DEIMoS: An Open-Source Tool for Processing High-Dimensional Mass Spectrometry Data

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 data sets, and (iii) mitigate convolution artifacts in tandem mass spectra. We demonstrate DEIMoS with LC-IMS-MS/MS metabolomics data to illustrate the advantages of a multidimensional approach in each data processing step.

Automated summary

DEIMoS is a Python tool for high-dimensional mass spectrometry data analysis that provides features such as feature detection, feature alignment, and calibration. It operates on N-dimensional data and can improve detection sensitivity and data alignment confidence while reducing artifacts in tandem mass spectra. The demonstration with metabolomics data shows the advantages of using DEIMoS in each data processing step.