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Use of reconstituted metabolic networks to assist in metabolomic data visualization and mining.

Jourdan F, Cottret L, Huc L, Wildridge D, Scheltema R, Hillenweck A, Barrett MP, Zalko D, Watson DG, Debrauwer L - Metabolomics (2010)

Bottom Line: However, important information can be gleaned even from sparse datasets, which can be facilitated by placing the results within the context of known metabolic networks.Biologically relevant metabolic sub-networks were extracted from both datasets.ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0196-9) contains supplementary material, which is available to authorized users.

View Article: PubMed Central - PubMed

ABSTRACT
Metabolomics experiments seldom achieve their aim of comprehensively covering the entire metabolome. However, important information can be gleaned even from sparse datasets, which can be facilitated by placing the results within the context of known metabolic networks. Here we present a method that allows the automatic assignment of identified metabolites to positions within known metabolic networks, and, furthermore, allows automated extraction of sub-networks of biological significance. This latter feature is possible by use of a gap-filling algorithm. The utility of the algorithm in reconstructing and mining of metabolomics data is shown on two independent datasets generated with LC-MS LTQ-Orbitrap mass spectrometry. Biologically relevant metabolic sub-networks were extracted from both datasets. Moreover, a number of metabolites, whose presence eluded automatic selection within mass spectra, could be identified retrospectively by virtue of their inferred presence through gap filling. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0196-9) contains supplementary material, which is available to authorized users.

No MeSH data available.


aTrypanosoma brucei metabolic network (derived from the BioCyc database) with identified metabolites highlighted in green. b Sub-network extracted by our method where all the identified metabolites are present (green nodes), reactions and metabolites, whose presence is inferred based on the BioCyc reconstruction, were added to create a pseudo-complete and descriptive sub-network (smaller nodes)
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Fig2: aTrypanosoma brucei metabolic network (derived from the BioCyc database) with identified metabolites highlighted in green. b Sub-network extracted by our method where all the identified metabolites are present (green nodes), reactions and metabolites, whose presence is inferred based on the BioCyc reconstruction, were added to create a pseudo-complete and descriptive sub-network (smaller nodes)

Mentions: The representation proposed by the BioCyc Cellular Overview is an increasingly popular style of visualising individual pathways in many organisms (Paley and Karp 2006) (Fig. 1a, a′). Within the BioCyc architecture, nodes belonging to different pathways are duplicated, since the objective of that visualisation is to prioritise the pathway as the central feature (see (Bourqui et al. 2007) for a discussion on node duplication). Such duplication of nodes is advantageous when considering individual pathways, but fails to capture linkage information of metabolites belonging to multiple pathways. Insight into the whole network is gained by removing duplication and connecting metabolites to all possible neighbours. However, this results in a much higher complexity as can be seen in Fig. 1, where the nodes representing identified metabolites were coloured green (Fig. 1b, b′). Zooming into local parts of the total representation reveals a dense mesh of edges, caused by the high degree of connectivity across the network (Jeong et al. 2000) (Fig. 1c, c′). This complexity confounds the objective of creating interpretable metabolomics networks. A major aim therefore, is to reduce the complexity of the visual representations of metabolic networks, thus enabling extraction of relevant information. To do so, we have developed a method that constructs a sub-network containing the selected metabolites, regardless of their occurrence in disparate metabolic pathways. Fig. 2 shows the result of this method (note that all of the identified metabolites are present in the drawing and most of them are connected).Fig. 1


Use of reconstituted metabolic networks to assist in metabolomic data visualization and mining.

Jourdan F, Cottret L, Huc L, Wildridge D, Scheltema R, Hillenweck A, Barrett MP, Zalko D, Watson DG, Debrauwer L - Metabolomics (2010)

aTrypanosoma brucei metabolic network (derived from the BioCyc database) with identified metabolites highlighted in green. b Sub-network extracted by our method where all the identified metabolites are present (green nodes), reactions and metabolites, whose presence is inferred based on the BioCyc reconstruction, were added to create a pseudo-complete and descriptive sub-network (smaller nodes)
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Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2874485&req=5

Fig2: aTrypanosoma brucei metabolic network (derived from the BioCyc database) with identified metabolites highlighted in green. b Sub-network extracted by our method where all the identified metabolites are present (green nodes), reactions and metabolites, whose presence is inferred based on the BioCyc reconstruction, were added to create a pseudo-complete and descriptive sub-network (smaller nodes)
Mentions: The representation proposed by the BioCyc Cellular Overview is an increasingly popular style of visualising individual pathways in many organisms (Paley and Karp 2006) (Fig. 1a, a′). Within the BioCyc architecture, nodes belonging to different pathways are duplicated, since the objective of that visualisation is to prioritise the pathway as the central feature (see (Bourqui et al. 2007) for a discussion on node duplication). Such duplication of nodes is advantageous when considering individual pathways, but fails to capture linkage information of metabolites belonging to multiple pathways. Insight into the whole network is gained by removing duplication and connecting metabolites to all possible neighbours. However, this results in a much higher complexity as can be seen in Fig. 1, where the nodes representing identified metabolites were coloured green (Fig. 1b, b′). Zooming into local parts of the total representation reveals a dense mesh of edges, caused by the high degree of connectivity across the network (Jeong et al. 2000) (Fig. 1c, c′). This complexity confounds the objective of creating interpretable metabolomics networks. A major aim therefore, is to reduce the complexity of the visual representations of metabolic networks, thus enabling extraction of relevant information. To do so, we have developed a method that constructs a sub-network containing the selected metabolites, regardless of their occurrence in disparate metabolic pathways. Fig. 2 shows the result of this method (note that all of the identified metabolites are present in the drawing and most of them are connected).Fig. 1

Bottom Line: However, important information can be gleaned even from sparse datasets, which can be facilitated by placing the results within the context of known metabolic networks.Biologically relevant metabolic sub-networks were extracted from both datasets.ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0196-9) contains supplementary material, which is available to authorized users.

View Article: PubMed Central - PubMed

ABSTRACT
Metabolomics experiments seldom achieve their aim of comprehensively covering the entire metabolome. However, important information can be gleaned even from sparse datasets, which can be facilitated by placing the results within the context of known metabolic networks. Here we present a method that allows the automatic assignment of identified metabolites to positions within known metabolic networks, and, furthermore, allows automated extraction of sub-networks of biological significance. This latter feature is possible by use of a gap-filling algorithm. The utility of the algorithm in reconstructing and mining of metabolomics data is shown on two independent datasets generated with LC-MS LTQ-Orbitrap mass spectrometry. Biologically relevant metabolic sub-networks were extracted from both datasets. Moreover, a number of metabolites, whose presence eluded automatic selection within mass spectra, could be identified retrospectively by virtue of their inferred presence through gap filling. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-009-0196-9) contains supplementary material, which is available to authorized users.

No MeSH data available.