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Author: rwehrens

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+## The goal of the book
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+This book aims to provide a concise and yet complete overview of data analysis in metabolomics, concentrating on nuclear magnetic resonance and mass spectrometry, the two dominant detection techniques in the area. Like all -omics sciences, metabolomics is a multidisciplinary field encompassing biology, chemistry, computer science, statistics and data analysis. DNA and proteins, related to genomics and proteomics, respectively, in a sense, are much simpler to measure than metabolites: they consist of sequences of letters from alphabets with limited numbers of letters. Metabolites cannot be enumerated in this way, and show a much bigger diversity in chemical and physical properties. This implies that different analytical techniques are necessary to obtain a complete picture of the metabolome of a biological system. The choice of analytical technique determines to a large extent what is measured, and some knowledge of the underlying (analytical) chemistry is more important than in transcriptomics, for example. This large variability in chemical structure and analytics also leads to a data processing pipeline that is much more diverse than what we see in other omics sciences and has to be adapted for each experiment to the analytical techniques and to the experimental protocol used. In that sense, metabolomics is not only a science but also somewhat of an “art”. 
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+Textbooks in metabolomics typically dedicate a significant fraction of the content to the analytical techniques and treat data analysis as one of the many aspects of a metabolomics experiment. In this book, we have reversed the balance and focus mainly on the data analysis aspects. These are diverse and often complicated: typically we have a lot of data from relatively few samples, something that makes the application of classical statistical procedures difficult. The experimental aspects are only covered to the extent that is necessary to understand the consequences of data handling, analysis and interpretation. 
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+In this book, we are going further than just the identification of metabolic fingerprints, and try to interpret them in a biochemical context by constructing metabolic networks. The dynamic nature of the metabolism of the system under study is addressed through flux modelling. Finally, data standards data sharing has become an integral part in publishing metabolomics results.
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+## Who should read this book 
+The intended audience consists of scientists searching to obtain a comprehensive overview of the field of metabolomics, typically PhD students or postdocs entering the field. Such people rarely have experience in metabolomics itself, but very often they do come with a (very useful!) background in related disciplines such as chemistry, bioinformatics, data science, or statistics. To cater to the different backgrounds in our readers we have in several places included Boxes containing very brief explanations of words or idioms that may not be familiar. Even though this may not be sufficient for the non-expert to obtain a full understanding, it should provide enough background to be able to grasp the basic principles, as well as pointers to useful search terms. In addition, we have set up a supplementary material website containing many of the scripts, data sets and tutorials used in this book (see below). This allows the reader to get some hands-on experience, essential to grasp the finer points.
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