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.

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    The Disruptive 4IR in the Life Sciences: Metabolomics
    Tugizimana, Fidele ; Engel, Jasper ; Salek, Reza ; Dubery, Ian ; Piater, Lizelle ; Burgess, Karl - \ 2020
    In: The Disruptive Fourth Industrial Revolution Springer (Lecture Notes in Electrical Engineering ) - ISBN 9783030482299 - p. 227 - 256.
    Artificial intelligence (AI) - Big data - Cloud computing - Cloud metabolomics - Fourth industrial revolution—4IR - Machine learning - Metabolomics

    A new era of systems biology is disruptively emerging, holistically describing biochemical events at both organismal and cellular level. In this new era, emerging ‘-omics’ technologies have brought about a paradigm shift in biological sciences and research. Metabolomics, the youngest of the omics trilogy and defined as the qualitative and quantitative investigation of the entire metabolome of a biological system, has positioned itself as an indispensable methodology to investigate global biochemistry phenomena at a cellular level. Metabolomics is a multidisciplinary research field, involving a convergence of biology, chemistry, chemometrics, statistics and computer science. Metabolomics accordingly can provide unprecedented in-depth explanations and insights of the mechanisms responsible for various physiological conditions, given the innovative developments in analytical technologies (integrating artificial intelligence and machine learning), advancement in chemometric and statistical methods (big data analytics and management), and the integration of orthogonal biological approaches. Thus, the objective of this Chapter is to provide an overview of 4IR in life sciences, illustratively pointing to some aspects in the metabolomics field. The latter, in its ontology, applies different 4IR technologies including big data analytics, machine learning, cloud computing, and artificial intelligence, amongst others. The momentum and maturation of metabolomics is undeniably evident, positively disruptive, and the field has visibly revolutionised the life sciences. The application of metabolomics spans a wide spectrum of the afore-said sciences, including biomedical technology, natural products, and plant biochemistry and—biotechnology research to name a few.

    Mass Spectrometry Data Processing
    Neumann, Steffen ; Yanes, Oscar ; Mumm, Roland ; Franceschi, Pietro - \ 2019
    In: Metabolomics / Wehrens, Ron, Salek, Reza, Chapman and Hall/CRC - ISBN 9781315370583 - p. 73 - 99.
    This chapter focuses on the mass spectrometry data processing workflow. The first step consists of processing the raw MS data, leading to a feature matrix amenable for statistical analysis. The raw MS files contain the list of spectra acquired during the run, and each spectrum contains many pairs. In the field of metabolite fingerprinting, Direct Infusion Mass Spectrometry and Flow Injection Mass Spectrometry can be used to complement chromatography-based MS techniques. To reduce this complexity, several feature detection algorithms have been developed. A feature is characterized by a retention time, an m/z value and an intensity. In microarray data analysis it is relatively straightforward to compare the expression of some genes in one sample against the expression in a second one, because the RNA is hybridized to a particular “known” probe sequence. In case an accurate-mass spectrometer has been used, it is also important to determine the mass accuracy in the measurements.
    Statistics: The Essentials
    Wehrens, H.R.M.J. ; Franceschi, Pietro - \ 2019
    In: Metabolomics: Practical Guide to Design and Analysis / Wehrens, Ron, Salek, Reza, New York : Chapman and Hall - ISBN 9781315370583 - 28 p.
    A basic understanding of statistics is essential in analyzing metabolomics data. In particular, the reasons behind common assumptions made in statistical inference, such as normally distributed data, should be understood. Because of the highly multivariate nature of metabolomics data, multiple-testing issues arise and should be dealt with appropriately. Multivariate analysis and visualization methods are easily applied now that they are commonly available in many software packages, but the interpretation of the results, and in particular the choices of data pretreatment and model selection, are less easily understood. This chapter discusses the basics of these and some other approaches.
    Wehrens, H.R.M.J. ; Salek, Reza - \ 2019
    In: Metabolomics: Practical Guide to Design and Analysis / Wehrens, Ron, Salek, Reza, New York : Chapman and Hall - ISBN 9781315370583 - 6 p.
    Metabolomics is a vibrant field, developing fast and attracting many scientists from adjacent disciplines. Its multi-disciplinarity bases are one of the defining features of metabolomics. In order to quantify and understand the diversity of the metabolite composition of biological samples one needs chemistry, biology, mathematics and statistics, and computer science (to name just the most central disciplines). Such multi-disciplinarity has been reflected in the programs of many metabolomics conferences. Metabolomics conferences often contain parallel or breakout sessions on topics that may be entirely different from one another (e.g., mass-spectrometry-based or NMR-based metabolomics, computational metabolomics, medical applications, environmental applications, etcetera). Such diversity makes it difficult to obtain a bird’s eye view, especially for scientists new to the field. Whatever one’s background, one needs to know the basics of many of these fields, and this is where we had set the primary function of this work. We aim to provide a concise yet global overview of the field of metabolomics. If this book can contribute to a better understanding of each other’s viewpoints and vocabulary used, the most important goal has been achieved.
    Study Design and Preparation
    Franceschi, Pietro ; Yanes, Oscar ; Wehrens, H.R.M.J. - \ 2019
    In: Metabolomics: Practical Guide to Design and Analysis / Wehrens, Ron, Salek, Reza, New York : Chapman and Hall - ISBN 9781315370583 - 19 p.
    When designing a study there are several important considerations to be taken into account, irrespective of the measurement technology. The most important element is to precisely define the goals of the study. Even though these goals can be quite general, they must be more explicit than just expressing the desire to get to know the system under study. One easy rule of thumb is to think about the conditions under which the experiment can be called a success: “Finally, we are able to understand why this treatment leads to an upregulation of this particular metabolic pathway!” or “We have confirmed our hypothesis that compound X is not present in muscle tissue but is quite abundantly present in liver tissue.” If you are able to define a successful experiment, your goals are testable. The more specific your research questions, the more likely it is that you will find a clear answer, based on your experimental results. The definition of the research question will also direct the choice of the most suitable experimental design and, consequently, will shape the data analysis plan, which is a critical part of metabolomics.
    Measurement Technologies
    Yanes, Oscar ; Hollywood, Katherine ; Mumm, R. ; Vinaixa, Maria ; Rankin, Naomi ; Wehrens, H.R.M.J. ; Salek, Reza - \ 2019
    In: Metabolomics: Practical Guide to Design and Analysis / Wehrens, Ron, Salek, Reza, New York : Chapman and Hall - ISBN 9781315370583 - 38 p.
    The main objective of a metabolomics experiment is to determine the composition of the set of small molecules in one or more biological samples, both with respect to quantity and chemical characteristics. Two detection platforms are most commonly used in metabolomics: mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. Since biological samples often consist of very complex matrices, containing several hundred to thousands of metabolites, these techniques are often coupled to different chromatographic separation techniques. In this chapter, we will treat the basic principles of the most commonly used chromatographic techniques and the very foundations of MS and NMR as major detection techniques in metabolomics. Numerous chromatographic techniques exist, but here we focus on three that are commonly used in metabolomics: gas chromatography, liquid chromatography and capillary electrophoresis. The focus is on concepts that are of particular importance for subsequent analysis and interpretation of the data. Less common detection methods based on vibrational and UV-Vis spectroscopy are treated briefly at the end of the chapter.
    Metabolomics: Practical Guide to Design and Analysis
    Wehrens, H.R.M.J. ; Salek, Reza - \ 2019
    New York : Chapman and Hall - ISBN 9781315370583 - 290 p.
    Metabolomics is the scientific study of the chemical processes in a living system, environment and nutrition. It is a relatively new omics science, but the potential applications are wide, including medicine, personalized medicine and intervention studies, food and nutrition, plants, agriculture and environmental science. The topics presented and discussed in this book are based on the European Molecular Biology Organization (EMBO) practical courses in metabolomics bioinformatics taught to those working in the field, from masters to postgraduate students, PhDs, postdoctoral and early PIs. The book covers the basics and fundamentals of data acquisition and analytical technologies, but the primary focus is data handling and data analysis. The mentioning and usage of a particular data analysis tool has been avoided; rather, the focus is on the concepts and principles of data processing and analysis. The material has been class-tested and includes lots of examples, computing and exercises.
    The Metabolomics Society—Current State of the Membership and Future Directions
    Zanetti, Krista A. ; Hall, R.D. ; Griffin, J.L. ; Putri, Sastia ; Salek, Reza M. ; Styczynski, Mark P. ; Tugizimana, Fidele ; Hooft, J.J.J. van der - \ 2019
    Metabolites 9 (2019). - ISSN 2218-1989
    Metabolomics Society; membership; metabolomics; survey
    Background: In 2017, the Metabolomics Society conducted a survey among its members to assess the degree of its current success, define opportunities for improving its service to the community and make plans to establish future goals and direction of the Society. Methods: A 32-question online survey was sent via e-mail to all Metabolomics Society members as of 19 June 2017 (n = 644).
    In addition to the direct e-mails, the link to access the survey was made available through social media. The survey was open until 10 August 2017. Question-specific data were reported using the summary data generated by SurveyMonkey and additional stratified analyses performed using Stata 15. Results: The number of respondents was 394 (61%) with 348 (88%) completing the multiple-choice questions in survey. Metabolomics Society annual meetings, networking and the opportunity to join the global metabolomics community were among the most important benefits expressed by the Metabolomics
    Society members. Conclusions: The survey collected the first data focusing on membership issues from Society members. The Society should focus on collecting and monitoring of demographic data during the membership registration process; continuing to support the early-career members of
    the Society; and developing initiatives that focus on member networking to retain and increase Society membership.
    Automated assembly of species metabolomes through data submission into a public repository
    Salek, Reza ; Conesa, Pablo ; Cochrane, Keeva ; Haug, Kenneth ; Williams, M. ; Kale, Namrata ; Moreno, P. ; Jayaseelan, Kalai Vanii ; Macias, Jose Ramon ; Nainala, Venkata Chandrasekhar ; Hall, R.D. ; Reed, Laura ; Viant, Mark ; Donovan, C. ; Steinbeck, Christoph - \ 2017
    GigaScience 6 (2017)8. - ISSN 2047-217X - 4 p.
    Following similar global efforts to exchange genomic and other biomedical data, global databases in metabolomics have now been established. MetaboLights, the first general purpose, publically available, cross-species, cross-application database in metabolomics, has become the fastest growing data repository at the European Bioinformatics Institute in terms of data volume. Here we present the automated assembly of species metabolomes in MetaboLights, a crucial reference for chemical biology, which is growing through user submissions.
    The Time Is Right to Focus on Model Organism Metabolomes
    Edison, Arthur ; Hall, Robert ; Junot, Christophe ; Karp, Peter ; Kurland, Irwin ; Mistrik, Robert ; Reed, Laura ; Saito, Kazuki ; Salek, Reza ; Steinbeck, Christoph ; Sumner, Lloyd ; Viant, Mark - \ 2016
    Metabolites 6 (2016)1. - ISSN 2218-1989
    Model organisms are an essential component of biological and biomedical research that can be used to study specific biological processes. These organisms are in part selected for facile experimental study. However, just as importantly, intensive study of a small number of model organisms yields important synergies as discoveries in one area of science for a given organism shed light on biological processes in other areas, even for other organisms. Furthermore, the extensive knowledge bases compiled for each model organism enable systems-level understandings of these species, which enhance the overall biological and biomedical knowledge for all organisms, including humans. Building upon extensive genomics research, we argue that the time is now right to focus intensively on model organism metabolomes. We propose a grand challenge for metabolomics studies of model organisms: to identify and map all metabolites onto metabolic pathways, to develop quantitative metabolic models for model organisms, and to relate organism metabolic pathways within the context of evolutionary metabolomics, i.e., phylometabolomics. These efforts should focus on a series of established model organisms in microbial, animal and plant research.
    Radar techniques for identifying precipitation type and estimating quantity of precipitation
    Sálek, M. ; Cheze, J.L. ; Handwerker, J. ; Delobbe, L. ; Uijlenhoet, R. - \ 2004
    Luxembourg : Cost office (EUR 21368 EN) - ISBN 9789289800044 - 55
    neerslag - regenmeters - radar - meteorologische waarnemingen - modellen - hydrologie - precipitation - rain gauges - radar - meteorological observations - models - hydrology
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