Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds
Artikel i vetenskaplig tidskrift, 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
Författare
Dorrain Yanwen Low
Université Clermont Auvergne
Pierre Micheau
Université Clermont Auvergne
Ville Mikael Koistinen
Itä-Suomen Yliopisto
Turun Yliopisto
Kati Hanhineva
Turun Yliopisto
Chalmers, Biologi och bioteknik, Livsmedelsvetenskap
Itä-Suomen Yliopisto
László Abrankó
Szent István Egyetem
Ana Rodriguez-Mateos
King's College London
Andreia Bento da Silva
Universidade de Lisboa
Universidade NOVA de Lisboa
Christof Van Poucke
Instituut voor Landbouw-, Visserij- en Voedingsonderzoek
Conceição Almeida
Universidade NOVA de Lisboa
Cristina Andres-Lacueva
Universitat de Barcelona
Instituto de Salud Carlos III
Dilip K. Rai
Teagasc - Irish Agriculture and Food Development Authority
Esra Capanoglu
Istanbul Teknik Universitesi (ITÜ)
Francisco A. Tomás-Barberán
Taif University
Fulvio Mattivi
Universita degli Studi di Trento
Istituto Agrario San Michele all'Adige
Gesine Schmidt
Nofima
Gözde Gürdeniz
Köpenhamns universitet
Kateřina Valentová
Chinese Academy of Sciences
Letizia Bresciani
Universita degli Studi di Parma
Lucie Petrásková
Chinese Academy of Sciences
Lars Ove Dragsted
Köpenhamns universitet
Mark Philo
Institute of Food Research
Marynka M. Ulaszewska
Istituto Agrario San Michele all'Adige
Pedro Mena
Universita degli Studi di Parma
Raúl González-Domínguez
Instituto de Salud Carlos III
Universitat de Barcelona
Rocío Garcia-Villalba
CEBAS- CSIC, Centro de Edafología y Biología Aplicada del Segura
Senem Kamiloglu
Istanbul Teknik Universitesi (ITÜ)
Bursa Uludağ Üniversitesi
Sonia de Pascual-Teresa
CSIC - Instituto de Ciencia y Tecnologia de Alimentos y Nutricion (ICTAN)
Stéphanie Durand
Université Clermont Auvergne
Wiesław Wiczkowski
Polish Academy of Sciences
Maria Bronze
Instituto de Biologia Experimental e Tecnologica
Universidade NOVA de Lisboa
Universidade de Lisboa
Jan Stanstrup
Köpenhamns universitet
Claudine Manach
Université Clermont Auvergne
Food Chemistry
0308-8146 (ISSN) 1873-7072 (eISSN)
Vol. 357 129757Ämneskategorier
Datorteknik
Språkteknologi (språkvetenskaplig databehandling)
Bioinformatik och systembiologi
DOI
10.1016/j.foodchem.2021.129757
PubMed
33872868