Annotating the human genome with Disease Ontology

John D. Osborne, Jared Flatow, Michelle Holko, Simon M. Lin, Warren A. Kibbe, Lihua Julie Zhu, Maria I. Danila, Gang Feng, Rex L. Chisholm*

*Corresponding author for this work

Research output: Contribution to journalArticle

183 Scopus citations

Abstract

Background: The human genome has been extensively annotated with Gene Ontology for biological functions, but minimally computationally annotated for diseases. Results: We used the Unified Medical Language System (UMLS) MetaMap Transfer tool (MMTx) to discover gene-disease relationships from the GeneRIF database. We utilized a comprehensive subset of UMLS, which is disease-focused and structured as a directed acyclic graph (the Disease Ontology), to filter and interpret results from MMTx. The results were validated against the Homayouni gene collection using recall and precision measurements. We compared our results with the widely used Online Mendelian Inheritance in Man (OMIM) annotations. Conclusion: The validation data set suggests a 91% recall rate and 97% precision rate of disease annotation using GeneRIF, in contrast with a 22% recall and 98% precision using OMIM. Our thesaurus-based approach allows for comparisons to be made between disease containing databases and allows for increased accuracy in disease identification through synonym matching. The much higher recall rate of our approach demonstrates that annotating human genome with Disease Ontology and GeneRIF for diseases dramatically increases the coverage of the disease annotation of human genome.

Original languageEnglish (US)
Article numberS6
JournalBMC Genomics
Volume10
Issue numberSUPPL. 1
DOIs
StatePublished - Jul 7 2009

ASJC Scopus subject areas

  • Biotechnology
  • Genetics

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    Osborne, J. D., Flatow, J., Holko, M., Lin, S. M., Kibbe, W. A., Zhu, L. J., Danila, M. I., Feng, G., & Chisholm, R. L. (2009). Annotating the human genome with Disease Ontology. BMC Genomics, 10(SUPPL. 1), [S6]. https://doi.org/10.1186/1471-2164-10-S1-S6