Graduate School Experimental Plant Sciences, Wageningen, The Netherlands.
Anal Chim Acta. 2011 Oct 31;705(1-2):56-63. doi: 10.1016/j.aca.2011.03.050. Epub 2011 Apr 13.
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.
在后基因组时代，高通量技术已经在转录组学、代谢组学和蛋白质组学等领域带来了数据的大量收集，因此，大量的数据已经变得可用。然而，为了推进作物育种，这些组学数据集与表型性状的整合仍然存在问题。我们已经从二倍体马铃薯群体的块茎中获得了全群体的基因表达和代谢物（LC-MS）数据，并提出了一种新的方法来研究各种组学数据集，以构建整合基因表达、代谢物和表型性状的网络。我们使用随机森林回归选择与马铃薯块茎肉色和酶促变色相关的代谢物和转录本的子集。网络重建导致了与类胡萝卜素生物合成途径相关的已知和未表征的代谢物与基因的整合。我们表明，这种方法能够构建与已知和未知成分以及代谢物途径相关的有意义的网络。
