A hands-on introduction to querying evolutionary relationships across multiple data sources using SPARQL

The increasing use of Semantic Web technologies in the life sciences, in particular the use of the Resource Description Framework (RDF) and the RDF query language SPARQL, opens the path for novel integrative analyses, combining information from multiple data sources. However, analyzing evolutionary data in RDF is not trivial, due to the steep learning curve required to understand both the data models adopted by different RDF data sources, as well as the equivalent SPARQL constructs required to benefit from this data – in particular, recursive property paths. In this article, we provide a hands-on introduction to querying evolutionary data across several data sources that publish orthology information in RDF, namely: The Orthologous MAtrix (OMA), the European Bioinformatics Institute (EBI) RDF platform, the Database of Orthologous Groups (OrthoDB) and the Microbial Genome Database (MBGD). We present four protocols in increasing order of complexity. In these protocols, we demonstrate through SPARQL queries how to retrieve pairwise orthologs, homologous groups, and hierarchical orthologous groups. Finally, we show how orthology information in different data sources can be compared, through the use of federated SPARQL queries.

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A hands-on introduction to querying evolutionary relationships across multiple data sources using SPARQL

F1000Research ◽

10.12688/f1000research.21027.1 ◽

2019 ◽

Vol 8 ◽

pp. 1822 ◽

Cited By ~ 1

Author(s):

Ana Claudia Sima ◽

Christophe Dessimoz ◽

Kurt Stockinger ◽

Monique Zahn-Zabal ◽

Tarcisio Mendes de Farias

Keyword(s):

Query Language ◽

Data Sources ◽

Multiple Sources ◽

Semantic Web Technologies ◽

Genome Database ◽

Web Technologies ◽

Multiple Data ◽

Hands On ◽

Description Framework ◽

Different Sources

The increasing use of Semantic Web technologies in the life sciences, in particular the use of the Resource Description Framework (RDF) and the RDF query language SPARQL, opens the path for novel integrative analyses, combining information from multiple sources. However, analyzing evolutionary data in RDF is not trivial, due to the steep learning curve required to understand both the data models adopted by different RDF data sources, as well as the SPARQL query language. In this article, we provide a hands-on introduction to querying evolutionary data across multiple sources that publish orthology information in RDF, namely: The Orthologous MAtrix (OMA), the European Bioinformatics Institute (EBI) RDF platform, the Database of Orthologous Groups (OrthoDB) and the Microbial Genome Database (MBGD). We present four protocols in increasing order of complexity. In these protocols, we demonstrate through SPARQL queries how to retrieve pairwise orthologs, homologous groups, and hierarchical orthologous groups. Finally, we show how orthology information in different sources can be compared, through the use of federated SPARQL queries.

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A SPATIAL/TEMPORAL QUERY LANGUAGE FOR MULTIPLE DATA SOURCES IN A HETEROGENEOUS INFORMATION SYSTEM ENVIRONMENT

International Journal of Cooperative Information Systems ◽

10.1142/s021884309800009x ◽

1998 ◽

Vol 07 (02n03) ◽

pp. 167-186 ◽

Cited By ~ 12

Author(s):

SHI-KUO CHANG ◽

ERLAND JUNGERT

Keyword(s):

Information System ◽

Virtual World ◽

Query Language ◽

Data Sources ◽

Heterogeneous Information ◽

Multiple Data Sources ◽

Multiple Data ◽

Temporal Query ◽

Operator Sequence ◽

Temporal Query Language

To support the retrieval, fusion and discovery of multimedia information, a spatial/temporal query language for multiple data sources is needed. In this paper we describe a spatial/temporal query language, the ∑QL, which is based upon the σ-operator sequence and in practice expressible in an SQL-like syntax. The general σ-operator and temporal σ-operator are explained, and applications of the σ-query language to vertical/horizontal reasoning and hypermapped virtual world are discussed.

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Linked Data Driven Information Systems as an Enabler for Integrating Financial Data

Information Systems for Global Financial Markets - Advances in Finance, Accounting, and Economics ◽

10.4018/978-1-61350-162-7.ch010 ◽

2011 ◽

pp. 239-270 ◽

Cited By ~ 6

Author(s):

Seán O’Riain ◽

Andreas Harth ◽

Edward Curry

Keyword(s):

Semantic Web ◽

Data Integration ◽

Information Integration ◽

Financial Services ◽

Linked Data ◽

Financial Data ◽

Data Sources ◽

Semantic Web Technologies ◽

Web Technologies ◽

Multiple Data

With increased dependence on efficient use and inclusion of diverse corporate and Web based data sources for business information analysis, financial information providers will increasingly need agile information integration capabilities. Linked Data is a set of technologies and best practices that provide such a level of agility for information integration, access, and use. Current approaches struggle to cope with multiple data sources inclusion in near real-time, and have looked to Semantic Web technologies for assistance with infrastructure access, and dealing with multiple data formats and their vocabularies. This chapter discusses the challenges of financial data integration, provides the component architecture of Web enabled financial data integration and outlines the emergence of a financial ecosystem, based upon existing Web standards usage. Introductions to Semantic Web technologies are given, and the chapter supports this with insight and discussion gathered from multiple financial services use case implementations. Finally, best practice for integrating Web data based on the Linked Data principles and emergent areas are described.

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