Classification of SARS-CoV-2 Variants in Turkey

Öz

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) causes the COVID-19 disease, which turns into a pandemic and threatens public health. Appearing of SARS-CoV-2 variants show a significant challenge to determine the risk of infection, develop vaccines as well as antiviral agents, monitor the changes, and assess the evolution of SARS-CoV-2. In this study, we propose a method identifying SARS-CoV-2 variants in Turkey. To achieve this goal, nucleotide occurrences are computed from the whole genome sequences that include four nucleotides, A, C, T, and G. Thus, 30 000 bps genome sequences are represented by only four integer numbers. After features are extracted, four classification methods, support vector machines, k-nearest neighbor, neural network, and decision tree are employed to identify SARS-CoV-2 variants. Experimental results are conducted on a dataset including 1403 genome sequences from Turkey and belonging to variants of SARS-CoV-2, B.1.1.7 (Alpha), B.1.351 (Beta), P.1. (Gamma), as well as B.1.617 (Delta). Experimental results present that the KNN classifier achieves an accuracy of 0.94, a precision of 0.81, a recall of 0.80, and an F-score of 0.80 on average.

Anahtar Kelimeler

• SARS-CoV-2 variants
• COVID-19
• Coronavirus
• Classifiers
• COVID-19 variants in Turkey

Kaynakça

1. [1] Volz, E. et al., 2021. Assessing transmissibility of SARS-CoV-2 lineage B.1.1.7 in England. Nature 593, 266–269.
2. [2] Tegally, H. et al., 2021. Detection of a SARS-CoV-2 variant of concern in South Africa. Nature 592, 438–443.
3. [3] Sabino, E. C. et al., 2021. Resurgence of COVID-19 in Manaus, Brazil, despite high seroprevalence. Lancet 397, 452–455.
4. [4] Mlcochova, P., et al., 2021. SARS-CoV-2 B.1.617.2 Delta variant replication and immune evasion. Nature.
5. [5] Sahoo, J. P., & Samal, K. C., 2021. World on Alert: WHO Designated South African New COVID Strain (Omicron/B. 1.1. 529) as a Variant of Concern. Biotica Research Today, 3(11), 1086-1088.
6. [7] Ali S., Sahoo B., Ullah N., Zelikovskiy A., Patterson M., Khan I. 2021 A k-mer Based Approach for SARS-CoV-2 Variant Identification. In: Wei Y., Li M., Skums P., Cai Z. (eds) Bioinformatics Research and Applications. ISBRA 2021. Lecture Notes in Computer Science, vol 13064. Springer, Cham.
7. [8] Noble, W. S., 2006. What is a support vector machine?. Nature biotechnology, 24(12), 1565-1567.
8. [9] Guo, G., Wang, H., Bell, D., Bi, Y., & Greer, K., 2003. KNN model-based approach in classification. In OTM Confederated International Conferences" On the Move to Meaningful Internet Systems" (pp. 986-996). Springer.

9. [10] Hagan, M. T., Demuth, H. B., & Beale, M., 1997. Neural network design. PWS Publishing Co.
10. [11] Galar, M., Fernandez, A., Barrenechea, E., Bustince, H., & Herrera, F., 2011. A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(4), 463-484.
11. [12] Shu, Y., & McCauley, J., 2017. GISAID: Global initiative on sharing all influenza data–from vision to reality. Eurosurveillance, 22(13), 30494.
12. [13] Refaeilzadeh, P., Tang, L., & Liu, H., 2009. Cross-validation. Encyclopedia of database systems, 5, 532-538.
13. [14] Khateeb, J., Li, Y., & Zhang, H., 2021. Emerging SARS-CoV-2 variants of concern and potential intervention approaches. In Critical Care (Vol. 25, Issue 1). Springer Science and Business Media LLC.
14. [15] Lauring, A. S., & Malani, P. N., 2021. Variants of SARS-CoV-2. In JAMA (Vol. 326, Issue 9, p. 880). American Medical Association (AMA).
15. [16] Simon-Loriere, E., Schwartz, O. 2022. Towards SARS-CoV-2 serotypes?. Nat Rev Microbiol 20, 187–188.
16. [17] Han, X., & Ye, Q. 2021. The variants of SARS‐CoV‐2 and the challenges of vaccines. In Journal of Medical Virology (Vol. 94, Issue 4, pp. 1366–1372). Wiley.
17. [18] Burioni, R., Topol, E.J. Assessing the human immune response to SARS-CoV-2 variants. Nat Med 27, 571–572, 2021.
18. [19] Kandeel, M., Mohamed, M. E. M., Abd El‐Lateef, H. M., Venugopala, K. N., & El‐Beltagi, H. S. (2021). Omicron variant genome evolution and phylogenetics. In Journal of Medical Virology (Vol. 94, Issue 4, pp. 1627–1632). Wiley. https://doi.org/10.1002/jmv.27515
19. [20] Arora, P., Kumar, H., & Panigrahi, B. K. (2020). Prediction and analysis of COVID-19 positive cases using deep learning models: A descriptive case study of India. In Chaos, Solitons & Fractals (Vol. 139, p. 110017). Elsevier BV. https://doi.org/10.1016/j.chaos.2020.110017
20. [21] Arslan, H., & Arslan, H. (2021). A new COVID-19 detection method from human genome sequences using CpG island features and KNN classifier. In Engineering Science and Technology, an International Journal (Vol. 24, Issue 4, pp. 839–847). Elsevier BV. https://doi.org/10.1016/j.jestch.2020.12.026
21. [22] Arslan, H. (2021). COVID-19 prediction based on genome similarity of human SARS-CoV-2 and bat SARS-CoV-like coronavirus. In Computers & Industrial Engineering (Vol. 161, p. 107666). Elsevier BV. https://doi.org/10.1016/j.cie.2021.107666
22. [23] Garcia-Beltran et al.(2021). COVID-19-neutralizing antibodies predict disease severity and survival. In Cell (Vol. 184, Issue 2, pp. 476-488.e11). Elsevier BV. https://doi.org/10.1016/j.cell.2020.12.015
23. [24] Hatirnaz Ng, O., Akyoney, S., Sahin, I., Soykam, H. O., Bayram Akcapinar, G., Ozdemir, O., Kancagi, D. D., Sir Karakus, G., Yurtsever, B., Kocagoz, A. S., Ovali, E., & Ozbek, U. (2021). Mutational landscape of SARS-CoV-2 genome in Turkey and impact of mutations on spike protein structure. In M. Adnan (Ed.), PLOS ONE (Vol. 16, Issue 12, p. e0260438). Public Library of Science (PLoS). https://doi.org/10.1371/journal.pone.0260438
24. [25] Tao, K., Tzou, P. L., Nouhin, J., Gupta, R. K., de Oliveira, T., Kosakovsky Pond, S. L., Fera, D., & Shafer, R. W. (2021). The biological and clinical significance of emerging SARS-CoV-2 variants. In Nature Reviews Genetics (Vol. 22, Issue 12, pp. 757–773). Springer Science and Business Media LLC. https://doi.org/10.1038/s41576-021-00408-x
25. [26] Faria, N. R. et al. (2021). Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil. In Science (Vol. 372, Issue 6544, pp. 815–821). American Association for the Advancement of Science (AAAS). https://doi.org/10.1126/science.abh2644
26. [27] Shu, Y., & McCauley, J. (2017). GISAID: Global initiative on sharing all influenza data – from vision to reality. In Eurosurveillance (Vol. 22, Issue 13). European Centre for Disease Control and Prevention (ECDC). https://doi.org/10.2807/1560-7917.es.2017.22.13.30494
27. [28] H. Arslan and B. Aygün, "Performance Analysis of Machine Learning Algorithms in Detection of COVID-19 from Common Symptoms," 2021 29th Signal Processing and Communications Applications Conference (SIU), 2021, pp. 1-4, doi: 10.1109/SIU53274.2021.9477809.
28. [29] Harvey, W.T., Carabelli, A.M., Jackson, B. et al. (2021). SARS-CoV-2 variants, spike mutations and immune escape. Nat Rev Microbiol 19, 409–424 (2021). https://doi.org/10.1038/s41579-021-00573-0
29. [30] Arslan, H. (2021). Machine Learning Methods for COVID-19 Prediction Using Human Genomic Data. In The 7th International Management Information Systems Conference. International Management Information Systems Conference. MDPI. https://doi.org/10.3390/proceedings2021074020
30. [31] Hamed, A., Sobhy, A., & Nassar, H. (2021). Accurate Classification of COVID-19 Based on Incomplete Heterogeneous Data using a KNN Variant Algorithm. In Arabian Journal for Science and Engineering (Vol. 46, Issue 9, pp. 8261–8272). Springer Science and Business Media LLC. https://doi.org/10.1007/s13369-020-05212-z
31. [32] Hasan, N. (2020). A Methodological Approach for Predicting COVID-19 Epidemic Using EEMD-ANN Hybrid Model. In Internet of Things (Vol. 11, p. 100228). Elsevier BV. https://doi.org/10.1016/j.iot.2020.100228
32. [33] Yoo, S. H. et al. (2020). Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging. In Frontiers in Medicine (Vol. 7). Frontiers Media SA. https://doi.org/10.3389/fmed.2020.00427