Semantic Technologies in Bioinformatics 1© Unicorn Solutions Inc. June 1, 2015

2© Unicorn Solutions Inc. Data Challenges of Bioinformatics Research Large diversity of data types Large number of data sources covering different aspects of biomedical information Diversity of terminology and subtlety of meaning. Diversity in representation and coding of biomedical data

June 1, 20153© Unicorn Solutions Inc. DesignManagementRun-Time Semantic Engine Footprint Unicorn Repository™ COMMON ONE LANGUAGE Ontology Model Unicorn Workbench™ Domain Conceptual View Model Browsing Query Generation Composite Applications Manage, Model, Map Information Resource Management Visualization & Semantic Discovery Stored & ad- hoc queries Metadata Semantic Mappings Portal Execution Engine Unicorn Server™ Generate Views Design & Management Metadata Data Sources, Warehouses/Marts, Applications Data Warehouse Scan Metadata Retrieve Data Generate Queries Generate Federated Schema Configure Wrappers, Servers, Nicknames Analysis & Data Mining Metadata Repository

June 1, 20154© Unicorn Solutions Inc. Semantic Mapping Mapping is done at two levels: –Table to Concept –Property to Field Mapping can be between multiple concepts to a single table or multiple tables to a single concept. Mapping between a property and a data field can be done only if they are of compatible types. If fields are not of compatible types a transformation rule needs to be applied.

June 1, 20155© Unicorn Solutions Inc. Table Semantic Mapping

June 1, 20156© Unicorn Solutions Inc. Column Semantic Mapping

June 1, 20157© Unicorn Solutions Inc. Semantic Query Generation Semantic queries are expressed in the language of the semantic model (Ontology.) Semantic query makes use of relationships between concepts to reach and navigate to associated data tables. Queries can be qualified with WHERE clauses selecting data instances that meet given criteria. Semantic query is translated to a SQL command in the dialect appropriate to the target database platform. Common queries can be canned and parameterized allowing users only change selection criteria

June 1, 20158© Unicorn Solutions Inc. Semantic Query

June 1, 20159© Unicorn Solutions Inc. Resulting SQL Command SELECT "ENTREZGENEID" AS "entrezGeneID", "GENENAME" AS "geneName", "GENESYMBOL" AS "hasPathway.geneSymbol", (CEIL("PATHWAYID")) AS "hasPathway.pathwayID", "ORGANISM" AS "organism" FROM ( SELECT A.ENTREZ_GENE_ID AS "ENTREZGENEID" /* "ENTREZGENEID" is property entrezGeneID */, A.GENE_NAME AS "GENENAME" /* "GENENAME" is property geneName */, B.GENE_SYMBOL AS "GENESYMBOL" /* "GENESYMBOL" is property hasPathway.geneSymbol */, B.PATHWAY_ID AS "PATHWAYID" /* "PATHWAYID" is property hasPathway.pathwayID */, A.ORGANISM AS "ORGANISM" /* "ORGANISM" is property organism */, A.GENE_SYMBOL AS "GENESYMBOL1" /* "GENESYMBOL1" is property geneSymbol */ FROM BISC_DATA.ENTREZ_GENE_DATA A, BISC_DATA.PATHWAY_DATA B WHERE A.ENTREZ_GENE_ID = B.ENTREZ_GENE_ID ) /* "ENTREZGENE" is class EntrezGene in package BISC_Data */ WHERE ("GENESYMBOL1"='ADA' OR "GENESYMBOL1"='AFP') AND ("ORGANISM"='Homo Sapiense')

June 1, © Unicorn Solutions Inc. Benefits of Semantic Technology Present a single unified front and vocabulary to any domain data on any platform. Shields the user from the complexities of database design. –Relieves the user from the need to know about primary keys foreign keys and to understand the relationships between tables. –No SQL knowledge is required. Potentially supports secondary data processing: queries, transformation, analysis and data mining. Easy to expand to include additional knowledge sub- domains and corresponding data assets.