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An alternative description of power law correlations in DNA sequences

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  • Silva, R.
  • Silva, J.R.P.
  • Anselmo, D.H.A.L.
  • Alcaniz, J.S.
  • da Silva, W.J.C.
  • Costa, M.O.

Abstract

We analyze the coding sequence for the Homo Sapiens via a model which naturally embraces power law correlations (PLC) among the bases in DNA sequences of living organisms. This model is based on a principle of universal optimization, which is the core of all statistical arguments, being associated with the power law distribution function of the length of DNA, measured in base pairs (bp). This distribution provides a PLC parameter introduced through a nonadditive framework in which such parameter measures the PLC in the DNA sequence. The results show that the Short-Range-Correlations (SRC), always present in coding DNA sequences, are appropriately captured through the power law distribution, adequately describing the cumulative length distribution of DNA bases, in contrast with the case of the traditional exponential statistical model. We use an Empirical cumulative distribution function and the database of proteins compiled by the Ensembl Project to show that the power law distribution provides the best description of the data. A Bayesian analysis of the data further confirms this result.

Suggested Citation

  • Silva, R. & Silva, J.R.P. & Anselmo, D.H.A.L. & Alcaniz, J.S. & da Silva, W.J.C. & Costa, M.O., 2020. "An alternative description of power law correlations in DNA sequences," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    • Handle: RePEc:eee:phsmap:v:545:y:2020:i:c:s0378437119320825
    DOI: 10.1016/j.physa.2019.123735
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    References listed on IDEAS

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    1. Stanley, H.E. & Buldyrev, S.V. & Goldberger, A.L. & Goldberger, Z.D. & Havlin, S. & Mantegna, R.N. & Ossadnik, S.M. & Peng, C.-K. & Simons, M., 1994. "Statistical mechanics in biology: how ubiquitous are long-range correlations?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 205(1), pages 214-253.
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    4. Katsaloulis, P & Theoharis, T & Provata, A, 2002. "Statistical distributions of oligonucleotide combinations: applications in human chromosomes 21 and 22," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 380-396.
    5. Th. Oikonomou & A. Provata, 2006. "Non-extensive trends in the size distribution of coding and non-coding DNA sequences in the human genome," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 50(1), pages 259-264, March.
    6. Oikonomou, Th. & Provata, A. & Tirnakli, U., 2008. "Nonextensive statistical approach to non-coding human DNA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(11), pages 2653-2659.
    7. 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. Gao, Shilong & Gao, Nunan & Kan, Bixia & Wang, Huiqi, 2021. "Stochastic resonance in coupled star-networks with power-law heterogeneity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    2. de Lima, M.M.F. & Costa, M.O. & Silva, R. & Fulco, U.L. & Oliveira, J.I.N. & Vasconcelos, M.S. & Anselmo, D.H.A.L., 2024. "Viral proteins length distributions: A comparative analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).

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