings
    PublisherSpringer Verlag
    Number of pages12
    ISBN (Print)3642122108, 9783642122101
    StatePublished - 2010
    Event8th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2010 - Istanbul, Turkey
    Duration: Apr 7 2010Apr 9 2010

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume6023 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference8th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2010


    • DNA motif
    • Evolutionary algorithm
    • Optimization
    • PSO

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)


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