A metabolome pipeline: from concept to data to knowledge

Brown, Marie; Dunn, Warwick B.; Ellis, David I.; Goodacre, Royston; Handl, Julia; Knowles, Joshua; O’Hagan, Steve; Spasić, Irena; Kell, Douglas B.

doi:10.1007/s11306-005-1106-4

Cited by 149 publications

(97 citation statements)

References 111 publications

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“…As MS measurements can provide quantitative data on thousands of features in a large number of samples, the discovery of significant features that differentiate certain conditions requires sophisticated statistical analysis (Brown et al 2005). Univariate and multivariate techniques are commonly applied to metabolome data sets (Liland 2011;Vinaixa et al 2012).…”

Section: Data Interpretationmentioning

confidence: 99%

Metabolomic Analysis of Schizosaccharomyces pombe: Sample Preparation, Detection, and Data Interpretation

Pluskal

Yanagida

2016

Cold Spring Harb Protoc

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Metabolomics is a modern field of chemical biology that strives to simultaneously quantify hundreds of cellular metabolites. Techniques for metabolomic analysis in Schizosaccharomyces pombe have only recently been developed. Here we introduce methods that provide a complete workflow for metabolomic analysis in S. pombe. Based on available literature, we estimate the yeast metabolome to comprise on the order of several thousand different metabolites. We discuss the feasibility of extraction and detection of such a large number of metabolites, and the influences of various parameters on the results. Among the parameters addressed are cell cultivation conditions, metabolite extraction techniques, and detection and quantification methods. Further, we provide recommendations on data management and data processing for metabolomic experiments, and describe possible pitfalls regarding the interpretation of metabolomic data. Finally, we briefly discuss potential future developments of this technique.

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Section: Data Interpretationmentioning

confidence: 99%

Metabolomic Analysis of Schizosaccharomyces pombe: Sample Preparation, Detection, and Data Interpretation

Pluskal

Yanagida

2016

Cold Spring Harb Protoc

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“…As was implicit almost from the beginning of such bioinformatic studies [96], the concept of the pipeline [97,98] or workflow [99][100][101][102] is now common in bioinformatics for the analysis of data. Here, the tools involved in the data analysis are stitched together using standardised environments or interfaces to form a workflow, after which they can then be enacted in a more or less automated manner.…”

Section: Integrating Metabolomics and Metabolic Modelling For Systemsmentioning

confidence: 99%

Systems biology, metabolic modelling and metabolomics in drug discovery and development

Kell

2006

Drug Discovery Today

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266

163

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Unlike signalling pathways, metabolic networks are subject to strict stoichiometric constraints. Metabolomics amplifies changes in the proteome, and represents more closely the phenotype of an organism. Recent advances enable the production (and computer-readable encoding as SBML) of metabolic network models reconstructed from genome sequences, as well as experimental measurements of much of the metabolome. There is increasing convergence between the number of human metabolites estimated via genomics ( 3000) and the number measured experimentally. It is thus both timely, and now possible, to bring these two approaches together as an integrated (if distributed) whole to help understand the genesis of metabolic biomarkers, the progress of disease, and the modes of action, efficacy, off-target effects and toxicity of pharmaceutical drugs. Systems biology and metabolic modelling in the 21st CenturyAlthough there are many individual definitions, most commentators (including this one [1,2]) take it that systems biology involves an iterative interplay between more or less high-throughput and high-content 'wet' experiments, technology development, theory and computational modelling, and that it is the involvement of computational modelling, in particular, in the process that sets systems biology apart from the more traditional and more reductionist molecular biology. Metabolomics illustrates this amply. There is also the view that the perceived decrease in the effectiveness of the target-based drug discovery process [3][4][5], including the still-high levels of attrition [6], means that we must move towards understanding organisms at something more akin to a whole-system level [7][8][9][10][11].The question then arises as to what part of a system one might first beneficially model? Although there has, unsurprisingly, been considerable interest in modelling the major signalling pathways (e.g. Refs [12,13]), there are several reasons why it is timely to turn our attention to the level of small molecule metabolism, which is the focus of this review. Metabolism is more discriminatingIt has long been known, and proven through the formalism of metabolic control analysis [7,10,[14][15][16], that whereas small changes in the concentrations of enzymes (and the transcripts that encode them) have only small effects on the fluxes through metabolic pathways, they have substantial effects on the concentrations of metabolic intermediates. Because the metabolome (nominally the concentrations of 'all' the metabolites measured in a system of interest [17]) is downstream of the proteome, it is thereby 'amplified' both in theory [18] and in practice [19,20] and represents a more sensitive level of organisation than do the macromolecular 'omes for understanding a complex biological system, and the changes in it that might be occasioned by disease or pharmaceutical intervention [21,22]. Metabolic reconstruction is now mature and timelyAn attractive feature for the purposes of modelling is that metabolism, in contrast to signalling pathways, ...

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“…In metabolomics, such a pipeline (largely illustrated rather shamelessly here with our own work) includes [43]:…”

Section: A Pipeline For Metabolomic Biomarkersmentioning

confidence: 99%

Metabolomic biomarkers: search, discovery and validation

Kell¹

2007

Expert Review of Molecular Diagnostics

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A metabolome pipeline: from concept to data to knowledge

Cited by 149 publications

References 111 publications

Metabolomic Analysis of Schizosaccharomyces pombe: Sample Preparation, Detection, and Data Interpretation

Metabolomic Analysis of Schizosaccharomyces pombe: Sample Preparation, Detection, and Data Interpretation

Systems biology, metabolic modelling and metabolomics in drug discovery and development

Metabolomic biomarkers: search, discovery and validation

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