MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis
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Xia, 2009, MetaboAnalyst: a web server for metabolomic data analysis and interpretation, Nucleic Acids Res., 37, W652, 10.1093/nar/gkp356
Xia, 2012, MetaboAnalyst 2.0—a comprehensive server for metabolomic data analysis, Nucleic Acids Res., 40, W127, 10.1093/nar/gks374
Xia, 2010, MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data, Nucleic Acids Res., 38, W71, 10.1093/nar/gkq329
Xia, 2010, MetPA: a web-based metabolomics tool for pathway analysis and visualization, Bioinformatics, 26, 2342, 10.1093/bioinformatics/btq418
Xia, 2011, MetATT: a web-based metabolomics tool for analyzing time-series and two-factor datasets, Bioinformatics, 27, 2455, 10.1093/bioinformatics/btr392
Xia, 2015, MetaboAnalyst 3.0—making metabolomics more meaningful, Nucleic Acids Res., 43, W251, 10.1093/nar/gkv380
Xia, 2013, Translational biomarker discovery in clinical metabolomics: an introductory tutorial, Metabolomics, 9, 280, 10.1007/s11306-012-0482-9
Tayyari, 2018, Metabolic profiles of triple-negative and luminal A breast cancer subtypes in African-American identify key metabolic differences, Oncotarget, 9, 11677, 10.18632/oncotarget.24433
Zhang, 2017, Metabotyping reveals distinct metabolic alterations in ketotic cows and identifies early predictive serum biomarkers for the risk of disease, Metabolomics, 13, 43, 10.1007/s11306-017-1180-4
Reynolds, 2017, Enteric helminths promote Salmonella coinfection by altering the intestinal metabolome, J. Infect. Dis., 215, 1245, 10.1093/infdis/jix141
Bahado-Singh, 2013, Metabolomic analysis for first-trimester Down syndrome prediction, Am. J. Obstet. Gynecol., 208, 371
Cox, 2016, Yap reprograms glutamine metabolism to increase nucleotide biosynthesis and enable liver growth, Nat. Cell Biol., 18, 886, 10.1038/ncb3389
Arts, 2016, Glutaminolysis and fumarate accumulation integrate immunometabolic and epigenetic programs in trained immunity, Cell Metab., 24, 807, 10.1016/j.cmet.2016.10.008
Paglia, 2016, Distinctive pattern of serum elements during the progression of Alzheimer's disease, Scientific Rep., 6, 22769, 10.1038/srep22769
Li, 2013, Predicting network activity from high throughput metabolomics, PLoS Comput. Biol., 9, e1003123, 10.1371/journal.pcbi.1003123
Ravanbakhsh, 2015, Accurate, fully-automated NMR spectral profiling for metabolomics, PLoS ONE, 10, e0124219, 10.1371/journal.pone.0124219
Huan, 2017, Systems biology guided by XCMS Online metabolomics, Nat. Methods, 14, 461, 10.1038/nmeth.4260
Wadi, 2016, Impact of outdated gene annotations on pathway enrichment analysis, Nat. Methods, 13, 705, 10.1038/nmeth.3963
Marco-Ramell, 2018, Evaluation and comparison of bioinformatic tools for the enrichment analysis of metabolomics data, BMC Bioinformatics, 19, 1, 10.1186/s12859-017-2006-0
Kanehisa, 2017, KEGG: new perspectives on genomes, pathways, diseases and drugs, Nucleic Acids Res., 45, D353, 10.1093/nar/gkw1092
Wishart, 2017, HMDB 4.0: the human metabolome database for 2018, Nucleic Acids Res., 46, D608, 10.1093/nar/gkx1089
Hastings, 2012, The ChEBI reference database and ontology for biologically relevant chemistry: enhancements for 2013, Nucleic Acids Res., 41, D456, 10.1093/nar/gks1146
Smith, 2005, METLIN: a metabolite mass spectral database, Therapeut. Drug Monitor., 27, 747, 10.1097/01.ftd.0000179845.53213.39
Kim, 2015, PubChem substance and compound databases, Nucleic Acids Res., 44, D1202, 10.1093/nar/gkv951
Jewison, 2013, SMPDB 2.0: big improvements to the small molecule pathway database, Nucleic Acids Res., 42, D478, 10.1093/nar/gkt1067
Pluskal, 2010, MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data, BMC Bioinformatics, 11, 395, 10.1186/1471-2105-11-395
Lommen, 2012, MetAlign 3.0: performance enhancement by efficient use of advances in computer hardware, Metabolomics, 8, 719, 10.1007/s11306-011-0369-1
Kind, 2006, Metabolomic database annotations via query of elemental compositions: mass accuracy is insufficient even at less than 1 ppm, BMC Bioinformatics, 7, 234, 10.1186/1471-2105-7-234
Kind, 2007, Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry, BMC Bioinformatics, 8, 105, 10.1186/1471-2105-8-105
Subramanian, 2005, Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles, Proc. Natl. Acad. Sci. U.S.A., 102, 15545, 10.1073/pnas.0506580102
Johnson, 2016, Metabolomics: beyond biomarkers and towards mechanisms, Nat. Rev. Mol. Cell Biol., 17, 451, 10.1038/nrm.2016.25
Hanash, 2008, Mining the plasma proteome for cancer biomarkers, Nature, 452, 571, 10.1038/nature06916
Goveia, 2016, Meta‐analysis of clinical metabolic profiling studies in cancer: challenges and opportunities, EMBO Mol. Med., 8, 1134, 10.15252/emmm.201606798
Tzoulaki, 2014, Design and analysis of metabolomics studies in epidemiologic research: a primer on-omic technologies, Am. J. Epidemiol., 180, 129, 10.1093/aje/kwu143
Tseng, 2012, Comprehensive literature review and statistical considerations for microarray meta-analysis, Nucleic Acids Res., 40, 3785, 10.1093/nar/gkr1265
Patti, 2012, Innovation: Metabolomics: the apogee of the omics trilogy, Nat. Rev. Mol. Cell Biol., 13, 263, 10.1038/nrm3314
Xia, 2013, INMEX–a web-based tool for integrative meta-analysis of expression data, Nucleic Acids Res., 41, W63, 10.1093/nar/gkt338
Walsh, 2015, Microarray meta-analysis and cross-platform normalization: integrative genomics for robust biomarker discovery, Microarrays, 4, 389, 10.3390/microarrays4030389
Cambiaghi, 2016, Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration, Brief. Bioinformatics, 18, 498
Ritchie, 2015, Methods of integrating data to uncover genotype-phenotype interactions, Nat. Rev. Genet., 16, 85, 10.1038/nrg3868
Chong, 2017, Computational approaches for integrative analysis of the metabolome and microbiome, Metabolites, 7, E62, 10.3390/metabo7040062
Xia, 2015, NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data, Nat. Protoc., 10, 823, 10.1038/nprot.2015.052
Yao, 2015, Global prioritization of disease candidate metabolites based on a multi-omics composite network, Scientific Rep., 5, 17201, 10.1038/srep17201
Thevenot, 2015, Analysis of the human adult urinary metabolome variations with age, body mass index, and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses, J. Proteome Res., 14, 3322, 10.1021/acs.jproteome.5b00354
Lê Cao, 2011, Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems, BMC Bioinformatics, 12, 253, 10.1186/1471-2105-12-253
Giacomoni, 2014, Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics, Bioinformatics, 31, 1493, 10.1093/bioinformatics/btu813
Davidson, 2016, Galaxy-M: a Galaxy workflow for processing and analyzing direct infusion and liquid chromatography mass spectrometry-based metabolomics data, Gigascience, 5, 10, 10.1186/s13742-016-0115-8