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

144 results found

Karaman I, Ferreira DL, Boulange CL, Kaluarachchi MR, Herrington D, Dona AC, Castagné R, Moayyeri A, Lehne B, Loh M, de Vries PS, Dehghan A, Franco O, Hofman A, Evangelou E, Tzoulaki I, Elliott P, Lindon JC, Ebbels TMet al., 2016, A workflow for integrated processing of multi-cohort untargeted 1H NMR metabolomics data in large scale metabolic epidemiology, Journal of Proteome Research, Vol: 15, Pages: 4188-4194, ISSN: 1535-3907

Large-scale metabolomics studies involving thousands of samples present multiple challenges in data analysis, particularly when an untargeted platform is used. Studies with multiple cohorts and analysis platforms exacerbate existing problems such as peak alignment and normalization. Therefore, there is a need for robust processing pipelines which can ensure reliable data for statistical analysis. The COMBI-BIO project incorporates serum from approximately 8000 individuals, in 3 cohorts, profiled by 6 assays in 2 phases using both 1H-NMR and UPLC-MS. Here we present the COMBI-BIO NMR analysis pipeline and demonstrate its fitness for purpose using representative quality control (QC) samples. NMR spectra were first aligned and normalized. After eliminating interfering signals, outliers identified using Hotelling’s T2 were removed and a cohort/phase adjustment was applied, resulting in two NMR datasets (CPMG and NOESY). Alignment of the NMR data was shown to increase the correlation-based alignment quality measure from 0.319 to 0.391 for CPMG and from 0.536 to 0.586 for NOESY, showing that the improvement was present across both large and small peaks. End-to-end quality assessment of the pipeline was achieved using Hotelling’s T2 distributions. For CPMG spectra, the interquartile range decreased from 1.425 in raw QC data to 0.679 in processed spectra, while the corresponding change for NOESY spectra was from 0.795 to 0.636 indicating an improvement in precision following processing. PCA indicated that gross phase and cohort differences were no longer present. These results illustrate that the pipeline produces robust and reproducible data, successfully addressing the methodological challenges of this large multi-faceted study.

Journal article

Tredwell GD, Bundy JG, De lorio M, Ebbels TMDet al., 2016, Modelling the acid/base 1H NMR chemical shift limits of metabolites in human urine, Metabolomics, Vol: 12, ISSN: 1573-3890

IntroductionDespite the use of buffering agents the 1H NMR spectra of biofluid samples in metabolic profiling investigations typically suffer from extensive peak frequency shifting between spectra. These chemical shift changes are mainly due to differences in pH and divalent metal ion concentrations between the samples. This frequency shifting results in a correspondence problem: it can be hard to register the same peak as belonging to the same molecule across multiple samples. The problem is especially acute for urine, which can have a wide range of ionic concentrations between different samples.ObjectivesTo investigate the acid, base and metal ion dependent 1H NMR chemical shift variations and limits of the main metabolites in a complex biological mixture.MethodsUrine samples from five different individuals were collected and pooled, and pre-treated with Chelex-100 ion exchange resin. Urine samples were either treated with either HCl or NaOH, or were supplemented with various concentrations of CaCl2, MgCl2, NaCl or KCl, and their 1H NMR spectra were acquired.ResultsNonlinear fitting was used to derive acid dissociation constants and acid and base chemical shift limits for peaks from 33 identified metabolites. Peak pH titration curves for a further 65 unidentified peaks were also obtained for future reference. Furthermore, the peak variations induced by the main metal ions present in urine, Na+, K+, Ca2+ and Mg2+, were also measured.ConclusionThese data will be a valuable resource for 1H NMR metabolite profiling experiments and for the development of automated metabolite alignment and identification algorithms for 1H NMR spectra.

Journal article

Blaise B, Correia G, Tin A, Young J, Vergnaud A, Lewis M, Pearce J, Elliott P, Nicholson J, Holmes E, Ebbels TMDet al., 2016, A novel method for power analysis and sample size determination in metabolic phenotyping, Analytical Chemistry, Vol: 88, Pages: 5179-5188, ISSN: 1520-6882

Estimation of statistical power and sample size is a key aspect of experimental design. However, in metabolic phenotyping, there is currently no accepted approach for these tasks, in large part due to the unknown nature of the expected effect. In such hypothesis free science, neither the number or class of important analytes nor the effect size are known a priori. We introduce a new approach, based on multivariate simulation, which deals effectively with the highly correlated structure and high-dimensionality of metabolic phenotyping data. First, a large data set is simulated based on the characteristics of a pilot study investigating a given biomedical issue. An effect of a given size, corresponding either to a discrete (classification) or continuous (regression) outcome is then added. Different sample sizes are modeled by randomly selecting data sets of various sizes from the simulated data. We investigate different methods for effect detection, including univariate and multivariate techniques. Our framework allows us to investigate the complex relationship between sample size, power, and effect size for real multivariate data sets. For instance, we demonstrate for an example pilot data set that certain features achieve a power of 0.8 for a sample size of 20 samples or that a cross-validated predictivity QY2 of 0.8 is reached with an effect size of 0.2 and 200 samples. We exemplify the approach for both nuclear magnetic resonance and liquid chromatography–mass spectrometry data from humans and the model organism C. elegans.

Journal article

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: 1552-9924

BACKGROUND: Chemical carcinogens such as benzo[a]pyrene (BaP) and 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP) may contribute to the etiology of human diet-associated cancer. Individually, these are genotoxic, but the consequences of exposure to mixtures of these chemicals have not been systematically examined. OBJECTIVES: To determine the mutagenic response to mixtures of BaP and PhIP at concentrations relevant to human exposure (mM to sub-nM). METHODS: Human MCL-5 cells (metabolically competent) were exposed to BaP or PhIP individually or in mixtures. Mutagenicity was assessed at the thymidine kinase (TK) locus, CYP1A activity and message determined by Ethoxyresorufin-O-deethylase (EROD) activity and Q-PCR respectively, and cell cycle measured by flow cytometry. RESULTS: Mixtures gave modified dose-responses compared to the individual chemicals; a remarkable increased mutant frequency (MF) at low concentration combinations (not mutagenic individually), and decreased MF at higher concentration combinations, compared to the calculated predicted additive MF of the individual chemicals. EROD activity and CYP1A1 mRNA levels correlated with TK MF supporting involvement of the CYP1A family in mutation. Moreover, a cell cycle G2/M phase block was observed at high dose combinations, consistent with DNA damage sensing and repair. CONCLUSIONS: Mixtures of these genotoxic chemicals produced mutation responses that differed from expectations for additive effects of the individual chemicals. The increase in MF for some combinations of chemicals at low concentrations that were not genotoxic for the individual chemicals, and the non-monotonic dose response, may be important for understanding the mutagenic potential of food and the etiology of diet-associated cancers.

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., 2015, Data standards can boost metabolomics research, and if there is a will, there is a way, Metabolomics, Vol: 12, ISSN: 1573-3890

Thousands of articles using metabolomics approaches are published every year. With the increasing amounts of data being produced, mere description of investigations as text in manuscripts is not sufficient to enable re-use anymore: the underlying data needs to be published together with the findings in the literature to maximise the benefit from public and private expenditure and to take advantage of an enormous opportunity to improve scientific reproducibility in metabolomics and cognate disciplines. Reporting recommendations in metabolomics started to emerge about a decade ago and were mostly concerned with inventories of the information that had to be reported in the literature for consistency. In recent years, metabolomics data standards have developed extensively, to include the primary research data, derived results and the experimental description and importantly the metadata in a machine-readable way. This includes vendor independent data standards such as mzML for mass spectrometry and nmrML for NMR raw data that have both enabled the development of advanced data processing algorithms by the scientific community. Standards such as ISA-Tab cover essential metadata, including the experimental design, the applied protocols, association between samples, data files and the experimental factors for further statistical analysis. Altogether, they pave the way for both reproducible research and data reuse, including meta-analyses. Further incentives to prepare standards compliant data sets include new opportunities to publish data sets, but also require a little “arm twisting” in the author guidelines of scientific journals to submit the data sets to public repositories such as the NIH Metabolomics Workbench or MetaboLights at EMBL-EBI. In the present article, we look at standards for data sharing, investigate their impact in metabolomics and give suggestions to improve their adoption.

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

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, Erratum to: COordination of Standards in MetabOlomicS (COSMOS): facilitating integrated metabolomics data access, Metabolomics, Vol: 11, Pages: 1587-1597, ISSN: 1573-3882

Metabolomics has become a crucial phenotyping technique in a range of research fields including medicine, the life sciences, biotechnology and the environmental sciences. This necessitates the transfer of experimental information between research groups, as well as potentially to publishers and funders. After the initial efforts of the metabolomics standards initiative, minimum reporting standards were proposed which included the concepts for metabolomics databases. Built by the community, standards and infrastructure for metabolomics are still needed to allow storage, exchange, comparison and re-utilization of metabolomics data. The Framework Programme 7 EU Initiative ‘coordination of standards in metabolomics’ (COSMOS) is developing a robust data infrastructure and exchange standards for metabolomics data and metadata. This is to support workflows for a broad range of metabolomics applications within the European metabolomics community and the wider metabolomics and biomedical communities’ participation. Here we announce our concepts and efforts asking for re-engagement of the metabolomics community, academics and industry, journal publishers, software and hardware vendors, as well as those interested in standardisation worldwide (addressing missing metabolomics ontologies, complex-metadata capturing and XML based open source data exchange format), to join and work towards updating and implementing metabolomics standards.

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, Pages: 1-16, ISSN: 1946-6234

Obesity is a major public health problem worldwide. We used 24-hour urinary metabolic profiling by proton (1H) nuclear magnetic resonance (NMR) spectroscopy and ion exchange chromatography to characterize the metabolic signatures of adiposity in the U.S. (n = 1880) and UK (n = 444) cohorts of the INTERMAP (International Study of Macro- and Micronutrients and Blood Pressure) epidemiologic study. Metabolic profiling of urine samples collected over two 24-hour time periods 3 weeks apart showed reproducible patterns of metabolite excretion associated with adiposity. Exploratory analysis of the urinary metabolome using 1H NMR spectroscopy of the U.S. samples identified 29 molecular species, clustered in interconnecting metabolic pathways, that were significantly associated (P = 1.5 × 10−5 to 2.0 × 10−36) with body mass index (BMI); 25 of these species were also found in the UK validation cohort. We found multiple associations between urinary metabolites and BMI including urinary glycoproteins and N-acetyl neuraminate (related to renal function), trimethylamine, dimethylamine, 4-cresyl sulfate, phenylacetylglutamine and 2-hydroxyisobutyrate (gut microbial co-metabolites), succinate and citrate (tricarboxylic acid cycle intermediates), ketoleucine and the ketoleucine/leucine ratio (linked to skeletal muscle mitochondria and branched-chain amino acid metabolism), ethanolamine (skeletal muscle turnover), and 3-methylhistidine (skeletal muscle turnover and meat intake). We mapped the multiple BMI-metabolite relationships as part of an integrated systems network that describes the connectivities between the complex pathway and compartmental signatures of human adiposity.

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

Liebeke M, Bruford MW, Donnelly RK, Ebbels TM, Hao J, Kille P, Lahive E, Madison RM, Morgan AJ, Pinto-Juma GA, Spurgeon DJ, Svendsen C, Bundy JGet al., 2014, Identifying biochemical phenotypic differences between cryptic species., Biology Letters, Vol: 10, ISSN: 1744-9561

Molecular genetic methods can distinguish divergent evolutionary lineages in what previously appeared to be single species, but it is not always clear what functional differences exist between such cryptic species. We used a metabolomic approach to profile biochemical phenotype (metabotype) differences between two putative cryptic species of the earthworm Lumbricus rubellus. There were no straightforward metabolite biomarkers of lineage, i.e. no metabolites that were always at higher concentration in one lineage. Multivariate methods, however, identified a small number of metabolites that together helped distinguish the lineages, including uncommon metabolites such as Nε-trimethyllysine, which is not usually found at high concentrations. This approach could be useful for characterizing functional trait differences, especially as it is applicable to essentially any species group, irrespective of its genome sequencing status.

Journal article

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

In a recent article, we derived a probability distribution that was shown toclosely approximate that of the data produced by liquid chromatography time-of-flightmass spectrometry (LC/TOFMS) instruments employing time-to-digital converters(TDCs) as part of their detection system. The approach of formulating detailed andhighly accurate mathematical models of LC/MS data via probability distributions thatare parameterized by quantities of analytical interest does not appear to have beenfully explored before. However, we believe it could lead to a statistically rigorousframework for addressing many of the data analytical problems that arise in LC/MSstudies. In this article, we present new procedures for correcting for TDC saturationusing such an approach and demonstrate that there is potential for significantimprovements in the effective dynamic range of TDC-based mass spectrometers, which could make them muchmore competitive with the alternative analog-to-digital converters (ADCs). The degree of improvement dependson our ability to generate mass and chromatographic peaks that conform to known mathematical functions andour ability to accurately describe the state of the detector dead time—tasks that may be best addressed throughengineering efforts

Journal article

Tzoulaki I, Ebbels TMD, Valdes A, Elliott P, Ioannidis JPAet al., 2014, Design and Analysis of Metabolomics Studies in Epidemiologic Research: A Primer on -Omic Technologies, AMERICAN JOURNAL OF EPIDEMIOLOGY, Vol: 180, Pages: 129-139, ISSN: 0002-9262

Journal article

Hao J, Liebeke M, Astle W, De Iorio M, Bundy JG, Ebbels TMDet 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

Journal article

Milner JJ, Wang J, Sheridan PA, Ebbels T, Beck MA, Saric Jet 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

Journal article

Valcarcel B, Ebbels TMD, Kangas AJ, Soininen P, Elliot P, Ala-Korpela M, Jarvelin M-R, de Iorio Met al., 2014, Genome metabolome integrated network analysis to uncover connections between genetic variants and complex traits: an application to obesity, Journal of the Royal Society Interface, Vol: 11, ISSN: 1742-5689

Current studies of phenotype diversity by genome-wide association studies(GWAS) are mainly focused on identifying genetic variants that influencelevel changes of individual traits without considering additional alterations atthe system-level. However, in addition to level alterations of single phenotypes,differences in association between phenotype levels are observed across differentphysiological states. Such differences in molecular correlations betweenstates can potentially reveal information about the system state beyond thatreported by changes in mean levels alone. In this study, we describe a novelmethodological approach, which we refer to as genome metabolome integratednetwork analysis (GEMINi) consisting of a combination of correlation networkanalysis and genome-wide correlation study. The proposed methodologyexploits differences in molecular associations to uncover genetic variantsinvolved in phenotype variation. We test the performance of the GEMINiapproach in a simulation study and illustrate its use in the context of obesity anddetailed quantitative metabolomics data on systemic metabolism. Applicationof GEMINi revealed a set of metabolic associations which differ betweennormal and obese individuals. While no significant associations were foundbetween genetic variants and body mass index using a standard GWASapproach, further investigation of the identified differences in metabolic associationrevealed a number of loci, several of which have been previouslyimplicated with obesity-related processes. This study highlights the advantageof using molecular associations as an alternative phenotype when studying thegenetic basis of complex traits and diseases

Journal article

Salamanca BV, Ebbels TMD, De Iorio M, 2014, Variance and covariance heterogeneity analysis for detection of metabolites associated with cadmium exposure, STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY, Vol: 13, Pages: 191-201, ISSN: 2194-6302

Journal article

Stamler J, Brown IJ, Yap IKS, Chan Q, Wijeyesekera A, Garcia-Perez I, Chadeau-Hyam M, Ebbels TMD, De Iorio M, Posma J, Daviglus ML, Carnethon M, Holmes E, Nicholson JK, Elliott Pet al., 2013, Dietary and Urinary Metabonomic Factors Possibly Accounting for Higher Blood Pressure of Black Compared With White Americans Results of International Collaborative Study on Macro-/Micronutrients and Blood Pressure, HYPERTENSION, Vol: 62, Pages: 1074-1080, ISSN: 0194-911X

Journal article

Ebbels T, Dunn W, 2013, Report on the 9th Annual International Conference of the Metabolomics Society, METABOLOMICS, Vol: 9, Pages: 935-937, ISSN: 1573-3882

Journal article

, 2013, The continuing growth and development of YOUR metabolomics society, Metabolomics, Vol: 9, Pages: 529-531, ISSN: 1573-3882

Journal article

Liebeke M, Hao J, Ebbels TMD, Bundy JGet al., 2013, Combining Spectral Ordering with Peak Fitting for One-Dimensional NMR Quantitative Metabolomics, ANALYTICAL CHEMISTRY, Vol: 85, Pages: 4605-4612, ISSN: 0003-2700

Journal article

Blazquez M, Carretero A, Ellis JK, Athersuch TJ, Cavill R, Ebbels TMD, Keun HC, Castell JV, Lahoz A, Bort Ret al., 2013, A Combination of Transcriptomics and Metabolomics Uncovers Enhanced Bile Acid Biosynthesis in HepG2 Cells Expressing CCAAT/Enhancer-Binding Protein β (C/EBPβ), Hepatocyte Nuclear Factor 4α (HNF4α), and Constitutive Androstane Receptor (CAR), Journal of proteome research, Pages: 130515154744008-130515154744008

Journal article

Cantor GH, Beckonert O, Bollard ME, Keun HC, Ebbels TMD, Antti H, Wijsman JA, Bible RH, Breau AP, Cockerell GL, Holmes E, Lindon JC, Nicholson JKet al., 2013, Integrated Histopathological and Urinary Metabonomic Investigation of the Pathogenesis of Microcystin-LR Toxicosis, VETERINARY PATHOLOGY, Vol: 50, Pages: 159-171, ISSN: 0300-9858

Journal article

Astle W, De Iorio M, Richardson S, Stephens D, Ebbels Tet al., 2012, A Bayesian Model of NMR Spectra for the Deconvolution and Quantification of Metabolites in Complex Biological Mixtures, JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, Vol: 107, Pages: 1259-1271, ISSN: 0162-1459

Journal article

Vinken M, Maes M, Cavill R, Valkenborg D, Ellis JK, Decrock E, Leybaert L, Staes A, Gevaert K, Oliveira AG, Menezes GB, Cogliati B, Dagli MLZ, Ebbels TMD, Witters E, Keun HC, Vanhaecke T, Rogiers Vet al., 2012, Proteomic and metabolomic responses to connexin43 silencing in primary hepatocyte cultures, Archives of Toxicology, Vol: 87, Pages: 883-894

Journal article

Posma JM, Garcia-Perez I, De Iorio M, Lindon JC, Elliott P, Holmes E, Ebbels TMD, Nicholson JKet al., 2012, Subset Optimization by Reference Matching (STORM): An optimized statistical approach for recovery of metabolic biomarker structural information from ¹H NMR spectra of biofluids, Analytical Chemistry, Vol: 84, Pages: 10694-10701, ISSN: 0003-2700

We describe a new multivariate statistical approach to recover metabolite structure information from multiple 1H NMR spectra in population sample sets. SubseT Optimization by Reference Matching (STORM) was developed to select subsets of 1H NMR spectra that contain specific spectroscopic signatures of biomarkers differentiating between different human populations. STORM aims to improve the visualization of structural correlations in spectroscopic data using these reduced spectral subsets containing smaller numbers of samples than the number of variables (n<<p). We have used ‘statistical shrinkage’ to limit the number of false positive associations and to simplify the overall interpretation of the auto-correlation matrix. The STORM approach has been applied to findings from an on-going human Metabolome-Wide Association study on Body Mass Index to identify a biomarker metabolite present in a subset of the population. Moreover, we have shown how STORM improves the visualization of more abundant NMR peaks compared to a previously published method (STOCSY). STORM is a useful new tool for biomarker discovery in the ‘omic’ sciences that has a widespread applicability. It can be applied to any type of data, provided that there is interpretable correlation among variables, and can also be applied to data with more than 1 dimension (e.g. 2D-NMR spectra).

Journal article

Hao J, Astle W, De Iorio M, Ebbels TMDet al., 2012, BATMAN-an R package for the automated quantification of metabolites from nuclear magnetic resonance spectra using a Bayesian model, BIOINFORMATICS, Vol: 28, Pages: 2088-2090, ISSN: 1367-4803

Journal article

Hao J, Astle W, De Iorio M, Ebbels TMDet al., 2012, BATMAN - The Bayesian AuTomated Metabolite Analyser for NMR Spectra

Software

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