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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Protein ontology on the semantic web for knowledge discovery.

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Summary

The Protein Ontology (PRO) Linked Open Data (LOD) offers a FAIR-compliant resource for protein information. It enables biological knowledge discovery by connecting protein entities and facilitating data access through various methods.

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Area of Science:

  • Proteomics
  • Bioinformatics
  • Semantic Web Technologies

Background:

  • The Protein Ontology (PRO) organizes protein-related entities, including families, proteoforms, and complexes.
  • Integrating biological data is crucial for advancing knowledge discovery.

Purpose of the Study:

  • To expose, share, and connect protein knowledge on the Semantic Web using RDF.
  • To enable biological knowledge discovery by integrating PRO data with other Linked Open Data.

Main Methods:

  • Utilizing Resource Description Framework (RDF) for data representation.
  • Implementing a faceted browser interface and SPARQL endpoint for data exploration.
  • Providing RESTful APIs for programmatic data access and downloadable RDF data dumps.

Main Results:

  • PRO Linked Open Data (LOD) facilitates the retrieval of proteins and variants based on disease associations.
  • The resource adheres to Findability, Accessibility, Interoperability, and Reusability (FAIR) principles.
  • Multiple data access and querying methods are supported for diverse user needs.

Conclusions:

  • PRO LOD enhances biological knowledge discovery through linked data principles.
  • The resource provides accessible and interoperable protein information for scientists and programmers.
  • Adherence to FAIR principles ensures the long-term value and usability of the PRO LOD resource.