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Metabolomics data analysis strategies are central to transforming raw metabolomics data files into meaningful biochemical interpretations that answer biological questions or generate novel hypotheses. This book contains a variety of papers from a Special Issue around the theme “Best Practices in Metabolomics Data Analysis”. Reviews and strategies for the whole metabolomics pipeline are included, whereas key areas such as metabolite annotation and identification, compound and spectral databases and repositories, and statistical analysis are highlighted in various papers. Altogether, this book contains valuable information for researchers just starting in their metabolomics career as well as those that are more experienced and look for additional knowledge and best practice to complement key parts of their metabolomics workflows.

Research & information: general --- metabolic networks --- mass spectral libraries --- metabolite annotation --- metabolomics data mapping --- nontarget analysis --- liquid chromatography mass spectrometry --- compound identification --- tandem mass spectral library --- forensics --- wastewater --- gut microbiome --- meta-omics --- metagenomics --- metabolomics --- metabolic reconstructions --- genome-scale metabolic modeling --- constraint-based modeling --- flux balance --- host–microbiome --- metabolism --- global metabolomics --- LC-MS --- spectra processing --- pathway analysis --- enrichment analysis --- mass spectrometry --- liquid chromatography --- MS spectral prediction --- metabolite identification --- structure-based chemical classification --- rule-based fragmentation --- combinatorial fragmentation --- time series --- PLS --- NPLS --- variable selection --- bootstrapped-VIP --- data repository --- computational metabolomics --- reanalysis --- lipidomics --- data processing --- triplot --- multivariate risk modeling --- environmental factors --- disease risk --- chemical classification --- in silico workflows --- metabolome mining --- molecular families --- networking --- substructures --- mass spectrometry imaging --- metabolomics imaging --- biostatistics --- ion selection algorithms --- liquid chromatography high-resolution mass spectrometry --- data-independent acquisition --- all ion fragmentation --- targeted analysis --- untargeted analysis --- R programming --- full-scan MS/MS processing --- R-MetaboList 2 --- liquid chromatography–mass spectrometry (LC/MS) --- fragmentation (MS/MS) --- data-dependent acquisition (DDA) --- simulator --- in silico --- untargeted metabolomics --- liquid chromatography–mass spectrometry (LC-MS) --- experimental design --- sample preparation --- univariate and multivariate statistics --- metabolic pathway and network analysis --- LC–MS --- metabolic profiling --- computational statistical --- unsupervised learning --- supervised learning --- metabolic networks --- mass spectral libraries --- metabolite annotation --- metabolomics data mapping --- nontarget analysis --- liquid chromatography mass spectrometry --- compound identification --- tandem mass spectral library --- forensics --- wastewater --- gut microbiome --- meta-omics --- metagenomics --- metabolomics --- metabolic reconstructions --- genome-scale metabolic modeling --- constraint-based modeling --- flux balance --- host–microbiome --- metabolism --- global metabolomics --- LC-MS --- spectra processing --- pathway analysis --- enrichment analysis --- mass spectrometry --- liquid chromatography --- MS spectral prediction --- metabolite identification --- structure-based chemical classification --- rule-based fragmentation --- combinatorial fragmentation --- time series --- PLS --- NPLS --- variable selection --- bootstrapped-VIP --- data repository --- computational metabolomics --- reanalysis --- lipidomics --- data processing --- triplot --- multivariate risk modeling --- environmental factors --- disease risk --- chemical classification --- in silico workflows --- metabolome mining --- molecular families --- networking --- substructures --- mass spectrometry imaging --- metabolomics imaging --- biostatistics --- ion selection algorithms --- liquid chromatography high-resolution mass spectrometry --- data-independent acquisition --- all ion fragmentation --- targeted analysis --- untargeted analysis --- R programming --- full-scan MS/MS processing --- R-MetaboList 2 --- liquid chromatography–mass spectrometry (LC/MS) --- fragmentation (MS/MS) --- data-dependent acquisition (DDA) --- simulator --- in silico --- untargeted metabolomics --- liquid chromatography–mass spectrometry (LC-MS) --- experimental design --- sample preparation --- univariate and multivariate statistics --- metabolic pathway and network analysis --- LC–MS --- metabolic profiling --- computational statistical --- unsupervised learning --- supervised learning

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Metabolomics data analysis strategies are central to transforming raw metabolomics data files into meaningful biochemical interpretations that answer biological questions or generate novel hypotheses. This book contains a variety of papers from a Special Issue around the theme “Best Practices in Metabolomics Data Analysis”. Reviews and strategies for the whole metabolomics pipeline are included, whereas key areas such as metabolite annotation and identification, compound and spectral databases and repositories, and statistical analysis are highlighted in various papers. Altogether, this book contains valuable information for researchers just starting in their metabolomics career as well as those that are more experienced and look for additional knowledge and best practice to complement key parts of their metabolomics workflows.

Research & information: general --- metabolic networks --- mass spectral libraries --- metabolite annotation --- metabolomics data mapping --- nontarget analysis --- liquid chromatography mass spectrometry --- compound identification --- tandem mass spectral library --- forensics --- wastewater --- gut microbiome --- meta-omics --- metagenomics --- metabolomics --- metabolic reconstructions --- genome-scale metabolic modeling --- constraint-based modeling --- flux balance --- host–microbiome --- metabolism --- global metabolomics --- LC-MS --- spectra processing --- pathway analysis --- enrichment analysis --- mass spectrometry --- liquid chromatography --- MS spectral prediction --- metabolite identification --- structure-based chemical classification --- rule-based fragmentation --- combinatorial fragmentation --- time series --- PLS --- NPLS --- variable selection --- bootstrapped-VIP --- data repository --- computational metabolomics --- reanalysis --- lipidomics --- data processing --- triplot --- multivariate risk modeling --- environmental factors --- disease risk --- chemical classification --- in silico workflows --- metabolome mining --- molecular families --- networking --- substructures --- mass spectrometry imaging --- metabolomics imaging --- biostatistics --- ion selection algorithms --- liquid chromatography high-resolution mass spectrometry --- data-independent acquisition --- all ion fragmentation --- targeted analysis --- untargeted analysis --- R programming --- full-scan MS/MS processing --- R-MetaboList 2 --- liquid chromatography–mass spectrometry (LC/MS) --- fragmentation (MS/MS) --- data-dependent acquisition (DDA) --- simulator --- in silico --- untargeted metabolomics --- liquid chromatography–mass spectrometry (LC-MS) --- experimental design --- sample preparation --- univariate and multivariate statistics --- metabolic pathway and network analysis --- LC–MS --- metabolic profiling --- computational statistical --- unsupervised learning --- supervised learning