Improving integrative searching of systems chemical biology data using semantic annotation

We developed a generalized chemogenomics and systems chemical biology OWL ontology called Chem2Bio2OWL that describes the semantics of chemical compounds, drugs, protein targets, pathways, genes, diseases and side-effects, and the relationships between them. The ontology also includes data provenance. We used it to annotate our Chem2Bio2RDF dataset, making it a rich semantic resource. Through a series of scientific case studies we demonstrate how this (i) simplifies the process of building SPARQL queries, (ii) enables useful new kinds of queries on the data and (iii) makes possible intelligent reasoning and semantic graph mining in chemogenomics and systems chemical biology.Chem2Bio2OWL is available at http://chem2bio2rdf.org/owl webcite. The document is available at http://chem2bio2owl.wikispaces.com webcite.Recent efforts [1-3] in the Semantic web have involved conversion of various chemical and biological data sources into semantic formats (e.g., RDF, OWL) and linked them into very large networks. The number of bubbles in Linked Open Data (LOD) [4] has expanded rapidly from 12 in 2007 to 203 in 2010. This richly linked data allows answering of complex scientific questions using the SPARQL query language [5], finding paths among objects [6], and ranking associations of different entities [7,8]. Our previous work on Chem2Bio2RDF [3] offers a framework to data mine systems chemical biology and chemogenomics data, as exemplified by the examples given in our paper: compound selection in polypharmacology, multiple pathway inhibitor identification and adverse drug reaction - pathway mapping. However, without an ontology and associated annotation, the utility of the resource is semantically very limited - for example results cannot be refined based on criteria of the type of relationship between entities (e.g., activation or inhibition between compound and protein). Even when it is possible to create a SPARQL query, the lack of ontology increases the complexity of the qu