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COVIDomic: A multi-modal cloud-based platform for identification of risk factors associated with COVID-19 severity

Coronavirus disease 2019 (COVID-19) is an acute infection of the respiratory tract that emerged in December 2019 in Wuhan, China. It was quickly established that both the symptoms and the disease severity may vary from one case to another and several strains of SARS-CoV-2 have been identified. To ga...

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Autores principales: Naumov, Vladimir, Putin, Evgeny, Pushkov, Stefan, Kozlova, Ekaterina, Romantsov, Konstantin, Kalashnikov, Alexander, Galkin, Fedor, Tihonova, Nina, Shneyderman, Anastasia, Galkin, Egor, Zinkevich, Arsenii, Cope, Stephanie M., Sethuraman, Ramanathan, Oprea, Tudor I., Pearson, Alexander T., Tay, Savas, Agrawal, Nishant, Dubovenko, Alexey, Vanhaelen, Quentin, Ozerov, Ivan, Aliper, Alex, Izumchenko, Evgeny, Zhavoronkov, Alex
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8312936/
https://www.ncbi.nlm.nih.gov/pubmed/34260589
http://dx.doi.org/10.1371/journal.pcbi.1009183
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author Naumov, Vladimir
Putin, Evgeny
Pushkov, Stefan
Kozlova, Ekaterina
Romantsov, Konstantin
Kalashnikov, Alexander
Galkin, Fedor
Tihonova, Nina
Shneyderman, Anastasia
Galkin, Egor
Zinkevich, Arsenii
Cope, Stephanie M.
Sethuraman, Ramanathan
Oprea, Tudor I.
Pearson, Alexander T.
Tay, Savas
Agrawal, Nishant
Dubovenko, Alexey
Vanhaelen, Quentin
Ozerov, Ivan
Aliper, Alex
Izumchenko, Evgeny
Zhavoronkov, Alex
author_facet Naumov, Vladimir
Putin, Evgeny
Pushkov, Stefan
Kozlova, Ekaterina
Romantsov, Konstantin
Kalashnikov, Alexander
Galkin, Fedor
Tihonova, Nina
Shneyderman, Anastasia
Galkin, Egor
Zinkevich, Arsenii
Cope, Stephanie M.
Sethuraman, Ramanathan
Oprea, Tudor I.
Pearson, Alexander T.
Tay, Savas
Agrawal, Nishant
Dubovenko, Alexey
Vanhaelen, Quentin
Ozerov, Ivan
Aliper, Alex
Izumchenko, Evgeny
Zhavoronkov, Alex
author_sort Naumov, Vladimir
collection PubMed
description Coronavirus disease 2019 (COVID-19) is an acute infection of the respiratory tract that emerged in December 2019 in Wuhan, China. It was quickly established that both the symptoms and the disease severity may vary from one case to another and several strains of SARS-CoV-2 have been identified. To gain a better understanding of the wide variety of SARS-CoV-2 strains and their associated symptoms, thousands of SARS-CoV-2 genomes have been sequenced in dozens of countries. In this article, we introduce COVIDomic, a multi-omics online platform designed to facilitate the analysis and interpretation of the large amount of health data collected from patients with COVID-19. The COVIDomic platform provides a comprehensive set of bioinformatic tools for the multi-modal metatranscriptomic data analysis of COVID-19 patients to determine the origin of the coronavirus strain and the expected severity of the disease. An integrative analytical workflow, which includes microbial pathogens community analysis, COVID-19 genetic epidemiology and patient stratification, allows to analyze the presence of the most common microbial organisms, their antibiotic resistance, the severity of the infection and the set of the most probable geographical locations from which the studied strain could have originated. The online platform integrates a user friendly interface which allows easy visualization of the results. We envision this tool will not only have immediate implications for management of the ongoing COVID-19 pandemic, but will also improve our readiness to respond to other infectious outbreaks.
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spelling pubmed-83129362021-07-31 COVIDomic: A multi-modal cloud-based platform for identification of risk factors associated with COVID-19 severity Naumov, Vladimir Putin, Evgeny Pushkov, Stefan Kozlova, Ekaterina Romantsov, Konstantin Kalashnikov, Alexander Galkin, Fedor Tihonova, Nina Shneyderman, Anastasia Galkin, Egor Zinkevich, Arsenii Cope, Stephanie M. Sethuraman, Ramanathan Oprea, Tudor I. Pearson, Alexander T. Tay, Savas Agrawal, Nishant Dubovenko, Alexey Vanhaelen, Quentin Ozerov, Ivan Aliper, Alex Izumchenko, Evgeny Zhavoronkov, Alex PLoS Comput Biol Research Article Coronavirus disease 2019 (COVID-19) is an acute infection of the respiratory tract that emerged in December 2019 in Wuhan, China. It was quickly established that both the symptoms and the disease severity may vary from one case to another and several strains of SARS-CoV-2 have been identified. To gain a better understanding of the wide variety of SARS-CoV-2 strains and their associated symptoms, thousands of SARS-CoV-2 genomes have been sequenced in dozens of countries. In this article, we introduce COVIDomic, a multi-omics online platform designed to facilitate the analysis and interpretation of the large amount of health data collected from patients with COVID-19. The COVIDomic platform provides a comprehensive set of bioinformatic tools for the multi-modal metatranscriptomic data analysis of COVID-19 patients to determine the origin of the coronavirus strain and the expected severity of the disease. An integrative analytical workflow, which includes microbial pathogens community analysis, COVID-19 genetic epidemiology and patient stratification, allows to analyze the presence of the most common microbial organisms, their antibiotic resistance, the severity of the infection and the set of the most probable geographical locations from which the studied strain could have originated. The online platform integrates a user friendly interface which allows easy visualization of the results. We envision this tool will not only have immediate implications for management of the ongoing COVID-19 pandemic, but will also improve our readiness to respond to other infectious outbreaks. Public Library of Science 2021-07-14 /pmc/articles/PMC8312936/ /pubmed/34260589 http://dx.doi.org/10.1371/journal.pcbi.1009183 Text en © 2021 Naumov et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Naumov, Vladimir
Putin, Evgeny
Pushkov, Stefan
Kozlova, Ekaterina
Romantsov, Konstantin
Kalashnikov, Alexander
Galkin, Fedor
Tihonova, Nina
Shneyderman, Anastasia
Galkin, Egor
Zinkevich, Arsenii
Cope, Stephanie M.
Sethuraman, Ramanathan
Oprea, Tudor I.
Pearson, Alexander T.
Tay, Savas
Agrawal, Nishant
Dubovenko, Alexey
Vanhaelen, Quentin
Ozerov, Ivan
Aliper, Alex
Izumchenko, Evgeny
Zhavoronkov, Alex
COVIDomic: A multi-modal cloud-based platform for identification of risk factors associated with COVID-19 severity
title COVIDomic: A multi-modal cloud-based platform for identification of risk factors associated with COVID-19 severity
title_full COVIDomic: A multi-modal cloud-based platform for identification of risk factors associated with COVID-19 severity
title_fullStr COVIDomic: A multi-modal cloud-based platform for identification of risk factors associated with COVID-19 severity
title_full_unstemmed COVIDomic: A multi-modal cloud-based platform for identification of risk factors associated with COVID-19 severity
title_short COVIDomic: A multi-modal cloud-based platform for identification of risk factors associated with COVID-19 severity
title_sort covidomic: a multi-modal cloud-based platform for identification of risk factors associated with covid-19 severity
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8312936/
https://www.ncbi.nlm.nih.gov/pubmed/34260589
http://dx.doi.org/10.1371/journal.pcbi.1009183
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