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Although several studies have assessed topologies in metabolomic correlation networks, it remains unclear whether highly connected metabolites in these networks have specific functions in known tissue- and\/or genotype-dependent biochemical pathways.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>In our study of metabolite profiles we subjected root tissues to gas chromatography-time-of-flight\/mass spectrometry (GC-TOF\/MS) and used published information on the aerial parts of 3 <jats:italic>Arabidopsis<\/jats:italic> genotypes, Col-0 wild-type, <jats:italic>methionine over-accumulation 1<\/jats:italic> (<jats:italic>mto1<\/jats:italic>), and <jats:italic>transparent testa4<\/jats:italic> (<jats:italic>tt4<\/jats:italic>) to compare systematically the metabolomic correlations in samples of roots and aerial parts. We then applied graph clustering to the constructed correlation networks to extract densely connected metabolites and evaluated the clusters by biochemical-pathway enrichment analysis. We found that the number of significant correlations varied by tissue and genotype and that the obtained clusters were significantly enriched for metabolites included in biochemical pathways.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>We demonstrate that the graph-clustering approach identifies tissue- and\/or genotype-dependent metabolomic clusters related to the biochemical pathway. Metabolomic correlations complement information about changes in mean metabolite levels and may help to elucidate the organization of metabolically functional modules.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1752-0509-5-1","type":"journal-article","created":{"date-parts":[[2011,1,5]],"date-time":"2011-01-05T20:03:04Z","timestamp":1294257784000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":150,"title":["Metabolomic correlation-network modules in Arabidopsis based on a graph-clustering approach"],"prefix":"10.1186","volume":"5","author":[{"given":"Atsushi","family":"Fukushima","sequence":"first","affiliation":[]},{"given":"Miyako","family":"Kusano","sequence":"additional","affiliation":[]},{"given":"Henning","family":"Redestig","sequence":"additional","affiliation":[]},{"given":"Masanori","family":"Arita","sequence":"additional","affiliation":[]},{"given":"Kazuki","family":"Saito","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2011,1,1]]},"reference":[{"issue":"9","key":"591_CR1","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1016\/j.tplants.2004.07.004","volume":"9","author":"RJ Bino","year":"2004","unstructured":"Bino RJ, Hall RD, Fiehn O, Kopka J, Saito K, Draper J, Nikolau BJ, Mendes P, Roessner-Tunali U, Beale MH, et al.: Potential of metabolomics as a functional genomics tool. 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