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MetHoS: a platform for large-scale processing, storage and analysis of metabolomics data
BACKGROUND: Modern mass spectrometry has revolutionized the detection and analysis of metabolites but likewise, let the data skyrocket with repositories for metabolomics data filling up with thousands of datasets. While there are many software tools for the analysis of individual experiments with a...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9270834/ https://www.ncbi.nlm.nih.gov/pubmed/35804309 http://dx.doi.org/10.1186/s12859-022-04793-w |
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author | Tzanakis, Konstantinos Nattkemper, Tim W. Niehaus, Karsten Albaum, Stefan P. |
author_facet | Tzanakis, Konstantinos Nattkemper, Tim W. Niehaus, Karsten Albaum, Stefan P. |
author_sort | Tzanakis, Konstantinos |
collection | PubMed |
description | BACKGROUND: Modern mass spectrometry has revolutionized the detection and analysis of metabolites but likewise, let the data skyrocket with repositories for metabolomics data filling up with thousands of datasets. While there are many software tools for the analysis of individual experiments with a few to dozens of chromatograms, we see a demand for a contemporary software solution capable of processing and analyzing hundreds or even thousands of experiments in an integrative manner with standardized workflows. RESULTS: Here, we introduce MetHoS as an automated web-based software platform for the processing, storage and analysis of great amounts of mass spectrometry-based metabolomics data sets originating from different metabolomics studies. MetHoS is based on Big Data frameworks to enable parallel processing, distributed storage and distributed analysis of even larger data sets across clusters of computers in a highly scalable manner. It has been designed to allow the processing and analysis of any amount of experiments and samples in an integrative manner. In order to demonstrate the capabilities of MetHoS, thousands of experiments were downloaded from the MetaboLights database and used to perform a large-scale processing, storage and statistical analysis in a proof-of-concept study. CONCLUSIONS: MetHoS is suitable for large-scale processing, storage and analysis of metabolomics data aiming at untargeted metabolomic analyses. It is freely available at: https://methos.cebitec.uni-bielefeld.de/. Users interested in analyzing their own data are encouraged to apply for an account. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04793-w. |
format | Online Article Text |
id | pubmed-9270834 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-92708342022-07-10 MetHoS: a platform for large-scale processing, storage and analysis of metabolomics data Tzanakis, Konstantinos Nattkemper, Tim W. Niehaus, Karsten Albaum, Stefan P. BMC Bioinformatics Software BACKGROUND: Modern mass spectrometry has revolutionized the detection and analysis of metabolites but likewise, let the data skyrocket with repositories for metabolomics data filling up with thousands of datasets. While there are many software tools for the analysis of individual experiments with a few to dozens of chromatograms, we see a demand for a contemporary software solution capable of processing and analyzing hundreds or even thousands of experiments in an integrative manner with standardized workflows. RESULTS: Here, we introduce MetHoS as an automated web-based software platform for the processing, storage and analysis of great amounts of mass spectrometry-based metabolomics data sets originating from different metabolomics studies. MetHoS is based on Big Data frameworks to enable parallel processing, distributed storage and distributed analysis of even larger data sets across clusters of computers in a highly scalable manner. It has been designed to allow the processing and analysis of any amount of experiments and samples in an integrative manner. In order to demonstrate the capabilities of MetHoS, thousands of experiments were downloaded from the MetaboLights database and used to perform a large-scale processing, storage and statistical analysis in a proof-of-concept study. CONCLUSIONS: MetHoS is suitable for large-scale processing, storage and analysis of metabolomics data aiming at untargeted metabolomic analyses. It is freely available at: https://methos.cebitec.uni-bielefeld.de/. Users interested in analyzing their own data are encouraged to apply for an account. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-022-04793-w. BioMed Central 2022-07-08 /pmc/articles/PMC9270834/ /pubmed/35804309 http://dx.doi.org/10.1186/s12859-022-04793-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Tzanakis, Konstantinos Nattkemper, Tim W. Niehaus, Karsten Albaum, Stefan P. MetHoS: a platform for large-scale processing, storage and analysis of metabolomics data |
title | MetHoS: a platform for large-scale processing, storage and analysis of metabolomics data |
title_full | MetHoS: a platform for large-scale processing, storage and analysis of metabolomics data |
title_fullStr | MetHoS: a platform for large-scale processing, storage and analysis of metabolomics data |
title_full_unstemmed | MetHoS: a platform for large-scale processing, storage and analysis of metabolomics data |
title_short | MetHoS: a platform for large-scale processing, storage and analysis of metabolomics data |
title_sort | methos: a platform for large-scale processing, storage and analysis of metabolomics data |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9270834/ https://www.ncbi.nlm.nih.gov/pubmed/35804309 http://dx.doi.org/10.1186/s12859-022-04793-w |
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