COVID-19 Transcriptomic Atlas: A Comprehensive Analysis of COVID-19 Related Transcriptomics Datasets - Alqutami, Fatma; Senok, Abiola; Hachim, Mahmood (Frontiers in Genetics, 2021)
Abstract Background To develop anti-viral drugs and vaccines, it is crucial to understand the molecular basis and pathology of COVID-19. Research needs to be doubled and results need to be generated faster, which is why bioinformatics plays a significant role in COVID-19 research. Aim In this study, we aim to use the publicly available transcriptomic data from multiple datasets published in GEO to decipher the molecular basis of COVID-19 pathology and identify differentially expressed genes. This should allow us to identify biomarkers that are expressed in COVID-19 patients and which markers are present in specific severities and conditions. Materials and Methods A list of datasets was generated from NCBI's Gene Expression Omnibus (GEO) using the GEOmetadb package through R software. Search keywords included total-RNA, SARS-COV-2, and COVID-19. Datasets containing more than ten samples were selected, and the organism used was Homo sapiens. Differentially expressed genes in each dataset were identified. Results Using publicly available transcriptomic data, we identified differentially expressed genes in SARS-CoV-2 in multiple data sets. Of the nine data sets that were analyzed, several genes have been present in several datasets, such as MX1, CRTAM, and IL7R. Conclusion This analysis can conclude that several genes appear to be commonly expressed in different indications; however, their significance varies on the disease state and sample source.