126 results found
Ebbels TMD, Rodriguez-Martinez A, Dumas ME, et al., 2018, CHAPTER 12: Advances in Computational Analysis of Metabolomic NMR Data, New Developments in NMR, Pages: 310-323
© The Royal Society of Chemistry 2018. In this chapter we discuss some of the more recent developments in preprocessing and statistical analysis of NMR spectra in metabolomics. Bayesian methods for analyzing NMR spectra are summarized and we describe one particular approach, BATMAN, in more detail. We consider techniques based on statistical associations, such as correlation spectroscopy (e.g. STOCSY and recent variants), as well as approaches that model the associations as a network and how these change under different biological conditions. The link between metabolism and genotype is explored by looking at metabolic GWAS and related techniques. Finally, we describe the relevance and current status of data standards for NMR metabolomics.
Harada S, Hirayama A, Chan Q, et al., 2018, Reliability of plasma polar metabolite concentrations in a large-scale cohort study using capillary electrophoresis-mass spectrometry, PLOS ONE, Vol: 13, ISSN: 1932-6203
Kaluarachchi M, Boulangé CL, Karaman I, et al., 2018, A comparison of human serum and plasma metabolites using untargeted<sup>1</sup>H NMR spectroscopy and UPLC-MS, Metabolomics, Vol: 14, ISSN: 1573-3882
© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Introduction: Differences in the metabolite profiles between serum and plasma are incompletely understood. Objectives: To evaluate metabolic profile differences between serum and plasma and among plasma sample subtypes. Methods: We analyzed serum, platelet rich plasma (PRP), platelet poor plasma (PPP), and platelet free plasma (PFP), collected from 8 non-fasting apparently healthy women, using untargeted standard 1D and CPMG 1 H NMR and reverse phase and hydrophilic (HILIC) UPLC-MS. Differences between metabolic profiles were evaluated using validated principal component and orthogonal partial least squares discriminant analysis. Results: Explorative analysis showed the main source of variation among samples was due to inter-individual differences with no grouping by sample type. After correcting for inter-individual differences, lipoproteins, lipids in VLDL/LDL, lactate, glutamine, and glucose were found to discriminate serum from plasma in NMR analyses. In UPLC-MS analyses, lysophosphatidylethanolamine (lysoPE)(18:0) and lysophosphatidic acid(20:0) were higher in serum, and phosphatidylcholines (PC)(16:1/18:2, 20:3/18:0, O-20:0/22:4), lysoPC(16:0), PE(O-18:2/20:4), sphingomyelin(18:0/22:0), and linoleic acid were lower. In plasma subtype analyses, isoleucine, leucine, valine, phenylalanine, glutamate, and pyruvate were higher among PRP samples compared with PPP and PFP by NMR while lipids in VLDL/LDL, citrate, and glutamine were lower. By UPLC-MS, PE(18:0/18:2) and PC(P-16:0/20:4) were higher in PRP compared with PFP samples. Conclusions: Correction for inter-individual variation was required to detect metabolite differences between serum and plasma. Our results suggest the potential importance of inter-individual effects and sample type on the results from serum and plasma metabolic phenotyping studies.
Posma JM, Garcia-Perez I, Ebbels TMD, et al., 2018, Optimized Phenotypic Biomarker Discovery and Confounder Elimination via Covariate-Adjusted Projection to Latent Structures from Metabolic Spectroscopy Data., J Proteome Res
Metabolism is altered by genetics, diet, disease status, environment, and many other factors. Modeling either one of these is often done without considering the effects of the other covariates. Attributing differences in metabolic profile to one of these factors needs to be done while controlling for the metabolic influence of the rest. We describe here a data analysis framework and novel confounder-adjustment algorithm for multivariate analysis of metabolic profiling data. Using simulated data, we show that similar numbers of true associations and significantly less false positives are found compared to other commonly used methods. Covariate-adjusted projections to latent structures (CA-PLS) are exemplified here using a large-scale metabolic phenotyping study of two Chinese populations at different risks for cardiovascular disease. Using CA-PLS, we find that some previously reported differences are actually associated with external factors and discover a number of previously unreported biomarkers linked to different metabolic pathways. CA-PLS can be applied to any multivariate data where confounding may be an issue and the confounder-adjustment procedure is translatable to other multivariate regression techniques.
Schober D, Jacob D, Wilson M, et al., 2018, nmrML: A Community Supported Open Data Standard for the Description, Storage, and Exchange of NMR Data, ANALYTICAL CHEMISTRY, Vol: 90, Pages: 649-656, ISSN: 0003-2700
Buesen R, Chorley BN, Lima BDS, et al., 2017, Applying 'omics technologies in chemicals risk assessment: Report of an ECETOC workshop, REGULATORY TOXICOLOGY AND PHARMACOLOGY, Vol: 91, Pages: S3-S13, ISSN: 0273-2300
Castagne R, Boulange CL, Karaman I, et al., 2017, Improving Visualization and Interpretation of Metabolome-Wide Association Studies: An Application in a Population-Based Cohort Using Untargeted H-1 NMR Metabolic Profiling, JOURNAL OF PROTEOME RESEARCH, Vol: 16, Pages: 3623-3633, ISSN: 1535-3893
Chan Q, Loo RL, Ebbels TMD, et al., 2017, Metabolic phenotyping for discovery of urinary biomarkers of diet, xenobiotics and blood pressure in the INTERMAP Study: an overview, HYPERTENSION RESEARCH, Vol: 40, Pages: 336-345, ISSN: 0916-9636
Kauffmann H-M, Kamp H, Fuchs R, et al., 2017, Framework for the quality assurance of 'omics technologies considering GLP requirements, REGULATORY TOXICOLOGY AND PHARMACOLOGY, Vol: 91, Pages: S27-S35, ISSN: 0273-2300
© 2017 van Rijswijk M et al. Metabolomics, the youngest of the major omics technologies, is supported by an active community of researchers and infrastructure developers across Europe. To coordinate and focus efforts around infrastructure building for metabolomics within Europe, a workshop on the "Future of metabolomics in ELIXIR" was organised at Frankfurt Airport in Germany. This one-day strategic workshop involved representatives of ELIXIR Nodes, members of the PhenoMeNal consortium developing an e-infrastructure that supports workflow-based metabolomics analysis pipelines, and experts from the international metabolomics community. The workshop established metabolite identification as the critical area, where a maximal impact of computational metabolomics and data management on other fields could be achieved. In particular, the existing four ELIXIR Use Cases, where the metabolomics community - both industry and academia - would benefit most, and which could be exhaustively mapped onto the current five ELIXIR Platforms were discussed. This opinion article is a call for support for a new ELIXIR metabolomics Use Case, which aligns with and complements the existing and planned ELIXIR Platforms and Use Cases.
Tan LSL, Jasra A, De Iorio M, et al., 2017, BAYESIAN INFERENCE FOR MULTIPLE GAUSSIAN GRAPHICAL MODELS WITH APPLICATION TO METABOLIC ASSOCIATION NETWORKS, ANNALS OF APPLIED STATISTICS, Vol: 11, Pages: 2222-2251, ISSN: 1932-6157
Weber RJM, Lawson TN, Salek RM, et al., 2017, Computational tools and workflows in metabolomics: An international survey highlights the opportunity for harmonisation through Galaxy, METABOLOMICS, Vol: 13, ISSN: 1573-3882
Blaise BJ, Correia G, Tin A, et al., 2016, Power Analysis and Sample Size Determination in Metabolic Phenotyping, ANALYTICAL CHEMISTRY, Vol: 88, Pages: 5179-5188, ISSN: 0003-2700
David R, Ebbels T, Gooderham N, 2016, Synergistic and Antagonistic Mutation Responses of Human MCL-5 Cells to Mixtures of Benzo[a]pyrene and 2-Amino-1-Methyl-6-Phenylimidazo[4,5-b]pyridine: Dose-Related Variation in the Joint Effects of Common Dietary Carcinogens, ENVIRONMENTAL HEALTH PERSPECTIVES, Vol: 124, Pages: 88-96, ISSN: 0091-6765
Griep LMO, Chekmeneva E, Stamler J, et al., 2016, Urinary hippurate and proline betaine relative to fruit intake, blood pressure, and body mass index, Publisher: CAMBRIDGE UNIV PRESS, Pages: E178-E178, ISSN: 0029-6651
Karaman I, Ferreira DLS, Boulange CL, et al., 2016, Workflow for Integrated Processing of Multicohort Untargeted H-1 NMR Metabolomics Data in Large-Scale Metabolic Epidemiology, JOURNAL OF PROTEOME RESEARCH, Vol: 15, Pages: 4188-4194, ISSN: 1535-3893
Rocca-Serra P, Salek RM, Arita M, et al., 2016, Data standards can boost metabolomics research, and if there is a will, there is a way, METABOLOMICS, Vol: 12, ISSN: 1573-3882
Tredwell GD, Bundy JG, De Iorio M, et al., 2016, Modelling the acid/base H-1 NMR chemical shift limits of metabolites in human urine, METABOLOMICS, Vol: 12, ISSN: 1573-3882
Hendrickx DM, Aerts HJWL, Caiment F, et al., 2015, diXa: a data infrastructure for chemical safety assessment, BIOINFORMATICS, Vol: 31, Pages: 1505-1507, ISSN: 1367-4803
Pomyen Y, Segura M, Ebbels TMD, et al., 2015, Over-representation of correlation analysis (ORCA): a method for identifying associations between variable sets, BIOINFORMATICS, Vol: 31, Pages: 102-108, ISSN: 1367-4803
Roessner U, Bearden DW, Ebbels T, 2015, The international Metabolomics Society in 2015: the path forward to success, METABOLOMICS, Vol: 11, Pages: 1-2, ISSN: 1573-3882
Roessner U, Ebbels T, 2015, One minute with the Metabolomics Society's Honorary Fellows 2015, METABOLOMICS, Vol: 11, Pages: 779-781, ISSN: 1573-3882
Salek RM, Arita M, Dayalan S, et al., 2015, Embedding standards in metabolomics: the Metabolomics Society data standards task group, METABOLOMICS, Vol: 11, Pages: 782-783, ISSN: 1573-3882
Salek RM, Neumann S, Schober D, et al., 2015, COordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access, METABOLOMICS, Vol: 11, Pages: 1587-1597, ISSN: 1573-3882
Salek RM, Neumann S, Schober D, et al., 2015, Coordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access (vol 11, pg 1587, 2015), METABOLOMICS, Vol: 11, Pages: 1598-1599, ISSN: 1573-3882
Hao J, Liebeke M, Astle W, et al., 2014, Bayesian deconvolution and quantification of metabolites in complex 1D NMR spectra using BATMAN, NATURE PROTOCOLS, Vol: 9, Pages: 1416-1427, ISSN: 1754-2189
Ipsen A, Ebbels TMD, 2014, Orders of Magnitude Extension of the Effective Dynamic Range of TDC-Based TOFMS Data Through Maximum Likelihood Estimation, JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY, Vol: 25, Pages: 1824-1827, ISSN: 1044-0305
Liebeke M, Bruford MW, Donnelly RK, et al., 2014, Identifying biochemical phenotypic differences between cryptic species, BIOLOGY LETTERS, Vol: 10, ISSN: 1744-9561
Milner JJ, Wang J, Sheridan PA, et al., 2014, H-1 NMR-Based Profiling Reveals Differential Immune-Metabolic Networks during Influenza Virus Infection in Obese Mice, PLOS ONE, Vol: 9, ISSN: 1932-6203
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