COVID-19 Transcriptomic Atlas: A Comprehensive Analysis of COVID-19 Related Transcriptomics ‎Datasets

Abstract

Background:‎ To develop anti-viral drugs and vaccines, it is crucial to understand the molecular basis and pathology of ‎COVID-19. An increase in research output is required to generate data and results at a faster rate, ‎therefore bioinformatics plays a crucial role in COVID- 19 research. There is an abundance of ‎transcriptomic data from studies carried out on COVID- 19, however, their use is limited by the ‎confounding factors pertaining to each study. The reanalysis of all these datasets in a unified approach ‎should help in understanding the molecular basis of COVID-19. This should allow for the identification of ‎COVID-19 biomarkers expressed in patients and the presence of markers specific to disease severity ‎and condition.‎

Aim: In this study, we aim to use the multiple publicly available transcriptomic datasets retrieved from the ‎Gene Expression Omnibus (GEO) database to identify consistently differential expressed genes in ‎different tissues and clinical settings.‎

Materials and Methods: A list of datasets was generated from NCBI’s GEO using the GEOmetadb package through R software. ‎Search keywords included SARS-COV-2 and COVID-19. Datasets in human tissues containing more than ‎ten samples were selected for this study. Differentially expressed genes (DEGs) in each dataset were ‎identified. Then the common DEGs between different datasets, conditions, tissues and clinical settings ‎were shortlisted.‎

Results: Using a unified approach, we were able to identify common DEGs based on the disease conditions, ‎samples source and clinical settings. For each indication, a different set of genes have been identified, ‎revealing that a multitude of factors play a role in the level of gene expression.‎

Conclusion: Unified reanalysis of publically available transcriptomic data showed promising potential in identifying ‎core targets that can explain the molecular pathology.