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


Low D. Y. , Micheau P., Koistinen V. M. , Hanhineva K., Abrankó L., Rodriguez-Mateos A., ...More

Food chemistry, vol.357, pp.129757, 2021 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 357
  • Publication Date: 2021
  • Doi Number: 10.1016/j.foodchem.2021.129757
  • Journal Name: Food chemistry
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Chemical Abstracts Core, Chimica, Communication Abstracts, Compendex, EMBASE, Food Science & Technology Abstracts, MEDLINE, Metadex, Veterinary Science Database, Civil Engineering Abstracts
  • Page Numbers: pp.129757
  • Keywords: Predicted retention time, Metabolomics, Plant food bioactive compounds, Metabolites, Data sharing, UHPLC, METABOLOME, SUSPECT
  • Bursa Uludag University Affiliated: Yes

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/) topredict 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.