This article is part of the supplement: The 2008 International Conference on Bioinformatics & Computational Biology (BIOCOMP'08) .

Research

Annotating the human genome with Disease Ontology

John D Osborne¹ , Jared Flatow² , Michelle Holko³ , Simon M Lin² , Warren A Kibbe² , Lihua (Julie) Zhu⁵ , Maria I Danila⁶ , Gang Feng² and Rex L Chisholm⁴

¹  Department of Microbiology, University of Alabama at Birmingham, Birmingham, AL 35294, USA

²  The Biomedical Informatics Center, Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL 60611, USA

³  Department of Preventive Medicine, Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL 60611, USA

⁴  The Center for Genetic Medicine, Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, IL 60611, USA

⁵  Program in Gene Function and Expression, University of Massachusetts Medical School, Worcester, MA 01605, USA

⁶  Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL 35294, USA

author email corresponding author email

BMC Genomics 2009, 10(Suppl 1):S6doi:10.1186/1471-2164-10-S1-S6

Published:	7 July 2009

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.