Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 362311
Title MetaNetwork: a computational protocol for the genetic study of metabolic networks
Author(s) Fu, J.; Swertz, M.A.; Keurentjes, J.J.B.; Jansen, R.C.
Source Nature protocols 2 (2007). - ISSN 1754-2189 - p. 685 - 694.
DOI https://doi.org/10.1038/nprot.2007.96
Department(s) Laboratory of Genetics
Laboratory of Plant Physiology
PRI Biometris
EPS-3
Publication type Refereed Article in a scientific journal
Publication year 2007
Keyword(s) quantitative trait loci - expression - arabidopsis - genomics - pathway - association - discovery - linkage - aracyc - yeast
Abstract We here describe the MetaNetwork protocol to reconstruct metabolic networks using metabolite abundance data from segregating populations. MetaNetwork maps metabolite quantitative trait loci (mQTLs) underlying variation in metabolite abundance in individuals of a segregating population using a two-part model to account for the often observed spike in the distribution of metabolite abundance data. MetaNetwork predicts and visualizes potential associations between metabolites using correlations of mQTL profiles, rather than of abundance profiles. Simulation and permutation procedures are used to assess statistical significance. Analysis of about 20 metabolite mass peaks from a mass spectrometer takes a few minutes on a desktop computer. Analysis of 2,000 mass peaks will take up to 4 days. In addition, MetaNetwork is able to integrate high-throughput data from subsequent metabolomics, transcriptomics and proteomics experiments in conjunction with traditional phenotypic data. This way MetaNetwork will contribute to a better integration of such data into systems biology.
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