Metabolomics Data Processing and Data Analysis-Current Best Practices
Hanhineva, Kati
Metabolomics Data Processing and Data Analysis-Current Best Practices - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021 - 1 electronic resource (276 p.)
Open Access
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.
Creative Commons
English
books978-3-0365-1195-5 9783036511948 9783036511955
10.3390/books978-3-0365-1195-5 doi
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
Metabolomics Data Processing and Data Analysis-Current Best Practices - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021 - 1 electronic resource (276 p.)
Open Access
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.
Creative Commons
English
books978-3-0365-1195-5 9783036511948 9783036511955
10.3390/books978-3-0365-1195-5 doi
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