Identifying aberrant pathways through integrated analysis of knowledge in pharmacogenomics.
BACKGROUND
Many complex diseases are the result of abnormal pathway functions instead of single abnormalities. Disease diagnosis and intervention strategies must target these pathways while minimizing the interference with normal physiological processes. Large-scale identification of disease pathways and chemicals that may be used to perturb them requires the integration of information about drugs, genes, diseases and pathways. This information is currently distributed over several pharmacogenomics databases. An integrated analysis of the information in these databases can reveal disease pathways and facilitate novel biomedical analyses.


RESULTS
We demonstrate how to integrate pharmacogenomics databases through integration of the biomedical ontologies that are used as meta-data in these databases. The additional background knowledge in these ontologies can then be used to enable novel analyses. We identify disease pathways using a novel multi-ontology enrichment analysis over the Human Disease Ontology, and we identify significant associations between chemicals and pathways using an enrichment analysis over a chemical ontology. The drug-pathway and disease-pathway associations are a valuable resource for research in disease and drug mechanisms and can be used to improve computational drug repurposing.


BACKGROUND
http://pharmgkb-owl.googlecode.com
Department of Genetics, University of Cambridge, Downing Street, Cambridge CB2 3EH, UK. rh497@cam.ac.uk
Robert
R
Hoehndorf
Michel
M
Dumontier
Georgios V
GV
Gkoutos
D016208:Databases, Factual; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D051188:Knowledge Bases; D010597:Pharmacogenetics / Q000379:methods*; D012984:Software; D018511:Systems Integration; D018875:Vocabulary, Controlled