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Analysis workflow of publicly available RNA-sequencing datasets
Pablo Sanchis1,2, Rosario Lavignolle1,2, Mercedes Abbate1,2
1Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Química Biológica, Buenos Aires C1428EGA, Argentina.
STAR Protocols
|April 28, 2021
Summary
This study analyzes gene expression changes related to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and host antiviral responses. It identifies associations between gene expression and phenotypic features in public samples.
Area of Science:
- Genomics
- Virology
- Bioinformatics
Background:
- Differential gene expression analysis is crucial for understanding biological variations between sample groups.
- Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection triggers complex host cell responses.
- Publicly available sample repositories offer valuable resources for retrospective analysis.
Purpose of the Study:
- To identify gene expression alterations linked to SARS-CoV-2 infection.
- To investigate the host cell's antiviral response at the gene expression level.
- To correlate gene expression patterns with observed phenotypic features.
Main Methods:
- Utilized differential gene expression analysis on pre-selected gene sets.
- Analyzed gene expression data from samples in public repositories.
- Focused on genes associated with SARS-CoV-2 infection and antiviral responses.
Main Results:
- Identified specific gene expression changes associated with SARS-CoV-2 infection.
- Characterized gene expression patterns related to the host cell's antiviral defense mechanisms.
- Established correlations between gene expression profiles and phenotypic characteristics.
Conclusions:
- Gene expression analysis provides insights into SARS-CoV-2 pathogenesis and host response.
- Informatics approaches using public data can reveal significant biological associations.
- Findings contribute to understanding viral infection mechanisms and host-pathogen interactions.
