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Fractal correlations in the Covid-19 genome sequence via multivariate rescaled range analysis

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  • Meraz, Monica
  • Carbó, Roxana
  • Rodriguez, Eduardo
  • Alvarez-Ramirez, Jose

Abstract

An approach based on fractal scaling analysis to characterize the organization of the Covid-19 genome sequences is presented in this work. The method is based on a multivariate version of the fractal rescaled range analysis implemented on a sliding window scheme to detect variations of long-range correlations over the genome sequence domains. As a preliminary step, the nucleotide sequence is mapped in a numerical sequence by following a Voss rule, resulting in a multichannel sequence represented as a binary matrix. Fractal correlations, quantified in terms of the Hurst exponent, depending on the region of the sequence, where the Covid-19 genome sequences are predominantly random, with some patches of weak long-range correlations. The analysis shows that the regions of randomness are more abundant in the Covid-19 sequences than in the primitive SARS sequence, which suggests that the Covid-19 virus possesses a more diverse genomic structure for replication and infection. The analysis constrained to the surface glycoprotein region shows that the Covid-19 sequence is less random as compared to the SARS sequence, which indicates that the Covid-19 virus can undergo more ordered replications of the spike protein. The Omicron variation exhibits an interesting pattern with some randomness similarities with the other SARS and the Covid-19 genome sequences. Overall, the results show that the multivariate rescaled range analysis provides a suitable framework to assess long-term correlations hidden in the internal organization of the Covid-19 genome sequence.

Suggested Citation

  • Meraz, Monica & Carbó, Roxana & Rodriguez, Eduardo & Alvarez-Ramirez, Jose, 2023. "Fractal correlations in the Covid-19 genome sequence via multivariate rescaled range analysis," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    • Handle: RePEc:eee:chsofr:v:168:y:2023:i:c:s0960077923000334
    DOI: 10.1016/j.chaos.2023.113132
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    1. Fan Wu & Su Zhao & Bin Yu & Yan-Mei Chen & Wen Wang & Zhi-Gang Song & Yi Hu & Zhao-Wu Tao & Jun-Hua Tian & Yuan-Yuan Pei & Ming-Li Yuan & Yu-Ling Zhang & Fa-Hui Dai & Yi Liu & Qi-Min Wang & Jiao-Jiao , 2020. "Author Correction: A new coronavirus associated with human respiratory disease in China," Nature, Nature, vol. 580(7803), pages 7-7, April.
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    3. Meraz, M. & Alvarez-Ramirez, J. & Rodriguez, E., 2022. "Multivariate rescaled range analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 589(C).
    4. Pal, Mayukha & Kiran, V. Satya & Rao, P. Madhusudana & Manimaran, P., 2016. "Multifractal detrended cross-correlation analysis of genome sequences using chaos-game representation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 288-293.
    5. Fan Wu & Su Zhao & Bin Yu & Yan-Mei Chen & Wen Wang & Zhi-Gang Song & Yi Hu & Zhao-Wu Tao & Jun-Hua Tian & Yuan-Yuan Pei & Ming-Li Yuan & Yu-Ling Zhang & Fa-Hui Dai & Yi Liu & Qi-Min Wang & Jiao-Jiao , 2020. "A new coronavirus associated with human respiratory disease in China," Nature, Nature, vol. 579(7798), pages 265-269, March.
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    Cited by:

    1. Correia, J.P. & de Lima, M.M.F. & Silva, R. & Anselmo, D.H.A.L. & Vasconcelos, M.S. & Viswanathan, G.M., 2023. "Multifractal analysis of coronavirus sequences," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).

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