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Mass-Suite: a novel open-source python package for high-resolution mass spectrometry data analysis
Mass-Suite (MSS) is a Python-based, open-source software package designed to analyze high-resolution mass spectrometry (HRMS)-based non-targeted analysis (NTA) data, particularly for water quality assessment and other environmental applications. MSS provides flexible, user-defined workflows for HRMS...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10517472/ https://www.ncbi.nlm.nih.gov/pubmed/37741995 http://dx.doi.org/10.1186/s13321-023-00741-9 |
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author | Hu, Ximin Mar, Derek Suzuki, Nozomi Zhang, Bowei Peter, Katherine T. Beck, David A. C. Kolodziej, Edward P. |
author_facet | Hu, Ximin Mar, Derek Suzuki, Nozomi Zhang, Bowei Peter, Katherine T. Beck, David A. C. Kolodziej, Edward P. |
author_sort | Hu, Ximin |
collection | PubMed |
description | Mass-Suite (MSS) is a Python-based, open-source software package designed to analyze high-resolution mass spectrometry (HRMS)-based non-targeted analysis (NTA) data, particularly for water quality assessment and other environmental applications. MSS provides flexible, user-defined workflows for HRMS data processing and analysis, including both basic functions (e.g., feature extraction, data reduction, feature annotation, data visualization, and statistical analyses) and advanced exploratory data mining and predictive modeling capabilities that are not provided by currently available open-source software (e.g., unsupervised clustering analyses, a machine learning-based source tracking and apportionment tool). As a key advance, most core MSS functions are supported by machine learning algorithms (e.g., clustering algorithms and predictive modeling algorithms) to facilitate function accuracy and/or efficiency. MSS reliability was validated with mixed chemical standards of known composition, with 99.5% feature extraction accuracy and ~ 52% overlap of extracted features relative to other open-source software tools. Example user cases of laboratory data evaluation are provided to illustrate MSS functionalities and demonstrate reliability. MSS expands available HRMS data analysis workflows for water quality evaluation and environmental forensics, and is readily integrated with existing capabilities. As an open-source package, we anticipate further development of improved data analysis capabilities in collaboration with interested users. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-023-00741-9. |
format | Online Article Text |
id | pubmed-10517472 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-105174722023-09-24 Mass-Suite: a novel open-source python package for high-resolution mass spectrometry data analysis Hu, Ximin Mar, Derek Suzuki, Nozomi Zhang, Bowei Peter, Katherine T. Beck, David A. C. Kolodziej, Edward P. J Cheminform Software Mass-Suite (MSS) is a Python-based, open-source software package designed to analyze high-resolution mass spectrometry (HRMS)-based non-targeted analysis (NTA) data, particularly for water quality assessment and other environmental applications. MSS provides flexible, user-defined workflows for HRMS data processing and analysis, including both basic functions (e.g., feature extraction, data reduction, feature annotation, data visualization, and statistical analyses) and advanced exploratory data mining and predictive modeling capabilities that are not provided by currently available open-source software (e.g., unsupervised clustering analyses, a machine learning-based source tracking and apportionment tool). As a key advance, most core MSS functions are supported by machine learning algorithms (e.g., clustering algorithms and predictive modeling algorithms) to facilitate function accuracy and/or efficiency. MSS reliability was validated with mixed chemical standards of known composition, with 99.5% feature extraction accuracy and ~ 52% overlap of extracted features relative to other open-source software tools. Example user cases of laboratory data evaluation are provided to illustrate MSS functionalities and demonstrate reliability. MSS expands available HRMS data analysis workflows for water quality evaluation and environmental forensics, and is readily integrated with existing capabilities. As an open-source package, we anticipate further development of improved data analysis capabilities in collaboration with interested users. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-023-00741-9. Springer International Publishing 2023-09-23 /pmc/articles/PMC10517472/ /pubmed/37741995 http://dx.doi.org/10.1186/s13321-023-00741-9 Text en © The Author(s) 2023 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 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 Hu, Ximin Mar, Derek Suzuki, Nozomi Zhang, Bowei Peter, Katherine T. Beck, David A. C. Kolodziej, Edward P. Mass-Suite: a novel open-source python package for high-resolution mass spectrometry data analysis |
title | Mass-Suite: a novel open-source python package for high-resolution mass spectrometry data analysis |
title_full | Mass-Suite: a novel open-source python package for high-resolution mass spectrometry data analysis |
title_fullStr | Mass-Suite: a novel open-source python package for high-resolution mass spectrometry data analysis |
title_full_unstemmed | Mass-Suite: a novel open-source python package for high-resolution mass spectrometry data analysis |
title_short | Mass-Suite: a novel open-source python package for high-resolution mass spectrometry data analysis |
title_sort | mass-suite: a novel open-source python package for high-resolution mass spectrometry data analysis |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10517472/ https://www.ncbi.nlm.nih.gov/pubmed/37741995 http://dx.doi.org/10.1186/s13321-023-00741-9 |
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