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Exploring COVID-19 pathogenesis on command-line: A bioinformatics pipeline for handling and integrating omics data
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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9095070/ https://www.ncbi.nlm.nih.gov/pubmed/35871895 http://dx.doi.org/10.1016/bs.apcsb.2022.04.002 |
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author | Macedo-da-Silva, Janaina Coutinho, João Victor Paccini Rosa-Fernandes, Livia Marie, Suely Kazue Nagahashi Palmisano, Giuseppe |
author_facet | Macedo-da-Silva, Janaina Coutinho, João Victor Paccini Rosa-Fernandes, Livia Marie, Suely Kazue Nagahashi Palmisano, Giuseppe |
author_sort | Macedo-da-Silva, Janaina |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9095070 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90950702022-05-12 Exploring COVID-19 pathogenesis on command-line: A bioinformatics pipeline for handling and integrating omics data Macedo-da-Silva, Janaina Coutinho, João Victor Paccini Rosa-Fernandes, Livia Marie, Suely Kazue Nagahashi Palmisano, Giuseppe Adv Protein Chem Struct Biol Article 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. Elsevier Inc. 2022 2022-05-12 /pmc/articles/PMC9095070/ /pubmed/35871895 http://dx.doi.org/10.1016/bs.apcsb.2022.04.002 Text en Copyright © 2022 Elsevier Inc. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Macedo-da-Silva, Janaina Coutinho, João Victor Paccini Rosa-Fernandes, Livia Marie, Suely Kazue Nagahashi Palmisano, Giuseppe Exploring COVID-19 pathogenesis on command-line: A bioinformatics pipeline for handling and integrating omics data |
title | Exploring COVID-19 pathogenesis on command-line: A bioinformatics pipeline for handling and integrating omics data |
title_full | Exploring COVID-19 pathogenesis on command-line: A bioinformatics pipeline for handling and integrating omics data |
title_fullStr | Exploring COVID-19 pathogenesis on command-line: A bioinformatics pipeline for handling and integrating omics data |
title_full_unstemmed | Exploring COVID-19 pathogenesis on command-line: A bioinformatics pipeline for handling and integrating omics data |
title_short | Exploring COVID-19 pathogenesis on command-line: A bioinformatics pipeline for handling and integrating omics data |
title_sort | exploring covid-19 pathogenesis on command-line: a bioinformatics pipeline for handling and integrating omics data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9095070/ https://www.ncbi.nlm.nih.gov/pubmed/35871895 http://dx.doi.org/10.1016/bs.apcsb.2022.04.002 |
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