Perspective abstract


Nature Biotechnology 25, 1127 - 1133 (2007)
Published online: 5 October 2007 | doi:10.1038/nbt1347

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

Andrew R Jones1,2,16, Michael Miller3, Ruedi Aebersold4,5, Rolf Apweiler6, Catherine A Ball7, Alvis Brazma6, James DeGreef8, Nigel Hardy9, Henning Hermjakob6, Simon J Hubbard2, Peter Hussey10, Mark Igra10, Helen Jenkins9, Randall K Julian, Jr11, Kent Laursen11, Stephen G Oliver2, Norman W Paton1, Susanna-Assunta Sansone6, Ugis Sarkans6, Christian J Stoeckert, Jr12, Chris F Taylor6, Patricia L Whetzel12, Joseph A White13, Paul Spellman14 & Angel Pizarro15,16


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.

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  1. School of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL, UK.
  2. Faculty of Life Sciences, University of Manchester, Simon Building, Brunswick Street, Manchester, M13 9PL, UK.
  3. Rosetta Biosoftware, 401 Terry Avenue North, Seattle, Washington 98109, USA.
  4. Institute of Molecular Systems Biology, HPT E 78, Wolfgang-Pauli-Str. 16, 8093 Zurich, Switzerland, Faculty of Science, University of Zurich, Switzerland and Center for Systems Physiology and Metabolic Disease at ETH Zurich.
  5. The Institute for Systems Biology, 1441 North 34th Street, Seattle, Washington, 98103, USA.
  6. European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
  7. Microarray and Genome Informatics, Department of Biochemistry, Stanford School of Medicine, CCSR, Room 2255a, Stanford, California 94305, USA.
  8. GenoLogics Life Sciences Software, Suite 2302-4464 Markham Street, Victoria, British Columbia, V8Z 7X8, Canada.
  9. Department of Computer Science, Aberystwyth University, Ceredigion, SY23 3DB, Wales, UK.
  10. LabKey Software, 312 North 49th Street, Seattle, Washington, 98103, USA.
  11. Indigo BioSystems, Inc., 111 Congressional Blvd, Suite 160, Carmel, Indiana, 46032, USA.
  12. Department of Genetics, University of Pennsylvania, Center for Bioinformatics, 1415 Blockley Hall, 423 Guardian Drive, Philadelphia, Pennsylvania, 19104, USA.
  13. Dana-Farber Cancer Institute, 44 Binney Street, Boston, Massachusetts, 02115, USA.
  14. Lawrence Berkeley National Laboratory, University of California, 1 Cyclotron Road Mail Stop 977R225A, Berkeley, CA 94720, USA.
  15. Institute for Translational Medicine and Therapeutics, University of Pennsylvania, 421 Curie Blvd., Philadelphia, Pennsylvania, 19104, USA.
  16. These authors contributed equally to this work.

Correspondence to: Angel Pizarro15,16 e-mail: angel@mail.med.upenn.edu



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