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COVIDanno, COVID-19 annotation in human
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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10366449/ https://www.ncbi.nlm.nih.gov/pubmed/37497545 http://dx.doi.org/10.3389/fmicb.2023.1129103 |
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author | Feng, Yuzhou Yang, Mengyuan Fan, Zhiwei Zhao, Weiling Kim, Pora Zhou, Xiaobo |
author_facet | Feng, Yuzhou Yang, Mengyuan Fan, Zhiwei Zhao, Weiling Kim, Pora Zhou, Xiaobo |
author_sort | Feng, Yuzhou |
collection | PubMed |
description | 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 in vitro 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 in vitro 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 in vitro 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. |
format | Online Article Text |
id | pubmed-10366449 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103664492023-07-26 COVIDanno, COVID-19 annotation in human Feng, Yuzhou Yang, Mengyuan Fan, Zhiwei Zhao, Weiling Kim, Pora Zhou, Xiaobo Front Microbiol Microbiology 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 in vitro 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 in vitro 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 in vitro 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. Frontiers Media S.A. 2023-07-11 /pmc/articles/PMC10366449/ /pubmed/37497545 http://dx.doi.org/10.3389/fmicb.2023.1129103 Text en Copyright © 2023 Feng, Yang, Fan, Zhao, Kim and Zhou. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Microbiology Feng, Yuzhou Yang, Mengyuan Fan, Zhiwei Zhao, Weiling Kim, Pora Zhou, Xiaobo COVIDanno, COVID-19 annotation in human |
title | COVIDanno, COVID-19 annotation in human |
title_full | COVIDanno, COVID-19 annotation in human |
title_fullStr | COVIDanno, COVID-19 annotation in human |
title_full_unstemmed | COVIDanno, COVID-19 annotation in human |
title_short | COVIDanno, COVID-19 annotation in human |
title_sort | covidanno, covid-19 annotation in human |
topic | Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10366449/ https://www.ncbi.nlm.nih.gov/pubmed/37497545 http://dx.doi.org/10.3389/fmicb.2023.1129103 |
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