Imperial College London

DrTimothyEbbels

Faculty of MedicineDepartment of Metabolism, Digestion and Reproduction

Reader in Computational Bioinformatics
 
 
 
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Contact

 

+44 (0)20 7594 3160t.ebbels Website

 
 
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Location

 

131Sir Alexander Fleming BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

139 results found

Alves AC, Li JV, Garcia-Perez I, Sands C, Barbas C, Holmes E, Ebbels TMDet al., 2012, Characterization of data analysis methods for information recovery from metabolic 1H NMR spectra using artificial complex mixtures, Metabolomics, Vol: 8, Pages: 1170-1180

Journal article

Veselkov KA, Vingara LK, Masson P, Robinette SL, Want E, Li JV, Barton RH, Boursier-Neyret C, Walther B, Ebbels TM, Pelczer I, Holmes E, Lindon JC, Nicholson JKet al., 2011, Response to Comment on "Optimized Preprocessing of Ultra-Performance Liquid Chromatography/Mass Spectrometry Urinary Metabolic Profiles for Improved Information Recovery", ANALYTICAL CHEMISTRY, Vol: 83, Pages: 9721-9722, ISSN: 0003-2700

Journal article

Kamburov A, Cavill R, Ebbels TMD, Herwig R, Keun HCet al., 2011, Integrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA, BIOINFORMATICS, Vol: 27, Pages: 2917-2918, ISSN: 1367-4803

Journal article

Valcarcel B, Wurtz P, al Basatena N-KS, Tukiainen T, Kangas AJ, Soininen P, Jarvelin M-R, Ala-Korpela M, Ebbels TM, de Iorio Met al., 2011, A Differential Network Approach to Exploring Differences between Biological States: An Application to Prediabetes, PLOS ONE, Vol: 6, ISSN: 1932-6203

Journal article

Ebbels TMD, De Iorio M, 2011, Statistical Data Analysis in Metabolomics, Handbook of Statistical Systems Biology, Editors: Stumpf, Balding, Girolami, Publisher: Wiley, ISBN: 9781119970613

8 Statistical Data Analysis in Metabolomics Timothy MD Ebbels1 and Maria De Iorio2 1Department of Surgery and Cancer, Imperial College, London, UK 2Department of Epidemiology and Biostatistics, Imperial College, London, ...

Book chapter

Veselkov KA, Vingara LK, Masson P, Robinette SL, Want E, Li JV, Barton RH, Boursier-Neyret C, Walther B, Ebbels TM, Pelczer I, Holmes E, Lindon JC, Nicholson JKet al., 2011, Optimized Preprocessing of Ultra-Performance Liquid Chromatography/Mass Spectrometry Urinary Metabolic Profiles for Improved Information Recovery, ANALYTICAL CHEMISTRY, Vol: 83, Pages: 5864-5872, ISSN: 0003-2700

Journal article

Berk M, Ebbels T, Montana G, 2011, A statistical framework for biomarker discovery in metabolomic time course data, BIOINFORMATICS, Vol: 27, Pages: 1979-1985, ISSN: 1367-4803

Journal article

Ellis JK, Athersuch TJ, Cavill R, Radford R, Slattery C, Jennings P, McMorrow T, Ryan MP, Ebbels TMD, Keun HCet al., 2011, Erratum: Metabolic response to low level toxicant exposure in a novel renal tubule epithelial cell system (Molecular BioSystems (2010) DOI:10.1039/c0mb00146e), Molecular BioSystems, Vol: 7, Pages: 2081-2086, ISSN: 1742-206X

Journal article

Montana G, Berk M, Ebbels TMD, 2010, Modelling Short Time Series in Metabolomics: A Functional Data Analysis Approach, Software Tools and Algorithms for Biological Systems, Editors: Arabnia, Publisher: Springer

Book chapter

Sands CJ, Coen M, Ebbels TMD, Holmes E, Lindon JC, Nicholson JKet al., 2011, Data-Driven Approach for Metabolite Relationship Recovery in Biological H-1 NMR Data Sets Using Iterative Statistical Total Correlation Spectroscopy, ANALYTICAL CHEMISTRY, Vol: 83, Pages: 2075-2082, ISSN: 0003-2700

Journal article

Cavill R, Kamburov A, Ellis JK, Athersuch TJ, Blagrove MSC, Herwig R, Ebbels TMD, Keun HCet al., 2011, Consensus-Phenotype Integration of Transcriptomic and Metabolomic Data Implies a Role for Metabolism in the Chemosensitivity of Tumour Cells, PLOS COMPUTATIONAL BIOLOGY, Vol: 7

Journal article

Chadeau-Hyam M, Athersuch TJ, Keun HC, De Iorio M, Ebbels TMD, Jenab M, Sacerdote C, Bruce SJ, Holmes E, Vineis Pet al., 2011, Meeting-in-the-middle using metabolic profiling - a strategy for the identification of intermediate biomarkers in cohort studies, BIOMARKERS, Vol: 16, Pages: 83-88, ISSN: 1354-750X

Journal article

Ellis JK, Athersuch TJ, Cavill R, Radford R, Slattery C, Jennings P, McMorrow T, Ryan MP, Ebbels TMD, Keun HCet al., 2011, Metabolic response to low-level toxicant exposure in a novel renal tubule epithelial cell system, MOLECULAR BIOSYSTEMS, Vol: 7, Pages: 247-257, ISSN: 1742-206X

Journal article

Muncey H, Jones R, De Iorio M, Ebbels TMDet al., 2011, MetAssimulo

MetAssimulo is a MATLAB-based package which simulates 1H-NMR spectra of complex mixtures such as metabolic profiles. Drawing data from a metabolite standard spectral database in conjunction with concentration information input by the user or constructed automatically from the Human Metabolome Database, MetAssimulo is able to create realistic metabolic profiles containing large numbers of metabolites with a range of user-defined properties. Current features include the simulation of two groups ('case' and 'control') specified by means and standard deviations of concentrations for each metabolite. The software also allows addition of spectral noise with a realistic autocorrelation structure at user controllable levels. A crucial feature of the algorithm is its ability to simulate both intra- and inter-metabolite correlations, the analysis of which is fundamental to many techniques in the field. Further, MetAssimulo is able to simulate shifts in NMR peak positions that result from matrix effects such as pH differences which are often observed in metabolic NMR spectra and pose serious challenges for statistical algorithms.

Software

Ebbels TMD, Lindon JC, Coen M, 2011, Processing and modeling of nuclear magnetic resonance (NMR) metabolic profiles., Pages: 365-388

Modern nuclear magnetic resonance (NMR) spectroscopy generates complex and information-rich metabolic profiles. These require robust, accurate, and often sophisticated statistical techniques to yield the maximum meaningful knowledge. In this chapter, we describe methods typically used to analyze such data. We begin by describing seven goals of metabolic profile analysis, ranging from production of a data table to multi-omic integration for systems biology. Methods for preprocessing and pretreatment are then presented, including issues such as instrument-level spectral processing, data reduction and deconvolution, normalization, scaling, and transformations of the data. We then discuss methods for exploratory modeling and exemplify three techniques: principal components analysis, hierarchical clustering, and self-organizing maps. Moving to predictive modeling, we focus our discussion on partial least squares regression, orthogonal partial least squares regression, and genetic algorithm approaches. A typical set of in vitro metabolic profiles is used where possible to compare and contrast the methods. The importance of validating statistical models is highlighted, and standard techniques for doing so, such as training/test set and cross-validation are described. Finally, we discuss the contributions of statistical techniques such as statistical total correlation spectroscopy, and other correlation-based methods have made to the process of structural characterization for unknown metabolites.

Book chapter

Fonville JM, Richards SE, Barton RH, Boulange CL, Ebbels TMD, Nicholson JK, Holmes EC, Dumas MEet al., 2010, The Evolution of Partial Least Squares Models and Related Chemometric Approaches in Metabonomics and Metabolic Phenotyping, J. Chemometrics, Vol: 24, Pages: 636-649

Metabonomics is a key element in systems biology, and with current analytical methods, generates vast amounts ofquantitative or qualitative metabolic data. Understanding of the global function of the living organism can beachieved by integration of ‘omics’ approaches including metabonomics, genomics, transcriptomics and proteomics,increasing the complexity of the full data sets. Multivariate statistical approaches are well suited to extract thecharacterizing metabolic information associated with each level of dynamic process. In this review, we discusstechniques that have evolved from principal component analysis and partial least squares (PLS) methods with a focuson improved interpretation and modeling with respect to biomarker recovery and data visualization in the context ofmetabonomic applications. Visualization is of paramount importance to investigate complex metabolic signatures,the power and potential of which is illustrated with key papers. Recent improvements based on the removal oforthogonal variation are discussed in terms of interpretation enhancement, and are supported by relevantapplications. Flexibility of PLS methods in general and of O-PLS in particular allows implementation of derivativemethods such as O2-PLS, O-PLS-variance components, nonlinear methods, and batch modeling to improve analysis ofcomplex data sets, which facilitates extraction of information related to subtle biological processes. These approachescan be used to address issues present in complex multi-factorial data sets. Thus, we highlight the key advantages andlimitations of the different latent variable applications for top-down systems biology and assess the differencesbetween the methods available.

Journal article

Yap IKS, Brown IJ, Chan Q, Wijeyesekera A, Garcia-Perez I, Bictash M, Loo RL, Chadeau-Hyam M, Ebbeis T, De Iorio M, Maibaum E, Zhao L, Kesteloot H, Daviglus ML, Stamler J, Nicholson JK, Elliott P, Holmes Eet al., 2010, Metabolome-Wide Association Study Identifies Multiple Biomarkers that Discriminate North and South Chinese Populations at Differing Risks of Cardiovascular Disease INTERMAP Study, JOURNAL OF PROTEOME RESEARCH, Vol: 9, Pages: 6647-6654, ISSN: 1535-3893

Journal article

Richards SE, Dumas M-E, Fonville JM, Ebbels TMD, Holmes E, Nicholson JKet al., 2010, Intra- and inter-omic fusion of metabolic profiling data in a systems biology framework, CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, Vol: 104, Pages: 121-131, ISSN: 0169-7439

Journal article

Muncey HJ, Jones R, De Iorio M, Ebbels TMDet al., 2010, MetAssimulo:Simulation of Realistic NMR Metabolic Profiles, BMC BIOINFORMATICS, Vol: 11, ISSN: 1471-2105

Journal article

Benton HP, Want EJ, Ebbels TMD, 2010, Correction of mass calibration gaps in liquid chromatography-mass spectrometry metabolomics data, BIOINFORMATICS, Vol: 26, Pages: 2488-2489, ISSN: 1367-4803

Journal article

Bictash M, Ebbels TM, Chan Q, Loo RL, Yap IKS, Brown IJ, de Iorio M, Daviglus ML, Holmes E, Stamler J, Nicholson JK, Elliott Pet al., 2010, Opening up the "Black Box": Metabolic phenotyping and metabolome-wide association studies in epidemiology, JOURNAL OF CLINICAL EPIDEMIOLOGY, Vol: 63, Pages: 970-979, ISSN: 0895-4356

Journal article

Chadeau-Hyam M, Ebbels TMD, Brown IJ, Chan Q, Stemler J, Huang CC, Daviglus ML, Ueshima H, Zhao L, Holmes E, Nicholson JK, Elliott P, De Iorio Met al., 2010, Metabolic Profiling and the Metabolome-Wide Association Study: Significance Level For Biomarker Identification, JOURNAL OF PROTEOME RESEARCH, Vol: 9, Pages: 4620-4627, ISSN: 1535-3893

Journal article

Ipsen A, Want EJ, Ebbels TMD, 2010, Construction of Confidence Regions for Isotopic Abundance Patterns in LC/MS Data Sets for Rigorous Determination of Molecular Formulas, ANALYTICAL CHEMISTRY, Vol: 82, Pages: 7319-7328, ISSN: 0003-2700

Journal article

Cavill R, Sidhu JK, Kilarski W, Javerzat S, Hagedorn M, Timothy MDE, Bikfalvi A, Keunt HCet al., 2010, A Combined Metabonomic and Transcriptomic Approach to Investigate Metabolism during Development in the Chick Chorioallantoic Membrane, JOURNAL OF PROTEOME RESEARCH, Vol: 9, Pages: 3126-3134, ISSN: 1535-3893

Journal article

Ipsen A, Want E, Lindon J, Ebbels Tet al., 2010, Identification of parent-fragment pairs via rigorous statistical modeling of LC-MS metabolomic data, Publisher: AMER CHEMICAL SOC, ISSN: 0065-7727

Conference paper

Ipsen A, Want EJ, Lindon JC, Ebbels TMDet al., 2010, A statistically rigorous test for the identification of parent-fragment pairs in LC-MS datasets, Analytical Chemistry, Vol: 82, Pages: 1766-1778, ISSN: 1086-4377

Journal article

Bollard ME, Contel NR, Ebbels TMD, Smith L, Beckonert O, Cantor GH, Lehman-McKeeman L, Holmes EC, Lindon JC, Nicholson JK, Keun HCet al., 2010, NMR-Based Metabolic Profiling Identifies Biomarkers of Liver Regeneration Following Partial Hepatectomy in the Rat, JOURNAL OF PROTEOME RESEARCH, Vol: 9, Pages: 59-69, ISSN: 1535-3893

Journal article

Beckonert O, Coen M, Keun HC, Wang Y, Ebbels TMD, Holmes E, Lindon JC, Nicholson JKet al., 2010, High-resolution magic-angle-spinning NMR spectroscopy for metabolic profiling of intact tissues, NATURE PROTOCOLS, Vol: 5, Pages: 1019-1032, ISSN: 1754-2189

Journal article

Ellis JK, Chan PH, Doktorova T, Athersuch TJ, Cavill R, Vanhaecke T, Rogiers V, Vinken M, Nicholson JK, Ebbels TMD, Keun HCet al., 2010, Effect of the Histone Deacetylase Inhibitor Trichostatin A on the Metabolome of Cultured Primary Hepatocytes, JOURNAL OF PROTEOME RESEARCH, Vol: 9, Pages: 413-419, ISSN: 1535-3893

Journal article

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