RDFScape: Semantic Web meets systems biology.

Andrea Splendiani

doi:10.1186/1471-2105-9-s4-s6

Abstract

BackgroundThe recent availability of high-throughput data in molecular biology has increased the need for a formal representation of this knowledge domain. New ontologies are being developed to formalize knowledge, e.g. about the functions of proteins. As the Semantic Web is being introduced into the Life Sciences, the basis for a distributed knowledge-base that can foster biological data analysis is laid. However, there still is a dichotomy, in tools and methodologies, between the use of ontologies in biological investigation, that is, in relation to experimental observations, and their use as a knowledge-base.ResultsRDFScape is a plugin that has been developed to extend a software oriented to biological analysis with support for reasoning on ontologies in the semantic web framework. We show with this plugin how the use of ontological knowledge in biological analysis can be extended through the use of inference. In particular, we present two examples relative to ontologies representing biological pathways: we demonstrate how these can be abstracted and visualized as interaction networks, and how reasoning on causal dependencies within elements of pathways can be implemented.ConclusionsThe use of ontologies for the interpretation of high-throughput biological data can be improved through the use of inference. This allows the use of ontologies not only as annotations, but as a knowledge-base from which new information relevant for specific analysis can be derived.

Highlights

The recent availability of high-throughput data in molecular biology has increased the need for a formal representation of this knowledge domain
As ontologies represented in the semantic web framework are networks of concepts themselves, they can be treated as graphs within Cytoscape and visualized taking advantage of its interactive features
We illustrated this approach in two examples with ontologies relative to biological pathways, but it can be extended to a heterogeneous domain [13]

Summary

Introduction

The recent availability of high-throughput data in molecular biology has increased the need for a formal representation of this knowledge domain. New ontologies are being developed to formalize knowledge, e.g. about the functions of proteins. The role of ontologies in the Life Sciences domain has increased in recent years, as the development of highthroughput measurement technologies has made it a data-intensive discipline. GO is a unique resource for uniform annotation of gene products across organisms; and it is used to relate experimental data and knowl-. Bio-ontologies are shifting from almost terminological resources used for annotation of biological entities, to a formal representation of a knowledge domain that allows inference of biologically meaningful facts [7][8]

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Discussion

Conclusion