Data integration and network reconstruction with ~omics data using Random Forest regression in potato.

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.