MeltDB: a software platform for the analysis and integration of metabolomics experiment data

Neuweger, Heiko; Albaum, Stefan P.; Dondrup, Michael; Persicke, Marcus; Watt, Tony; Niehaus, Karsten; Stoye, Jens; Goesmann, Alexander

doi:10.1093/bioinformatics/btn452

Cited by 93 publications

(70 citation statements)

References 31 publications

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“…Despite being a stand-alone application, it is able to communicate with the well-established genome annotation software GenDB (c) [77], which can be complemented with SAMS for the analysis of shorter DNA sequence data [269]. Specialized tools facilitate the genome-based interpretation of microarray-based transcriptome data (d; EMMA; [129]), proteome data (e; QuPE; [172]), and gas chromatography-mass spectrometry (GC-MS)-based metabolome data (f; MeltDB; [181]). Recently, in addition, the ALLocator software (g) became available for liquid chromatography (LC)-MS-based metabolite analyses [184], as well as applications for enhanced data visualization (h; ProMeTra; [172]) and for the automated generation of metabolic networks in Systems Biology Markup Language (SBML) format (i; CARMEN; [199]).…”

Section: Genomic Basicsmentioning

confidence: 99%

“…The web-based software platform MeltDB (Fig. 1, f) has been evaluated for the analysis of Xcc metabolomics data by means of GC-MS [180][181][182]. This software tool supports storage, sharing, analysis and integration of metabolomics experiments and mapping of the measured metabolites onto the metabolic pathway maps provided by an integrated KEGG [183] database.…”

Section: Metabolomicsmentioning

confidence: 99%

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Systems and synthetic biology perspective of the versatile plant-pathogenic and polysaccharide-producing bacterium Xanthomonas campestris

et al. 2017

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Bacteria of the genus Xanthomonas are a major group of plant pathogens. They are hazardous to important crops and closely related to human pathogens. Being collectively a major focus of molecular phytopathology, an increasing number of diverse and intricate mechanisms are emerging by which they communicate, interfere with host signalling and keep competition at bay. Interestingly, they are also biotechnologically relevant polysaccharide producers. Systems biotechnology techniques have revealed their central metabolism and a growing number of remarkable features. Traditional analyses of Xanthomonas metabolism missed the Embden-Meyerhof-Parnas pathway (glycolysis) as being a route by which energy and molecular building blocks are derived from glucose. As a consequence of the emerging full picture of their metabolism process, xanthomonads were discovered to have three alternative catabolic pathways and they use an unusual and reversible phosphofructokinase as a key enzyme. In this review, we summarize the synthetic and systems biology methods and the bioinformatics tools applied to reconstruct their metabolic network and reveal the dynamic fluxes within their complex carbohydrate metabolism. This is based on insights from omics disciplines; in particular, genomics, transcriptomics, proteomics and metabolomics. Analysis of high-throughput omics data facilitates the reconstruction of organism-specific large-and genome-scale metabolic networks. Reconstructed metabolic networks are fundamental to the formulation of metabolic models that facilitate the simulation of actual metabolic activities under specific environmental conditions. SYSTEMS BIOLOGYIn recent years, systems and synthetic biology have substantially increased our understanding of many processes in life sciences at molecular and cellular levels [1][2][3]. Systems biology intends to understand the cell from a system-wide perspective. For over 100 years, life sciences have aimed at elucidating the details of molecular processes in organisms. In systems biology, scientists have started to inter-relate this data on a large scale in order to analyse the interactions of all elements [4,5], and thus initiate the decoding of entire systems, aiming at their quantitative understanding. This became possible with the development of high-throughput techniques associated with diverse computational methods and the availability of faster computing. Fundamental highthroughput methods are now routinely available in the fields of genomics, transcriptomics, metabolomics and proteomics, while additional specialized branches of omics disciplines have been developed, such as lipidomics [6][7][8], glycomics [9][10][11] and 13 C fluxomics [12,13].

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Section: Genomic Basicsmentioning

confidence: 99%

Section: Metabolomicsmentioning

confidence: 99%

Systems and synthetic biology perspective of the versatile plant-pathogenic and polysaccharide-producing bacterium Xanthomonas campestris

et al. 2017

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“…This task is usually not handled by the frameworks described in this chapter. Many web-based analysis tools allow to put the data into a larger context, by providing name-or id-based mapping of the experimentally determined metabolite concentrations onto biochemical pathways like MetaboAnalyst , MetabolomeExpress (Carroll et al, 2010), or MeltDB (Neuweger et al, 2008). The latter allows association of the metabolomics data with other results for the same subjects under study or with results from other "omics" experiments on the same target subjects, but this is beyond the scope of the frameworks presented herein.…”

Section: Evaluation Of Hypothesismentioning

confidence: 99%

“…However, other tools aim to integrate a more complete metabolomics workflow including preprocessing, peakfinding, alignment and statistical analysis combined with pathway mapping information like MetaboAnalyst , MetabolomeExpress (Carroll et al, 2010), or MeltDB (Neuweger et al, 2008). These larger web-based frameworks integrate other functionality for time-course analysis , pathway mapping (Neuweger et al, 2009;Xia & Wishart, 2010a) and metabolite set enrichment analysis (Kankainen et al, 2011;Xia & Wishart, 2010b).…”

Section: Introductionmentioning

confidence: 99%

Generic Software Frameworks for GC-MS Based Metabolomics

Hoffmann¹,

Stoye²

2012

Metabolomics

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“…The accumulation of metabolomics data is therefore limited to a smaller scale than other omics fields such as genomics and transcriptomics (Kind et al 2009;Tohge and Fernie 2009). Many tools and systems for metabolome analysis have been developed to improve various analytical processes for gas chromatographymass spectrometry (GC-MS; Duran et al 2003;Jonsson et al 2005;Tikunov et al 2005;Broeckling et al 2006;Bunk et al 2006;Luedemann et al 2008;Neuweger et al 2008;Hiller et al 2009;Oishi et al 2009), liquid chromatography-mass spectrometry (LC-MS; Katajamaa et al 2006;Smith et al 2006;Sturm et al 2008) and capillary electrophoresis-mass spectrometry (CE-MS; Baran et al 2006;Morohashi et al 2007). However, throughput of comparative analysis of metabolome data, especially for quantitative differential analysis, is very low since there are many time-consuming processes.…”

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confidence: 99%

Improvement of the quantitative differential metabolome pipeline for gas chromatography-mass spectrometry data by automated reliable peak selection

Ara¹,

Sakurai²,

Tange³

et al. 2009

Plant Biotechnology

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Recent advances in metabolomics technology have enabled large-scale comprehensive analyses of metabolites, but the throughput of data processing of non-targeted, quantitative differential analyses is very low. It is crucial to solve this problem to generate biological hypotheses from a large-scale dataset. To improve the analysis of metabolite data, we focused on the processing of quantitative differential analysis after multiple peak alignment. We have developed a program named FAQuant that automatically selects reliable peaks from each chromatogram, quantifies the mean of peak intensity to compare between sample groups, and selects the peaks with differences in accumulation of metabolites. This program was incorporated into a quantitative differential metabolome pipeline as a module to improve the throughput of gas chromatography-mass spectrometry dataset analysis. As a result, the module incorporation largely reduced the total processing time. Furthermore, differential analysis of metabolites in soybean (Glycine max) cultivars was demonstrated by use of the system. This system might facilitate biological hypothesis generation from large-scale comparative metabolome analysis.Key words: Differential analysis, GC-TOF-MS, mass spectrometry, metabolomics, quantification.Plant Biotechnology 26, 445-449 (2009) Bioinformatics NoteAbbreviations: GC-TOF-MS, gas chromatography-time-of-flight-mass spectrometry; MST, mass spectral tag. This article can be found at

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MeltDB: a software platform for the analysis and integration of metabolomics experiment data

Abstract: The system is publicly available at http://meltdb.cebitec.uni-bielefeld.de.

Cited by 93 publications

References 31 publications

Systems and synthetic biology perspective of the versatile plant-pathogenic and polysaccharide-producing bacterium Xanthomonas campestris

Systems and synthetic biology perspective of the versatile plant-pathogenic and polysaccharide-producing bacterium Xanthomonas campestris

Generic Software Frameworks for GC-MS Based Metabolomics

Improvement of the quantitative differential metabolome pipeline for gas chromatography-mass spectrometry data by automated reliable peak selection

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