The Functional Genomics Experiment model (FuGE): an extensible framework for standards in functional genomics.

Authors Organisations
  • Andrew R. Jones(Author)
  • Michael Miller(Author)
  • Ruedi Aebersold(Author)
  • Rolf Apweiler(Author)
  • Catherine A. Ball(Author)
  • Alvis Brazma(Author)
  • James DeGreef(Author)
  • Nigel Hardy(Author)
  • Henning Hermjakob(Author)
  • Simon J. Hubbard(Author)
  • Peter Hussey(Author)
  • Mark Igra(Author)
  • Helen Jenkins(Author)
  • Randall K. Julian Jr(Author)
  • Kent Laursen(Author)
  • Stephen G. Oliver(Author)
  • Norman W. Paton(Author)
  • Susanna-Assunta Sansone(Author)
  • Ugis Sarkans(Author)
  • Christian J. Stoeckert Jr(Author)
  • Chris F. Taylor(Author)
  • Patricia L. Whetzel(Author)
  • Joseph A. White(Author)
  • Paul Spellman(Author)
  • Angel Pizarro(Author)
Type Article
Original languageEnglish
Pages (from-to)1127-1133
Number of pages7
JournalNature Biotechnology
Publication statusPublished - 05 Oct 2007
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The Functional Genomics Experiment data model (FuGE) has been developed to facilitate convergence of data standards for high-throughput, comprehensive analyses in biology. FuGE models the components of an experimental activity that are common across different technologies, including protocols, samples and data. FuGE provides a foundation for describing entire laboratory workflows and for the development of new data formats. The Microarray Gene Expression Data society and the Proteomics Standards Initiative have committed to using FuGE as the basis for defining their respective standards, and other standards groups, including the Metabolomics Standards Initiative, are evaluating FuGE in their development efforts. Adoption of FuGE by multiple standards bodies will enable uniform reporting of common parts of functional genomics workflows, simplify data-integration efforts and ease the burden on researchers seeking to fulfill multiple minimum reporting requirements. Such advances are important for transparent data management and mining in functional genomics and systems biology.