Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds
Journal article, 2021
Prediction of retention times (RTs) is increasingly considered in untargeted metabolomics to complement MS/MS matching for annotation of unidentified peaks. We tested the performance of PredRet (http://predret.org/) to predict RTs for plant food bioactive metabolites in a data sharing initiative containing entry sets of 29–103 compounds (totalling 467 compounds, >30 families) across 24 chromatographic systems (CSs). Between 27 and 667 predictions were obtained with a median prediction error of 0.03–0.76 min and interval width of 0.33–8.78 min. An external validation test of eight CSs showed high prediction accuracy. RT prediction was dependent on shape and type of LC gradient, and number of commonly measured compounds. Our study highlights PredRet's accuracy and ability to transpose RT data acquired from one CS to another CS. We recommend extensive RT data sharing in PredRet by the community interested in plant food bioactive metabolites to achieve a powerful community-driven open-access tool for metabolomics annotation.
Predicted retention time
Plant food bioactive compounds
Data sharing
UHPLC
Metabolomics
Metabolites
Author
Dorrain Yanwen Low
Clermont Auvergne University
Pierre Micheau
Clermont Auvergne University
Ville Mikael Koistinen
University of Eastern Finland
University of Turku
Kati Hanhineva
University of Turku
University of Eastern Finland
Chalmers, Biology and Biological Engineering, Food and Nutrition Science
László Abrankó
Szent István University
Ana Rodriguez-Mateos
King's College London
Andreia Bento da Silva
University of Lisbon
Nova University of Lisbon
Christof Van Poucke
Flanders Research Institute for Agriculture Fisheries and Food (ILVO),
Conceição Almeida
Nova University of Lisbon
Cristina Andres-Lacueva
University of Barcelona
Institute of Health Carlos III
Dilip K. Rai
Teagasc - Irish Agriculture and Food Development Authority
Esra Capanoglu
Istanbul Technical University (ITÜ)
Francisco A. Tomás-Barberán
Taif University
Fulvio Mattivi
University of Trento
Istituto Agrario San Michele all'Adige
Gesine Schmidt
Nofima
Gözde Gürdeniz
University of Copenhagen
Kateřina Valentová
Chinese Academy of Sciences
Letizia Bresciani
University of Parma
Lucie Petrásková
Chinese Academy of Sciences
Lars Ove Dragsted
University of Copenhagen
Mark Philo
Institute of Food Research
Marynka M. Ulaszewska
Istituto Agrario San Michele all'Adige
Pedro Mena
University of Parma
Raúl González-Domínguez
University of Barcelona
Institute of Health Carlos III
Rocío Garcia-Villalba
Spanish National Research Council (CSIC)
Senem Kamiloglu
Bursa Uludağ Üniversitesi
Istanbul Technical University (ITÜ)
Sonia de Pascual-Teresa
Spanish National Research Council (CSIC)
Stéphanie Durand
Clermont Auvergne University
Wiesław Wiczkowski
Polish Academy of Sciences
Maria Bronze
Instituto de Biologia Experimental e Tecnologica
Nova University of Lisbon
University of Lisbon
Jan Stanstrup
University of Copenhagen
Claudine Manach
Clermont Auvergne University
Food Chemistry
0308-8146 (ISSN) 1873-7072 (eISSN)
Vol. 357 129757Subject Categories (SSIF 2011)
Computer Engineering
Language Technology (Computational Linguistics)
Bioinformatics and Systems Biology
DOI
10.1016/j.foodchem.2021.129757
PubMed
33872868