Computational Methods and Data Analysis for Metabolomics

<p>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.&nbsp;</p>