Steffen Heuckeroth, Tito Damiani, Aleksandr Smirnov, Olena Mokshyna, Corinna Brungs, Ansgar Korf, Joshua David Smith, Paolo Stincone, Nicola Dreolin, Louis-Félix Nothias, Tuulia Hyötyläinen, Matej Orešič, Uwe Karst, Pieter C. Dorrestein, Daniel Petras, Xiuxia Du, Justin J. J. van der Hooft, Robin Schmid, Tomáš Pluskal
{"title":"Reproducible mass spectrometry data processing and compound annotation in MZmine 3","authors":"Steffen Heuckeroth, Tito Damiani, Aleksandr Smirnov, Olena Mokshyna, Corinna Brungs, Ansgar Korf, Joshua David Smith, Paolo Stincone, Nicola Dreolin, Louis-Félix Nothias, Tuulia Hyötyläinen, Matej Orešič, Uwe Karst, Pieter C. Dorrestein, Daniel Petras, Xiuxia Du, Justin J. J. van der Hooft, Robin Schmid, Tomáš Pluskal","doi":"10.1038/s41596-024-00996-y","DOIUrl":null,"url":null,"abstract":"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. Untargeted mass spectrometry (MS) produces complex, multidimensional data. The MZmine open-source project enables processing of spectral data from various MS platforms, e.g., liquid chromatography–MS, gas chromatography–MS, MS–imaging and ion mobility spectrometry–MS, and is specialized for metabolomics.","PeriodicalId":18901,"journal":{"name":"Nature Protocols","volume":"19 9","pages":"2597-2641"},"PeriodicalIF":13.1000,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Protocols","FirstCategoryId":"99","ListUrlMain":"https://www.nature.com/articles/s41596-024-00996-y","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
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. Untargeted mass spectrometry (MS) produces complex, multidimensional data. The MZmine open-source project enables processing of spectral data from various MS platforms, e.g., liquid chromatography–MS, gas chromatography–MS, MS–imaging and ion mobility spectrometry–MS, and is specialized for metabolomics.
期刊介绍:
Nature Protocols focuses on publishing protocols used to address significant biological and biomedical science research questions, including methods grounded in physics and chemistry with practical applications to biological problems. The journal caters to a primary audience of research scientists and, as such, exclusively publishes protocols with research applications. Protocols primarily aimed at influencing patient management and treatment decisions are not featured.
The specific techniques covered encompass a wide range, including but not limited to: Biochemistry, Cell biology, Cell culture, Chemical modification, Computational biology, Developmental biology, Epigenomics, Genetic analysis, Genetic modification, Genomics, Imaging, Immunology, Isolation, purification, and separation, Lipidomics, Metabolomics, Microbiology, Model organisms, Nanotechnology, Neuroscience, Nucleic-acid-based molecular biology, Pharmacology, Plant biology, Protein analysis, Proteomics, Spectroscopy, Structural biology, Synthetic chemistry, Tissue culture, Toxicology, and Virology.