Reproducible mass spectrometry data processing and compound annotation in MZmine 3

质谱法 注释 计算机科学 色谱法 化学 情报检索 人工智能
作者
Tito Damiani,Steffen Heuckeroth,Aleksandr Smirnov,Olena Mokshyna,Corinna Brungs,Ansgar Korf,Joshua R. Smith,Paolo Stincone,Nicola Dreolin,Louis‐Félix Nothias,Tuulia Hyötyläinen,Matej Orešič,Uwe Kärst,Pieter C. Dorrestein,Daniel Petras,Xiuxia Du,Justin J. J. van der Hooft,Robin Schmid,Tomáš Pluskal
标识
DOI:10.26434/chemrxiv-2023-98n6q-v2
摘要

Untargeted MS experiments produce complex, multi-dimensional 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. Based on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is open-source software for the processing of raw spectral data generated by different MS platforms: liquid chromatography–MS (LC–MS), gas chromatography–MS (GC–MS), and MS–imaging. 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: LC–(IMS–)MS, GC–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 hours for new MZmine users and non-experts. 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.
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