In conclusion, we have identified Macedonian and British patients with hypouricaemia, who presented with symptoms including renal stone disease and haematuria. We have identified missense mutations in VE-821 1232410-49-9 SLC22A12 encoding URAT1. This data highlights the importance of renal urate transporters in determining serum urate concentrations and the of clinical phenotypes that should lead the clinician to suspect an inherited form of renal hypouricaemia. Cancer is caused by an accumulation of mutations, often in a subset of genes that confer survival and growth advantage. The protein kinase gene family, which controls key signaling pathways associated with cell growth and survival, is one of the most overrepresented families of oncogenes. Targeted sequencing of 518 protein kinase exons encoded in the human genome has revealed hundreds of mutations in the protein kinase domain. Although these mutations are currently catalogued in various databases, identification and experimental characterization of key cancer-causing mutations is essential for developing new therapies for cancer. Experimental characterization of cancer mutations, however, requires that one first formulate the right hypotheses based on analysis of existing data. In particular, analysis of mutation data in light of other forms of data available on protein kinases such as sequence, structure, function and pathway is necessary to develop and test new hypotheses regarding the functional impact of cancer mutations. Integrative analysis of protein kinase data, however, is a challenge because of the disparate nature of protein kinase data sources and formats. For example, a researcher interested in the structural location of a cancer mutation, or distribution of kinase mutations in various cancer types, has to go through the time-consuming and error prone process of collecting and parsing data from disparate sources, often in different data formats. Although several kinase-specific resources such as KinBase, KING, PKR and KinMutBase have been developed, these resources largely focus on one, or few types, of protein kinase data, leaving aside the challenge of data integration. Ontologies have emerged as a powerful tool for integrative and quantitative analysis of biological data. By capturing domain knowledge in the form of concepts and relationships, ontologies provide a conceptual representation of data in a way that computers can read and humans can understand. For example, for an automated and informed response to the query “kinase mutations associated with cancer types”, the computer needs to understand the concepts, “kinase mutations” and “cancer types”, and the relationships between the concepts, namely, “associated with”. It is this conceptual representation of knowledge that distinguishes ontologies from relational databases, and enables efficient integration and mining of diverse data sets. Indeed, several ontologies have been developed to capture and mine the wealth of information on genes, sequence, pathways, protein modification and others. Focused ontologies on selected protein families such as the protein phosphatase family and transporter family have also been developed. However, up until now, a focused ontology capturing the state of knowledge on the protein kinase family has not been reported.