Guardia Gabriela D A, Ferreira Pires Luís, da Silva Eduardo G, de Farias Cléver R G
Department of Computer Science and Mathematics - Faculty of Philosophy, Sciences and Letters at Ribeirão Preto (FFCLRP) - University of São Paulo (USP), Ribeirão Preto, Brazil.
Faculty of Electrical Engineering, Mathematics and Computer Science - University of Twente, Enschede, Netherlands.
J Biomed Inform. 2017 Feb;66:116-128. doi: 10.1016/j.jbi.2016.12.014. Epub 2017 Jan 3.
Gene expression studies often require the combined use of a number of analysis tools. However, manual integration of analysis tools can be cumbersome and error prone. To support a higher level of automation in the integration process, efforts have been made in the biomedical domain towards the development of semantic web services and supporting composition environments. Yet, most environments consider only the execution of simple service behaviours and requires users to focus on technical details of the composition process. We propose a novel approach to the semantic composition of gene expression analysis services that addresses the shortcomings of the existing solutions. Our approach includes an architecture designed to support the service composition process for gene expression analysis, and a flexible strategy for the (semi) automatic composition of semantic web services. Finally, we implement a supporting platform called SemanticSCo to realize the proposed composition approach and demonstrate its functionality by successfully reproducing a microarray study documented in the literature. The SemanticSCo platform provides support for the composition of RESTful web services semantically annotated using SAWSDL. Our platform also supports the definition of constraints/conditions regarding the order in which service operations should be invoked, thus enabling the definition of complex service behaviours. Our proposed solution for semantic web service composition takes into account the requirements of different stakeholders and addresses all phases of the service composition process. It also provides support for the definition of analysis workflows at a high-level of abstraction, thus enabling users to focus on biological research issues rather than on the technical details of the composition process. The SemanticSCo source code is available at https://github.com/usplssb/SemanticSCo.
