Main description:

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.

Key Features:

Provides an overview of qualitative /quantitative methods in metabolomics

Offers an introduction to the key concepts of metabolomics, including experimental design and technology

Covers data handling, processing, analysis, data standards and sharing

Contains lots of examples to illustrate the topics

Includes contributions from some of the leading researchers in the field of metabolomics with extensive teaching experiences

Contents:

Part I - Setting the stage. What is metabolomics, definitions. Before you start. Measurement technologies. A short review of applications of metabolomics in different fields, to whet the appetite. Part II - Data processing. NMR. MS, same topics as with NMR. LCDAD. Part III - From data to information. A short review of relevant statistical techniques. Statistical analysis for platforms separately. Data fusion. Metabolic networks. Metabolic flux. Part IV- Data sharing. Data storage and accessibility. Data standards and databases of standards. Conclusion - what is next in metabolomics? Where are the bottlenecks in the bioinformatics part?