TidyMS: a Python library for preprocessing LC-MS data in untargeted metabolomics workflows

GABRIEL RIQUELME; NICOLÁS ZABALEGUI; PABLO MARCHI; CHRISTINA JONES; MARÍA EUGENIA MONGE

Conferencia Virtual

Conferencia; 16th Annual Conference of the Metabolomics Society Metabolomics2020 Online; 2020

The Metabolomics Society

One of the current challenges in untargeted metabolomics workflows is preprocessing data in a reproducible and robust way. In liquid chromatography-mass spectrometry (LC-MS), curation of features (retention time, m/z pairs) involves retaining only those analytically robust enough to allow subsequent data analysis and interpretation. Quality control practices are typically used in data preprocessing to remove biologically non-relevant features. Our research group has developed TidyMS, a library for the Python programming language, for preprocessing LC-MS data, with the goal of performing quality control procedures in untargeted metabolomics workflows. It includes tools to work with both raw MS data, such as feature extraction and detection; or data matrices, providing functionality to build customized pipelines for data curation. The capabilities of TidyMS were illustrated with pipelines for a system suitability check, system conditioning, signal drift evaluation, and data curation. These applications were applied to preprocess data from a new suite of candidate human plasma reference materials for untargeted metabolomics currently under development by the National Institute of Standards and Technology (NIST; hypertriglyceridemic, diabetic, and African-American plasma pools), in addition to NIST SRM 1950 Metabolites in Frozen Human Plasma. Overall, TidyMS offers a rapid and reproducible workflow that can be used in an automated or semi-automated way, and it is an open and free tool available to all users.