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Article

A Python-Based Pipeline for Preprocessing LC–MS Data for Untargeted Metabolomics Workflows

1
Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, Ciudad de Buenos Aires C1425FQD, Argentina
2
Departamento de Química Inorgánica Analítica y Química Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Ciudad Universitaria, Buenos Aires C1428EGA, Argentina
3
Facultad de Ingeniería, Universidad de Buenos Aires, Paseo Colón 850, Ciudad de Buenos Aires C1063ACV, Argentina
4
Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899-8392, USA
*
Author to whom correspondence should be addressed.
Metabolites 2020, 10(10), 416; https://doi.org/10.3390/metabo10100416
Received: 12 September 2020 / Revised: 9 October 2020 / Accepted: 14 October 2020 / Published: 16 October 2020
(This article belongs to the Special Issue Metabolomics Methodologies and Applications II)
Preprocessing data in a reproducible and robust way is one of the current challenges in untargeted metabolomics workflows. Data curation in liquid chromatography–mass spectrometry (LC–MS) involves the removal of biologically non-relevant features (retention time, m/z pairs) to retain only high-quality data for subsequent analysis and interpretation. The present work introduces TidyMS, a package for the Python programming language for preprocessing LC–MS data for quality control (QC) procedures in untargeted metabolomics workflows. It is a versatile strategy that can be customized or fit for purpose according to the specific metabolomics application. It allows performing quality control procedures to ensure accuracy and reliability in LC–MS measurements, and it allows preprocessing metabolomics data to obtain cleaned matrices for subsequent statistical analysis. The capabilities of the package are shown with pipelines for an LC–MS system suitability check, system conditioning, signal drift evaluation, and data curation. These applications were implemented to preprocess data corresponding to a new suite of candidate plasma reference materials developed by the National Institute of Standards and Technology (NIST; hypertriglyceridemic, diabetic, and African-American plasma pools) to be used in untargeted metabolomics studies in addition to NIST SRM 1950 Metabolites in Frozen Human Plasma. The package offers a rapid and reproducible workflow that can be used in an automated or semi-automated fashion, and it is an open and free tool available to all users. View Full-Text
Keywords: data cleaning; preprocessing; Python; untargeted metabolomics; reference materials; data curation; system suitability; signal drift; quality control data cleaning; preprocessing; Python; untargeted metabolomics; reference materials; data curation; system suitability; signal drift; quality control
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MDPI and ACS Style

Riquelme, G.; Zabalegui, N.; Marchi, P.; Jones, C.M.; Monge, M.E. A Python-Based Pipeline for Preprocessing LC–MS Data for Untargeted Metabolomics Workflows. Metabolites 2020, 10, 416. https://doi.org/10.3390/metabo10100416

AMA Style

Riquelme G, Zabalegui N, Marchi P, Jones CM, Monge ME. A Python-Based Pipeline for Preprocessing LC–MS Data for Untargeted Metabolomics Workflows. Metabolites. 2020; 10(10):416. https://doi.org/10.3390/metabo10100416

Chicago/Turabian Style

Riquelme, Gabriel, Nicolás Zabalegui, Pablo Marchi, Christina M. Jones, and María E. Monge 2020. "A Python-Based Pipeline for Preprocessing LC–MS Data for Untargeted Metabolomics Workflows" Metabolites 10, no. 10: 416. https://doi.org/10.3390/metabo10100416

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