Shuzhao Li - Computational Methods and Data Analysis for Metabolomics

For further volumes: http://www.springer.com/series/7651

For over 35 years, biological scientists have come to rely on the research protocols and methodologies in the critically acclaimedMethods in Molecular Biologyseries. The series was the first to introduce the step-by-step protocols approach that has become the standard in all biomedical protocol publishing. Each protocol is provided in readily-reproducible step-by-step fashion, opening with an introductory overview, a list of the materials and reagents needed to complete the experiment, and followed by a detailed procedure that is supported with a helpful notes section offering tips and tricks of the trade as well as troubleshooting advice. These hallmark features were introduced by series editor Dr. John Walker and constitute the key ingredient in each and every volume of theMethods in Molecular Biologyseries. Tested and trusted, comprehensive and reliable, all protocols from the series are indexed in PubMed.

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Metabolomics is the new biochemistry. It reinvigorates the old discipline by new data at a large scale: simultaneous measurement of thousands of chemicals in biological samples. Many of these chemicals are beyond the known metabolic intermediates. This new information fills an important gap between the interactions of genome and environment, thus conferring enormous potential for improving human health. The metabolomics data also overlap significantly with the exposome, which aims to quantify all environmental exposures. The explosive growth of metabolomics creates a large gap in training on metabolomics data analysis. This book shall provide a comprehensive guide to scientists, engineers, and students that employ metabolomics in their work, with an emphasis on the understanding and interpretation of the data.

The book is organized as follows. Chapter).

While it is not possible to cover all the databases and software tools, we aim to have representations of each major topic and give readers a foundation to work in this field. It is critical to note that the scientific landscape keeps evolving and tools keep changing. Therefore, it is more important to understand the rationale and principles than to replicate the protocols. This book is supplemented by example data and code at GitHub ( https://metabolomics-data.github.io ), which can be continuously updated by the community.

I would like to express my gratitude to the metabolomics group at Emory University, especially Dean Jones, Tianwei Yu, Young-Mi Go, Youngja Park, Karan Uppal, Douglas Walker, and Gary Miller. Their intellectual input and friendship made my scientific journey truly rewarding.