Annotating the human genome with Disease Ontology.
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.


CONCLUSIONS
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.
Osborne
John D
JD
Department of Microbiology, University of Alabama at Birmingham, Birmingham, AL 35294, USA. ozborn@uab.edu
Flatow
Jared
J
Holko
Michelle
M
Lin
Simon M
SM
Kibbe
Warren A
WA
Zhu
Lihua Julie
LJ
Danila
Maria I
MI
Feng
Gang
G
Chisholm
Rex L
RL
D019295:Computational Biology / Q000379:methods; D030541:Databases, Genetic; D015894:Genome, Human; D006801:Humans; D012984:Software; D017432:Unified Medical Language System