Comprehensive Foodomics Platform, TUM School of Life Sciences, Technical University of Munich, Freising 85354, Germany.
Analytical BioGeoChemistry, Helmholtz Zentrum Muenchen, Neuherberg 85764, Germany.
Bioinformatics. 2023 Feb 3;39(2). doi: 10.1093/bioinformatics/btad088.
Plasma ionization is rapidly gaining popularity for mass spectrometry (MS)-based studies of volatiles and aerosols. However, data from plasma ionization are delicate to interpret as competing ionization pathways in the plasma create numerous ion species. There is no tool for detection of adducts and in-source fragments from plasma ionization data yet, which makes data evaluation ambiguous.
We developed DBDIpy, a Python library for processing and formal analysis of untargeted, time-sensitive plasma ionization MS datasets. Its core functionality lies in the identification of in-source fragments and identification of rivaling ionization pathways of the same analytes in time-sensitive datasets. It further contains elementary functions for processing of untargeted metabolomics data and interfaces to an established ecosystem for analysis of MS data in Python.
DBDIpy is implemented in Python (Version ≥ 3.7) and can be downloaded from PyPI the Python package repository (https://pypi.org/project/DBDIpy) or from GitHub (https://github.com/leopold-weidner/DBDIpy).
Supplementary data are available at Bioinformatics online.
等离子体电离技术在基于质谱（MS）的挥发性和气溶胶研究中迅速普及。然而，由于等离子体中的竞争电离途径会产生多种离子物种，因此等离子体电离产生的数据难以解释。目前还没有用于检测等离子体电离数据中加合物和内源碎片的工具，这使得数据评估变得不确定。
我们开发了 DBDIpy，这是一个用于处理和正式分析无靶向、时间敏感的等离子体电离 MS 数据集的 Python 库。它的核心功能在于识别内源碎片以及在时间敏感数据集中识别同一分析物的竞争电离途径。它还包含用于处理无靶向代谢组学数据的基本功能，并提供了与 Python 中用于分析 MS 数据的成熟生态系统的接口。
DBDIpy 是用 Python 实现的（版本≥3.7），可以从 Python 包存储库 PyPI（https://pypi.org/project/DBDIpy）或从 GitHub（https://github.com/leopold-weidner/DBDIpy）下载。
补充数据可在 Bioinformatics 在线获取。
