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Inferring the genetic variability in Indian SARS-CoV-2 genomes using consensus of multiple sequence alignment techniques
Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is a threat to the human population and has created a worldwide pandemic. Daily thousands of people are getting affected by the SARS-CoV-2 virus; India being no exception. In this situation, there is no doubt that vaccine is the primary pr...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier B.V.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7462517/ https://www.ncbi.nlm.nih.gov/pubmed/32889094 http://dx.doi.org/10.1016/j.meegid.2020.104522 |
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author | Saha, Indrajit Ghosh, Nimisha Maity, Debasree Sharma, Nikhil Mitra, Kaushik |
author_facet | Saha, Indrajit Ghosh, Nimisha Maity, Debasree Sharma, Nikhil Mitra, Kaushik |
author_sort | Saha, Indrajit |
collection | PubMed |
description | Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is a threat to the human population and has created a worldwide pandemic. Daily thousands of people are getting affected by the SARS-CoV-2 virus; India being no exception. In this situation, there is no doubt that vaccine is the primary prevention strategy to contain the wave of COVID-19 pandemic. In this regard, genome-wide analysis of SARS-CoV-2 is important to understand its genetic variability. This has motivated us to analyse 566 Indian SARS-CoV-2 sequences using multiple sequence alignment techniques viz. ClustalW, MUSCLE, ClustalO and MAFFT to align and subsequently identify the lists of mutations as substitution, deletion, insertion and SNP. Thereafter, a consensus of these results, called as Consensus Multiple Sequence Alignment (CMSA), is prepared to have the final list of mutations so that the advantages of all four alignment techniques can be preserved. The analysis shows 767, 2025 and 54 unique substitutions, deletions and SNPs in Indian SARS-CoV-2 genomes. More precisely, out of 54 SNPs, 4 SNPs are present close to the 60% of the virus population. The results of this experiment can be useful for virus classification, designing and defining the dose of vaccine for the Indian population. |
format | Online Article Text |
id | pubmed-7462517 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-74625172020-09-02 Inferring the genetic variability in Indian SARS-CoV-2 genomes using consensus of multiple sequence alignment techniques Saha, Indrajit Ghosh, Nimisha Maity, Debasree Sharma, Nikhil Mitra, Kaushik Infect Genet Evol Short Communication Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is a threat to the human population and has created a worldwide pandemic. Daily thousands of people are getting affected by the SARS-CoV-2 virus; India being no exception. In this situation, there is no doubt that vaccine is the primary prevention strategy to contain the wave of COVID-19 pandemic. In this regard, genome-wide analysis of SARS-CoV-2 is important to understand its genetic variability. This has motivated us to analyse 566 Indian SARS-CoV-2 sequences using multiple sequence alignment techniques viz. ClustalW, MUSCLE, ClustalO and MAFFT to align and subsequently identify the lists of mutations as substitution, deletion, insertion and SNP. Thereafter, a consensus of these results, called as Consensus Multiple Sequence Alignment (CMSA), is prepared to have the final list of mutations so that the advantages of all four alignment techniques can be preserved. The analysis shows 767, 2025 and 54 unique substitutions, deletions and SNPs in Indian SARS-CoV-2 genomes. More precisely, out of 54 SNPs, 4 SNPs are present close to the 60% of the virus population. The results of this experiment can be useful for virus classification, designing and defining the dose of vaccine for the Indian population. Elsevier B.V. 2020-11 2020-09-01 /pmc/articles/PMC7462517/ /pubmed/32889094 http://dx.doi.org/10.1016/j.meegid.2020.104522 Text en © 2020 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Short Communication Saha, Indrajit Ghosh, Nimisha Maity, Debasree Sharma, Nikhil Mitra, Kaushik Inferring the genetic variability in Indian SARS-CoV-2 genomes using consensus of multiple sequence alignment techniques |
title | Inferring the genetic variability in Indian SARS-CoV-2 genomes using consensus of multiple sequence alignment techniques |
title_full | Inferring the genetic variability in Indian SARS-CoV-2 genomes using consensus of multiple sequence alignment techniques |
title_fullStr | Inferring the genetic variability in Indian SARS-CoV-2 genomes using consensus of multiple sequence alignment techniques |
title_full_unstemmed | Inferring the genetic variability in Indian SARS-CoV-2 genomes using consensus of multiple sequence alignment techniques |
title_short | Inferring the genetic variability in Indian SARS-CoV-2 genomes using consensus of multiple sequence alignment techniques |
title_sort | inferring the genetic variability in indian sars-cov-2 genomes using consensus of multiple sequence alignment techniques |
topic | Short Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7462517/ https://www.ncbi.nlm.nih.gov/pubmed/32889094 http://dx.doi.org/10.1016/j.meegid.2020.104522 |
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