Reproducible mass spectrometry data processing and compound annotation in MZmine 3

IF 13.1 1区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
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
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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.

Abstract Image

Abstract Image

MZmine 3 中可重复的质谱数据处理和化合物注释。
非靶向质谱(MS)实验会产生复杂的多维数据,而这些数据实际上无法通过人工方式进行研究。因此,需要使用计算管道从原始光谱数据中提取相关信息,并将其转换为更易于理解的格式。根据样品类型和/或研究目标的不同,可使用各种 MS 平台进行此类分析。MZmine 是一款开源软件,用于处理不同质谱平台生成的原始光谱数据。例如液相色谱-质谱、气相色谱-质谱和质谱成像。这些数据通常与代谢组学和脂质组学等各种应用有关。此外,本文介绍的第三版软件还支持离子迁移谱(IMS)数据的处理。本协议提供了三种不同的程序,用于对不同仪器设置产生的非目标质谱数据进行特征检测和注释:液相色谱-(IMS-)质谱、气相色谱-质谱和(IMS-)质谱成像。为便于培训,我们提供了示例数据集和配置批处理文件(即处理步骤和参数列表),以便新用户轻松复制所述工作流程。根据数据文件的数量和可用的计算资源,我们预计新的 MZmine 用户和非专业人员需要 2 到 24 小时才能完成这项工作。在每个程序中,我们都对所有处理参数进行了详细说明,并提供了优化说明/建议。生成的主要输出结果由对齐的特征表和碎片光谱列表表示,可用于其他第三方工具的进一步下游分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Nature Protocols
Nature Protocols 生物-生化研究方法
CiteScore
29.10
自引率
0.70%
发文量
128
审稿时长
4 months
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.
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