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Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds
Low, Dorrain Yanwen; Micheau, Pierre; Koistinen, Ville Mikael; Hanhineva, Kati; Abrankó, László; Rodriguez-Mateos, Ana; da Silva, Andreia Bento; van Poucke, Christof; Almeida, Conceição; Andres-Lacueva, Cristina; Rai, Dilip K.; Capanoglu, Esra; Tomás Barberán, Francisco A.; Mattivi, Fulvio; Schmidt, Gesine; Gürdeniz, Gözde; Valentová, Kateřina; Bresciani, Letizia; Petrásková, Lucie; Dragsted, Lars Ove; Philo, Mark; Ulaszewska, Marynka; Mena, Pedro; González-Domínguez, Raúl; Garcia-Villalba, Rocío; Kamiloglu, Senem; de Pascual-Teresa, Sonia; Durand, Stéphanie; Wiczkowski, Wieslaw; Bronze, Maria Rosário; Stanstrup, Jan; Manach, Claudine
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2021-09
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Dorrain Yanwen Low, Pierre Micheau, Ville Mikael Koistinen, Kati Hanhineva, László Abrankó, Ana Rodriguez-Mateos, Andreia Bento da Silva, Christof van Poucke, Conceição Almeida, Cristina Andres-Lacueva, Dilip K. Rai, Esra Capanoglu, Francisco A. Tomás Barberán, Fulvio Mattivi, Gesine Schmidt, Gözde Gürdeniz, Kateřina Valentová, Letizia Bresciani, Lucie Petrásková, Lars Ove Dragsted, Mark Philo, Marynka Ulaszewska, Pedro Mena, Raúl González-Domínguez, Rocío Garcia-Villalba, Senem Kamiloglu, Sonia de Pascual-Teresa, Stéphanie Durand, Wieslaw Wiczkowski, Maria Rosário Bronze, Jan Stanstrup, Claudine Manach, Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds, Food Chemistry, Volume 357, 2021, 129757, ISSN 0308-8146, https://doi.org/10.1016/j.foodchem.2021.129757.
Abstract
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.
Funder
European Union
Nanyang Technological University, Singapore
MetaboHUB French infrastructure
Czech Science Foundation
Faculty of Pharmacy of Lisbon University
Academy of Finland
Lantmännen Foundation and EU H2020 FP7-Marie Curie-COFUND MoRE Programme
Biocenter Finland
Generalitat de Catalunya’s Agency AGAUR
“Juan de la Cierva” program from MINECO
Spanish National Research program
Fundación Seneca Región de Murcia
Norwegian Agriculture and Food Industry Research Funds
Carlsberg Foundation
Hungarian Academy of Sciences
Spanish MINECO
Teagasc Walsh Fellowship
Nanyang Technological University, Singapore
MetaboHUB French infrastructure
Czech Science Foundation
Faculty of Pharmacy of Lisbon University
Academy of Finland
Lantmännen Foundation and EU H2020 FP7-Marie Curie-COFUND MoRE Programme
Biocenter Finland
Generalitat de Catalunya’s Agency AGAUR
“Juan de la Cierva” program from MINECO
Spanish National Research program
Fundación Seneca Región de Murcia
Norwegian Agriculture and Food Industry Research Funds
Carlsberg Foundation
Hungarian Academy of Sciences
Spanish MINECO
Teagasc Walsh Fellowship