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10.1016/bs.apcsb.2022.04.002 http://scihub22266oqcxt.onion/10.1016/bs.apcsb.2022.04.002 35871895!9095070!35871895     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=35871895&cmd=llinks): Failed to open stream: HTTP request failed! 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Exploring COVID-19 pathogenesis on command-line: A bioinformatics pipeline for handling and integrating omics data |pdf=|usr=}}{{35871895}} gab.com Text Exploring COVID-19 pathogenesis on command-line: A bioinformatics pipeline for handling and integrating omics data JO: Adv Protein Chem Struct Biol http://www.kidney.de/pget.php?&tw=1&pmid=35871895 #FOAvip Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first identified in late 2019 in Wuhan, China, and has proven to be highly pathogenic, making it a global public health threat. The immediate need to understand the mechanisms and impact of the virus made omics techniques stand out, as they can offer a holistic and comprehensive view of thousands of molecules in a single experiment. Mastering bioinformatics tools to process, analyze, integrate, and interpret omics data is a powerful knowledge to enrich results. We present a robust and open access computational pipeline for extracting information from quantitative proteomics and transcriptomics public data. We present the entire pipeline from raw data to differentially expressed genes. We explore processes and pathways related to mapped transcripts and proteins. A pipeline is presented to integrate and compare proteomics and transcriptomics data using also packages available in the Bioconductor and providing the codes used. Cholesterol metabolism, immune system activity, ECM, and proteasomal degradation pathways increased in infected patients. Leukocyte activation profile was overrepresented in both proteomics and transcriptomics data. Finally, we found a panel of proteins and transcripts regulated in the same direction in the lung transcriptome and plasma proteome that distinguish healthy and infected individuals. This panel of markers was confirmed in another cohort of patients, thus validating the robustness and functionality of the tools presented. Twit Text FOAVip Exploring COVID-19 pathogenesis on command-line: A bioinformatics pipeline for handling and integrating omics data ????Adv Protein Chem Struct Biol http://www.kidney.de/pget.php?&tw=1&pmid=35871895 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=35871895 #FOAvip English Wikipedia <ref>{{Cite pmid|35871895}}</ref> Exploring COVID-19 pathogenesis on command-line: A bioinformatics pipeline for handling and integrating omics data #MMPMID35871895 Macedo-da-Silva J; Coutinho JVP; Rosa-Fernandes L; Marie SKN; Palmisano G Adv Protein Chem Struct Biol 2022[]; 131 (ä): 311-339 PMID35871895show ga Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first identified in late 2019 in Wuhan, China, and has proven to be highly pathogenic, making it a global public health threat. The immediate need to understand the mechanisms and impact of the virus made omics techniques stand out, as they can offer a holistic and comprehensive view of thousands of molecules in a single experiment. Mastering bioinformatics tools to process, analyze, integrate, and interpret omics data is a powerful knowledge to enrich results. We present a robust and open access computational pipeline for extracting information from quantitative proteomics and transcriptomics public data. We present the entire pipeline from raw data to differentially expressed genes. We explore processes and pathways related to mapped transcripts and proteins. A pipeline is presented to integrate and compare proteomics and transcriptomics data using also packages available in the Bioconductor and providing the codes used. Cholesterol metabolism, immune system activity, ECM, and proteasomal degradation pathways increased in infected patients. Leukocyte activation profile was overrepresented in both proteomics and transcriptomics data. Finally, we found a panel of proteins and transcripts regulated in the same direction in the lung transcriptome and plasma proteome that distinguish healthy and infected individuals. This panel of markers was confirmed in another cohort of patients, thus validating the robustness and functionality of the tools presented. |*COVID-19/genetics[MESH] |Computational Biology[MESH] |Humans[MESH] |Proteome/metabolism[MESH] |Proteomics/methods[MESH]Exploring COVID-19 pathogenesis on command-line: A bioinformatics pipe(epmc).pdf DeepDyve Pubget Overpricing