COVIDanno, COVID-19 annotation in human

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.