Tidy-Direct-to-MS: An Open-Source Data-Processing Pipeline for Direct Mass Spectrometry-Based Metabolomics Experiments

Abstract

Direct-to-Mass Spectrometry and ambient ionization techniques can be used for biochemical fingerprinting in a fast way. Data processing is typically accomplished with vendor-provided software tools. Here, a novel, open-source functionality, entitled Tidy-Directto-MS, was developed for data processing of direct-to-MS data sets. It allows for fast and userfriendly processing using different modules for optional sample position detection and separation, mass-to-charge ratio drift detection and correction, consensus spectra calculation, and bracketing across sample positions as well as feature abundance calculation. The tool also provides functionality for the automated comparison of different sets of parameters, thereby assisting the user in the complex task of finding an optimal combination to maximize the total number of detected features while also checking for the detection of user-provided reference features. In addition, Tidy-Direct-to-MS has the capability for data quality review and subsequent data analysis, thereby simplifying the workflow of untargeted ambient MS-based metabolomics studies. Tidy-Direct-to-MS is implemented in the Python programming language as part of the TidyMS library and can thus be easily extended. Capabilities of Tidy-Direct-to-MS are showcased in a data set acquired in a marine metabolomics study reported in MetaboLights (MTBLS1198) using a transmission mode Direct Analysis in Real Time-Mass Spectrometry (TM-DART-MS)-based method.

Celebrating Women in Proteomics and Metabolomics

“…The Special issue also presents web-based tools for analyzing N-glycocapture data (Veneer from the Rebekah Gundry group) and for analyzing proteomics, metabolomics, lipidomics, and transcriptomics data (PMart by Kelly Stratton from Lisa Bramer’s group) . For MS-based metabolomics data, an open-source pipeline (Tidy-Direct-to-MS from the group of Maria Eugenia Monge) is described, as well as an ensemble learning-based spatial segmentation strategy for analyzing MS imaging data and characterizing metabolic heterogeneity in different tissue subregions (eLIMS from Jingjing Xu and colleagues) …”