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Data preprocessing workflow for exhaled breath analysis by GC/MS using open sources
The noninvasive diagnosis and monitoring of high prevalence diseases such as cardiovascular diseases, cancers and chronic respiratory diseases are currently priority objectives in the area of health. In this regard, the analysis of volatile organic compounds (VOCs) has been identified as a potential...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7738550/ https://www.ncbi.nlm.nih.gov/pubmed/33319832 http://dx.doi.org/10.1038/s41598-020-79014-6 |
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author | Sola Martínez, Rosa Alba Pastor Hernández, José María Lozano Terol, Gema Gallego-Jara, Julia García-Marcos, Luis Cánovas Díaz, Manuel de Diego Puente, Teresa |
author_facet | Sola Martínez, Rosa Alba Pastor Hernández, José María Lozano Terol, Gema Gallego-Jara, Julia García-Marcos, Luis Cánovas Díaz, Manuel de Diego Puente, Teresa |
author_sort | Sola Martínez, Rosa Alba |
collection | PubMed |
description | The noninvasive diagnosis and monitoring of high prevalence diseases such as cardiovascular diseases, cancers and chronic respiratory diseases are currently priority objectives in the area of health. In this regard, the analysis of volatile organic compounds (VOCs) has been identified as a potential noninvasive tool for the diagnosis and surveillance of several diseases. Despite the advantages of this strategy, it is not yet a routine clinical tool. The lack of reproducible protocols for each step of the biomarker discovery phase is an obstacle of the current state. Specifically, this issue is present at the data preprocessing step. Thus, an open source workflow for preprocessing the data obtained by the analysis of exhaled breath samples using gas chromatography coupled with single quadrupole mass spectrometry (GC/MS) is presented in this paper. This workflow is based on the connection of two approaches to transform raw data into a useful matrix for statistical analysis. Moreover, this workflow includes matching compounds from breath samples with a spectral library. Three free packages (xcms, cliqueMS and eRah) written in the language R are used for this purpose. Furthermore, this paper presents a suitable protocol for exhaled breath sample collection from infants under 2 years of age for GC/MS. |
format | Online Article Text |
id | pubmed-7738550 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-77385502020-12-17 Data preprocessing workflow for exhaled breath analysis by GC/MS using open sources Sola Martínez, Rosa Alba Pastor Hernández, José María Lozano Terol, Gema Gallego-Jara, Julia García-Marcos, Luis Cánovas Díaz, Manuel de Diego Puente, Teresa Sci Rep Article The noninvasive diagnosis and monitoring of high prevalence diseases such as cardiovascular diseases, cancers and chronic respiratory diseases are currently priority objectives in the area of health. In this regard, the analysis of volatile organic compounds (VOCs) has been identified as a potential noninvasive tool for the diagnosis and surveillance of several diseases. Despite the advantages of this strategy, it is not yet a routine clinical tool. The lack of reproducible protocols for each step of the biomarker discovery phase is an obstacle of the current state. Specifically, this issue is present at the data preprocessing step. Thus, an open source workflow for preprocessing the data obtained by the analysis of exhaled breath samples using gas chromatography coupled with single quadrupole mass spectrometry (GC/MS) is presented in this paper. This workflow is based on the connection of two approaches to transform raw data into a useful matrix for statistical analysis. Moreover, this workflow includes matching compounds from breath samples with a spectral library. Three free packages (xcms, cliqueMS and eRah) written in the language R are used for this purpose. Furthermore, this paper presents a suitable protocol for exhaled breath sample collection from infants under 2 years of age for GC/MS. Nature Publishing Group UK 2020-12-15 /pmc/articles/PMC7738550/ /pubmed/33319832 http://dx.doi.org/10.1038/s41598-020-79014-6 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Sola Martínez, Rosa Alba Pastor Hernández, José María Lozano Terol, Gema Gallego-Jara, Julia García-Marcos, Luis Cánovas Díaz, Manuel de Diego Puente, Teresa Data preprocessing workflow for exhaled breath analysis by GC/MS using open sources |
title | Data preprocessing workflow for exhaled breath analysis by GC/MS using open sources |
title_full | Data preprocessing workflow for exhaled breath analysis by GC/MS using open sources |
title_fullStr | Data preprocessing workflow for exhaled breath analysis by GC/MS using open sources |
title_full_unstemmed | Data preprocessing workflow for exhaled breath analysis by GC/MS using open sources |
title_short | Data preprocessing workflow for exhaled breath analysis by GC/MS using open sources |
title_sort | data preprocessing workflow for exhaled breath analysis by gc/ms using open sources |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7738550/ https://www.ncbi.nlm.nih.gov/pubmed/33319832 http://dx.doi.org/10.1038/s41598-020-79014-6 |
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