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.
@article{arxiv.2112.03466,
title = {DEIMoS: an open-source tool for processing high-dimensional mass spectrometry data},
author = {Sean M. Colby and Christine H. Chang and Jessica L. Bade and Jamie R. Nunez and Madison R. Blumer and Daniel J. Orton and Kent J. Bloodsworth and Ernesto S. Nakayasu and Richard D. Smith and Yehia M. Ibrahim and Ryan S. Renslow and Thomas O. Metz},
journal= {arXiv preprint arXiv:2112.03466},
year = {2021}
}