Change-point detection in biological sequences via genetic algorithm

Tatiana Polushina*, Georgy Sofronov

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

    10 Citations (Scopus)


    Genome research is one of the most interesting and important areas of the science nowadays. It is well-known that the genomes of complex organisms are highly organized. Many studies show that DNA sequence can be divided into a few segments, which have various properties of interest. Detection of this segments is extremely significant from the point of view of practical applications, as well as for understanding evolutional processes. We model genome sequences as a multiple change-point process, that is, a process in which sequential data are divided into segments by an unknown number of change-points, with each segment supposed to have been generated by a process with different parameters. Multiple change-point models are important in many biological applications and, specifically, in analysis of biomolecular sequences. In this paper, we propose to use genetic algorithm to identify change-points. Numerical experiments illustrate the effectiveness of our approach to the problem. We obtain estimates for the positions of change-points in artificially generated sequences and compare the accuracy of these estimates to those obtained via Markov chain Monte Carlo and the Cross-Entropy method. We also provide examples with real data sets to illustrate the usefulness of our method.

    Original languageEnglish
    Title of host publication2011 IEEE Congress of Evolutionary Computation, CEC 2011
    Place of PublicationPiscataway, N.J.
    PublisherInstitute of Electrical and Electronics Engineers (IEEE)
    Number of pages6
    ISBN (Electronic)9781424478354
    ISBN (Print)9781424478347
    Publication statusPublished - 2011
    Event2011 IEEE Congress of Evolutionary Computation, CEC 2011 - New Orleans, LA, United States
    Duration: 5 Jun 20118 Jun 2011


    Other2011 IEEE Congress of Evolutionary Computation, CEC 2011
    Country/TerritoryUnited States
    CityNew Orleans, LA


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