4.7 Article

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

期刊

FOOD CHEMISTRY
卷 357, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.foodchem.2021.129757

关键词

Predicted retention time; Metabolomics; Plant food bioactive compounds; Metabolites; Data sharing; UHPLC

资金

  1. European Cooperation in Science and Technology (COST) Action [FA 1403]
  2. EU [609398]
  3. Nanyang Technological University, Singapore [001991-00001]
  4. INRAE platform (PFEM, MetaboHUBClermont) [ANR-INBS-0010]
  5. Czech Science Foundation [19-00043S]
  6. FUNDACAO PARA A CIENCIA E TECNOLOGIA
  7. PORTUGAL 2020 [LISBOA-01-0145-FEDER-402-022125]
  8. Academy of Finland [277986, 312550]
  9. Lantmannen Foundation
  10. EU H2020 FP7-Marie Curie-COFUND MoRE Programme [754412]
  11. Biocenter Finland
  12. CIBERFES project
  13. ISCIII project [AC19/00111, AC19/00096]
  14. FEDER Program from EU, A way to make Europe
  15. Generalitat de Catalunya's Agency AGAUR [2017SGR1546]
  16. Juan de la Cierva program from MINECO [IJC2019-041867-I]
  17. ICREA Academia award
  18. Spanish National Research program [AGL-2015-73107EXP/AEI, CSIC 201870E014]
  19. Fundacion Seneca Region de Murcia [19900/GERM/15]
  20. Norwegian Agriculture and Food Industry Research Funds [262300]
  21. Carlsberg Foundation
  22. Hungarian Academy of Sciences
  23. EU
  24. ESF
  25. project of SZIU [EFOP-3.6.3-VEKOP-16-201700005]
  26. Spanish MINECO [AGL2016-76832-R]
  27. Walsh Fellowship [2016038]
  28. Academy of Finland (AKA) [312550, 277986, 312550] Funding Source: Academy of Finland (AKA)

向作者/读者索取更多资源

The study tested the performance of PredRet in predicting retention times for plant food bioactive metabolites and demonstrated high prediction accuracy in an external validation test. Retention time prediction depends on the shape and type of LC gradient, as well as the number of commonly measured compounds.
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.

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