Page:Wikidata as a knowledge graph for the life sciences.pdf/5

 Feature Article

SELECT * values ?gene ?gene }

Science Forum Wikidata as a knowledge graph for the life sciences

WHERE { ?symbol {"CDK2" "AKT1" "RORA" "VEGFA" "COL2A1" "NGLY1"}. wdt:P353 ?symbol. wdt:P351 ?entrez.

Input ID type

Input IDs

SELECT * WHERE { values ?rxnorm {"327361" "301542" "10582" "284924"}. ?compound wdt:P3345 ?rxnorm. ?compound wdt:P2115 ?ndfrt. } Output ID type

Figure 2. Generalizable SPARQL template for identifier translation. SPARQL is the primary query language for accessing Wikidata content. These simple SPARQL examples show how identifiers of any biological type can easily be translated using SPARQL queries. The top query demonstrates the translation of a small list of gene symbols (wdt:P353) to Entrez Gene IDs (wdt:P351), while the bottom example shows conversion of RxNorm concept IDs (wdt:P3345) to NDF-RT IDs (wdt:P2115). These queries can be submitted to the Wikidata Query Service (WDQS; https://query.wikidata.org/) to get real-time results. Translation to and from a wide variety of identifier types can be performed using slight modifications on these templates, and relatively simple extensions of these queries can filter mappings based on the statement references and/or qualifiers. A full list of Wikidata properties can be found at https://www.wikidata.org/wiki/Special:ListProperties. Note that for translating a large number of identifiers, it is often more efficient to perform a SPARQL query to retrieve all mappings and then perform additional filtering locally.

downloaded and used as part of larger scripts or analyses. There are a number of other tools that are also aimed at solving the identifier translation use case, including the BioThings APIs (Xin et al., 2018), BridgeDb (van Iersel et al., 2010), BioMart (Smedley et al., 2015), UMLS (Bodenreider, 2004), and NCI Thesaurus (de Coronado et al., 2009). Relative to these tools, Wikidata distinguishes itself with a unique combination of the following: an almost limitless scope including all entities in biology, chemistry, and medicine; a data model that can represent exact, broader, and narrow matches between items in different identifier namespaces (beyond semantically imprecise ’cross-references’); programmatic access through web services with a track record of high performance and high availability. Moreover, Wikidata is also unique as it is the only tool that allows real-time community editing. So while Wikidata is certainly not complete with respect to identifier mappings, it can be continually improved independent of any centralized effort or curation authority. As a database of assertions and not of absolute truth, Wikidata is able to represent conflicting information (with provenance) when, for example, different curation authorities produce different mappings between entities. (However, as with any bioinformatics integration exercise, harmonization of cross-references between resources

Waagmeester et al. eLife 2020;9:e52614. DOI: https://doi.org/10.7554/eLife.52614

can include relationships other than ‘exact match’. These instances can lead to Wikidata statements that are not explicitly declared, but rather the result of transitive inference.)

Integrative Queries Wikidata contains a much broader set of information than just identifier cross-references. Having biomedical data in one centralized data resource facilitates powerful integrative queries that span multiple domain areas and data sources. Performing these integrative queries through Wikidata obviates the need to perform many time-consuming and error-prone data integration steps. As an example, consider a pulmonologist who is interested in identifying candidate chemical compounds for testing in disease models (schematically illustrated in Figure 3). They may start by identifying genes with a genetic association to any respiratory disease, with a particular interest in genes that encode membrane-bound proteins (for ease in cell sorting). They may then look for chemical compounds that either directly inhibit those proteins, or finding none, compounds that inhibit another protein in the same pathway. Because they have collaborators with relevant expertise, they may specifically filter for proteins containing a serine-threonine kinase domain.

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