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

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.

Classifying COVID-19 based on amino acids encoding with machine learning algorithms

“…As further research directions, we aim to apply the same model using different feature extraction methods according to the sequence and the structure of the proteins to obtain more detailed biological information about the virus behavior and its infection cycle. Other classification methods will also be explored in future studies such as principal components analysis and its new derivations, including supervised and unsupervised approaches, as well as functional data analysis, partial least squares structures, and other recent methodologies [ [36] , [37] , [38] , [39] , [40] , [41] , [46] , [47] , [48] , [49] ].…”

Early warning of emerging infectious diseases based on multimodal data

“… / Nextstrain https://nextstrain.org/ Virus information (including influenza virus, SARS-CoV-2, dengue virus, Zika virus, Monkeypox, Ebola virus, etc.) [5] NCBI Influenza Virus Database https://www.ncbi.nlm.nih.gov/genomes/FLU/Database/nph-select.cgi?go = database Influenza virus information / NCBI SARS-CoV-2 Resources https://www.ncbi.nlm.nih.gov/sars-cov-2/ SARS-CoV-2 Information / UniProt https://www.uniprot.org/ Contains virus related proteins information [8] AlphaFold https://alphafold.com/ Contains structural biological information of virus / COVIDomic https://covidomic.com/ Multi-omics health data of COVID-19 patients Clinical data [6] ClinicalTrials.gov https://clinicaltrials.gov/ct2/home Clinical data / FluReassort https://www.jianglab.tech/FluReassort Genomic reassortments of influenza virus Database for specific studies [9] EpiGraphDB https://epigraphdb.org Contains epidemiological data on diseases caused by various viral infections Epidemiological data [11] KGCoV https://www.biosino.org/kgcov/ SARS-CoV genome-epidemiological knowledge graph [12] CoV-AbDab https://opig.stats.ox.ac.uk/webapps/covabdab/ Information on structures of coronavirus antibodies Immunology related information <...…”

“…Nextstrain, developed by James Hadfield et al [5] includes several virus-related information, with the most significant feature of using system dynamics analysis technology to generate a phylogenetic tree of the virus and collect spatiotemporal information when the data is updated, presenting a spatiotemporal view of the evolution and transmission of the virus to users. COVIDomic developed by Naumov et al [6] is a multiomics online platform that collects large amounts of health data from COVID-19 patients to determine the origin of the virus and the expected severity of the disease by analyzing multimodal genomic data from these patients. In addition, websites such as National Center for Biotechnology Information (NCBI) and European Nucleotide Archive collect genomic information on many viruses, while the platforms like Protein Data Bank (PDB) [7] , UniProt [8] , and AlphaFold collect the structural biology and protein information of viruses.…”

Using Mendelian randomization to unveil the truth: does COVID-19 impact QT interval prolongation?

“…While a growing body of research has identi ed genetic loci associated with COVID-19 susceptibility and severity, the precise relationship between these genetic markers and adverse outcomes in COVID-19 patients remains less clear 6, [23][24][25] . From a biological standpoint, although QT interval prolongation may be associated with SARS-CoV-2-induced myocarditis and changes in physiological parameters such as electrolytes and hormones 7,26 , the observational and cross-sectional nature of these studies, coupled with the complexity of this relationship in uenced by multiple factors, leaves us uncertain as to whether a genuine causal association exists between SARS-CoV-2 infection and QT interval prolongation.…”