title: Decoding genes with coexpression networks and metabolomics - 'majority report by precogs'.
abstract: Following the sequencing of whole genomes of model plants, high-throughput decoding of gene function is a major challenge in modern plant biology. In view of remarkable technical advances in transcriptomics and metabolomics, integrated analysis of these 'omics' by data-mining informatics is an excellent tool for prediction and identification of gene function, particularly for genes involved in complicated metabolic pathways. The availability of Arabidopsis public transcriptome datasets containing data of >1000 microarrays reinforces the potential for prediction of gene function by transcriptome coexpression analysis. Here, we review the strategy of combining transcriptome and metabolome as a powerful technology for studying the functional genomics of model plants and also crop and medicinal plants.
authors.affiliation: RIKEN Plant Science Center, Tsurumi-ku, Yokohama 230-0045, Japan. ksaito@faculty.chiba-u.jp
authors.forename: Kazuki
authors.initials: K
authors.lastname: Saito
authors.forename: Masami Y
authors.initials: MY
authors.lastname: Hirai
authors.forename: Keiko
authors.initials: K
authors.lastname: Yonekura-Sakakibara
mesh_terms: D019295:Computational Biology; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D023281:Genomics / Q000379:methods*; D010944:Plants / Q000235:genetics* / Q000378:metabolism*; D049490:Systems Biology