![]() To ensure compatibility between the App and the BioGateway data, upon startup the App fetches an XML-based configuration file from the NoSQL server. In addition to these curated resources, we have added a resource produced by a text mining effort, named EXTRI ( The selection of terms of interest, such as genes, proteins, ontology terms and relation types is facilitated by an autocomplete function that is driven by a REST API of a NoSQL database loaded with all the entity names and metadata from the BioGateway server, to allow quick response times. To allow a user a special focus on gene regulation, we also included several resources with regulatory relations (transcription factor–target gene) of transcription factors (TF) with one or more target genes (TG): TFactS ( Essaghir et al., 2010), TRRUST ( Han et al., 2015), IntAct ( Orchard et al., 2014), Signor ( Perfetto et al., 2016), HTRIdb ( Bovolenta et al., 2012) and GOA ( Gene Ontology Consortium, 2018). The main information that is subject to the query is obtained from IntAct (protein–protein interactions Orchard et al., 2014), UniProtKB (protein descriptions, their genes, related diseases UniProt Consortium, 2017) and the Gene Ontology database (protein annotations Gene Ontology Consortium, 2018). All materials can also be viewed at Screenshot of an example query in the BioGateway App Query Builder The Run Query command converts these to native SPARQL queries that are launched against the BioGateway 3.0 SPARQL endpoint ( The example in Figure 1 is further explained in Supplementary Material S1, and tutorial pages are provided in Supplementary Material S2. By adding additional query parts line by line ( Fig. 1), increasingly complex and restrictive or inclusive queries can be composed. The main feature of the BioGateway App is the Query Builder (see example in Fig. 1), which supports the design of queries that are built from definitions of proteins or genes and a relationship to either an ontology term or another protein or gene. This allows users to take advantage of the inherent advantages of a graph-based database while querying and assessing the results as graphs in the familiar setting of the Cytoscape network editor, further aided by its extensive functionality for network analysis. To improve ease of access, we reach out to the large user base of the Cytoscape platform ( Cline et al., 2007) and make the BioGateway content available for exploratory network building through a Cytoscape plugin: the BioGateway App. Therefore, the wealth of information that triple stores provide has only been fully used by experts able to build their own SPARQL queries. Powerful as this query language is for speed and query complexity, designing SPARQL queries tends to be rather intimidating to the average biologists, including many who would be interested in network assembly from available knowledge sources. Version BioGateway 3.0 ( contains mainly human proteome-centric data, which can be queried with the SPARQL query language ( Prud’hommeaux and Seaborne, 2008). Version BioGateway 2.1 hosts RDF data for ∼1500 proteomes (species) stored as graphs ( ), as opposed to the more common relational databases. The BioGateway knowledge base ( Antezana et al., 2009) was one of the first semantic web resources developed to service the domain of the Life Sciences. Many primary life science databases make their content available as RDF ( Lassila et al., 1998) graphs, through triple store endpoints, including UniProtKB ( UniProt Consortium, 2017, ) and IntAct ( Orchard et al., 2014) and Reactome ( Fabregat et al., 2018 In addition, resources like Bio2RDF ( Callahan et al., 2013, /), Pathway Commons ( Cerami et al., 2011, /) and BioGateway ( Antezana et al., 2009, provide RDF with content integrated from several public resources. Semantic web technologies provide a powerful framework for the integration of diverse datasets into one homogeneous, queryable resource. ![]()
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