Metabolomics Data Processing and Data Analysis-Current Best Practices

• Autore: Hanhineva Kati
• Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2021
• Descrizione fisica: 1 online resource (276 p.)
• Soggetto topico: Research & information: general
• Soggetto non controllato: all ion fragmentation; biostatistics; bootstrapped-VIP; chemical classification; combinatorial fragmentation; compound identification; computational metabolomics; computational statistical; constraint-based modeling; data processing; data repository; data-dependent acquisition (DDA); data-independent acquisition; disease risk; enrichment analysis; environmental factors; experimental design; flux balance; forensics; fragmentation (MS/MS); full-scan MS/MS processing; genome-scale metabolic modeling; global metabolomics; gut microbiome; host-microbiome; in silico; in silico workflows; ion selection algorithms; LC-MS; lipidomics; liquid chromatography; liquid chromatography high-resolution mass spectrometry; liquid chromatography mass spectrometry; liquid chromatography-mass spectrometry (LC-MS); liquid chromatography-mass spectrometry (LC/MS); mass spectral libraries; mass spectrometry; mass spectrometry imaging; meta-omics; metabolic networks; metabolic pathway and network analysis; metabolic profiling; metabolic reconstructions; metabolism; metabolite annotation; metabolite identification; metabolome mining; metabolomics; metabolomics data mapping; metabolomics imaging; metagenomics; molecular families; MS spectral prediction; multivariate risk modeling; networking; nontarget analysis; NPLS; pathway analysis; PLS; R programming; R-MetaboList 2; reanalysis; rule-based fragmentation; sample preparation; simulator; spectra processing; structure-based chemical classification; substructures; supervised learning; tandem mass spectral library; targeted analysis; time series; triplot; univariate and multivariate statistics; unsupervised learning; untargeted analysis; untargeted metabolomics; variable selection; wastewater
• Persona (resp. second.): Van der HooftJustin; HanhinevaKati
• Sommario/riassunto: 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.
• Titolo autorizzato: Metabolomics Data Processing and Data Analysis-Current Best Practices
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910557354403321
• Lo trovi qui: Univ. Federico II