Imperial College London

DrTimothyEbbels

Faculty of MedicineDepartment of Surgery & Cancer

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
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130 results found

Peters K, Bradbury J, Bergmann S, Capuccini M, Cascante M, de Atauri P, Ebbels TMD, Foguet C, Glen R, Gonzalez-Beltran A, Günther UL, Handakas E, Hankemeier T, Haug K, Herman S, Holub P, Izzo M, Jacob D, Johnson D, Jourdan F, Kale N, Karaman I, Khalili B, Emami Khonsari P, Kultima K, Lampa S, Larsson A, Ludwig C, Moreno P, Neumann S, Novella JA, O'Donovan C, Pearce JTM, Peluso A, Piras ME, Pireddu L, Reed MAC, Rocca-Serra P, Roger P, Rosato A, Rueedi R, Ruttkies C, Sadawi N, Salek RM, Sansone S-A, Selivanov V, Spjuth O, Schober D, Thévenot EA, Tomasoni M, van Rijswijk M, van Vliet M, Viant MR, Weber RJM, Zanetti G, Steinbeck Cet al., 2019, PhenoMeNal: processing and analysis of metabolomics data in the cloud., Gigascience, Vol: 8

BACKGROUND: Metabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological, and many other applied biological domains. Its computationally intensive nature has driven requirements for open data formats, data repositories, and data analysis tools. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution. FINDINGS: PhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm. CONCLUSIONS: PhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible, and shareable metabolomics data analysis platforms that are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy

JOURNAL ARTICLE

Peluso A, Ebbels T, Glen R, 2018, Empirical estimation of permutation-based metabolome-wide significance thresholds, Publisher: bioRxiv

A key issue in the omics literature is the search of statistically significant relationships between molecular markers and phenotype. The aim is to detect disease-related discriminatory features while controlling for false positive associations at adequate power. Metabolome-wide association studies have revealed significant relationships of metabolic phenotypes with disease risk by analysing hundreds to tens of thousands of molecular variables leading to multivariate data which are highly noisy and collinear. In this context, Bonferroni or Sidak correction are rather useful as these are valid for independent tests, while permutation procedures allow for the estimation of p-values from the null distribution without assuming independence among features. Nevertheless, under the permutation approach the distribution of p-values may presents systematic deviations from the theoretical null distribution which leads to biased adjusted threshold estimate, e.g. smaller than a Bonferroni or Sidak correction. We make use of parametric approximation methods based on a multivariate Normal distribution to derive stable estimates of the metabolome-wide significance level within a univariate approach based on a permutation procedure which effectively controls the maximum overall type I error rate at the α level. We illustrate the results for different model parametrizations and distributional features of the outcome measure, as well as for diverse correlation levels within the features and between the features and the phenotype in real data and simulated studies. MWSL is the open-source R software package for the empirical estimation of the metabolomic-wide significance level available at https://github.com/AlinaPeluso/MWSL.

WORKING PAPER

Kamp H, Beger R, Dorne J-LCM, Ebbels T, Ekman D, Guillou C, Goetz A, Loizou G, Leonards P, van Ravenzwaay B, Sperber S, Viant M, Walk Tet al., 2018, MEtabolomics standaRds Initiative in Toxicology (MERIT), 54th Congress of the European-Societies-of-Toxicology (EUROTOX) - Toxicology Out of the Box, Publisher: ELSEVIER IRELAND LTD, Pages: S214-S214, ISSN: 0378-4274

CONFERENCE PAPER

Ye L, De Iorio M, Ebbels TMD, 2018, Bayesian estimation of the number of protonation sites for urinary metabolites from NMR spectroscopic data, METABOLOMICS, Vol: 14, ISSN: 1573-3882

JOURNAL ARTICLE

Posma JM, Garcia-Perez I, Ebbels TMD, Lindon JC, Stamler J, Elliott P, Holmes E, Nicholson JKet al., 2018, Optimized Phenotypic Biomarker Discovery and Confounder Elimination via Covariate-Adjusted Projection to Latent Structures from Metabolic Spectroscopy Data, JOURNAL OF PROTEOME RESEARCH, Vol: 17, Pages: 1586-1595, ISSN: 1535-3893

JOURNAL ARTICLE

Kaluarachchi M, Boulange CL, Karaman I, Lindon JC, Ebbels TMD, Elliott P, Tracy RP, Olson NCet al., 2018, A comparison of human serum and plasma metabolites using untargeted H-1 NMR spectroscopy and UPLC-MS, METABOLOMICS, Vol: 14, ISSN: 1573-3882

JOURNAL ARTICLE

Harada S, Hirayama A, Chan Q, Kurihara A, Fukai K, Iida M, Kato S, Sugiyama D, Kuwabara K, Takeuchi A, Akiyama M, Okamura T, Ebbels TMD, Elliott P, Tomita M, Sato A, Suzuki C, Sugimoto M, Soga T, Takebayashi Tet 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

JOURNAL ARTICLE

Schober D, Jacob D, Wilson M, Cruz JA, Marcu A, Grant JR, Moing A, Deborde C, de Figueiredo LF, Haug K, Rocca-Serra P, Easton J, Ebbels TMD, Hao J, Ludwig C, Guenther UL, Rosato A, Klein MS, Lewis IA, Luchinat C, Jones AR, Grauslys A, Larralde M, Yokochi M, Kobayashi N, Porzel A, Griffin JL, Viant MR, Wishart DS, Steinbeck C, Salek RM, Neumann Set 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

JOURNAL ARTICLE

Ebbels TMD, Rodriguez-Martinez A, Dumas ME, Keun HCet 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.

BOOK CHAPTER

Buesen R, Chorley BN, Lima BDS, Daston G, Deferme L, Ebbels T, Gant TW, Goetz A, Greally J, Gribaldo L, Hackermueller J, Hubesch B, Jennen D, Johnson K, Kanno J, Kauffmann H-M, Laffont M, McMullen P, Meehan R, Pemberton M, Perdichizzi S, Piersma AH, Sauer UG, Schmidt K, Seitz H, Sumida K, Tollefsen KE, Tong W, Tralau T, van Ravenzwaay B, Weber RJM, Worth A, Yauk C, Poole Aet 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

JOURNAL ARTICLE

Tan LSL, Jasra A, De Iorio M, Ebbels TMDet 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

JOURNAL ARTICLE

Kauffmann H-M, Kamp H, Fuchs R, Chorley BN, Deferme L, Ebbels T, Hackermueller J, Perdichizzi S, Poole A, Sauer UG, Tollefsen KE, Tralau T, Yauk C, van Ravenzwaay Bet 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

JOURNAL ARTICLE

Castagne R, Boulange CL, Karaman I, Campanella G, Ferreira DLS, Kaluarachchi MR, Lehne B, Moayyeri A, Lewis MR, Spagou K, Dona AC, Evangelos V, Tracy R, Greenland P, Lindon JC, Herrington D, Ebbels TMD, Elliott P, Tzoulaki I, Chadeau-Hyam Met 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

JOURNAL ARTICLE

Chan Q, Loo RL, Ebbels TMD, Van Horn L, Daviglus ML, Stamler J, Nicholson JK, Holmes E, Elliott Pet 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

JOURNAL ARTICLE

Weber RJM, Lawson TN, Salek RM, Ebbels TMD, Glen RC, Goodacre R, Griffin JL, Haug K, Koulman A, Moreno P, Ralser M, Steinbeck C, Dunn WB, Viant MRet al., 2017, Computational tools and workflows in metabolomics: An international survey highlights the opportunity for harmonisation through Galaxy, METABOLOMICS, Vol: 13, ISSN: 1573-3882

JOURNAL ARTICLE

Steinbeck C, van Rijswijk M, Beirnaert C, Caron C, Cascante M, Dominguez V, Dunn WB, Ebbels TMD, Giacomoni F, Gonzalez-Beltran A, Hankemeier T, Haug K, Izquierdo-Garcia JL, Jimenez RC, Jourdan F, Kale N, Klapa MI, Kohlbacher O, Koort K, Kultima K, Le Corguillé G, Moschonas NK, Neumann S, O'Donovan C, Reczko M, Rocca-Serra P, Rosato A, Salek RM, Sansone SA, Satagopam V, Schober D, Shimmo R, Spicer RA, Spjuth O, Thévenot EA, Viant MR, Weber RJM, Willighagen EL, Zanetti Get al., 2017, The future of metabolomics in ELIXIR, F1000Research, Vol: 6, ISSN: 2046-1402

© 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.

JOURNAL ARTICLE

Karaman I, Ferreira DLS, Boulange CL, Kaluarachchi MR, Herrington D, Dona AC, Castagne R, Moayyeri A, Lehne B, Loh M, de Vries PS, Dehghan A, Franco OH, Hofman A, Evangelou E, Tzoulaki I, Elliott P, Lindon JC, Ebbels TMDet 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

JOURNAL ARTICLE

Tredwell GD, Bundy JG, De Iorio M, Ebbels TMDet al., 2016, Modelling the acid/base H-1 NMR chemical shift limits of metabolites in human urine, METABOLOMICS, Vol: 12, ISSN: 1573-3882

JOURNAL ARTICLE

Blaise BJ, Correia G, Tin A, Young JH, Vergnaud A-C, Lewis M, Pearce JTM, Elliott P, Nicholson JK, Holmes E, Ebbels TMDet al., 2016, Power Analysis and Sample Size Determination in Metabolic Phenotyping, ANALYTICAL CHEMISTRY, Vol: 88, Pages: 5179-5188, ISSN: 0003-2700

JOURNAL ARTICLE

Rocca-Serra P, Salek RM, Arita M, Correa E, Dayalan S, Gonzalez-Beltran A, Ebbels T, Goodacre R, Hastings J, Haug K, Koulman A, Nikolski M, Oresic M, Sansone S-A, Schober D, Smith J, Steinbeck C, Viant MR, Neumann Set al., 2016, Data standards can boost metabolomics research, and if there is a will, there is a way, METABOLOMICS, Vol: 12, ISSN: 1573-3882

JOURNAL ARTICLE

Griep LMO, Chekmeneva E, Stamler J, Van Horn L, Chan Q, Ebbels TMD, Holmes E, Frost GS, Elliott Pet 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

CONFERENCE PAPER

Salek RM, Neumann S, Schober D, Hummel J, Billiau K, Kopka J, Correa E, Reijmers T, Rosato A, Tenori L, Turano P, Marin S, Deborde C, Jacob D, Rolin D, Dartigues B, Conesa P, Haug K, Rocca-Serra P, O'Hagan S, Hao J, van Vliet M, Sysi-Aho M, Ludwig C, Bouwman J, Cascante M, Ebbels T, Griffin JL, Moing A, Nikolski M, Oresic M, Sansone S-A, Viant MR, Goodacre R, Guenther UL, Hankemeier T, Luchinat C, Walther D, Steinbeck Cet 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

JOURNAL ARTICLE

Salek RM, Neumann S, Schober D, Hummel J, Billiau K, Kopka J, Correa E, Reijmers T, Rosato A, Tenori L, Turano P, Marin S, Deborde C, Jacob D, Rolin D, Dartigues B, Conesa P, Haug K, Rocca-Serra P, O'Hagan S, Hao J, Vliet MV, Sysi-Aho M, Ludwig C, Bouwman J, Cascante M, Ebbels T, Griffin JL, Moing A, Nikolski M, Oresic M, Sansone S-A, Viant MR, Goodacre R, Guenther UL, Hankemeier T, Luchinat C, Walther D, Steinbeck Cet al., 2015, COordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access, METABOLOMICS, Vol: 11, Pages: 1587-1597, ISSN: 1573-3882

JOURNAL ARTICLE

Roessner U, Ebbels T, 2015, One minute with the Metabolomics Society's Honorary Fellows 2015, METABOLOMICS, Vol: 11, Pages: 779-781, ISSN: 1573-3882

JOURNAL ARTICLE

Salek RM, Arita M, Dayalan S, Ebbels T, Jones AR, Neumann S, Rocca-Serra P, Viant MR, Vizcaino J-Aet al., 2015, Embedding standards in metabolomics: the Metabolomics Society data standards task group, METABOLOMICS, Vol: 11, Pages: 782-783, ISSN: 1573-3882

JOURNAL ARTICLE

Hendrickx DM, Aerts HJWL, Caiment F, Clark D, Ebbels TMD, Evelo CT, Gmuender H, Hebels DGAJ, Herwig R, Hescheler J, Jennen DGJ, Jetten MJA, Kanterakis S, Keun HC, Matser V, Overington JP, Pilicheva E, Sarkans U, Segura-Lepe MP, Sotiriadou I, Wittenberger T, Wittwehr C, Zanzi A, Kleinjans JCSet al., 2015, diXa: a data infrastructure for chemical safety assessment, BIOINFORMATICS, Vol: 31, Pages: 1505-1507, ISSN: 1367-4803

JOURNAL ARTICLE

Elliott P, Posma JM, Chan Q, Garcia-Perez I, Wijeyesekera A, Bictash M, Ebbels TMD, Ueshima H, Zhao L, van Horn L, Daviglus M, Stamler J, Holmes E, Nicholson JKet al., 2015, Urinary metabolic signatures of human adiposity, SCIENCE TRANSLATIONAL MEDICINE, Vol: 7, ISSN: 1946-6234

JOURNAL ARTICLE

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

JOURNAL ARTICLE

Pomyen Y, Segura M, Ebbels TMD, Keun HCet 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

JOURNAL ARTICLE

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