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
2022-06-08T09:52:11Z
2022-06-08T09:52:11Z
2021-04
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.
Article
Published version
English
Metabolòmica; ADN - Bancs de dades; Genètica molecular; INFORMATION SCIENCE::Information Science::Information Services::Documentation::Molecular Sequence Data; PHENOMENA AND PROCESSES::Metabolism::Metabolome; DISCIPLINES AND OCCUPATIONS::Natural Science Disciplines::Biological Science Disciplines::Biochemistry::Molecular Biology; CIENCIA DE LA INFORMACIÓN::Ciencias de la información::servicios de información::documentación::datos de secuencia molecular; FENÓMENOS Y PROCESOS::metabolismo::metaboloma; DISCIPLINAS Y OCUPACIONES::disciplinas de las ciencias naturales::disciplinas de las ciencias biológicas::bioquímica::biología molecular
MDPI
Metabolites;11(4)
https://doi.org/10.3390/metabo11040211
Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/