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

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Standard

Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds. / Low, Dorrain Yanwen; Micheau, Pierre; Koistinen, Ville Mikael; Hanhineva, Kati; Abrankó, László; Rodriguez-Mateos, Ana; da Silva, Andreia Bento; van Poucke, Christof; Almeida, Conceição; Andres-Lacueva, Cristina; Rai, Dilip K; Capanoglu, Esra; Barberán, Francisco A Tomás; Mattivi, Fulvio; Schmidt, Gesine; Gürdeniz, Gözde; Valentová, Kateřina; Bresciani, Letizia; Petrásková, Lucie; Dragsted, Lars Ove; Philo, Mark; Ulaszewska, Marynka; Mena, Pedro; González-Domínguez, Raúl; Garcia-Villalba, Rocío; Kamiloglu, Senem; de Pascual-Teresa, Sonia; Durand, Stéphanie; Wiczkowski, Wieslaw; Rosário Bronze, Maria; Stanstrup, Jan; Manach, Claudine.

I: Food Chemistry, Bind 357, 129757, 2021.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Low, DY, Micheau, P, Koistinen, VM, Hanhineva, K, Abrankó, L, Rodriguez-Mateos, A, da Silva, AB, van Poucke, C, Almeida, C, Andres-Lacueva, C, Rai, DK, Capanoglu, E, Barberán, FAT, Mattivi, F, Schmidt, G, Gürdeniz, G, Valentová, K, Bresciani, L, Petrásková, L, Dragsted, LO, Philo, M, Ulaszewska, M, Mena, P, González-Domínguez, R, Garcia-Villalba, R, Kamiloglu, S, de Pascual-Teresa, S, Durand, S, Wiczkowski, W, Rosário Bronze, M, Stanstrup, J & Manach, C 2021, 'Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds', Food Chemistry, bind 357, 129757. https://doi.org/10.1016/j.foodchem.2021.129757

APA

Low, D. Y., Micheau, P., Koistinen, V. M., Hanhineva, K., Abrankó, L., Rodriguez-Mateos, A., da Silva, A. B., van Poucke, C., Almeida, C., Andres-Lacueva, C., Rai, D. K., Capanoglu, E., Barberán, F. A. T., Mattivi, F., Schmidt, G., Gürdeniz, G., Valentová, K., Bresciani, L., Petrásková, L., ... Manach, C. (2021). Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds. Food Chemistry, 357, [129757]. https://doi.org/10.1016/j.foodchem.2021.129757

Vancouver

Low DY, Micheau P, Koistinen VM, Hanhineva K, Abrankó L, Rodriguez-Mateos A o.a. Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds. Food Chemistry. 2021;357. 129757. https://doi.org/10.1016/j.foodchem.2021.129757

Author

Low, Dorrain Yanwen ; Micheau, Pierre ; Koistinen, Ville Mikael ; Hanhineva, Kati ; Abrankó, László ; Rodriguez-Mateos, Ana ; da Silva, Andreia Bento ; van Poucke, Christof ; Almeida, Conceição ; Andres-Lacueva, Cristina ; Rai, Dilip K ; Capanoglu, Esra ; Barberán, Francisco A Tomás ; Mattivi, Fulvio ; Schmidt, Gesine ; Gürdeniz, Gözde ; Valentová, Kateřina ; Bresciani, Letizia ; Petrásková, Lucie ; Dragsted, Lars Ove ; Philo, Mark ; Ulaszewska, Marynka ; Mena, Pedro ; González-Domínguez, Raúl ; Garcia-Villalba, Rocío ; Kamiloglu, Senem ; de Pascual-Teresa, Sonia ; Durand, Stéphanie ; Wiczkowski, Wieslaw ; Rosário Bronze, Maria ; Stanstrup, Jan ; Manach, Claudine. / Data sharing in PredRet for accurate prediction of retention time: Application to plant food bioactive compounds. I: Food Chemistry. 2021 ; Bind 357.

Bibtex

@article{00a166c90ad54a9e855019effaf99684,

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.",

keywords = "Faculty of Science, Predicted retention time, Metabolomics, Plant food bioactive compounds, Metabolites, Data sharing, PredRet, UHPLC",

author = "Low, {Dorrain Yanwen} and Pierre Micheau and Koistinen, {Ville Mikael} and Kati Hanhineva and L{\'a}szl{\'o} Abrank{\'o} and Ana Rodriguez-Mateos and {da Silva}, {Andreia Bento} and {van Poucke}, Christof and Concei{\c c}{\~a}o Almeida and Cristina Andres-Lacueva and Rai, {Dilip K} and Esra Capanoglu and Barber{\'a}n, {Francisco A Tom{\'a}s} and Fulvio Mattivi and Gesine Schmidt and G{\"o}zde G{\"u}rdeniz and Kate{\v r}ina Valentov{\'a} and Letizia Bresciani and Lucie Petr{\'a}skov{\'a} and Dragsted, {Lars Ove} and Mark Philo and Marynka Ulaszewska and Pedro Mena and Ra{\'u}l Gonz{\'a}lez-Dom{\'i}nguez and Roc{\'i}o Garcia-Villalba and Senem Kamiloglu and {de Pascual-Teresa}, Sonia and St{\'e}phanie Durand and Wieslaw Wiczkowski and {Ros{\'a}rio Bronze}, Maria and Jan Stanstrup and Claudine Manach",

note = "CURIS 2021 NEXS 147",

year = "2021",

doi = "10.1016/j.foodchem.2021.129757",

language = "English",

volume = "357",

journal = "Food Chemistry",

issn = "0308-8146",

publisher = "Elsevier",

}

RIS

TY - JOUR

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

AU - Low, Dorrain Yanwen

AU - Micheau, Pierre

AU - Koistinen, Ville Mikael

AU - Hanhineva, Kati

AU - Abrankó, László

AU - Rodriguez-Mateos, Ana

AU - da Silva, Andreia Bento

AU - van Poucke, Christof

AU - Almeida, Conceição

AU - Andres-Lacueva, Cristina

AU - Rai, Dilip K

AU - Capanoglu, Esra

AU - Barberán, Francisco A Tomás

AU - Mattivi, Fulvio

AU - Schmidt, Gesine

AU - Gürdeniz, Gözde

AU - Valentová, Kateřina

AU - Bresciani, Letizia

AU - Petrásková, Lucie

AU - Dragsted, Lars Ove

AU - Philo, Mark

AU - Ulaszewska, Marynka

AU - Mena, Pedro

AU - González-Domínguez, Raúl

AU - Garcia-Villalba, Rocío

AU - Kamiloglu, Senem

AU - de Pascual-Teresa, Sonia

AU - Durand, Stéphanie

AU - Wiczkowski, Wieslaw

AU - Rosário Bronze, Maria

AU - Stanstrup, Jan

AU - Manach, Claudine

N1 - CURIS 2021 NEXS 147

PY - 2021

Y1 - 2021

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.

KW - Faculty of Science

KW - Predicted retention time

KW - Metabolomics

KW - Plant food bioactive compounds

KW - Metabolites

KW - Data sharing

KW - PredRet

KW - UHPLC

U2 - 10.1016/j.foodchem.2021.129757

DO - 10.1016/j.foodchem.2021.129757

M3 - Journal article

C2 - 33872868

VL - 357

JO - Food Chemistry

JF - Food Chemistry

SN - 0308-8146

M1 - 129757

ER -

ID: 259674013