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)

Vol. 357 129757

Ämneskategorier

Datorteknik

Språkteknologi (språkvetenskaplig databehandling)

Bioinformatik och systembiologi

DOI

10.1016/j.foodchem.2021.129757

Mer information

Senast uppdaterat

2021-05-04