Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.3389/fgene.2021.755222 http://scihub22266oqcxt.onion/10.3389/fgene.2021.755222 35003208!8727884!35003208     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Front+Genet 2021 ; 12 (ä): 755222 Nephropedia Template TP {{tp|p=35003208|t=2021. COVID-19 Transcriptomic Atlas: A Comprehensive Analysis of COVID-19 Related Transcriptomics Datasets |pdf=|usr=}}{{35003208}} gab.com Text COVID-19 Transcriptomic Atlas: A Comprehensive Analysis of COVID-19 Related Transcriptomics Datasets JO: Front Genet http://www.kidney.de/pget.php?&tw=1&pmid=35003208 #FOAvip 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. Twit Text FOAVip COVID-19 Transcriptomic Atlas: A Comprehensive Analysis of COVID-19 Related Transcriptomics Datasets ????Front Genet http://www.kidney.de/pget.php?&tw=1&pmid=35003208 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=35003208 #FOAvip English Wikipedia <ref>{{Cite pmid|35003208}}</ref> COVID-19 Transcriptomic Atlas: A Comprehensive Analysis of COVID-19 Related Transcriptomics Datasets #MMPMID35003208 Alqutami F; Senok A; Hachim M Front Genet 2021[]; 12 (ä): 755222 PMID35003208show ga 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. äCOVID-19 Transcriptomic Atlas: A Comprehensive Analysis of COVID-19 Re(epmc).pdf DeepDyve Pubget Overpricing