04999nam 2201309z- 450 991055735440332120231214133335.0(CKB)5400000000042346(oapen)https://directory.doabooks.org/handle/20.500.12854/76855(EXLCZ)99540000000004234620202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierMetabolomics Data Processing and Data Analysis—Current Best PracticesBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 electronic resource (276 p.)3-0365-1194-6 3-0365-1195-4 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: generalbicsscmetabolic networksmass spectral librariesmetabolite annotationmetabolomics data mappingnontarget analysisliquid chromatography mass spectrometrycompound identificationtandem mass spectral libraryforensicswastewatergut microbiomemeta-omicsmetagenomicsmetabolomicsmetabolic reconstructionsgenome-scale metabolic modelingconstraint-based modelingflux balancehost–microbiomemetabolismglobal metabolomicsLC-MSspectra processingpathway analysisenrichment analysismass spectrometryliquid chromatographyMS spectral predictionmetabolite identificationstructure-based chemical classificationrule-based fragmentationcombinatorial fragmentationtime seriesPLSNPLSvariable selectionbootstrapped-VIPdata repositorycomputational metabolomicsreanalysislipidomicsdata processingtriplotmultivariate risk modelingenvironmental factorsdisease riskchemical classificationin silico workflowsmetabolome miningmolecular familiesnetworkingsubstructuresmass spectrometry imagingmetabolomics imagingbiostatisticsion selection algorithmsliquid chromatography high-resolution mass spectrometrydata-independent acquisitionall ion fragmentationtargeted analysisuntargeted analysisR programmingfull-scan MS/MS processingR-MetaboList 2liquid chromatography–mass spectrometry (LC/MS)fragmentation (MS/MS)data-dependent acquisition (DDA)simulatorin silicountargeted metabolomicsliquid chromatography–mass spectrometry (LC-MS)experimental designsample preparationunivariate and multivariate statisticsmetabolic pathway and network analysisLC–MSmetabolic profilingcomputational statisticalunsupervised learningsupervised learningResearch & information: generalHanhineva Katiedt1328811Van der Hooft JustinedtHanhineva KatiothVan der Hooft JustinothBOOK9910557354403321Metabolomics Data Processing and Data Analysis—Current Best Practices3038980UNINA