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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.
  • Naumov V; Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong.
  • Putin E; Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong.
  • Pushkov S; Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong.
  • Kozlova E; Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong.
  • Romantsov K; Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong.
  • Kalashnikov A; Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong.
  • Galkin F; Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong.
  • Tihonova N; Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong.
  • Shneyderman A; School of Biology, Lomonosov Moscow State University, Moscow, Russia.
  • Galkin E; Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong.
  • Zinkevich A; Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong.
  • Cope SM; Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong.
  • Sethuraman R; Intel Corporation, Santa Clara, California, United States of America.
  • Oprea TI; Intel Corporation, Bangalore India.
  • Pearson AT; Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, New Mexico, United States of America.
  • Tay S; University of New Mexico Comprehensive Cancer Center, Albuquerque, New Mexico, United States of America.
  • Agrawal N; Autophagy Inflammation and Metabolism Center of Biomedical Research Excellence, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, United States of America.
  • Dubovenko A; Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden.
  • Vanhaelen Q; Faculty of Health and Medical Sciences, Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.
  • Ozerov I; Department of Medicine, Section of Hematology and Oncology, University of Chicago, Chicago, Ilinois, United States of America.
  • Aliper A; Pritzker School of Molecular Engineering, University of Chicago, Chicago, Ilinois, United States of America.
  • Izumchenko E; Department of Surgery, Section of Otolaryngology-Head and Neck Surgery, University of Chicago, Chicago, Ilinois, United States of America.
  • Zhavoronkov A; Insilico Medicine Hong Kong Ltd, Pak Shek Kok, New Territories, Hong Kong.
PLoS Comput Biol ; 17(7): e1009183, 2021 07.
Article in English | MEDLINE | ID: covidwho-1309945
ABSTRACT
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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Full text: Available Collection: International databases Database: MEDLINE Main subject: User-Computer Interface / Computational Biology / Cloud Computing / COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Language: English Journal: PLoS Comput Biol Journal subject: Biology / Medical Informatics Year: 2021 Document Type: Article Affiliation country: Journal.pcbi.1009183

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Full text: Available Collection: International databases Database: MEDLINE Main subject: User-Computer Interface / Computational Biology / Cloud Computing / COVID-19 Type of study: Observational study / Prognostic study Limits: Humans Language: English Journal: PLoS Comput Biol Journal subject: Biology / Medical Informatics Year: 2021 Document Type: Article Affiliation country: Journal.pcbi.1009183