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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388971%3A_____%2F21%3A00543397" target="_blank" >RIV/61388971:_____/21:00543397 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0308814621007639?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0308814621007639?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.foodchem.2021.129757" target="_blank" >10.1016/j.foodchem.2021.129757</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10406 - Analytical chemistry

Projekt

<a href="/cs/project/GA19-00043S" target="_blank" >GA19-00043S: Aryl-sulfotrasferasy a jejich využití v přípravě sulfatovaných metabolitů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Food Chemistry

ISSN

0308-8146

e-ISSN

1873-7072

Svazek periodika

357

Číslo periodika v rámci svazku

SEP 30 2021

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

10

Strana od-do

129757

Kód UT WoS článku

000655533400011

EID výsledku v databázi Scopus

2-s2.0-85104344822