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Metabolomics Data Processing and Data Analysis—Current Best Practices
| Metabolomics Data Processing and Data Analysis—Current Best Practices |
| Autore | Hanhineva Kati |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (276 p.) |
| Soggetto topico | Research & information: general |
| Soggetto non controllato |
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 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557354403321 |
Hanhineva Kati
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Sample Preparation in Metabolomics
| Sample Preparation in Metabolomics |
| Autore | Kuligowski Julia |
| Pubbl/distr/stampa | Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 |
| Descrizione fisica | 1 electronic resource (208 p.) |
| Soggetto topico | Medicine |
| Soggetto non controllato |
metabolomics
sample preparation hydrophilic interaction liquid chromatography ion mobility spectrometry high resolution mass spectrometry design of experiments AMOPLS metabonomics metabolic profiling NMR nuclear magnetic resonance spectroscopy cell line human cell line MiaPaCa-2 Panc-1 AsPC-1 extracellular vesicles exosomes microvesicles biomarkers diagnostics metabolic pathways plant metabolomics forestry trees mass spectrometry metabolite extraction GC-MS LC-MS metadata standardization databases multicellular tumor spheroids metallodrugs oxaliplatin KP1339 method development IT-139 20% FCS harvesting extraction metabolites normalization electromembrane extraction cardiovascular disease multi-segment injection capillary electrophoresis–mass spectrometry liquid chromatography–mass spectrometry plant natural products drug discovery liquid chromatography gas chromatography human milk metabolome sampling liquid chromatography–mass spectrometry (LC-MS) nuclear magnetic resonance (NMR) gas chromatography–mass spectrometry (GC-MS) capillary electrophoresis—mass spectrometry (CE-MS) high-resolution magic angle spinning microscopic samples lipidomics LC-MS/MS human plasma |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557141203321 |
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Kuligowski Julia
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| Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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