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., Abranko L., Rodriguez-Mateos A., ...Daha Fazla

FOOD CHEMISTRY, cilt.357, 2021 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 357
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1016/j.foodchem.2021.129757
  • Dergi Adı: FOOD CHEMISTRY
  • Derginin Tarandığı İndeksler: 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
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

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 & nbsp;predict RTs for plant food bioactive metabolites in a data sharing initiative containing entry sets of 29 & ndash;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 & ndash;0.76 min and interval width of 0.33 & ndash;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 & rsquo;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.