TargetMine, an integrated data warehouse for candidate gene prioritisation and target discovery

Abstract

Prioritising candidate genes for further experimental characterisation is a non-trivial challenge in drug discovery and biomedical research in general. An integrated approach that combines results from multiple data types is best suited for optimal target selection. We developed TargetMine, a data warehouse for efficient target prioritisation. TargetMine utilises the InterMine framework, with new data models such as protein-DNA interactions integrated in a novel way. It enables complicated searches that are difficult to perform with existing tools and it also offers integration of custom annotations and in-house experimental data. We proposed an objective protocol for target prioritisation using TargetMine and set up a benchmarking procedure to evaluate its performance. The results show that the protocol can identify known disease-associated genes with high precision and coverage. A demonstration version of TargetMine is available at http://targetmine.nibio.go.jp/.

Figure 1. Schema for selected examples of newly created data sources in TargetMine.

The data model is depicted as a class diagram in the Unified Modeling Language (http://www.uml.org). Some details of the model are ignored to reduce the complexity of the diagram.

Figure 2. A schematic representation of the suggested objective protocol for candidate gene prioritisation with TargetMine.

Figure 3. Outline of the procedure for benchmarking candidate gene prioritisation on 19 sets of known disease-associated genes with TargetMine.

TP- True positive, FP- False positive (see text for details).

Figure 4. Benchmarking results for 19 sets of known disease-associated genes.

(The full disease names and their abbreviations are listed in Table 2.) Each line represents the F-score for a particular disease data set as a function of the threshold (the top N significant associations considered). The error bars show the standard deviation across ten benchmarking evaluations for each disease.