Translating standards into practice - one Semantic Web API for Gene Expression.

Area of Science:

• Bioinformatics
• Genomics
• Data Science

Background:

• Reproducible scientific research relies on unambiguous experimental result sharing.
• Standardized terminologies and ontologies are crucial for reporting high-throughput omics data.
• Integrating heterogeneous health care and life sciences (HCLSs) datasets is increasingly important.

Purpose of the Study:

• To develop a methodology for integrating microarray-based transcriptomic experiment results and context.
• To enable the integration of existing, disparate transcriptomic datasets.
• To encourage the reuse of established ontologies in gene expression studies.

Main Methods:

• Utilized SPARQL Construct for a posteriori mappings of concepts and properties.
• Developed linking rules to match entities based on query constraints.
• Integrated data from the Gene Expression Atlas, W3C BioRDF, and HSCI blood genomics project.

Main Results:

• Successfully integrated results and experimental context from three distinct microarray transcriptomic data representations.
• Demonstrated a method for linking entities across different datasets using query constraints.
• Facilitated the reuse of existing ontologies for reporting experimental data.

Conclusions:

• The proposed methodology enables the integration of existing microarray transcriptomic datasets without altering current standards.
• This approach promotes the reuse of ontologies like EFO and OBIs for consistent reporting of experimental context and results.
• Enhances data discoverability and interpretability in gene expression studies.