ArticleNo Access

COMPLEXITY-BASED CLASSIFICATION OF THE CORONAVIRUS GENOME VERSUS GENOMES OF THE HUMAN IMMUNODEFICIENCY VIRUS (HIV) AND DENGUE VIRUS

School of Engineering, Monash University, Selangor, Malaysia

https://doi.org/10.1142/S0218348X20501297Cited by:28 (Source: Crossref)

Abstract

Coronavirus disease (COVID-19) is a pandemic disease that has affected almost all around the world. The most crucial step in the treatment of patients with COVID-19 is to investigate about the coronavirus itself. In this research, for the first time, we analyze the complex structure of the coronavirus genome and compare it with the other two dangerous viruses, namely, dengue and HIV. For this purpose, we employ fractal theory, sample entropy, and approximate entropy to analyze the genome walk of coronavirus, dengue virus, and HIV. Based on the obtained results, the genome walk of coronavirus has greater complexity than the other two deadly viruses. The result of statistical analysis also showed the significant difference between the complexity of genome walks in case of all complexity measures. The result of this analysis opens new doors to scientists to consider the complexity of a virus genome as an index to investigate its danger for human life.

Keywords:

References

1. R. Lu et al., Genomic characterisation and epidemiology of 2019 novel coronavirus: Implications for virus origins and receptor binding, Lancet. 395(10224) (2020) 565–574. Web of Science, Google Scholar
2. A. Khailany, M. Safdar and M. Ozaslanc , Genomic characterization of a novel SARS-CoV-2, Gene. Rep. 19 (2020) 100682. Web of Science, Google Scholar
3. M. Pachetti et al., Emerging SARS-CoV-2 mutation hot spots include a novel RNA-dependent-RNA polymerase variant, J. Transl. Med. 18 (2020) 179, https://doi.org/10.1186/s12967-020-02344-6. Web of Science, Google Scholar
4. G. Zehender et al., Genomic characterization and phylogenetic analysis of SARS-COV-2 in Italy, J. Med. Virol. (2020), https://doi.org/10.1002/jmv.25794. Web of Science, Google Scholar
5. A. E. Castillo et al., Phylogenetic analysis of the first four SARS-CoV-2 cases in Chile, J. Med. Virol. (2020), https://doi.org/10.1002/jmv.25797. Web of Science, Google Scholar
6. C. Ceraolo and F. M. Giorgi , Genomic variance of the 2019-nCoV coronavirus, J. Med. Virol. 92(5) (2020) 522–528. Web of Science, Google Scholar
7. K. Kupferschmidt , Genome analyses help track coronavirus’ moves, Science 367(6483) (2020) 1176–1177. Web of Science, Google Scholar
8. T. Zhang, Q. Wu and Z. Zhang , Probable pangolin origin of SARS-CoV-2 associated with the COVID-19 outbreak, Curr. Biol. 30(7) (2020) 1346–1351.e2. Web of Science, Google Scholar
9. Y. Zhang and E. C. Holmes , A genomic perspective on the origin and emergence of SARS-CoV-2, Cell 181(2) (2020) 223–227. Web of Science, Google Scholar
10. H. Alipour et al., Complexity-based analysis of the relation between fractal visual stimuli and fractal eye movements, Fluct. Noise Lett. 18(3) (2019) 1950012. Link, Web of Science, Google Scholar
11. S. Mujib Kamal et al., Decoding of the relationship between human brain activity and walking paths, Technol. Health Care 28(4) (2020) 381–390. Web of Science, Google Scholar
12. H. Namazi and S. Jafari , Decoding of wrist movements’ direction by fractal analysis of Magnetoencephalography (MEG) signal, Fractals 27(2) (2019) 1950001. Link, Web of Science, Google Scholar
13. S. M. Kamal et al., Complexity-based analysis of the relation between human muscle reaction and walking path, Fluct. Noise Lett. (2020), https://doi.org/10.1142/S021947752050025X. Link, Web of Science, Google Scholar
14. J. M. Tapanainen et al., Fractal analysis of heart rate variability and mortality after an acute myocardial infarction, Am. J. Cardiol. 90(4) (2002) 347–352. Web of Science, Google Scholar
15. R. H. Namazi , Fractal-based analysis of the influence of music on human respiration, Fractals 25(6) (2017) 1750059. Link, Web of Science, Google Scholar
16. O. Shafiul et al., Complexity-based decoding of brain-skin relation in response to olfactory stimuli, Comput. Meth. Prog. Bio. 184 (2020) 105293, https://doi.org/10.1016/j.cmpb.2019.105293. Web of Science, Google Scholar
17. M. Mozaffarilegha, H. Namazi, A. A. Tahaei and S. Jafari , Complexity-based analysis of the difference between normal subjects and subjects with stuttering in speech evoked auditory brainstem response, J. Med. Biol. Eng. 39 (2019) 490–497. Web of Science, Google Scholar
18. H. Alipour, H. Namazi, H. Azarnoush and S. Jafari , Complexity-based analysis of the influence of visual stimulus color on human eye movement, Fractals 27(2) (2019) 1950002. Link, Web of Science, Google Scholar
19. H. Namazi et al., The fractal based analysis of human face and DNA variations during aging, Biosci. Trends. 10(6) (2017) 477–481. Web of Science, Google Scholar
20. M. R. Ashfaq Ahamed, M. Babini and H. Namazi , Complexity-based decoding of the relation between human voice and brain activity, Technol. Health Care. (2020), https://doi.org/10.3233/THC-192105. Web of Science, Google Scholar
21. H. Namazi and V. V. Kulish , Complexity-based classification of the coronavirus disease (COVID-19), Fractals (2020), https://doi.org/10.1142/S0218348X20501145. Link, Web of Science, Google Scholar
22. A. Carbone et al., 3D fractal DNA assembly from coding, geometry and protection, Nat. Comput. 3 (2004) 235–252. Google Scholar
23. C. Cattani and G. Pierro , On the fractal geometry of DNA by the binary image analysis, Bull. Math. Biol. 75(9) (2013) 1544–1570. Web of Science, Google Scholar
24. M. Babič, J. Mihelič and M. Calì , Complex network characterization using graph theory and fractal geometry: The case study of lung cancer DNA sequences, Appl. Sci. 10 (2020) 3037. Google Scholar
25. C. Cattani , Fractals and hidden symmetries in DNA, Math. Probl. Eng. 507056 (2010) 1–31. Google Scholar
26. H. Namazi and M. Kiminezhadmalaie , Diagnosis of lung cancer by fractal analysis of damaged DNA, Comput. Math. Methods Med. 2015 1–11, https://doi.org/10.1155/2015/242695. Web of Science, Google Scholar
27. H. Namazi, V. V. Kulish, F. Delaviz and A. Delaviz , Diagnosis of skin cancer by correlation and complexity analyses of damaged DNA, Oncotarget. 6(40) (2015) 42623–42631. Google Scholar
28. V. V. Solovyev , Fractal graphical representation and analysis of DNA and protein sequences, Biosystems. 30(1–3) (1993) 137–160. Web of Science, Google Scholar
29. K. Metze, R. Adam and J. B. Florindo , The fractal dimension of chromatin — A potential molecular marker for carcinogenesis, tumor progression and prognosis, Expert Rev. Mol. Diagn. 19(4) (2019) 299–312. Web of Science, Google Scholar
30. R. Sun, R. Song and K. Tong , Complexity analysis of EMG signals for patients after stroke during robot-aided rehabilitation training using fuzzy approximate entropy, IEEE T. Neur. Sys. Reh. 22(5) (2014) 1013–1019. Web of Science, Google Scholar
31. S. A. Ahmad and P. H. Chappell , Moving approximate entropy applied to surface electromyographic signals, Biomed. Signal Process. 3(1) (2008) 88–93. Web of Science, Google Scholar
32. H. Namazi, T. Seifi Ala and E. Aghasian , Complexity-based classification of EEG signal in normal subjects and patients with epilepsy, Technol. Health Care 28(1) (2020) 57–66. Web of Science, Google Scholar
33. H. Namazi et al., Analysis of the influence of memory content of auditory stimuli on the memory content of EEG signal, Oncotarget. 7(35) (2016) 56120–56128. Google Scholar
34. S. K. Botting et al., Sample entropy analysis of cervical neoplasia gene-expression signatures, BMC Bioinform 10 (2009) 66. Web of Science, Google Scholar
35. X. Zhang, X. A. Zhou and Y. H. Yu , Similarity analysis of DNA using improved approximate entropy, 2012 International Conference on Biomedical Engineering and Biotechnology, Macao, 2012, pp. 511–514, https://doi.org/10.1109/iCBEB.2012.353. Google Scholar
36. M. O. Sokunbi , Sample entropy reveals high discriminative power between young and elderly adults in short fMRI data sets, Front. Neuroinform. 8 (2014) 69. Web of Science, Google Scholar
37. X. Zhang and P. Zhou , Sample entropy analysis of surface EMG for improved muscle activity onset detection against spurious background spikes, J. Electromyogr. Kinesiol. 22(6) (2012) 901–907. Web of Science, Google Scholar
38. H. M. Al-Angari and A. V. Sahakian , Use of sample entropy approach to study heart rate variability in obstructive sleep apnea syndrome, IEEE Trans. Biomed. Eng. 54(10) (2007) 1900–1904. Web of Science, Google Scholar
39. M. Aktaruzzaman and R. Sassi , Sample entropy parametric estimation for heart rate variability analysis, Computing in Cardiology 2013, Zaragoza, (2013) pp. 429–432. Google Scholar
40. L. Montesinos, R. Castaldo and L. Pecchia , On the use of approximate entropy and sample entropy with centre of pressure time-series, J. Neuro Eng. Rehabil. 15 (2018) 116, https://doi.org/10.1186/s12984-018-0465-9. Web of Science, Google Scholar
41. A. P. Singh, S. Mishra and S. Jabin , Sequence based prediction of enhancer regions from DNA random walk, Sci. Rep. 8 (2018) 15912, https://doi.org/10.1038/s41598-018-33413-y. Web of Science, Google Scholar
42. C.-K. Peng et al., Long-range correlations in nucleotide sequences, Nature 356(6365) (1992) 168–170. Web of Science, Google Scholar
43. R. R. Sinden , Chapter 1 — Introduction to the structure, properties, and reactions of DNA, DNA Structure and Function (Academic press, USA, 1994), pp. 1–57. Google Scholar
44. H. Namazi, E. Aghasian and T. Seifi Ala , Fractal-based classification of Electroencephalography (EEG) signals in healthy adolescents and adolescents with symptoms of schizophrenia, Technol. Health Care (2019), https://doi.org/10.3233/THC-181497. Web of Science, Google Scholar
45. J. M. Yentes et al., The appropriate use of approximate entropy and sample entropy with short data sets, Ann. Biomed Eng. 41(2) (2013) 349–365. Web of Science, Google Scholar
46. K. K. Ho et al., Predicting survival in heart failure case and control subjects by use of fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics, Circulation. 96(3) (1997) 842–848. Web of Science, Google Scholar
47. https://www.ncbi.nlm.nih.gov/nuccore/. Google Scholar
48. S. Baron , Medical Microbiology, 4th edition (The University of Texas Medical Branch at Galveston, 1996). Google Scholar