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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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Using Genomics, Metagenomics and Other "Omics" to Assess Valuable Microbial Ecosystem Services and Novel Biotechnological Applications
| Using Genomics, Metagenomics and Other "Omics" to Assess Valuable Microbial Ecosystem Services and Novel Biotechnological Applications |
| Autore | Elizabeth Marco Diana |
| Pubbl/distr/stampa | Frontiers Media SA, 2019 |
| Descrizione fisica | 1 electronic resource (674 p.) |
| Soggetto topico |
Science: general issues
Medical microbiology & virology Microbiology (non-medical) |
| Soggetto non controllato |
genomics
metagenomics meta-omics microbial ecology ecosystem services biotechnology industrial processes natural products |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910557392703321 |
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Elizabeth Marco Diana
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| Frontiers Media SA, 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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