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PLoS One. 2014 Jun 18;9(6):e99864. doi: 10.1371/journal.pone.0099864. eCollection 2014.

Representing kidney development using the gene ontology.

Author information

  • 1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom.
  • 2The Jackson Laboratory, Bar Harbor, Maine, United States of America.
  • 3Cambridge Systems Biology Centre and Department of Biochemistry, Sanger Building, University of Cambridge, Cambridge, United Kingdom.
  • 4FlyBase, Department of Genetics, University of Cambridge, Cambridge, United Kingdom.
  • 5The Zebrafish Model Organism Database (ZFIN), University of Oregon Eugene, Oregon, United States of America.
  • 6IR4M UMR8081, Centre National de la Recherche Scientifique (CNRS), Université Paris-Sud, Orsay, Franc.
  • 7MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, Western General Hospital, Edinburgh, United Kingdom.
  • 8Institute of Human Development, Faculty of Medical and Human Sciences, University of Manchester, Manchester, United Kingdom.
  • 9Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America.


Gene Ontology (GO) provides dynamic controlled vocabularies to aid in the description of the functional biological attributes and subcellular locations of gene products from all taxonomic groups (www.geneontology.org). Here we describe collaboration between the renal biomedical research community and the GO Consortium to improve the quality and quantity of GO terms describing renal development. In the associated annotation activity, the new and revised terms were associated with gene products involved in renal development and function. This project resulted in a total of 522 GO terms being added to the ontology and the creation of approximately 9,600 kidney-related GO term associations to 940 UniProt Knowledgebase (UniProtKB) entries, covering 66 taxonomic groups. We demonstrate the impact of these improvements on the interpretation of GO term analyses performed on genes differentially expressed in kidney glomeruli affected by diabetic nephropathy. In summary, we have produced a resource that can be utilized in the interpretation of data from small- and large-scale experiments investigating molecular mechanisms of kidney function and development and thereby help towards alleviating renal disease.

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