Computational analysis of SARS-CoV-2, SARS-CoV, and MERS-CoV genome using MEGA
The novel coronavirus pandemic that has originated from China and spread throughout the world in three months. Genome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) predecessor, severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus...
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BioMed Central
2020-09-01
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| Series: | Genomics & Informatics |
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| Online Access: | http://genominfo.org/upload/pdf/gi-2020-18-3-e30.pdf |
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| author | Vipan Kumar Sohpal |
| author_facet | Vipan Kumar Sohpal |
| author_sort | Vipan Kumar Sohpal |
| collection | DOAJ |
| description | The novel coronavirus pandemic that has originated from China and spread throughout the world in three months. Genome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) predecessor, severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) play an important role in understanding the concept of genetic variation. In this paper, the genomic data accessed from National Center for Biotechnology Information (NCBI) through Molecular Evolutionary Genetic Analysis (MEGA) for statistical analysis. Firstly, the Bayesian information criterion (BIC) and Akaike information criterion (AICc) are used to evaluate the best substitution pattern. Secondly, the maximum likelihood method used to estimate of transition/transversions (R) through Kimura-2, Tamura-3, Hasegawa-Kishino-Yano, and Tamura-Nei nucleotide substitutions model. Thirdly and finally nucleotide frequencies computed based on genomic data of NCBI. The results indicate that general times reversible model has the lowest BIC and AICc score 347,394 and 347,287, respectively. The transition/transversions bias for nucleotide substitutions models varies from 0.56 to 0.59 in MEGA output. The average nitrogenous bases frequency of U, C, A, and G are 31.74, 19.48, 28.04, and 20.74, respectively in percentages. Overall the genomic data analysis of SARS-CoV-2, SARS-CoV, and MERS-CoV highlights the close genetic relationship. |
| format | Article |
| id | doaj-art-ddfbbd2f80e140708c0cb720a21219ab |
| institution | Kabale University |
| issn | 2234-0742 |
| language | English |
| publishDate | 2020-09-01 |
| publisher | BioMed Central |
| record_format | Article |
| series | Genomics & Informatics |
| spelling | doaj-art-ddfbbd2f80e140708c0cb720a21219ab2025-02-02T12:12:21ZengBioMed CentralGenomics & Informatics2234-07422020-09-01183e3010.5808/GI.2020.18.3.e30623Computational analysis of SARS-CoV-2, SARS-CoV, and MERS-CoV genome using MEGAVipan Kumar SohpalThe novel coronavirus pandemic that has originated from China and spread throughout the world in three months. Genome of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) predecessor, severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) play an important role in understanding the concept of genetic variation. In this paper, the genomic data accessed from National Center for Biotechnology Information (NCBI) through Molecular Evolutionary Genetic Analysis (MEGA) for statistical analysis. Firstly, the Bayesian information criterion (BIC) and Akaike information criterion (AICc) are used to evaluate the best substitution pattern. Secondly, the maximum likelihood method used to estimate of transition/transversions (R) through Kimura-2, Tamura-3, Hasegawa-Kishino-Yano, and Tamura-Nei nucleotide substitutions model. Thirdly and finally nucleotide frequencies computed based on genomic data of NCBI. The results indicate that general times reversible model has the lowest BIC and AICc score 347,394 and 347,287, respectively. The transition/transversions bias for nucleotide substitutions models varies from 0.56 to 0.59 in MEGA output. The average nitrogenous bases frequency of U, C, A, and G are 31.74, 19.48, 28.04, and 20.74, respectively in percentages. Overall the genomic data analysis of SARS-CoV-2, SARS-CoV, and MERS-CoV highlights the close genetic relationship.http://genominfo.org/upload/pdf/gi-2020-18-3-e30.pdfmiddle east respiratory syndromemolecular evolutionary genetic analysisnational center for biotechnology informationsars-covsars-cov-2 |
| spellingShingle | Vipan Kumar Sohpal Computational analysis of SARS-CoV-2, SARS-CoV, and MERS-CoV genome using MEGA Genomics & Informatics middle east respiratory syndrome molecular evolutionary genetic analysis national center for biotechnology information sars-cov sars-cov-2 |
| title | Computational analysis of SARS-CoV-2, SARS-CoV, and MERS-CoV genome using MEGA |
| title_full | Computational analysis of SARS-CoV-2, SARS-CoV, and MERS-CoV genome using MEGA |
| title_fullStr | Computational analysis of SARS-CoV-2, SARS-CoV, and MERS-CoV genome using MEGA |
| title_full_unstemmed | Computational analysis of SARS-CoV-2, SARS-CoV, and MERS-CoV genome using MEGA |
| title_short | Computational analysis of SARS-CoV-2, SARS-CoV, and MERS-CoV genome using MEGA |
| title_sort | computational analysis of sars cov 2 sars cov and mers cov genome using mega |
| topic | middle east respiratory syndrome molecular evolutionary genetic analysis national center for biotechnology information sars-cov sars-cov-2 |
| url | http://genominfo.org/upload/pdf/gi-2020-18-3-e30.pdf |
| work_keys_str_mv | AT vipankumarsohpal computationalanalysisofsarscov2sarscovandmerscovgenomeusingmega |