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Entropy based analysis of SARS-CoV-2 spread in India using informative subtype markers

India became one of the most COVID-19 affected countries with more than 4 million infected cases and 71,000 deaths by September 2020. We studied the temporal dynamics and geographic distribution of SARS-CoV-2 subtypes in India. Moreover, we analysed the RGD motif and D614G mutation in the spike prot...

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Autores principales: Mathur, Piyush, Goyal, Pratik, Verma, Garima, Yadav, Pankaj
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8342543/
https://www.ncbi.nlm.nih.gov/pubmed/34354142
http://dx.doi.org/10.1038/s41598-021-95247-5
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author Mathur, Piyush
Goyal, Pratik
Verma, Garima
Yadav, Pankaj
author_facet Mathur, Piyush
Goyal, Pratik
Verma, Garima
Yadav, Pankaj
author_sort Mathur, Piyush
collection PubMed
description India became one of the most COVID-19 affected countries with more than 4 million infected cases and 71,000 deaths by September 2020. We studied the temporal dynamics and geographic distribution of SARS-CoV-2 subtypes in India. Moreover, we analysed the RGD motif and D614G mutation in the spike protein of SARS-CoV-2. We used a previously proposed viral subtyping method based upon informative subtype markers (ISMs). The ISMs were identified on the basis of information entropy using 94,515 genome sequences of SARS-CoV-2 available publicly at the Global Initiative on Sharing All Influenza Data (GISAID). We identified 11 distinct positions in the SARS-CoV-2 genomes for defining ISMs resulting in 798 unique ISMs. The most abundant ISM in India was transferred from European countries. In contrast, the second most abundant ISM in India was found to be transferred via Australia. Moreover, the eastern regions in India were infected by the ISM most abundant in China due to geographical linkage. Our analysis confirmed higher rates of new cases in the countries abundant with S-G614 strain compared to countries with abundant S-D614 strain. In India, overall S-G614 was most prevalent compared to S-D614, except a few regions including New Delhi, Bihar, and Rajasthan.
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spelling pubmed-83425432021-08-06 Entropy based analysis of SARS-CoV-2 spread in India using informative subtype markers Mathur, Piyush Goyal, Pratik Verma, Garima Yadav, Pankaj Sci Rep Article India became one of the most COVID-19 affected countries with more than 4 million infected cases and 71,000 deaths by September 2020. We studied the temporal dynamics and geographic distribution of SARS-CoV-2 subtypes in India. Moreover, we analysed the RGD motif and D614G mutation in the spike protein of SARS-CoV-2. We used a previously proposed viral subtyping method based upon informative subtype markers (ISMs). The ISMs were identified on the basis of information entropy using 94,515 genome sequences of SARS-CoV-2 available publicly at the Global Initiative on Sharing All Influenza Data (GISAID). We identified 11 distinct positions in the SARS-CoV-2 genomes for defining ISMs resulting in 798 unique ISMs. The most abundant ISM in India was transferred from European countries. In contrast, the second most abundant ISM in India was found to be transferred via Australia. Moreover, the eastern regions in India were infected by the ISM most abundant in China due to geographical linkage. Our analysis confirmed higher rates of new cases in the countries abundant with S-G614 strain compared to countries with abundant S-D614 strain. In India, overall S-G614 was most prevalent compared to S-D614, except a few regions including New Delhi, Bihar, and Rajasthan. Nature Publishing Group UK 2021-08-05 /pmc/articles/PMC8342543/ /pubmed/34354142 http://dx.doi.org/10.1038/s41598-021-95247-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Mathur, Piyush
Goyal, Pratik
Verma, Garima
Yadav, Pankaj
Entropy based analysis of SARS-CoV-2 spread in India using informative subtype markers
title Entropy based analysis of SARS-CoV-2 spread in India using informative subtype markers
title_full Entropy based analysis of SARS-CoV-2 spread in India using informative subtype markers
title_fullStr Entropy based analysis of SARS-CoV-2 spread in India using informative subtype markers
title_full_unstemmed Entropy based analysis of SARS-CoV-2 spread in India using informative subtype markers
title_short Entropy based analysis of SARS-CoV-2 spread in India using informative subtype markers
title_sort entropy based analysis of sars-cov-2 spread in india using informative subtype markers
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8342543/
https://www.ncbi.nlm.nih.gov/pubmed/34354142
http://dx.doi.org/10.1038/s41598-021-95247-5
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