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STOP using just GO: a multi-ontology hypothesis generation tool for high throughput experimentation

Tobias Wittkop1, Emily TerAvest1, Uday S Evani1, K Mathew Fleisch1, Ari E Berman1, Corey Powell2, Nigam H Shah3 and Sean D Mooney14*

Author Affiliations

1 Buck Institute for Research on Aging, Novato, CA, USA

2 University of Michigan Medical School, Ann Arbor, MI, USA

3 National Center for Biomedical Ontology, Biomedical Informatics, Stanford University, Stanford, CA, USA

4 Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA

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BMC Bioinformatics 2013, 14:53  doi:10.1186/1471-2105-14-53

Published: 14 February 2013



Gene Ontology (GO) enrichment analysis remains one of the most common methods for hypothesis generation from high throughput datasets. However, we believe that researchers strive to test other hypotheses that fall outside of GO. Here, we developed and evaluated a tool for hypothesis generation from gene or protein lists using ontological concepts present in manually curated text that describes those genes and proteins.


As a consequence we have developed the method Statistical Tracking of Ontological Phrases (STOP) that expands the realm of testable hypotheses in gene set enrichment analyses by integrating automated annotations of genes to terms from over 200 biomedical ontologies. While not as precise as manually curated terms, we find that the additional enriched concepts have value when coupled with traditional enrichment analyses using curated terms.


Multiple ontologies have been developed for gene and protein annotation, by using a dataset of both manually curated GO terms and automatically recognized concepts from curated text we can expand the realm of hypotheses that can be discovered. The web application STOP is available at webcite.