RDFizing the biosynthetic pathway of E.coli O-antigen to enable semantic sharing of microbiology data

Abstract

Abstract Background The abundance of glycomics data that have accumulated has led to the development of many useful databases to aid in the understanding of the function of the glycans and their impact on cellular activity. At the same time, the endeavor for data sharing between glycomics databases with other biological databases have contributed to the creation of new knowledgebases. However, different data types in data description have impeded the data sharing for knowledge integration. To solve this matter, Semantic Web techniques including Resource Description Framework (RDF) and ontology development have been adopted by various groups to standardize the format for data exchange. These semantic data have contributed to the expansion of knowledgebases and hold promises of providing data that can be intelligently processed. On the other hand, bench biologists who are experts in experimental finding are end users and data producers. Therefore, it is indispensable to reduce the technical barrier required for bench biologists to manipulate their experimental data to be compatible with standard formats for data sharing. Results There are many essential concepts and practical techniques for data integration but there is no method to enable researchers to easily apply Semantic Web techniques to their experimental data. We implemented our procedure on unformatted information of E.coli O-antigen structures collected from the web and show how this information can be expressed as formatted data applicable to Semantic Web standards. In particular, we described the E-coli O-antigen biosynthesis pathway using the BioPAX ontology developed to support data exchange between pathway databases. Conclusions The method we implemented to semantically describe O-antigen biosynthesis should be helpful for biologists to understand how glycan information, including relevant pathway reaction data, can be easily shared. We hope this method can contribute to lower the technical barrier that is required when experimental findings are formulated into formal representations and can lead bench scientists to readily participate in the construction of new knowledgebases that are integrated with existing ones. Such integration over the Semantic Web will enable future work in artificial intelligence and machine learning to enable computers to infer new relationships and hypotheses in the life sciences.