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Ontology engineering

Nature Biotechnology volume 28, pages 128–130 (2010)Cite this article

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To the Editor:

Gene Ontology (GO)1 and similar biomedical ontologies are critical tools of today's genetic research. These ontologies are crafted through a painstaking process of manual editing, and their organization relies on the intuition of human curators. Here we describe a method that uses information theory to automatically organize the structure of GO and optimize the distribution of the information within it. We used this approach to analyze the evolution of GO, and we identified several areas where the information was suboptimally organized. We optimized the structure of GO and used it to analyze 10,117 gene expression signatures. The use of this new version changed the functional interpretations of 97.5% (P < 10−3) of the signatures by, on average, 14.6%. As a result of this analysis, several changes will be introduced in the next releases of GO. We expect that these formal methods will become the standard to engineer biomedical ontologies.

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Figure 1
Figure 2: Three-dimensional evolution of GO over ten releases from 2005 to 2007 along the three dimensions of structural inefficiency.

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Acknowledgements

This work was supported in part by the National Library of Medicine (NLM/NIH) under grants 1K99LM009826 and 5T15LM007092 and by the National Human Genome Research Institute (NHGRI/NIH) under grants 2P41HG02273, 1R01HG003354, and 1R01HG004836. The authors are grateful to the anonymous reviewers for their helpful suggestions.

Author information

Authors and Affiliations

  1. Harvard-MIT Division of Health Sciences and Technology, Children's Hospital Informatics Program, Harvard Medical School, Boston, Massachusetts, USA

    Gil Alterovitz, Michael Xiang, Jonathan Dreyfuss & Marco F Ramoni

  2. Partners Healthcare Center for Personalized Genetic Medicine, Boston, Massachusetts, USA

    Gil Alterovitz, Michael Xiang, Michael Cherkassky, Jonathan Dreyfuss & Marco F Ramoni

  3. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Gil Alterovitz

  4. Jackson Laboratory, Bar Harbor, Maine, USA

    David P Hill, Mary E Dolan & Judith A Blake

  5. EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, UK

    Jane Lomax & Midori A Harris

  6. Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Jonathan Liu

  7. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Chris Mungall

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  1. Gil Alterovitz
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Corresponding authors

Correspondence to Gil Alterovitz or Marco F Ramoni.

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The authors declare no competing financial interests.

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Alterovitz, G., Xiang, M., Hill, D. et al. Ontology engineering. Nat Biotechnol 28, 128–130 (2010). https://doi.org/10.1038/nbt0210-128

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