Genomic Characterization of Emerging SARS-CoV-2: A Systematic
Review

Shikha      Sharma; Rinkle      Rani; Nidhi      Kalra
Abstract

Introduction: Severe Acute Respiratory Syndrome Coronavirus – 2, SARS-CoV-2, is a wellknown virus for its fatal infectivity and widespread impact on the health of the worldwide population. Genome sequencing is critical in understanding the virus’s behavior, origin, and genetic variants. This article conducts an extensive literature review on the SARS-CoV-2 genome, including its Genome Structure, Genome Analysis, Evolution, Mutation, and, Genome Computation. It highlights the summary of clinical and evolutionary research along with the applicability of computational methods in the areas. It lucidly presents the structural detail and mutation analysis of SARS-CoV-2 without overwhelming the readers with difficult terms. In the pandemic, machine learning and deep learning emerged as a paradigm change, that when combined with genome analysis, enabled more precise identification and prognosis of the virus's impact. Molecular detailing is crucial in extracting features from the SARS-CoV-2 genome before computation models are applied.
Methods: Further, in this systematic study we investigate the usage of Machine Learning and Deep Learning models mapped to SARS-CoV-2 genome samples to see their applicability in virus detection and disease severity prediction. We searched research articles from various reputed journals explaining the structure, evolution, mutations, and computational methods published until June 2022.
Results: The paper summarizes significant trends in the research of SARS-COV-2 genomes. Furthermore, this research also identifies the limitations and research gaps that yet have to be explored more and indicates future directions.
Impact Statement: There are few review articles on the SARS-CoV-2 genome; these reviews target various aspects of the SARS-COV2 genome individually. This article considers all the aspects simultaneously and provides in-depth knowledge about the SARS-CoV-2 genome.
Conclusion: This article provides a detailed description about the type of samples, volumes of selection, processes, and tools used by various researchers in their studies. Further, the computational techniques applied to the SARS-COV2 genome are also discussed and analysed thoroughly.
Graphical Abstract

[1]
Fazeli-Varzaneh M, Ghorbi A, Ausloos M, Sallinger E, Vahdati S. Sleeping beauties of coronavirus research. IEEE Access  2021; 9: 21192-05.
 [http://dx.doi.org/10.1109/ACCESS.2021.3052918]

[2]
Coronavirus Graphs: Worldwide Cases and Deaths.  Available from: https://www.worldometers.info/coronavirus/worldwide-graphs/

[3]
Tu YF, Chien CS, Yarmishyn AA, et al. A review of sars-cov-2 and the ongoing clinical trials. Int J Mol Sci  2020; 21(7): 2657.
 [http://dx.doi.org/10.3390/ijms21072657] [PMID:  32290293]

[4]
Helmy YA, Fawzy M, Elaswad A, Sobieh A, Kenney SP, Shehata AA. The COVID-19 pandemic: A comprehensive review of taxonomy, genetics, epidemiology, diagnosis, treatment, and control. J Clin Med  2020; 9(4): 1225.
 [http://dx.doi.org/10.3390/jcm9041225] [PMID:  32344679]

[5]
Li Y. View from public health to molecular biology on coronavirus
disease 2019 (COVID-19). Proceedings of 7th international conference
on Biomedical and Bioinformatics Engineering, ICBBE  2020; 194-7.
 [http://dx.doi.org/10.1145/3444884.3444922]

[6]
Liu J. From Evolution to solution: Battling SARS-CoV-2. Proceedings of International Conference on Public Health and Data Science,
ICPHDS  2020; 1-7.
 [http://dx.doi.org/10.1109/ICPHDS51617.2020.00007]

[7]
Tang D, Comish P, Kang R. The hallmarks of COVID-19 disease. PLoS Pathog  2020; 16(5): e1008536.
 [http://dx.doi.org/10.1371/journal.ppat.1008536] [PMID:  32442210]

[8]
Zhu G, Zhu C, Zhu Y, Sun F. Minireview of progress in the structural study of SARS-CoV-2 proteins. Curr Res Microbial Sci  2020; 1: 53-61.
 [http://dx.doi.org/10.1016/j.crmicr.2020.06.003] [PMID:  33236001]

[9]
Alanagreh L, Alzoughool F, Atoum M. The human coronavirus disease COVID-19: Its origin, characteristics, and insights into potential drugs and its mechanisms. Pathogens  2020; 9(5): 331.
 [http://dx.doi.org/10.3390/pathogens9050331] [PMID:  32365466]

[10]
Jamshidi M, Lalbakhsh A, Talla J, et al. Artificial intelligence and
COVID-19: Deep learning approaches for diagnosis and treatment. IEEE Access  2020; 8: 109581-95.
 [http://dx.doi.org/10.1109/ACCESS.2020.3001973]

[11]
Pham QV, Nguyen DC, Huynh-The T, Hwang WJ, Pathirana PN. Artificial Intelligence (AI) and big data for Coronavirus (COVID-
19) pandemic: A survey on the State-of-the-Arts. IEEE Access  2020; 8: 130820-39.
 [http://dx.doi.org/10.1109/ACCESS.2020.3009328]

[12]
Islam MN, Inan TT, Rafi S, Akter SS, Sarker IH, Islam AKMN. A systematic review on the use of AI and ML for fighting the COVID-19 pandemic. IEEE Trans Artif Intell  2020; 1(3): 258-70.
 [http://dx.doi.org/10.1109/TAI.2021.3062771] [PMID:  35784006]

[13]
John CC, Ponnusamy V, Krishnan Chandrasekaran S. R N. A survey on mathematical, machine learning and deep learning models for COVID-19 transmission and diagnosis. IEEE Rev Biomed Eng  2022; 15: 325-40.
 [http://dx.doi.org/10.1109/RBME.2021.3069213] [PMID:  33769936]

[14]
Wang C, Horby PW, Hayden FG, Gao GF. A novel coronavirus outbreak of global health concern. Lancet  2020; 395(10223): 470-3.
 [http://dx.doi.org/10.1016/S0140-6736(20)30185-9] [PMID:  31986257]

[15]
Chan JFW, Yuan S, Kok KH, et al. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: A study of a family cluster. Lancet  2020; 395(10223): 514-23.
 [http://dx.doi.org/10.1016/S0140-6736(20)30154-9] [PMID:  31986261]

[16]
Chen N, Zhou M, Dong X, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study. Lancet  2020; 395(10223): 507-13.
 [http://dx.doi.org/10.1016/S0140-6736(20)30211-7] [PMID:  32007143]

[17]
Guo YR, Cao QD, Hong ZS, et al. The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak – an update on the status. Mil Med Res  2020; 7(1): 11.
 [http://dx.doi.org/10.1186/s40779-020-00240-0] [PMID:  32169119]

[18]
Wu F, Zhao S, Yu B, et al. Holmes EC and, Zhang Y-Z Severe acute respiratory syndrome coronavirus 2 isolate Wuhan-Hu-1, complete genome. Nature  2020; 579(7798): 265-9.
 [http://dx.doi.org/10.1038/s41586-020-2008-3] [PMID:  32015508]

[19]
Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet  2020; 395(10223): 497-506.
 [http://dx.doi.org/10.1016/S0140-6736(20)30183-5] [PMID:  31986264]

[20]
Wang D, Hu B, Hu C, et al. Clinical Characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China. JAMA  2020; 323(11): 1061-9.
 [http://dx.doi.org/10.1001/jama.2020.1585] [PMID:  32031570]

[21]
Guan W, Ni Z, Hu Y, et al. Clinical characteristics of coronavirus disease 2019 in china. N Engl J Med  2020; 382(18): 1708-20.
 [http://dx.doi.org/10.1056/NEJMoa2002032] [PMID:  32109013]

[22]
Ma L, Li H, Lan J, et al. Comprehensive analyses of bioinformatics applications in the fight against COVID-19 pandemic. Comput Biol Chem  2021; 95: 107599.
 [http://dx.doi.org/10.1016/j.compbiolchem.2021.107599] [PMID:  34773807]

[23]
Yang Y, Xiao Z, Ye K, et al. SARS-CoV-2: Characteristics and current advances in research. Virol J  2020; 17(1): 117.
 [http://dx.doi.org/10.1186/s12985-020-01369-z] [PMID:  32727485]

[24]
Atzrodt CL, Maknojia I, McCarthy RDP, et al. A Guide to COVID‐19: A global pandemic caused by the novel coronavirus SARS‐CoV‐2. FEBS J  2020; 287(17): 3633-50.
 [http://dx.doi.org/10.1111/febs.15375] [PMID:  32446285]

[25]
Shah N, Davariya V, Gupta SK, Gajjar P, Parmar J, D’Cruz L. Review: An insight into coronaviruses: Challenges, security and scope. Rev Med Virol  2020; 30(6): 1-8.
 [http://dx.doi.org/10.1002/rmv.2138] [PMID:  32754974]

[26]
Riggioni C, Comberiati P, Giovannini M, et al. A compendium answering 150 questions on COVID‐19 and SARS‐CoV‐2. Allergy  2020; 75(10): 2503-41.
 [http://dx.doi.org/10.1111/all.14449] [PMID:  32535955]

[27]
Long MJC, Aye Y. Science’s Response to CoVID‐19. ChemMedChem  2021; 16(15): 2288-314.
 [http://dx.doi.org/10.1002/cmdc.202100079] [PMID:  33811458]

[28]
Davis IM. SARS‐CoV: Lessons learned; opportunities missed for SARS‐CoV ‐2. Rev Med Virol  2021; 31(1): 1-6.
 [http://dx.doi.org/10.1002/rmv.2152] [PMID:  32808446]

[29]
Drosten C, Günther S, Preiser W, et al. Identification of a novel coronavirus in patients with severe acute respiratory syndrome. N Engl J Med  2003; 348(20): 1967-76.
 [http://dx.doi.org/10.1056/NEJMoa030747] [PMID:  12690091]

[30]
Lu R, Zhao X, Li J, et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: Implications for virus origins and receptor binding. Lancet  2020; 395(10224): 565-74.
 [http://dx.doi.org/10.1016/S0140-6736(20)30251-8] [PMID:  32007145]

[31]
Xu X, Chen P, Wang J, et al. Evolution of the novel coronavirus from the ongoing Wuhan outbreak and modeling of its spike protein for risk of human transmission. Sci China Life Sci  2020; 63(3): 457-60.
 [http://dx.doi.org/10.1007/s11427-020-1637-5] [PMID:  32009228]

[32]
Wu A, Peng Y, Huang B, et al. Genome composition and divergence of the novel coronavirus (2019-nCoV) originating in China. Cell Host Microbe  2020; 27(3): 325-8.
 [http://dx.doi.org/10.1016/j.chom.2020.02.001] [PMID:  32035028]

[33]
Sharma M. The SARS-CoV-2 and its similarity to other bat-derived
SARS-like coronaviruses: A data-driven study. 7th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics
and Computer Engineering, UPCON  2020; 1-6.
 [http://dx.doi.org/10.1109/UPCON50219.2020.9376568]

[34]
Adebowale A, Letebele P. Phylogenomics for tracking the epidemiology of COVID-19: The genomic data gap for the african continent. Proceedings of 2nd International Multidisciplinary Information Technology and Engineering Conference IMITEC  2020; 1-5.
 [http://dx.doi.org/10.1109/IMITEC50163.2020.9334111]

[35]
Tabibzadeh A, Zamani F, Laali A, et al. SARS-CoV-2 Molecular and phylogenetic analysis in COVID-19 patients: A preliminary] report from Iran. Infect Genet Evol  2020; 84: 104387.
 [http://dx.doi.org/10.1016/j.meegid.2020.104387] [PMID:  32485332]

[36]
Kumari P, Poddar R. A computational analysis of molecular evolution for virulence genes of zoonotic novel coronavirus (COVID-19). Comput Biol Chem  2021; 93: 107532.
 [http://dx.doi.org/10.1016/j.compbiolchem.2021.107532] [PMID:  34171504]

[37]
Wang C, Liu Z, Chen Z, et al. The establishment of reference sequence for SARS‐CoV‐2 and variation analysis. J Med Virol  2020; 92(6): 667-74.
 [http://dx.doi.org/10.1002/jmv.25762] [PMID:  32167180]

[38]
Sun C, Bai Y, Chen D, et al. Accurate classification of COVID‐19 patients with different severity via machine learning. Clin Transl Med  2021; 11(3): e323.
 [http://dx.doi.org/10.1002/ctm2.323] [PMID:  33784017]

[39]
Béreux S, Delmas B, Cazals F. Boosting the analysis of protein interfaces with multiple interface string alignments: Illustration on the spikes of coronaviruses. Proteins  2022; 90(3): 848-57.
 [http://dx.doi.org/10.1002/prot.26279] [PMID:  34779026]

[40]
Ahmed Z, Renart EG, Mishra D, Zeeshan S. JWES: A new pipeline for whole genome/exome sequence data processing, management, and gene‐variant discovery, annotation, prediction, and genotyping. FEBS Open Bio  2021; 11(9): 2441-52.
 [http://dx.doi.org/10.1002/2211-5463.13261] [PMID:  34370400]

[41]
Cheng JH, Liu WC, Chang TT, Hsieh SY, Tseng VS. An effective
and efficient algorithm for detecting exact deletion breakpoints
from viral next-generation sequencing data. Proceedings of 2020 International Computer Symposium, ICS  2020; 147-52.
 [http://dx.doi.org/10.1109/ICS51289.2020.00038]

[42]
Angeletti S, Benvenuto D, Bianchi M, Giovanetti M, Pascarella S, Ciccozzi M. COVID‐2019: The role of the nsp2 and nsp3 in its pathogenesis. J Med Virol  2020; 92(6): 584-8.
 [http://dx.doi.org/10.1002/jmv.25719] [PMID:  32083328]

[43]
Pereira F. Evolutionary dynamics of the SARS-CoV-2 ORF8 accessory
gene. Infect Genetics Evol  2020; 2 85-1-10
 [http://dx.doi.org/10.1016/j.meegid.2020.104525]

[44]
Yi X, Zhang Y, Xu T, Su X, Fu C. Study on pathological mechanism
of pneumonia infected by coronavirus based on time-series
gene co-expression network analysis. Proceedings of 9th International
Conference on Bioinformatics and Computational Biology,
ICBCB  2021; 168-73.
 [http://dx.doi.org/10.1109/ICBCB52223.2021.9459223]

[45]
Digard P, Lee HM, Sharp C, Grey F, Gaunt E. Intra-genome variability in the dinucleotide composition of SARS-CoV-2. Virus Evol  2020; 6(2): veaa057.
 [http://dx.doi.org/10.1093/ve/veaa057] [PMID:  33029383]

[46]
Pan YQ, Bahoussi AN, Guo F, Xing L. A single nucleotide distinguishes the SARS-CoV-2 in the Wuhan outbreak in December 2019 from that in Beijing-Xinfadi in June 2020, China. New Microbes New Infect  2021; 39: 100835.
 [http://dx.doi.org/10.1016/j.nmni.2020.100835] [PMID:  33425367]

[47]
Tort FL, Castells M, Cristina J. A comprehensive analysis of genome composition and codon usage patterns of emerging coronaviruses. Virus Res  2020; 283: 197976.
 [http://dx.doi.org/10.1016/j.virusres.2020.197976] [PMID:  32294518]

[48]
Huang W, Guo Y, Li N, Feng Y, Xiao L. Codon usage analysis of zoonotic coronaviruses reveals lower adaptation to humans by SARS-CoV-2. Infect Genet Evol  2021; 89: 104736.
 [http://dx.doi.org/10.1016/j.meegid.2021.104736] [PMID:  33516969]

[49]
Rout RK, Hassan SS, Sheikh S, Umer S, Sahoo KS, Gandomi AH. Feature-extraction and analysis based on spatial distribution of amino acids for SARS-CoV-2 Protein sequences. Comput Biol Med  2022; 141: 105024.
 [http://dx.doi.org/10.1016/j.compbiomed.2021.105024] [PMID:  34815067]

[50]
Ghorbani A, Samarfard S, Ramezani A, et al. Quasi-species nature and differential gene expression of severe acute respiratory syndrome coronavirus 2 and phylogenetic analysis of a novel Iranian strain. Infect Genet Evol  2020; 85: 104556.
 [http://dx.doi.org/10.1016/j.meegid.2020.104556] [PMID:  32937193]

[51]
Cleemput S, Dumon W, Fonseca V, et al. Genome detective coronavirus typing tool for rapid identification and characterization of novel coronavirus genomes. Bioinformatics  2020; 36(11): 3552-5.
 [http://dx.doi.org/10.1093/bioinformatics/btaa145] [PMID:  32108862]

[52]
Gene.  Available from: https://en.wikipedia.org/wiki/Gene [Accessed on: 12 Jan 2022].

[53]
Jiang C. A molecular view of coronavirus disease-2019 (COVID-19). Proceedings of 7th international conference on biomedical and
bioinformatics engineering, ICBBE  2020; 125-9.
 [http://dx.doi.org/10.1145/3444884.3444908]

[54]
Banerjee S, Wang X, Du S, et al. Comprehensive role of SARS‐CoV‐2 spike glycoprotein in regulating host signaling pathway. J Med Virol  2022; 94(9): 4071-87.
 [http://dx.doi.org/10.1002/jmv.27820] [PMID:  35488404]

[55]
Cui C, Huang C, Zhou W, et al. AGTR2, one possible novel key
gene for the entry of SARS-CoV-2 into human cells. Proceed IEEE/ACM Transac Comput Biol Bioinform  2021; 18(4): 1230-3.
 [http://dx.doi.org/10.1109/TCBB.2020.3009099]

[56]
Mousavizadeh L, Ghasemi S. Genotype and phenotype of COVID-19: Their roles in pathogenesis. J Microbiol Immunol Infect  2020; 159-63.
 [http://dx.doi.org/10.1016/j.jmii.2020.03.022] [PMID:  32265180]

[57]
Hu B, Guo H, Zhou P, Shi ZL. Characteristics of SARS-CoV-2 and COVID-19. Nat Rev Microbiol  2021; 19(3): 141-54.
 [http://dx.doi.org/10.1038/s41579-020-00459-7] [PMID:  33024307]

[58]
Liu Z, Xiao X, Wei X, et al. Composition and divergence of coronavirus spike proteins and host ACE2 receptors predict potential intermediate hosts of SARS‐CoV‐2. J Med Virol  2020; 92(6): 595-601.
 [http://dx.doi.org/10.1002/jmv.25726] [PMID:  32100877]

[59]
Severe acute respiratory syndrome coronavirus 2 isolate Wuhan-
Hu-1, complete genome. Severe acute respiratory syndrome coronavirus
2 isolate Wuhan-Hu-1, complete genome  Available from: https://www.ncbi.nlm.nih.gov/nuccore/NC_045512

[60]
Nakagawa S, Miyazawa T. Correction to: Genome evolution of SARS-CoV-2 and its virological characteristics. Inflamm Regen  2020; 40(1): 41.
 [http://dx.doi.org/10.1186/s41232-020-00151-6] [PMID:  33349265]

[61]
Genome-wide structure and function modeling of SARS-CoV-2
virus.  Available from: https://zhanggroup.org//COVID-19/

[62]
Osipiuk J, Azizi SA, Dvorkin S, et al. Structure of papain-like protease from SARS-CoV-2 and its complexes with non-covalent inhibitors. Nat Commun  2021; 12(1): 743.
 [http://dx.doi.org/10.1038/s41467-021-21060-3] [PMID:  33531496]

[63]
Wolfe G, Belhoussine O, Dawson A, Lisaius M, Jagodzinski F. Impactful mutations in Mpro of the SARS-CoV-2. Proteome  2020; 32(10): 1-3.
 [http://dx.doi.org/10.1145/3388440.3414706]

[64]
Zhang C, Li L, He J, Chen C, Su D. Nonstructural protein 7 and 8 complexes of SARS‐CoV‐2. Protein Sci  2021; 30(4): 873-81.
 [http://dx.doi.org/10.1002/pro.4046] [PMID:  33594727]

[65]
Reshamwala SMS, Likhite V, Degani MS, Deb SS, Noronha SB. Mutations in SARS‐CoV‐2 nsp7 and nsp8 proteins and their predicted impact on replication/transcription complex structure. J Med Virol  2021; 93(7): 4616-9.
 [http://dx.doi.org/10.1002/jmv.26791] [PMID:  33433004]

[66]
Littler DR, Gully BS, Colson RN, Rossjohn J. Crystal Structure of the SARS-CoV-2 Non-structural Protein 9, Nsp9. iScience  2020; 23(7): 101258.
 [http://dx.doi.org/10.1016/j.isci.2020.101258] [PMID:  32592996]

[67]
Krafcikova P, Silhan J, Nencka R, Boura E. Structural analysis of the SARS-CoV-2 methyltransferase complex involved in RNA cap creation bound to sinefungin. Nat Commun  2020; 11(1): 3717.
 [http://dx.doi.org/10.1038/s41467-020-17495-9] [PMID:  32709887]

[68]
Gadhave K, Kumar P, Kumar A, Bhardwaj T, Garg N, Giri R. Conformational dynamics of 13 amino acids long NSP11 of SARS-CoV-2 under membrane mimetics and different solvent conditions. Microb Pathog  2021; 158: 105041.
 [http://dx.doi.org/10.1016/j.micpath.2021.105041] [PMID:  34119626]

[69]
Mariano G, Farthing RJ, Lale-Farjat SLM, Bergeron JRC. Structural Characterization of SARS-CoV-2: Where we are, and where we need to be. Front Mol Biosci  2020; 7: 605236.
 [http://dx.doi.org/10.3389/fmolb.2020.605236] [PMID:  33392262]

[70]
Yang R, Zhao Q, Rao J, et al. SARS-CoV-2 accessory protein ORF7b mediates tumor necrosis factor-α-induced apoptosis in cells. Front Microbiol  2021; 12: 654709.
 [http://dx.doi.org/10.3389/fmicb.2021.654709] [PMID:  34484133]

[71]
Flower TG, Buffalo CZ, Hooy RM, Allaire M, Ren X, Hurley JH. Structure of SARS-CoV-2 ORF8, a rapidly evolving immune evasion protein. Proc Natl Acad Sci USA  2021; 118(2): e2021785118.
 [http://dx.doi.org/10.1073/pnas.2021785118] [PMID:  33361333]

[72]
Li JY, Liao CH, Wang Q, et al. The ORF6, ORF8 and nucleocapsid proteins of SARS-CoV-2 inhibit type I interferon signaling pathway. Virus Res  2020; 286: 198074.
 [http://dx.doi.org/10.1016/j.virusres.2020.198074] [PMID:  32589897]

[73]
Pancer K, Milewska A, Owczarek K, et al. The SARS-CoV-2 ORF10 is not essential in vitro or in vivo in humans. PLoS Pathog  2020; 16(12): e1008959.
 [http://dx.doi.org/10.1371/journal.ppat.1008959] [PMID:  33301543]

[74]
Elhabyan A, Elyaacoub S, Sanad E, Abukhadra A, Elhabyan A, Dinu V. The role of host genetics in susceptibility to severe viral infections in humans and insights into host genetics of severe COVID-19: A systematic review. Virus Res  2020; 289: 198163.
 [http://dx.doi.org/10.1016/j.virusres.2020.198163] [PMID:  32918943]

[75]
Fang S, Zheng R, Lei C, Zhou R, Wang J, Li M. In silico prediction
of new mutations that can improve the binding abilities between
2019-nCoV coronavirus and human ACE2. IEEE/ACM Trans Comput Biol Bioinform  2022; 19(3): 1694-702.
 [http://dx.doi.org/10.1109/TCBB.2021.3058265]

[76]
Sharawy L, Tantawy M, Ahmed Y, et al. In-silico comparative
analysis of Egyptian SARS CoV-2 with other populations: A phylogeny
and mutation analysis. 2nd Novel Intelligent and Leading
Emerging Sciences Conference, NILES  2020; 618-22.
 [http://dx.doi.org/10.1109/NILES50944.2020.9257918]

[77]
Khan MI, Khan ZA, Baig MH, et al. Comparative genome analysis of novel coronavirus (SARS-CoV-2) from different geographical locations and the effect of mutations on major target proteins: An in silico insight. PLoS One  2020; 15(9): e0238344.
 [http://dx.doi.org/10.1371/journal.pone.0238344] [PMID:  32881907]

[78]
Tang X, Wu C, Li X, et al. On the origin and continuing evolution of SARS-CoV-2. Natl Sci Rev  2020; 7(6): 1012-23.
 [http://dx.doi.org/10.1093/nsr/nwaa036] [PMID:  34676127]

[79]
Almubaid Z, Al-Mubaid H. Analysis and comparison of genetic variants and mutations of the novel coronavirus SARS-CoV-2. Gene Rep  2021; 23: 101064.
 [http://dx.doi.org/10.1016/j.genrep.2021.101064] [PMID:  33681535]

[80]
Kumar BK, Rohit A, Prithvisagar KS, Rai P, Karunasagar I, Karunasagar I. Deletion in the C-terminal region of the envelope glycoprotein in some of the Indian SARS-CoV-2 genome. Virus Res  2021; 291: 198222.
 [http://dx.doi.org/10.1016/j.virusres.2020.198222] [PMID:  33166565]

[81]
Xie Y, Du D, Karki CB, et al. Revealing the mechanism of SARS-CoV-2 spike protein binding with ACE2. Comput Sci Eng  2020; 22(6): 21-9.
 [http://dx.doi.org/10.1109/MCSE.2020.3015511] [PMID:  33762895]

[82]
Khan A, Hussain S, Ahmad S, et al. Computational modelling of potentially emerging SARS-CoV-2 spike protein RBDs mutations with higher binding affinity towards ACE2: A structural modelling study. Comput Biol Med  2022; 141: 105163.
 [http://dx.doi.org/10.1016/j.compbiomed.2021.105163] [PMID:  34979405]

[83]
Mullick B, Magar R, Jhunjhunwala A, Barati Farimani A. Understanding mutation hotspots for the SARS-CoV-2 spike protein using Shannon Entropy and K-means clustering. Comput Biol Med  2021; 138: 104915.
 [http://dx.doi.org/10.1016/j.compbiomed.2021.104915] [PMID:  34655896]

[84]
Mercatelli D, Giorgi FM. Geographic and genomic distribution of SARS-CoV-2 mutations. Front Microbiol  2020; 11: 1800.
 [http://dx.doi.org/10.3389/fmicb.2020.01800] [PMID:  32793182]

[85]
Sen Gupta A, Zhao Z, Rosen G. Spatiotemporal tracking of SARS-CoV-2 Variants using informative subtype markers and association graphs. Proceedings of 54th Asilomar Conference on Signals, Systems and Computers  2020; 516-9.
 [http://dx.doi.org/10.1109/IEEECONF51394.2020.9443496]

[86]
Li M, Prasad N, Hall D, Wu H. Analysis of SARS-CoV-2 sequences
reveals transmission path and emergence of SD 614G mutation. Proceedings of IEEE International Conference on Bioinformatics
and Biomedicine,  BIBM. 1995-8.
 [http://dx.doi.org/10.1109/BIBM49941.2020.9313091]

[87]
Kwon SB, Ernst J. Single-nucleotide conservation state annotation of the SARS-CoV-2 genome. Commun Biol  2021; 4(1): 698.
 [http://dx.doi.org/10.1038/s42003-021-02231-w] [PMID:  33398033]

[88]
Singh J, Samal J, Kumar V, et al. Structure-function analyses of new sars-cov-2 variants b.1.1.7, b.1.351 and b.1.1.28.1: Clinical, diagnostic, therapeutic and public health implications. Viruses  2021; 13(3): 439.
 [http://dx.doi.org/10.3390/v13030439] [PMID:  33803400]

[89]
Sarkar R, Mitra S, Chandra P, et al. Comprehensive analysis of genomic diversity of SARS-CoV-2 in different geographic regions of India: An endeavour to classify Indian SARS-CoV-2 strains on the basis of co-existing mutations. Arch Virol  2021; 166(3): 801-12.
 [http://dx.doi.org/10.1007/s00705-020-04911-0] [PMID:  33464421]

[90]
Li X, Gao X, Zou M, et al. SARS-CoV-2 Genomic Sequencing Revealed N501Y and L452R Mutants of S/A Lineage in Tianjin Municipality, China. Virol Sin  2021; 36(5): 1228-31.
 [http://dx.doi.org/10.1007/s12250-021-00432-5] [PMID:  34379315]

[91]
Vopson MM, Robson SC. A new method to study genome mutations using the information entropy. Physica A  2021; 584: 126383.
 [http://dx.doi.org/10.1016/j.physa.2021.126383]

[92]
Islam MR, Hoque MN, Rahman MS, et al. Genome-wide analysis of SARS-CoV-2 virus strains circulating worldwide implicates heterogeneity. Sci Rep  2020; 10(1): 14004.
 [http://dx.doi.org/10.1038/s41598-020-70812-6] [PMID:  32814791]

[93]
Wilkin GA, Huang X. K-Means clustering algorithms: Implementation
and comparison. Proceedings of 2nd International Multi-
Symposiums on Computer and Computational Sciences IMSCCS  2007; 133-6.
 [http://dx.doi.org/10.1109/IMSCCS.2007.51]

[94]
Chappell T, Geva S, Hogan J. K-Means clustering of biological sequences. Proceedings of ACM International Conference Proceeding Series  2017; 1-4.
 [http://dx.doi.org/10.1145/3166072.3166076]

[95]
Stamoulakatou E, Gulino A, Pinoli P. DLA: A distributed, location-based and apriori-based algorithm for biological sequence pattern mining. Proceedings of IEEE International Conference on Big Data, Big Data  2018; 1121-6.
 [http://dx.doi.org/10.1109/BigData.2018.8622007]

[96]
Muhammad LJ, Algehyne EA, Usman SS, Ahmad A, Chakraborty C, Mohammed IA. Supervised machine learning models for prediction of COVID-19 infection using epidemiology dataset. SN Comp Sci  2021; 2(1): 11.
 [http://dx.doi.org/10.1007/s42979-020-00394-7] [PMID:  33263111]

[97]
Noble WS. A biologist ’s introduction to support vector machines.  Noble Research Lab 2006; pp. 1-22.

[98]
Myles AJ, Feudale RN, Liu Y, Woody NA, Brown SD. An introduction to decision tree modeling. J Chemometr  2004; 18(6): 275-85.
 [http://dx.doi.org/10.1002/cem.873]

[99]
Qi Y. Random forest for bioinformatics. Ensemble Machine Learning: Methods and Applications.   2012; pp. 307-23.
 [http://dx.doi.org/10.1007/978-1-4419-9326-7_11]

[100]
An TK, Kim MH. A new diverse adaboost classifier. Proceedings of International Conference on Artificial Intelligence and Computational
Intelligence, AICI  1: 359-63.
 [http://dx.doi.org/10.1109/AICI.2010.82]

[101]
Singh OP, Vallejo M, El-Badawy IM, Aysha A, Madhanagopal J, Mohd Faudzi AA. Classification of SARS-CoV-2 and non-SARS-CoV-2 using machine learning algorithms. Comput Biol Med  2021; 136: 104650.
 [http://dx.doi.org/10.1016/j.compbiomed.2021.104650] [PMID:  34329865]

[102]
Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining KDD  785-94.
 [http://dx.doi.org/10.1145/2939672.2939785]

[103]
Nawaz MS, Fournier-Viger P, Shojaee A, Fujita H. Using artificial intelligence techniques for COVID-19 genome analysis. Appl Intell  2021; 51(5): 3086-103.
 [http://dx.doi.org/10.1007/s10489-021-02193-w] [PMID:  34764587]

[104]
Wang B, Jiang L. Principal component analysis applications in COVID-19 genome sequence studies. Cognit Comput  2021; 2021: 1-12.
 [http://dx.doi.org/10.1007/s12559-020-09790-w] [PMID:  33456620]

[105]
Oberer L, Carral AD, Fyta M. Simple classification of RNA sequences of respiratory-related coronaviruses. ACS Omega  2021; 6(31): 20158-65.
 [http://dx.doi.org/10.1021/acsomega.1c01625] [PMID:  34395967]

[106]
Ahmed I, Jeon G. Enabling artificial intelligence for genome sequence analysis of COVID-19 and alike viruses. Interdiscip Sci  2022; 14(2): 504-19.
 [http://dx.doi.org/10.1007/s12539-021-00465-0] [PMID:  34357528]

[107]
Dlamini GS, Mïller SJ, Meraba RL, et al. Classification of COVID-19 and other pathogenic sequences: A dinucleotide frequency and machine learning approach. IEEE Access  2020; 8: 195263-73.
 [http://dx.doi.org/10.1109/ACCESS.2020.3031387]

[108]
Arslan H. COVID-19 prediction based on genome similarity of human SARS-CoV-2 and bat SARS-CoV-like coronavirus. Comput Ind Eng  2021; 161: 107666.
 [http://dx.doi.org/10.1016/j.cie.2021.107666] [PMID:  34511707]

[109]
Danda S, Vasighizaker A, Rueda L. Unsupervised identification of
SARS-CoV-2 target cell groups via nonlinear dimensionality reduction
on single-cell RNA-Seq data. Proceedings of IEEE International
Conference on Bioinformatics and Biomedicine, BIBM  2020; 2737-44.
 [http://dx.doi.org/10.1109/BIBM49941.2020.9313378]

[110]
Kaden M, Bohnsack KS, Weber M, et al. Learning vector quantization as an interpretable classifier for the detection of SARS-CoV-2 types based on their RNA sequences. Neural Comput Appl  2022; 34(1): 67-78.
 [http://dx.doi.org/10.1007/s00521-021-06018-2] [PMID:  33935376]

[111]
Cabaro S, D’Esposito V, Di Matola T, et al. Cytokine signature and COVID-19 prediction models in the two waves of pandemics. Sci Rep  2021; 11(1): 20793.
 [http://dx.doi.org/10.1038/s41598-021-00190-0] [PMID:  34675240]

[112]
Moustafa AM, Planet PJ. Emerging SARS-CoV-2 diversity revealed by rapid whole-genome sequence typing. Genome Biol Evol  2021; 13(9): evab197.
 [http://dx.doi.org/10.1093/gbe/evab197] [PMID:  34432021]

[113]
Lopez-Rincon A, Tonda A, Mendoza-Maldonado L, et al. Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning. Sci Rep  2021; 11(1): 1-11.
 [http://dx.doi.org/10.1038/s41598-020-80363-5]

[114]
Deif MA, Solyman AAA, Kamarposhti MA, Band SS, Hammam RE. A deep bidirectional recurrent neural network for identification of SARS-CoV-2 from viral genome sequences. Math Biosci Eng  2021; 18(6): 8933-50.
 [http://dx.doi.org/10.3934/mbe.2021440] [PMID:  34814329]

[115]
Pathan RK, Biswas M, Khandaker MU. Time series prediction of COVID-19 by mutation rate analysis using recurrent neural network-based LSTM model. Chaos Solitons Fractals  2020; 138: 110018.
 [http://dx.doi.org/10.1016/j.chaos.2020.110018] [PMID:  32565626]

[116]
Whata A, Chimedza C. Deep Learning for SARS COV-2 Genome Sequences. IEEE Access  2021; 9: 59597-611.
 [http://dx.doi.org/10.1109/ACCESS.2021.3073728]

[117]
Chen J, Wang R, Wei GW. SARS-CoV-2 becoming more infectious as revealed by algebraic topology and deep learning. Commun Inf Syst  2021; 21(1): 31-6.
 [http://dx.doi.org/10.4310/CIS.2021.v21.n1.a2] [PMID:  34675755]

[118]
Kille B, Liu Y, Sapoval N, et al. Accelerating SARS-CoV-2 low
frequency variant calling on ultra deep sequencing datasets. Proceedings of IEEE International Parallel and Distributed Processing
Symposium Workshops, IPDPSW  2021; 204-8.
 [http://dx.doi.org/10.1109/IPDPSW52791.2021.00038]

[119]
Ali S, Sahoo B, Ullah N, Zelikovskiy A, Patterson M, Khan I. A kmer Based Approach for SARS-CoV-2 Variant Identification.  Lecture Notes in Comput Sci.   2021; pp. 153-64.
 [http://dx.doi.org/10.1007/978-3-030-91415-8_14]

[120]
Hossain MS, Pathan AQMSU, Islam MN, et al. Genome-wide identification and prediction of SARS-CoV-2 mutations show an abundance of variants: Integrated study of bioinformatics and deep neural learning. Informat Med Unlocked  2021; 27: 100798.
 [http://dx.doi.org/10.1016/j.imu.2021.100798] [PMID:  34812411]
Cite As
Current Bioinformatics

Genomic Characterization of Emerging SARS-CoV-2: A Systematic Review

Abstract

Graphical Abstract
