Estimating change-points in biological sequences via the cross-entropy method

G. Evans (), G. Sofronov, J. Keith and D. Kroese

Annals of Operations Research, 2011, vol. 189, issue 1, 155-165

Abstract: The genomes of complex organisms, including the human genome, are known to vary in GC content along their length. That is, they vary in the local proportion of the nucleotides G and C, as opposed to the nucleotides A and T. Changes in GC content are often abrupt, producing well-defined regions. We model DNA sequences as a multiple change-point process in which the sequence is separated into segments by an unknown number of change-points, with each segment supposed to have been generated by a different process. Multiple change-point problems are important in many biological applications, particularly in the analysis of DNA sequences. Multiple change-point problems also arise in segmentation of protein sequences according to hydrophobicity. We use the Cross-Entropy method to estimate the positions of the change-points. Parameters of the process for each segment are approximated with maximum likelihood estimates. Numerical experiments illustrate the effectiveness of the approach. We obtain estimates of the locations of change-points in artificially generated sequences and compare the accuracy of these estimates with those obtained via other methods such as IsoFinder (Oliver et al. in Nucl. Acids Res. 32:W283–W292, 2004 ) and Markov Chain Monte Carlo. Lastly, we provide examples with real data sets to illustrate the usefulness of our method. Copyright Springer Science+Business Media, LLC 2011

Date: 2011
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DOI: 10.1007/s10479-010-0687-0

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