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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/2146| Characterizing geographical and temporal dynamics of novel coronavirus SARS-CoV-2 using informative subtype markers | |
| Zhengqiao Zhao. Bahrad A. Sokhansanj. Gail Rosen. | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| 10.1101/2020.04.07.030759 | |
| We propose an efficient framework for genetic subtyping of a pandemic virus, with application to the novel coronavirus SARS-CoV-2. Efficient identification of subtypes is particularly important for tracking the geographic distribution and temporal dynamics of infectious spread in real-time. In this paper, we utilize an entropy analysis to identify nucleotide sites within SARS-CoV-2 genome sequences that are highly informative of genetic variation, and thereby define an Informative Subtype Marker (ISM) for each sequence. We further apply an error correction technique to the ISMs, for more robust subtype definition given ambiguity and noise in sequence data. We show that, by analyzing the ISMs of global SARS-CoV-2 sequence data, we can distinguish interregional differences in viral subtype distribution, and track the emergence of subtypes in different regions over time. Based on publicly available data up to April 5, 2020, we show, for example: (1) distinct genetic subtypes of infections in Europe, with earlier transmission linked to subtypes prevalent in Italy with later development of subtypes specific to other countries over time; (2) within the United States, the emergence of an endogenous U.S. subtype that is distinct from the outbreak in New York, which is linked instead to subtypes found in Europe; and (3) dynamic emergence of SARS-CoV-2 from localization in China to a pattern of distinct regional subtypes in different countries around the world over time. Our results demonstrate that utilizing ISMs for genetic subtyping can be an important complement to conventional phylogenetic tree-based analyses of the COVID-19 pandemic. Particularly, because ISMs are efficient and compact subtype identifiers, they will be useful for modeling, data-mining, and machine learning tools to help enhance containment, therapeutic, and vaccine targeting strategies for fighting the COVID-19 pandemic. We have made the subtype identification pipeline described in this paper publicly available at https://github.com/EESI/ISM. | |
| www.biorxiv.org | |
| 2020 | |
| Artículo | |
| https://www.biorxiv.org/content/10.1101/2020.04.07.030759v4.full.pdf | |
| Inglés | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Artículos científicos |
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