Environmental Modeling & Assessment

, Volume 14, Issue 5, pp 577–584 | Cite as

Identifying Change-Points in Biological Sequences via Sequential Importance Sampling

  • George Yu. Sofronov
  • Gareth E. Evans
  • Jonathan M. Keith
  • Dirk P. Kroese


The genomes of complex organisms, including the human genome, are highly structured. This structure takes the form of segmental patterns of variation in various properties and may be caused by the division of genomes into regions of distinct function, by the contingent evolutionary processes that gave rise to genomes, or by a combination of both. Whatever the cause, identifying the change-points between segments is potentially important, as a means of discovering the functional components of a genome, understanding the evolutionary processes involved, and fully describing genomic architecture. One property of genomes that is known to display a segmental pattern of variation is GC content. The GC content of a portion of DNA is the proportion of GC pairs that it contains. Sharp changes in GC content can be observed in human and other genomes. Such change-points may be the boundaries of functional elements or may play a structural role. We model genome sequences as a multiple change-point process, that is, a process in which sequential data are separated into segments by an unknown number of change-points, with each segment supposed to have been generated by a different process. We consider a Sequential Importance Sampling approach to change-point modeling using Monte Carlo simulation to find estimates of change-points as well as parameters of the process on each segment. Numerical experiments illustrate the effectiveness of the approach. We obtain estimates for the locations of change-points in artificially generated sequences and compare the accuracy of these estimates to those obtained via Markov chain Monte Carlo and a well-known method, IsoFinder. We also provide examples with real data sets to illustrate the usefulness of this method.


Comparative genomics Multiple change-point problem 



G. Yu. Sofronov and D. P. Kroese acknowledge the support of an Australian Research Council discovery grant (DP0556631). J. M. Keith would like to acknowledge the support of the Australian Research Council discovery grants (DP0452412, DP0556631) and a National Medical and Health Research Council grant “Statistical methods and algorithms for analysis of high-throughput genetics and genomics platforms” (389892).


  1. 1.
    Braun, J. V., & Muller, H.-G. (1998). Statistical methods for DNA sequence segmentation. Statistical Science, 13, 142–162.CrossRefGoogle Scholar
  2. 2.
    Keith, J., Kroese, D. P., & Bryant D. (2004). A generalized markov sampler. Methodology and Computing in Applied Probability, 6(1), 29–53.CrossRefGoogle Scholar
  3. 3.
    Keith, J. M. (2006). Segmenting eukaryotic genomes with the generalized Gibbs sampler. Journal of Computational Biology, 13(7), 1369–1383.CrossRefGoogle Scholar
  4. 4.
    Keith, J. M., Adams, P., Stephen, S., & Mattick, J. S. (2008). Delineating slowly and rapidly evolving fractions of the drosophila genome. Journal of Computational Biology, 15(4), 407–430.CrossRefGoogle Scholar
  5. 5.
    Oliver, J. L., Bernaola-Galvan, P., Carpena, P., & Roman-Roldan, R. (2001). Isochore chromosome maps of eukaryotic genomes. Gene, 276, 47–56.CrossRefGoogle Scholar
  6. 6.
    Oliver, J. L., Carpena, P., Hackenberg, M., & Bernaola-Galvan, P. (2005). IsoFinder.
  7. 7.
    Oliver, J. L., Carpena, P., Hackenberg, M., & Bernaola-Galvan, P. (2004). IsoFinder: computational prediction of isochores in genome sequences. Nucleic Acids Research, 32(Web Server issue), W287–W292.CrossRefGoogle Scholar
  8. 8.
    Oliver, J. L., Carpena, P., Roman-Roldan, R., Mata-Balaguer, T., et al. (2002). Isochore chromosome maps of the human genome. Gene, 300, 117–127.CrossRefGoogle Scholar
  9. 9.
    Oliver, J. L., Roman-Roldan, R., Perez, J., & Bernaola-Galvan, P. (1999). Segment: identifying compositional domains in DNA sequences. Bioinformatics, 15, 974–979.CrossRefGoogle Scholar
  10. 10.
    Rubinstein, R. Y., & Kroese, D. P. (2007). Simulation and the Monte Carlo method, 2nd edition. Wiley, New York.Google Scholar
  11. 11.
    Waterston, R. H., Lindblad-Toh, K., Birney, E., Rogers, J., et al. (2002). Initial sequencing and comparative analysis of the mouse genome. Nature, 420, 520–562.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • George Yu. Sofronov
    • 1
  • Gareth E. Evans
    • 2
  • Jonathan M. Keith
    • 3
  • Dirk P. Kroese
    • 2
  1. 1.School of Mathematics and Applied StatisticsUniversity of WollongongWollongongAustralia
  2. 2.Department of MathematicsThe University of QueenslandBrisbaneAustralia
  3. 3.School of Mathematical SciencesQueensland University of TechnologyBrisbaneAustralia

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