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

Chen YA, Tripathi LP, Mizuguchi K - PLoS ONE (2011)

Bottom Line: An integrated approach that combines results from multiple data types is best suited for optimal target selection.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.The results show that the protocol can identify known disease-associated genes with high precision and coverage.

View Article: PubMed Central - PubMed

Affiliation: National Institute of Biomedical Innovation, Saito-Asagi, Ibaraki, Osaka, Japan.

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/.

Show MeSH
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).
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3050930&req=5

pone-0017844-g003: 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).

Mentions: To evaluate the effectiveness of TargetMine in identifying suitable targets for further characterisation, we performed target gene prioritisation tests (as described above) on 19 sets of known disease-associated genes compiled from the literature [51] (Table 2 and Figures 3 and 4; see Materials and Methods for details). In all instances, our prioritisation approach was supported by high sensitivity and precision values, and enforcing a threshold of collecting only the genes mapped to top seven associations (that satisfied a p-value cutoff of p≤0.05 after a multiple test correction with the Benajmini and Hochberg procedure) was by and large most suited to ensuring maximum coverage and minimum over-prediction (Table S2). Though for cirrhosis and cervical carcinoma, the number of false positives was slightly larger than those for the other diseases, the sensitivity and precision remained high.


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

Chen YA, Tripathi LP, Mizuguchi K - PLoS ONE (2011)

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).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3050930&req=5

pone-0017844-g003: 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).
Mentions: To evaluate the effectiveness of TargetMine in identifying suitable targets for further characterisation, we performed target gene prioritisation tests (as described above) on 19 sets of known disease-associated genes compiled from the literature [51] (Table 2 and Figures 3 and 4; see Materials and Methods for details). In all instances, our prioritisation approach was supported by high sensitivity and precision values, and enforcing a threshold of collecting only the genes mapped to top seven associations (that satisfied a p-value cutoff of p≤0.05 after a multiple test correction with the Benajmini and Hochberg procedure) was by and large most suited to ensuring maximum coverage and minimum over-prediction (Table S2). Though for cirrhosis and cervical carcinoma, the number of false positives was slightly larger than those for the other diseases, the sensitivity and precision remained high.

Bottom Line: An integrated approach that combines results from multiple data types is best suited for optimal target selection.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.The results show that the protocol can identify known disease-associated genes with high precision and coverage.

View Article: PubMed Central - PubMed

Affiliation: National Institute of Biomedical Innovation, Saito-Asagi, Ibaraki, Osaka, Japan.

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/.

Show MeSH