TidyMass an object-oriented reproducible analysis framework for LC–MS data

Reproducibility, traceability, and transparency have been long-standing issues for metabolomics data analysis. Multiple tools have been developed, but limitations still exist. Here, we present the tidyMass project (https://www.tidymass.org/), a comprehensive R-based computational framework that can...

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Autores principales: Shen, Xiaotao, Yan, Hong, Wang, Chuchu, Gao, Peng, Johnson, Caroline H., Snyder, Michael P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9334349/
https://www.ncbi.nlm.nih.gov/pubmed/35902589
http://dx.doi.org/10.1038/s41467-022-32155-w
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author Shen, Xiaotao
Yan, Hong
Wang, Chuchu
Gao, Peng
Johnson, Caroline H.
Snyder, Michael P.
author_facet Shen, Xiaotao
Yan, Hong
Wang, Chuchu
Gao, Peng
Johnson, Caroline H.
Snyder, Michael P.
author_sort Shen, Xiaotao
collection PubMed
description Reproducibility, traceability, and transparency have been long-standing issues for metabolomics data analysis. Multiple tools have been developed, but limitations still exist. Here, we present the tidyMass project (https://www.tidymass.org/), a comprehensive R-based computational framework that can achieve the traceable, shareable, and reproducible workflow needs of data processing and analysis for LC-MS-based untargeted metabolomics. TidyMass is an ecosystem of R packages that share an underlying design philosophy, grammar, and data structure, which provides a comprehensive, reproducible, and object-oriented computational framework. The modular architecture makes tidyMass a highly flexible and extensible tool, which other users can improve and integrate with other tools to customize their own pipeline.
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spelling pubmed-93343492022-07-30 TidyMass an object-oriented reproducible analysis framework for LC–MS data Shen, Xiaotao Yan, Hong Wang, Chuchu Gao, Peng Johnson, Caroline H. Snyder, Michael P. Nat Commun Article Reproducibility, traceability, and transparency have been long-standing issues for metabolomics data analysis. Multiple tools have been developed, but limitations still exist. Here, we present the tidyMass project (https://www.tidymass.org/), a comprehensive R-based computational framework that can achieve the traceable, shareable, and reproducible workflow needs of data processing and analysis for LC-MS-based untargeted metabolomics. TidyMass is an ecosystem of R packages that share an underlying design philosophy, grammar, and data structure, which provides a comprehensive, reproducible, and object-oriented computational framework. The modular architecture makes tidyMass a highly flexible and extensible tool, which other users can improve and integrate with other tools to customize their own pipeline. Nature Publishing Group UK 2022-07-28 /pmc/articles/PMC9334349/ /pubmed/35902589 http://dx.doi.org/10.1038/s41467-022-32155-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Shen, Xiaotao
Yan, Hong
Wang, Chuchu
Gao, Peng
Johnson, Caroline H.
Snyder, Michael P.
TidyMass an object-oriented reproducible analysis framework for LC–MS data
title TidyMass an object-oriented reproducible analysis framework for LC–MS data
title_full TidyMass an object-oriented reproducible analysis framework for LC–MS data
title_fullStr TidyMass an object-oriented reproducible analysis framework for LC–MS data
title_full_unstemmed TidyMass an object-oriented reproducible analysis framework for LC–MS data
title_short TidyMass an object-oriented reproducible analysis framework for LC–MS data
title_sort tidymass an object-oriented reproducible analysis framework for lc–ms data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9334349/
https://www.ncbi.nlm.nih.gov/pubmed/35902589
http://dx.doi.org/10.1038/s41467-022-32155-w
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