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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Updated: Dec 16, 2025

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

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

Ana Claudia Sima^1,2,3, Christophe Dessimoz^2,3,4,5,6, Kurt Stockinger¹

¹ZHAW Zurich University of Applied Sciences, Winterthur, Zurich, Switzerland.

|August 6, 2020

Summary

This study introduces SPARQL querying for evolutionary data in life sciences. It simplifies accessing orthology information from multiple RDF data sources, enabling integrative analyses.

Keywords:

Comparative Genomics Orthology Resource Description Framework (RDF)SPARQL Sequence Homology

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Last Updated: Dec 16, 2025

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Published on: February 5, 2014

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Published on: December 7, 2021

Area of Science:

Bioinformatics
Computational Biology
Semantic Web Technologies

Background:

Semantic Web technologies like RDF and SPARQL are increasingly used in life sciences for data integration.
Analyzing evolutionary data, particularly orthology information, using RDF presents challenges due to complex data models and SPARQL constructs like recursive property paths.

Purpose of the Study:

To provide a practical guide for querying evolutionary data from diverse RDF sources.
To demonstrate how to retrieve and compare orthology information across multiple databases using SPARQL.

Main Methods:

Utilizing SPARQL queries to access orthology data from The Orthologous MAtrix (OMA), EBI RDF, OrthoDB, and MBGD.
Developing four protocols of increasing complexity to illustrate querying techniques.
Employing federated SPARQL queries for cross-database comparisons.

Main Results:

Demonstrated retrieval of pairwise orthologs, homologous groups, and hierarchical orthologous groups.
Successfully compared orthology data across different RDF data sources.
Provided practical examples of SPARQL queries for evolutionary data analysis.

Conclusions:

Querying evolutionary data in RDF is feasible and beneficial for integrative analyses.
The presented protocols simplify the process of accessing and comparing orthology information.
SPARQL, including federated queries, is a powerful tool for life science data integration.