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Statistical analysis of gene and intergenic DNA sequences

Author

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  • Kugiumtzis, D.
  • Provata, A.

Abstract

Much of the on-going statistical analysis of DNA sequences is focused on the estimation of characteristics of coding and non-coding regions that would possibly allow discrimination of these regions. In the current approach, we concentrate specifically on genes and intergenic regions. To estimate the level and type of correlation in these regions we apply various statistical methods inspired from nonlinear time series analysis, namely the probability distribution of tuplets, the Mutual Information and the Identical Neighbour Fit. The methods are suitably modified to work on symbolic sequences and they are first tested for validity on sequences obtained from well-known simple deterministic and stochastic models. Then they are applied to the DNA sequence of chromosome 1 of arabidopsis thaliana. The results suggest that correlations do exist in the DNA sequence but they are weak and that intergenic sequences tend to be more correlated than gene sequences. The use of statistical tests with surrogate data establish these findings in a rigorous statistical manner.

Suggested Citation

  • Kugiumtzis, D. & Provata, A., 2004. "Statistical analysis of gene and intergenic DNA sequences," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 342(3), pages 623-638.
    • Handle: RePEc:eee:phsmap:v:342:y:2004:i:3:p:623-638
    DOI: 10.1016/j.physa.2004.05.070
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    References listed on IDEAS

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    1. Buldyrev, S.V. & Dokholyan, N.V. & Goldberger, A.L. & Havlin, S. & Peng, C.-K. & Stanley, H.E. & Viswanathan, G.M., 1998. "Analysis of DNA sequences using methods of statistical physics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 249(1), pages 430-438.
    2. Stanley, H.E & Buldyrev, S.V & Goldberger, A.L & Havlin, S & Peng, C.-K & Simons, M, 1999. "Scaling features of noncoding DNA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 273(1), pages 1-18.
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    Cited by:

    1. Papapetrou, M. & Kugiumtzis, D., 2013. "Markov chain order estimation with conditional mutual information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(7), pages 1593-1601.
    2. Papapetrou, M. & Kugiumtzis, D., 2020. "Tsallis conditional mutual information in investigating long range correlation in symbol sequences," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).

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