COVIDanno

COVIDanno analyzes RNA-seq data from SARS-CoV-2-infected in vitro models to identify differentially expressed genes and provide functional annotations for studying virus–host interactions.

Key Features:

• Integrated RNA-seq database: Aggregates 136 individual RNA-seq datasets covering 13 distinct human tissue types to capture transcriptomic changes induced by SARS-CoV-2 infection.
• Differential expression analysis: Performs rigorous differential expression analysis across infection time points, identifying 4,935 differentially expressed genes (DEGs).
• Functional annotation: Supplies functional annotations for identified DEGs to enable exploration of biological roles and pathways involved in SARS-CoV-2 pathogenesis.
• Predictive infection-status classifier: Provides a predictive component that determines sample infection status based on gene expression profiles.
• Systematic translation of RNA-seq data: Translates raw RNA-seq data into interpretable functional insights to support hypothesis generation and experimental follow-up.

Scientific Applications:

• Gene identification: Identification of key genes involved in SARS-CoV-2 infection across tissues and time points.
• Virus–host interaction studies: Analysis of transcriptomic changes to elucidate molecular interactions between SARS-CoV-2 and host cells.
• Translational research: Prioritization of candidate therapeutic targets and vaccine-related targets based on differential expression and functional annotation.
• Temporal and tissue-specific profiling: Comparative analysis of gene expression dynamics across infection stages and multiple human tissue types.

Methodology:

Comprehensive differential expression analysis of RNA-seq datasets from SARS-CoV-2-infected in vitro models, combined with integration of multiple bioinformatics and computational biology studies.