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Metabolomics as a hypothesis-generating functional genomics tool for the annotation of Arabidopsis thaliana genes of “unknown function”
Frontiers in Plant Science
  • Stephanie M. Quanbeck, Iowa State University
  • Libuse Brachova, Iowa State University
  • Alexis A. Campbell, Iowa State University
  • Xin Guan, Iowa State University
  • Ann Perera, Iowa State University
  • Kun He, Carnegie Institution for Science
  • Seung Y. Rhee, Carnegie Institution for Scienec
  • Preeti Bais, Iowa State University
  • Julie A. Dickerson, Iowa State University
  • Philip M. Dixon, Iowa State University
  • Gert Wohlgemuth, University of California, Davis
  • Oliver Fiehn, University of California, Davis
  • Lenore Barkan, Washington State University
  • Iris Lange, Washington State University
  • B. Markus Lange, Washington State University
  • Insuk Lee, Yonsei University
  • Diego Cortes, Virginia Commonwealth University
  • Carolina Salazar, University of North Texas
  • Joel Shuman, Virginia Polytechnic Institute and State University
  • Vladimir Shulaev, University of North Texas
  • David V. Huhman, The Samuel Roberts Noble Foundation
  • Lloyd W. Sumner, The Samuel Roberts Noble Foundation
  • Mary R. Roth, Kansas State University
  • Ruth Welti, Kansas State University
  • Hilal Ilarslan, Kansas State University
  • Eve S. Wurtele, Iowa State University
  • Basil J. Nikolau, Iowa State University
Document Type
Article
Publication Version
Published Version
Publication Date
2-10-2012
DOI
10.3389/fpls.2012.00015
Abstract
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.
Comments

This article is from Front. Plant Sci. 3:15. doi: 10.3389/fpls.2012.00015. Posted with permission.

Rights
© 2012 Quanbeck, Brachova, Campbell, Guan, Perera, He, Rhee, Bais, Dickerson, Dixon, Wohlgemuth, Fiehn, Barkan, Lange, Lange, Lee, Cortes, Salazar, Shuman, Shulaev, Huhman, Sumner, Roth, Welti, Ilarslan, Wurtele and Nikolau. This is an open-access article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits non-commercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.
Copyright Owner
Quanbeck, Brachova, Campbell, Guan, Perera, He, Rhee, Bais, Dickerson, Dixon, Wohlgemuth, Fiehn, Barkan, Lange, Lange, Lee, Cortes, Salazar, Shuman, Shulaev, Huhman, Sumner, Roth, Welti, Ilarslan, Wurtele and Nikolau
Language
en
File Format
application/pdf
Citation Information
Stephanie M. Quanbeck, Libuse Brachova, Alexis A. Campbell, Xin Guan, et al.. "Metabolomics as a hypothesis-generating functional genomics tool for the annotation of Arabidopsis thaliana genes of “unknown function”" Frontiers in Plant Science Vol. 3 (2012) p. 15
Available at: http://works.bepress.com/basil-nikolau/24/