Visualizing plant metabolomic correlation networks using
  clique–metabolite matrices

Kose, Frank; Weckwerth, Wolfram; Linke, Thomas; Fiehn, Oliver

doi:10.1093/bioinformatics/17.12.1198

Cited by 151 publications

(97 citation statements)

References 29 publications

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“…influenced by increased or decreased fluxes through linear pathways, or transcription factors controlling different pathways simultaneously. Therefore, the complete data set was comprehensively analyzed for metabolic co-regulations using Pearson correlation assessments and applying a relevance threshold of r xy > 0.80 (Kose et al, 2001). Long tables of metabolite:metabolite correlations are resulting from such calculations, and a network overview may be given by graph visualization (Fig.…”

Section: Resultsmentioning

confidence: 99%

Metabolic networks of Cucurbita maxima phloem

2003

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Section: Resultsmentioning

confidence: 99%

Metabolic networks of Cucurbita maxima phloem

2003

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“…However, the information power of graph visualization rapidly decreases with increasing total numbers of edges and nodes. In a study on pairwise metabolitemetabolite correlations in a metabolomic data set, 1.5% of all theoretical correlations could be found as linear relationships (Kose et al, 2001). In order to maintain the structural information inherent in the resulting metabolic networks, graph visualization was performed in clique-metabolite matrices instead of using edges and nodes.…”

Section: Data Miningmentioning

confidence: 99%

“…Instead of losing information in the process of averaging metabolite levels, each metabolite profile represents a true and valid response of metabolism upon subtle (but unknown) changes in parameters of the system. By computing correlation coefficients of metabolite-metabolite plots, the detection of homeostatic regulation of metabolite ratios could be set onto a more rigid statistical basis (Arkin et al, 1997;Roessner et al, 2001;Kose et al, 2001). A computation of metabolic models based on a combination of co-response MCA theory and experimentally detected metabolomic correlations, however, remains to be shown.…”

Section: Modelling Based On Metabolic Flux Measurementsmentioning

confidence: 99%

Metabolomics — the link between genotypes and phenotypes

Fiehn

2002

Functional Genomics

1,611

1,337

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Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. In parallel to the terms 'transcriptome' and 'proteome', the set of metabolites synthesized by a biological system constitute its 'metabolome'. Yet, unlike other functional genomics approaches, the unbiased simultaneous identification and quantification of plant metabolomes has been largely neglected. Until recently, most analyses were restricted to profiling selected classes of compounds, or to fingerprinting metabolic changes without sufficient analytical resolution to determine metabolite levels and identities individually. As a prerequisite for metabolomic analysis, careful consideration of the methods employed for tissue extraction, sample preparation, data acquisition, and data mining must be taken. In this review, the differences among metabolite target analysis, metabolite profiling, and metabolic fingerprinting are clarified, and terms are defined. Current approaches are examined, and potential applications are summarized with a special emphasis on data mining and mathematical modelling of metabolism.

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“…However, these output sensitive algorithms tend not to perform as well as the worst case optimal algorithms in practice [40,13]. Other works on sequential MCE include Kose et al [24], who take a breadth first search approach, an external memory algorithm due to Cheng et al [7], and pruning strategies for enumerating large cliques, due to Modani and Dey [32].…”

Section: Related Workmentioning

confidence: 99%

“…Early works in the area of parallel MCE include Zhang et al [45] and Du et al [12]. Zhang et al developed an algorithm based on the Kose et al [24] algorithm. Since these algorithms are based on breadth first search, they are able to enumerate maximal cliques in increasing order of size, but this makes the memory requirements very large.…”

Section: Related Workmentioning

confidence: 99%

Mining maximal cliques from a large graph using MapReduce: Tackling highly uneven subproblem sizes

Svendsen

Mukherjee

Tirthapura

2015

Journal of Parallel and Distributed Computing

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We consider Maximal Clique Enumeration (MCE) from a large graph. A maximal clique is perhaps the most fundamental dense substructure in a graph, and MCE is an important tool to discover densely connected subgraphs, with numerous applications to data mining on web graphs, social networks, and biological networks. While effective sequential methods for MCE are known, scalable parallel methods for MCE are still lacking.We present a new parallel algorithm for MCE, Parallel Enumeration of Cliques using Ordering (PECO" role="presentation" style="box-sizing: border-box; display: inline-block; line-height: normal; font-size: 14.4px; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; direction: ltr; maxwidth: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative;">PECO), designed for the MapReduce framework. Unlike previous works, which required a post-processing step to remove duplicate and non-maximal cliques, PECO" role="presentation" style="boxsizing: border-box; display: inline-block; line-height: normal; font-size: 14.4px; word-spacing: normal; wordwrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; minwidth: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative;">PECOenumerates only maximal cliques with no duplicates. The key technical ingredient is a total ordering of the vertices of the graph which is used in a novel way to achieve a load balanced distribution of work, and to eliminate redundant work among processors. We implemented PECO" role="presentation" style="box-sizing: border-box; display: inline-block; line-height: normal; font-size: 14.4px; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px; margin: 0px; position: relative;">PECO on Hadoop MapReduce, and our experiments on a cluster show that the algorithm can effectively process a variety of large real-world graphs with millions of vertices and tens of millions of maximal cliques, and scales well with the degree of available parallelism. KeywordsGraph mining, Maximal clique enumeration, Enumeration algorithm, MapReduce, Hadoop, Parallel algorithm, Clique, Load balancing Disciplines Electrical and Computer EngineeringComments This is a manuscript of an article from Svendsen, Michael, Arko Provo Mukherjee, and Srikanta Tirthapura. "Mining maximal cliques from a large graph using mapreduce: Tackling highly uneven subproblem sizes. h i g h l i g h t s• Scalable method for enumerating maximal cliques in a graph using MapReduce.• Effective solution to load balancing.• Experimental evaluation of our solution on large real world graphs.• Outperforms previous MapReduce solutions by orders of magnitude. a r t i c l e i n f o b s t r a c tWe consider Maximal Clique Enumeration (MCE) from a large graph. A maximal clique is perhaps the most fundamental dense substru...

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Visualizing plant metabolomic correlation networks using clique–metabolite matrices

Abstract: kose@mpimp-golm.mpg.de

Cited by 151 publications

References 29 publications

Metabolic networks of Cucurbita maxima phloem

Metabolic networks of Cucurbita maxima phloem

Metabolomics — the link between genotypes and phenotypes

Mining maximal cliques from a large graph using MapReduce: Tackling highly uneven subproblem sizes

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