ArticleF1000Research2019
A hands-on introduction to querying evolutionary relationships across multiple data sources using SPARQL.
Article in F1000Research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Ten Years of Collaborative Progress in the Quest for Orthologs.Molecular biology and evolution · 2021Article
- Functionathon: a manual data mining workflow to generate functional hypotheses for uncharacterized human proteins and its application by undergraduate students.Database : the journal of biological databases and curation · 2021Article
- OMA orthology in 2021: website overhaul, conserved isoforms, ancestral gene order and more.Nucleic acids research · 2021Article
- Making the most of genomic data with OMA.F1000Research · 2020Article
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5 authors.
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Abstract
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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