gcProfileMakeR: An R Package for Automatic Classification of Constitutive and Non-Constitutive Metabolites

Other authors

Institut Català de la Salut

[Perez-Sanz F] Instituto Murciano de Investigaciones Biomédicas El Palmar, 30120 Murcia, Spain. [Ruiz-Hernández V] Department of Biosciences, University Salzburg, 5020 Salzburg, Austria. [Terry MI, Weiss J, Egea-Cortines M] Genética Molecular, Instituto de Biotecnología Vegetal, Edificio I+D+I, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain. [Arce-Gallego S] Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain. [Navarro PJ] DSIE Cuartel de Antiguones, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain

Vall d'Hebron Barcelona Hospital Campus

Publication date

2022-06-08T09:52:11Z

2022-06-08T09:52:11Z

2021-04



Abstract

Circadian clock; Constitutive metabolome; Machine learning


Reloj circadiano; Metaboloma constitutivo; Aprendizaje automático


Rellotge circadià; Metaboloma constitutiu; Aprenentatge automàtic


Metabolomes comprise constitutive and non-constitutive metabolites produced due to physiological, genetic or environmental effects. However, finding constitutive metabolites and non-constitutive metabolites in large datasets is technically challenging. We developed gcProfileMakeR, an R package using standard Excel output files from an Agilent Chemstation GC-MS for automatic data analysis using CAS numbers. gcProfileMakeR has two filters for data preprocessing removing contaminants and low-quality peaks. The first function NormalizeWithinFiles, samples assigning retention times to CAS. The second function NormalizeBetweenFiles, reaches a consensus between files where compounds in close retention times are grouped together. The third function getGroups, establishes what is considered as Constitutive Profile, Non-constitutive by Frequency i.e., not present in all samples and Non-constitutive by Quality. Results can be plotted with the plotGroup function. We used it to analyse floral scent emissions in four snapdragon genotypes. These included a wild type, Deficiens nicotianoides and compacta affecting floral identity and RNAi:AmLHY targeting a circadian clock gene. We identified differences in scent constitutive and non-constitutive profiles as well as in timing of emission. gcProfileMakeR is a very useful tool to define constitutive and non-constitutive scent profiles. It also allows to analyse genotypes and circadian datasets to identify differing metabolites.


This research was funded by Ministerio de Ciencia, Innovación y Universidades and FEDER grant numbers BFU2017-88300-C2-1R to M.E.-C. and J.W.; BFU2017-88300-C2-2R to P.J.N.; and a PhD contract by the Ministerio de Educación Cultura y Deporte FPU13/03606 to V.R.-H.

Document Type

Article


Published version

Language

English

Publisher

MDPI

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Metabolites;11(4)

https://doi.org/10.3390/metabo11040211

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Rights

Attribution 4.0 International

http://creativecommons.org/licenses/by/4.0/

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