COVID-19 Transcriptomic Atlas: A Comprehensive Analysis of COVID-19 Related Transcriptomics Datasets
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 a...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-12-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2021.755222/full |
| _version_ | 1831758997522219008 |
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| author | Fatma Alqutami Abiola Senok Mahmood Hachim Mahmood Hachim |
| author_facet | Fatma Alqutami Abiola Senok Mahmood Hachim Mahmood Hachim |
| author_sort | Fatma Alqutami |
| collection | DOAJ |
| description | 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 and be used as biomarkers for COVID-19. |
| first_indexed | 2024-12-22T00:59:34Z |
| format | Article |
| id | doaj.art-3c27b25f335442fe9abb092d6bd5788e |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-22T00:59:34Z |
| publishDate | 2021-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-3c27b25f335442fe9abb092d6bd5788e2022-12-21T18:44:14ZengFrontiers Media S.A.Frontiers in Genetics1664-80212021-12-011210.3389/fgene.2021.755222755222COVID-19 Transcriptomic Atlas: A Comprehensive Analysis of COVID-19 Related Transcriptomics DatasetsFatma Alqutami0Abiola Senok1Mahmood Hachim2Mahmood Hachim3College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab EmiratesCollege of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab EmiratesCollege of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab EmiratesCenter for Genomic Discovery, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab EmiratesBackground: 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 and be used as biomarkers for COVID-19.https://www.frontiersin.org/articles/10.3389/fgene.2021.755222/fullCOVID-19SARS – CoV – 2omics analysesdifferentially expressed gene analysisatlas |
| spellingShingle | Fatma Alqutami Abiola Senok Mahmood Hachim Mahmood Hachim COVID-19 Transcriptomic Atlas: A Comprehensive Analysis of COVID-19 Related Transcriptomics Datasets Frontiers in Genetics COVID-19 SARS – CoV – 2 omics analyses differentially expressed gene analysis atlas |
| title | COVID-19 Transcriptomic Atlas: A Comprehensive Analysis of COVID-19 Related Transcriptomics Datasets |
| title_full | COVID-19 Transcriptomic Atlas: A Comprehensive Analysis of COVID-19 Related Transcriptomics Datasets |
| title_fullStr | COVID-19 Transcriptomic Atlas: A Comprehensive Analysis of COVID-19 Related Transcriptomics Datasets |
| title_full_unstemmed | COVID-19 Transcriptomic Atlas: A Comprehensive Analysis of COVID-19 Related Transcriptomics Datasets |
| title_short | COVID-19 Transcriptomic Atlas: A Comprehensive Analysis of COVID-19 Related Transcriptomics Datasets |
| title_sort | covid 19 transcriptomic atlas a comprehensive analysis of covid 19 related transcriptomics datasets |
| topic | COVID-19 SARS – CoV – 2 omics analyses differentially expressed gene analysis atlas |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2021.755222/full |
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