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 409864
Title Data integration and network reconstruction with ~omics data using Random Forest regression in potato
Author(s) Acharjee, A.; Kloosterman, B.A.; Vos, R.C.H. de; Werij, J.S.; Bachem, C.W.B.; Visser, R.G.F.; Maliepaard, C.A.
Source Analytica Chimica Acta 705 (2011)1-2. - ISSN 0003-2670 - p. 56 - 63.
DOI http://dx.doi.org/10.1016/j.aca.2011.03.050
Department(s) Laboratory of Plant Breeding
PRI BIOS Applied Metabolic Systems
EPS-4
Publication type Refereed Article in a scientific journal
Publication year 2011
Keyword(s) mass-spectrometry - systems biology - microarray data - classification - metabolomics - proteomics - tomato - qtl - biomarkers - phenotypes
Abstract In the post-genomic era, high-throughput technologies have led to data collection in fields like transcriptomics, metabolomics and proteomics and, as a result, large amounts of data have become available. However, the integration of these ~omics data sets in relation to phenotypic traits is still problematic in order to advance crop breeding. We have obtained population-wide gene expression and metabolite (LC–MS) data from tubers of a diploid potato population and present a novel approach to study the various ~omics datasets to allow the construction of networks integrating gene expression, metabolites and phenotypic traits. We used Random Forest regression to select subsets of the metabolites and transcripts which show association with potato tuber flesh color and enzymatic discoloration. Network reconstruction has led to the integration of known and uncharacterized metabolites with genes associated with the carotenoid biosynthesis pathway. We show that this approach enables the construction of meaningful networks with regard to known and unknown components and metabolite pathways.
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