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

Abstract 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 s...

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Autores principales:	Piyush Mathur, Pratik Goyal, Garima Verma, Pankaj Yadav
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/426a243c9c2f4fb3b0a53ba3e557a7f8
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spelling	oai:doaj.org-article:426a243c9c2f4fb3b0a53ba3e557a7f82021-12-02T14:53:34ZEntropy based analysis of SARS-CoV-2 spread in India using informative subtype markers10.1038/s41598-021-95247-52045-2322https://doaj.org/article/426a243c9c2f4fb3b0a53ba3e557a7f82021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95247-5https://doaj.org/toc/2045-2322Abstract 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.Piyush MathurPratik GoyalGarima VermaPankaj YadavNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Piyush Mathur Pratik Goyal Garima Verma Pankaj Yadav Entropy based analysis of SARS-CoV-2 spread in India using informative subtype markers
description	Abstract 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.
format	article
author	Piyush Mathur Pratik Goyal Garima Verma Pankaj Yadav
author_facet	Piyush Mathur Pratik Goyal Garima Verma Pankaj Yadav
author_sort	Piyush Mathur
title	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_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_sort	entropy based analysis of sars-cov-2 spread in india using informative subtype markers
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/426a243c9c2f4fb3b0a53ba3e557a7f8
work_keys_str_mv	AT piyushmathur entropybasedanalysisofsarscov2spreadinindiausinginformativesubtypemarkers AT pratikgoyal entropybasedanalysisofsarscov2spreadinindiausinginformativesubtypemarkers AT garimaverma entropybasedanalysisofsarscov2spreadinindiausinginformativesubtypemarkers AT pankajyadav entropybasedanalysisofsarscov2spreadinindiausinginformativesubtypemarkers
_version_	1718389442745466880

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

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