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.

UHPLC

Metabolomics

Data sharing

Predicted retention time

Plant food bioactive compounds

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

Chalmers, Biology and Biological Engineering, Food and Nutrition Science

University of Eastern Finland

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

Institute of Health Carlos III

University of Barcelona

Rocío Garcia-Villalba

CEBAS- CSIC, Centro de Edafología y Biología Aplicada del Segura

Senem Kamiloglu

Istanbul Technical University (ITÜ)

Bursa Uludağ Üniversitesi

Sonia de Pascual-Teresa

CSIC - Instituto de Ciencia y Tecnologia de Alimentos y Nutricion (ICTAN)

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 129757

Subject Categories

Computer Engineering

Language Technology (Computational Linguistics)

Bioinformatics and Systems Biology

DOI

10.1016/j.foodchem.2021.129757

PubMed

33872868

More information

Latest update

5/4/2021 3