DEIMoS：一个用于处理高维质谱数据的开源工具
DEIMoS: an open-source tool for processing high-dimensional mass spectrometry data
摘要：
我们介绍DEIMOS：用于集成多维光谱分析的数据提取，一个Python应用程序编程接口（API）和命令行工具，用于高维质谱数据分析工作流程，提供易于开发和高效算法实现的访问。功能包括特征检测、特征比对、碰撞截面（CCS）校准、同位素检测和MS/MS光谱去卷积，输出包括跨研究样品比对并通过质量、CCS、串联质谱和同位素特征表征的检测到的特征。值得注意的是，DEIMoS对N维数据进行操作，在很大程度上与采集仪器无关;算法实现同时利用所有维度来（i）提供特征之间的更大分离，从而提高检测灵敏度，（ii）增加数据集之间的对齐/特征匹配置信度，以及（iii）减轻串联质谱中的卷积伪影。我们演示DEIMoS与LC-IMS-MS/MS数据，以说明在每个数据处理步骤的多维方法的优势。
英文摘要：
We present DEIMoS: Data Extraction for Integrated Multidimensional Spectrometry, a Python application programming interface (API) and command-line tool for high-dimensional mass spectrometry data analysis workflows that offers ease of development and access to efficient algorithmic implementations. Functionality includes feature detection, feature alignment, collision cross section (CCS) calibration, isotope detection, and MS/MS spectral deconvolution, with the output comprising detected features aligned across study samples and characterized by mass, CCS, tandem mass spectra, and isotopic signature. Notably, DEIMoS operates on N-dimensional data, largely agnostic to acquisition instrumentation; algorithm implementations simultaneously utilize all dimensions to (i) offer greater separation between features, thus improving detection sensitivity, (ii) increase alignment/feature matching confidence among datasets, and (iii) mitigate convolution artifacts in tandem mass spectra. We demonstrate DEIMoS with LC-IMS-MS/MS data to illustrate the advantages of a multidimensional approach in each data processing step.
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