https://doi.org/10.1140/epjp/s13360-025-07136-9
Regular Article
Characterizing coronavirus RNA via complexity-entropy curves
1
Programa de Pós-Graduação em Física, Universidade do Estado do Rio Grande do Norte, 59625-620, Mossoró, RN, Brasil
2
Departamento de Física, Universidade Federal do Rio Grande do Norte, 59072-970, Natal, RN, Brasil
3
Departamento de Tecnologia e Ciência de Dados, FGV EAESP, 01313-902, São Paulo, Brasil
4
Departamento de Ciências Vegetais, Universidade Federal Rural do Semi-Árido, 59625-900, Mossoró, RN, Brasil
5
Centro Brasileiro de Pesquisas Físicas, Instituto Nacional de Ciências e Tecnologia de Sistemas Complexos, 22290-180, Rio de Janeiro, RJ, Brasil
a
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Received:
24
May
2025
Accepted:
30
November
2025
Published online:
13
December
2025
Abstract
This work applies information theory-based techniques, such as the complexity-entropy plane associated with Shannon, Tsallis, and Rényi entropies, to characterize six coronavirus species (HCoV-229E, HCoV-OC43, HCoV-NL63, HCoV-HKU1, MERS-CoV, and SARS-CoV-2) and their respective Spike proteins. Our results indicate MERS-CoV as the species with the most diverse structure of the complete sequences and SARS-CoV-2 by the Spike region. HCoV-HKU1 was characterized as the most predictable by k-mers and amino acid counts using Rényi entropy. The planes generated with Shannon and Tsallis entropies indicate HCoV-NL63. It was also observed that all analyzed species exhibited behaviors governed by stochastic processes, occupying distinct regions in the complexity-entropy plane, thereby facilitating their differentiation. However, some Spike proteins were located in nearby regions. Furthermore, a similar behavior was observed between the complexity-entropy planes generated from k-mer counts in nucleotide sequences and amino acid counts in Spike proteins, suggesting that a possible fractal pattern present in both datasets may be contributing to these results.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
