Computational Methods and Data Analysis for Metabolomics

This book provides 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. Chapters guide readers through common tools for data processing, using database resources, major techniques in data analysis, and integration with other data types and specific scientific domains. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, practical guidance of methods and techniques, useful web supplements, and connect the steps from experimental metabolomics to scientific discoveries.

Authoritative and cutting-edge, Computational Methods and Data Analysis for Metabolomics to ensure successful results in the further study of this vital field.

Overview of Experimental Methods and Study Design in Metabolomics, and Statistical and Pathway Considerations.- Metabolomics Data Processing using XCMS.- Metabolomics Data Preprocessing using ADAP and MZmine 2.- Metabolomics Data Processing using OpenMS.- Analysis of NMR Metabolomics Data.- Key Concepts Surrounding Studies of Stable Isotope Resolved Metabolomics.- Extracting Biological Insight from Untargeted Lipidomics Data.- Overview of Tandem Mass Spectral and Metabolite Databases for Metabolite Identification in Metabolomics.- METLIN: A Metabolite Mass Spectral Database.- Metabolomic Data Exploration and Analysis with the Human Metabolome Database.- De Novo Molecular Formula Annotation and Structure Elucidation using SIRIUS 4.- Annotation of Specialized Metabolites from High-throughput and High-resolution Mass Spectrometry Metabolomics.- Feature Based Molecular Networking for Metabolite Annotation. A Bioinformatics Primer to Data Science, with Examples for Metabolomics.- The Essential Toolbox of Data Science: Python, R, Git and Docker.- Predictive Modeling for Metabolomics Data.- Using MetaboAnalyst 4.0 for Metabolomics Data Analysis, Interpretation, and Integration with Other Omics Data.- Using Genome Scale Metabolic Networks for Analysis, Visualization, and Integration of Targeted Metabolomics Data.- Pathway Analysis for Targeted and Untargeted Metabolomics.- Application of Metabolomics to Renal and Cardiometabolic Diseases.- Using the IDEOM Workflow for LCMS-Based Metabolomics Studies of Drug Mechanisms.- Analyzing Metabolomics Data for Environmental Health and Exposome Research.- Network-based Approaches for Multi-omics Integration.