| URL: | http://biomedbdc.wchscu.cn/COVIDanno |
| Full name: | COVID-19 annotation in human |
| Description: | COVIDanno, COVID-19 annotation in human, which is an integrated RNA-seq database of SARS-CoV-2-infected in vitro models. Here, we accumulated publicly available data of RNA-seq data about SARS-CoV-2-infected in vitro models and did systematic translation/interpretation analysis. COVIDanno aims to provide resources and references for intensive functional annotations of the significantly differentially expressed genes among different time points of COVID-19 infection of human in vitro models. |
| Year founded: | 2023 |
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| Accessibility: |
Accessible
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| Country/Region: | United States |
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| University/Institution: | The University of Texas Health Science Center at Houston |
| Address: | Center for Computational Systems Medicine, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin Street, Houston, TX 77030 |
| City: | Houston |
| Province/State: | Texas |
| Country/Region: | United States |
| Contact name (PI/Team): | Xiaobo Zhou |
| Contact email (PI/Helpdesk): | Xiaobo.Zhou@uth.tmc.edu |
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COVIDanno, COVID-19 annotation in human. [PMID: 37497545]
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiologic agent of coronavirus disease 19 (COVID-19), has caused a global health crisis. Despite ongoing efforts to treat patients, there is no universal prevention or cure available. One of the feasible approaches will be identifying the key genes from SARS-CoV-2-infected cells. SARS-CoV-2-infected model, allows easy control of the experimental conditions, obtaining reproducible results, and monitoring of infection progression. Currently, accumulating RNA-seq data from SARS-CoV-2 models urgently needs systematic translation and interpretation. To fill this gap, we built COVIDanno, COVID-19 annotation in humans, available at http://biomedbdc.wchscu.cn/COVIDanno/. The aim of this resource is to provide a reference resource of intensive functional annotations of differentially expressed genes (DEGs) among different time points of COVID-19 infection in human models. To do this, we performed differential expression analysis for 136 individual datasets across 13 tissue types. In total, we identified 4,935 DEGs. We performed multiple bioinformatics/computational biology studies for these DEGs. Furthermore, we developed a novel tool to help users predict the status of SARS-CoV-2 infection for a given sample. COVIDanno will be a valuable resource for identifying SARS-CoV-2-related genes and understanding their potential functional roles in different time points and multiple tissue types. |