The gene ontology's reference genome project: A unified framework for functional annotation across species

Pascale Gaudet*, Rex Chisholm, Tanya Berardini, Emily Dimmer, Stacia R. Engel, Petra Fey, David P. Hill, Doug Howe, James C. Hu, Rachael Huntley, Varsha K. Khodiyar, Ranjana Kishore, Donghui Li, Ruth C. Lovering, Fiona McCarthy, Li Ni, Victoria Petri, Deborah A. Siegele, Susan Tweedie, Kimberly Van AukenValerie Wood, Siddhartha Basu, Seth Carbon, Mary Dolan, Christopher J. Mungall, Kara Dolinski, Paul Thomas, Michael Ashburner, Judith A. Blake, J. Michael Cherry, Suzanna E. Lewis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

134 Scopus citations


The Gene Ontology (GO) is a collaborative effort that provides structured vocabularies for annotating the molecular function, biological role, and cellular location of gene products in a highly systematic way and in a species-neutral manner with the aim of unifying the representation of gene function across different organisms. Each contributing member of the GO Consortium independently associates GO terms to gene products from the organism(s) they are annotating. Here we introduce the Reference Genome project, which brings together those independent efforts into a unified framework based on the evolutionary relationships between genes in these different organisms. The Reference Genome project has two primary goals: to increase the depth and breadth of annotations for genes in each of the organisms in the project, and to create data sets and tools that enable other genome annotation efforts to infer GO annotations for homologous genes in their organisms. In addition, the project has several important incidental benefits, such as increasing annotation consistency across genome databases, and providing important improvements to the GO's logical structure and biological content.

Original languageEnglish (US)
Article numbere1000431
JournalPLoS computational biology
Issue number7
StatePublished - Jul 2009

ASJC Scopus subject areas

  • Genetics
  • Ecology, Evolution, Behavior and Systematics
  • Cellular and Molecular Neuroscience
  • Molecular Biology
  • Ecology
  • Computational Theory and Mathematics
  • Modeling and Simulation


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