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

Dorrain Yanwen  Low; Ville Mikael  Koistinen; Pierre Micheau; Kati  Hanhineva; Lazslo Abranko; Ana Maria Rodriguez Mateos; Andreia Bento da Silva; Christof  van Poucke; Conceicao Almeida; Cristina Andres-Lacueva; Dilip K Rai; Esra Capanoglu; Francisco Tomas-Barberan; Fulvio  Mattivi; Gesine Schmidt; Gozde Gurdeniz; Katerina Valentova; Leticia  Bresciani; Lucie Petraskova; Lars Ove Dragsted; Mark Philo; Marynka Ulaszewska; Pedro  Mena; Raul  Gonzalez-Dominguez; Rocio  Garcia-Villalba; Senem Kamiloglu; Sonia de Pascual-Teresa; Stephanie Durand; Wieslaw  Wiczkowski; Maria Rosario  Bronze; Stanstrup Jan; Claudine  Manach

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

Dorrain Yanwen Low, Ville Mikael Koistinen, Pierre Micheau, Kati Hanhineva, Lazslo Abranko, Ana Maria Rodriguez Mateos, Andreia Bento da Silva, Christof van Poucke, Conceicao Almeida, Cristina Andres-Lacueva, Dilip K Rai, Esra Capanoglu, Francisco Tomas-Barberan , Fulvio Mattivi, Gesine Schmidt, Gozde Gurdeniz, Katerina Valentova, Leticia Bresciani, Lucie Petraskova, Lars Ove DragstedMark Philo, Marynka Ulaszewska, Pedro Mena, Raul Gonzalez-Dominguez, Rocio Garcia-Villalba, Senem Kamiloglu, Sonia de Pascual-Teresa, Stephanie Durand, Wieslaw Wiczkowski , Maria Rosario Bronze, Stanstrup Jan, Claudine Manach

Nutritional Sciences

Research output: Contribution to journal › Article › peer-review

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/) 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.

Original language	English
Journal	FOOD CHEMISTRY
Publication status	Accepted/In press - 8 Apr 2021

Cite this

Low, D. Y., Koistinen, V. M., Micheau, P., Hanhineva, K., Abranko, L., Rodriguez Mateos, A. M., Bento da Silva, A., van Poucke, C., Almeida, C., Andres-Lacueva, C., Rai, D. K., Capanoglu, E., Tomas-Barberan , F., Mattivi, F., Schmidt, G., Gurdeniz, G., Valentova, K., Bresciani, L., Petraskova, L., ... Manach, C. (Accepted/In press). Data sharing in PredRet for accurate prediction of retention time: application to plant food bioactive compounds. FOOD CHEMISTRY.

@article{44571f5385ee41e79f17d76f96ba98c2,

title = "Data sharing in PredRet for accurate prediction of retention time: application to plant food bioactive compounds",

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/) 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{\textquoteright}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.",

author = "Low, {Dorrain Yanwen} and Koistinen, {Ville Mikael} and Pierre Micheau and Kati Hanhineva and Lazslo Abranko and {Rodriguez Mateos}, {Ana Maria} and {Bento da Silva}, Andreia and {van Poucke}, Christof and Conceicao Almeida and Cristina Andres-Lacueva and Rai, {Dilip K} and Esra Capanoglu and Francisco Tomas-Barberan and Fulvio Mattivi and Gesine Schmidt and Gozde Gurdeniz and Katerina Valentova and Leticia Bresciani and Lucie Petraskova and Dragsted, {Lars Ove} and Mark Philo and Marynka Ulaszewska and Pedro Mena and Raul Gonzalez-Dominguez and Rocio Garcia-Villalba and Senem Kamiloglu and {de Pascual-Teresa}, Sonia and Stephanie Durand and Wieslaw Wiczkowski and Bronze, {Maria Rosario} and Stanstrup Jan and Claudine Manach",

year = "2021",

month = apr,

day = "8",

language = "English",

journal = "FOOD CHEMISTRY",

issn = "0308-8146",

publisher = "Elsevier Limited",

}

Low, DY, Koistinen, VM, Micheau, P, Hanhineva, K, Abranko, L, Rodriguez Mateos, AM, Bento da Silva, A, van Poucke, C, Almeida, C, Andres-Lacueva, C, Rai, DK, Capanoglu, E, Tomas-Barberan , F, Mattivi, F, Schmidt, G, Gurdeniz, G, Valentova, K, Bresciani, L, Petraskova, L, Dragsted, LO, Philo, M, Ulaszewska, M, Mena, P, Gonzalez-Dominguez, R, Garcia-Villalba, R, Kamiloglu, S, de Pascual-Teresa, S, Durand, S, Wiczkowski , W, Bronze, MR, Jan, S & Manach, C 2021, 'Data sharing in PredRet for accurate prediction of retention time: application to plant food bioactive compounds', FOOD CHEMISTRY.

TY - JOUR

T1 - Data sharing in PredRet for accurate prediction of retention time: application to plant food bioactive compounds

AU - Low, Dorrain Yanwen

AU - Koistinen, Ville Mikael

AU - Micheau, Pierre

AU - Hanhineva, Kati

AU - Abranko, Lazslo

AU - Rodriguez Mateos, Ana Maria

AU - Bento da Silva, Andreia

AU - van Poucke, Christof

AU - Almeida, Conceicao

AU - Andres-Lacueva, Cristina

AU - Rai, Dilip K

AU - Capanoglu, Esra

AU - Tomas-Barberan , Francisco

AU - Mattivi, Fulvio

AU - Schmidt, Gesine

AU - Gurdeniz, Gozde

AU - Valentova, Katerina

AU - Bresciani, Leticia

AU - Petraskova, Lucie

AU - Dragsted, Lars Ove

AU - Philo, Mark

AU - Ulaszewska, Marynka

AU - Mena, Pedro

AU - Gonzalez-Dominguez, Raul

AU - Garcia-Villalba, Rocio

AU - Kamiloglu, Senem

AU - de Pascual-Teresa, Sonia

AU - Durand, Stephanie

AU - Wiczkowski , Wieslaw

AU - Bronze, Maria Rosario

AU - Jan, Stanstrup

AU - Manach, Claudine

PY - 2021/4/8

Y1 - 2021/4/8

N2 - 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.

AB - 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.

M3 - Article

SN - 0308-8146

JO - FOOD CHEMISTRY

JF - FOOD CHEMISTRY

ER -

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

Abstract

Fingerprint

Cite this