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

Naumov V, Putin E, Pushkov S, Kozlova E, Romantsov K, Kalashnikov A, Galkin F, Tihonova N, Shneyderman A, Galkin E, Zinkevich A, Cope SM, Sethuraman R, Oprea TI, Pearson AT, Tay S, Agrawal N, Dubovenko A, Vanhaelen Q, Ozerov I, Aliper A, Izumchenko E, Zhavoronkov A

PLoS Comput Biol 17 (7) e1009183 [2021-07-00; online 2021-07-14]

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.

Category: Health

Category: Other

Type: Journal article

PubMed 34260589

DOI 10.1371/journal.pcbi.1009183

Crossref 10.1371/journal.pcbi.1009183

pmc: PMC8312936
pii: PCOMPBIOL-D-21-00029

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