Reproducible mass spectrometry data processing and compound annotation in MZmine 3
Jazyk angličtina Země Anglie, Velká Británie Médium print-electronic
Typ dokumentu časopisecké články, přehledy
Grantová podpora
R01DK136117
U.S. Department of Health & Human Services | National Institutes of Health (NIH)
U01CA235507
U.S. Department of Health & Human Services | National Institutes of Health (NIH)
891397
EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)
R03 CA222450
NCI NIH HHS - United States
R03OD034493
U.S. Department of Health & Human Services | National Institutes of Health (NIH)
2152526
National Science Foundation (NSF)
PubMed
38769143
DOI
10.1038/s41596-024-00996-y
PII: 10.1038/s41596-024-00996-y
Knihovny.cz E-zdroje
- MeSH
- chromatografie kapalinová metody MeSH
- hmotnostní spektrometrie * metody MeSH
- iontová mobilní spektrometrie metody MeSH
- metabolomika metody MeSH
- plynová chromatografie s hmotnostně spektrometrickou detekcí metody MeSH
- reprodukovatelnost výsledků MeSH
- software * MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography-MS, gas chromatography-MS and MS-imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography-(IMS-)MS, gas chromatography-MS and (IMS-)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis.
1st Faculty of Medicine Charles University Prague Czech Republic
Örebro University Örebro Sweden
Université Côte d'Azur CNRS ICN Nice France
University of California Riverside Riverside CA USA
University of Geneva Geneva Switzerland
University of Johannesburg Johannesburg South Africa
University of Münster Münster Germany
University of North Carolina at Charlotte Charlotte NC USA
University of Tuebingen Tuebingen Germany
University of Turku and Åbo Akademi University Turku Finland
Wageningen University and Research Wageningen the Netherlands
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