Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

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Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies
Ulaszewska, Marynka M. ; Weinert, Christoph H. ; Trimigno, Alessia ; Portmann, Reto ; Andres Lacueva, Cristina ; Badertscher, René ; Brennan, Lorraine ; Brunius, Carl ; Bub, Achim ; Capozzi, Francesco ; Cialiè Rosso, Marta ; Cordero, Chiara E. ; Daniel, Hannelore ; Durand, Stéphanie ; Egert, Bjoern ; Ferrario, Paola G. ; Feskens, Edith J.M. ; Franceschi, Pietro ; Garcia-Aloy, Mar ; Giacomoni, Franck ; Giesbertz, Pieter ; González-Domínguez, Raúl ; Hanhineva, Kati ; Hemeryck, Lieselot Y. ; Kopka, Joachim ; Kulling, Sabine E. ; Llorach, Rafael ; Manach, Claudine ; Mattivi, Fulvio ; Migné, Carole ; Münger, Linda H. ; Ott, Beate ; Picone, Gianfranco ; Pimentel, Grégory ; Pujos-Guillot, Estelle ; Riccadonna, Samantha ; Rist, Manuela J. ; Rombouts, Caroline ; Rubert, Josep ; Skurk, Thomas ; Sri Harsha, Pedapati S.C. ; Meulebroek, Lieven Van; Vanhaecke, Lynn ; Vázquez-Fresno, Rosa ; Wishart, David ; Vergères, Guy - \ 2018
Molecular Nutrition & Food Research 63 (2018)1. - ISSN 1613-4125
GC–MS - LC–MS - metabolomics - NMR - nutrition
The life sciences are currently being transformed by an unprecedented wave of developments in molecular analysis, which include important advances in instrumental analysis as well as biocomputing. In light of the central role played by metabolism in nutrition, metabolomics is rapidly being established as a key analytical tool in human nutritional studies. Consequently, an increasing number of nutritionists integrate metabolomics into their study designs. Within this dynamic landscape, the potential of nutritional metabolomics (nutrimetabolomics) to be translated into a science, which can impact on health policies, still needs to be realized. A key element to reach this goal is the ability of the research community to join, to collectively make the best use of the potential offered by nutritional metabolomics. This article, therefore, provides a methodological description of nutritional metabolomics that reflects on the state-of-the-art techniques used in the laboratories of the Food Biomarker Alliance (funded by the European Joint Programming Initiative “A Healthy Diet for a Healthy Life” (JPI HDHL)) as well as points of reflections to harmonize this field. It is not intended to be exhaustive but rather to present a pragmatic guidance on metabolomic methodologies, providing readers with useful “tips and tricks” along the analytical workflow.
metaMS: An open-source pipeline for GC–MS-based untargeted metabolomics
Wehrens, H.R.M.J. ; Weingart, G. ; Mattivi, F. - \ 2014
Journal of Chromatography. B, Analytical technologies in the biomedical and life sciences 966 (2014). - ISSN 1570-0232 - p. 109 - 116.
chromatography/mass spectrometry data - mass-spectrometry - compound identification - gas-chromatography - metabonomics - extraction - alignment - toolkit
Untargeted metabolomics are rapidly becoming an important tool for studying complex biological samples. Gas chromatography–mass spectrometry (GC–MS) is the most widely used analytical technology for metabolomic analysis of compounds that are volatile or can be chemically derivatised into volatile compounds. Unfortunately, data processing and analysis are not straightforward and the field is dominated by vendor-supplied software that does not always allow easy integration for large laboratories with different instruments. This paper presents an open-source pipeline for high-throughput GC–MS data processing, written in the R language and available as package metaMS. It features rapid annotation using in-house databases, and also provides support for building and validating such databases. The results are presented in simple-to-use tables, summarising the relative concentrations of identified compounds and unknowns in all samples. The use of the pipeline is illustrated using three experimental data sets.
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