title: Transcriptome data modeling for targeted plant metabolic engineering.
abstract: The massive data generated by omics technologies require the power of bioinformatics, especially network analysis, for data mining and doing data-driven biology. Gene coexpression analysis, a network approach based on comprehensive gene expression data using microarrays, is becoming a standard tool for predicting gene function and elucidating the relationship between metabolic pathways. Differential and comparative gene coexpression analyses suggest a change in coexpression relationships and regulators controlling common and/or specific biological processes. In conjunction with the newly emerging genome editing technology, network analysis integrated with other omics data should pave the way for robust and practical plant metabolic engineering.
authors.affiliation: RIKEN Plant Science Center, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama 230-0045 Japan.
authors.forename: Keiko
authors.initials: K
authors.lastname: Yonekura-Sakakibara
authors.forename: Atsushi
authors.initials: A
authors.lastname: Fukushima
authors.forename: Kazuki
authors.initials: K
authors.lastname: Saito
mesh_terms: D001709:Biotechnology; D019295:Computational Biology; D018556:Crops, Agricultural / Q000235:genetics / Q000378:metabolism; D053263:Gene Regulatory Networks / Q000235:genetics; D018745:Genome, Plant / Q000235:genetics; D060847:Metabolic Engineering; D053858:Metabolic Networks and Pathways / Q000235:genetics; D010944:Plants / Q000235:genetics* / Q000378:metabolism*; D059467:Transcriptome