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Data sharing in PredRet for accurate prediction of retention time: application to plant food bioactive compounds

Research output: Contribution to journalArticlepeer-review

Dorrain Yanwen Low, Ville Mikael Koistinen, Pierre Micheau, Kati Hanhineva, Lazslo Abranko, Ana Maria Rodriguez Mateos, Andreia Bento da Silva, Christof van Poucke, Conceicao Almeida, Cristina Andres-Lacueva, Dilip K Rai, Esra Capanoglu, Francisco Tomas-Barberan , Fulvio Mattivi, Gesine Schmidt, Gozde Gurdeniz, Katerina Valentova, Leticia Bresciani, Lucie Petraskova, Lars Ove Dragsted & 12 more Mark Philo, Marynka Ulaszewska, Pedro Mena, Raul Gonzalez-Dominguez, Rocio Garcia-Villalba, Senem Kamiloglu, Sonia de Pascual-Teresa, Stephanie Durand, Wieslaw Wiczkowski , Maria Rosario Bronze, Stanstrup Jan, Claudine Manach

Original languageEnglish
Accepted/In press8 Apr 2021

King's Authors


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 ( 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.

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