TY - JOUR AU - Quanbeck, Stephanie AU - Brachova, Libuse AU - Campbell, Alexis AU - Guan, Xin AU - Perera, Ann AU - He, Kun AU - Rhee, Seung AU - Bais, Preeti AU - Dickerson, Julie AU - Dixon, Philip AU - Wohlgemuth, Gert AU - Fiehn, Oliver AU - Barkan, Lenore AU - Lange, B. Markus AU - Lee, Insuk AU - Cortes, Diego AU - Salazar, Carolina AU - Shuman, Joel AU - Shulaev, Vladimir AU - Huhman, David AU - Sumner, Lloyd AU - Roth, Mary AU - Welti, Ruth AU - Ilarslan, Hilal AU - Wurtele, Eve AU - Nikolau, Basil PY - 2012 M3 - Technology Report TI - Metabolomics as a Hypothesis-Generating Functional Genomics Tool for the Annotation of Arabidopsis thaliana Genes of “Unknown Function” JO - Frontiers in Plant Science UR - https://www.frontiersin.org/articles/10.3389/fpls.2012.00015 VL - 3 SN - 1664-462X N2 - Metabolomics is the methodology that identifies and measures global pools of small molecules (of less than about 1,000 Da) of a biological sample, which are collectively called the metabolome. Metabolomics can therefore reveal the metabolic outcome of a genetic or environmental perturbation of a metabolic regulatory network, and thus provide insights into the structure and regulation of that network. Because of the chemical complexity of the metabolome and limitations associated with individual analytical platforms for determining the metabolome, it is currently difficult to capture the complete metabolome of an organism or tissue, which is in contrast to genomics and transcriptomics. This paper describes the analysis of Arabidopsis metabolomics data sets acquired by a consortium that includes five analytical laboratories, bioinformaticists, and biostatisticians, which aims to develop and validate metabolomics as a hypothesis-generating functional genomics tool. The consortium is determining the metabolomes of Arabidopsis T-DNA mutant stocks, grown in standardized controlled environment optimized to minimize environmental impacts on the metabolomes. Metabolomics data were generated with seven analytical platforms, and the combined data is being provided to the research community to formulate initial hypotheses about genes of unknown function (GUFs). A public database (www.PlantMetabolomics.org) has been developed to provide the scientific community with access to the data along with tools to allow for its interactive analysis. Exemplary datasets are discussed to validate the approach, which illustrate how initial hypotheses can be generated from the consortium-produced metabolomics data, integrated with prior knowledge to provide a testable hypothesis concerning the functionality of GUFs. ER -