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COVIDanno, COVID-19 annotation in human

Yuzhou Feng1,2, Mengyuan Yang3, Zhiwei Fan4,5

  • 1West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.

Frontiers in Microbiology
|July 27, 2023
PubMed

Insights

Researchers identified key genes involved in COVID-19 infection using SARS-CoV-2 models. This study provides a valuable resource for understanding the functional roles of these differentially expressed genes.

Area of Science:

  • Genomics
  • Bioinformatics
  • Infectious Diseases

Background:

  • Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19, a global health crisis with no universal cure.
  • Identifying key genes in infected cells is crucial for understanding and potentially treating the disease.
  • Existing RNA-seq data from SARS-CoV-2 in vitro models requires systematic interpretation.

Purpose of the Study:

  • To create a comprehensive annotation resource for differentially expressed genes (DEGs) in human in vitro models of COVID-19.
  • To facilitate the identification and functional analysis of SARS-CoV-2-related genes.
  • To develop a tool for predicting SARS-CoV-2 infection status.

Main Methods:

  • Performed differential expression analysis on 136 RNA-seq datasets from SARS-CoV-2 infected human in vitro models across 13 tissue types.
  • Conducted extensive bioinformatics and computational biology analyses on identified DEGs.
  • Developed a novel prediction tool for SARS-CoV-2 infection status.

Main Results:

  • Identified a total of 4,935 differentially expressed genes (DEGs) across various time points and tissue types.
  • Generated intensive functional annotations for these DEGs.
  • Developed a functional tool to predict SARS-CoV-2 infection status.

Conclusions:

  • COVIDanno serves as a valuable reference resource for COVID-19 research.
  • The identified DEGs and their annotations aid in understanding gene functions during SARS-CoV-2 infection.
  • The developed tool assists in sample infection status prediction, advancing COVID-19 diagnostics and research.
Keywords:
COVID-19expressionin vitro modelsinfection stateregulatory network

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