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Structural Changes in Gene Ontology Reveal Modular and Complex Representations of Biological Function.
Sergi Valverde1,2,3, Blai Vidiella4, Gemma I Martínez-Redondo5
1Evolution of Networks Lab, Institute of Evolutionary Biology (CSIC-UPF), Passeig Marítim de la Barceloneta 37-49, Barcelona 08003, Spain.
The Gene Ontology (GO) evolves through restructuring, not just growth. A 2019 Cellular Component reorganization modularized terms, impacting computational analyses and highlighting the need for version-aware models.
Area of Science:
- Bioinformatics
- Computational Biology
- Ontology Engineering
Background:
- The Gene Ontology (GO) is a foundational resource for biological knowledge representation.
- Its dynamic evolution and internal structure are often overlooked in computational analyses.
- Understanding GO's changes is crucial for accurate biological data interpretation.
Purpose of the Study:
- To analyze 15 years of Gene Ontology evolution using network-based methods.
- To investigate the impact of curator-driven restructuring on GO's internal organization.
- To assess the implications of GO's evolution for computational biology and data reproducibility.
Main Methods:
- Network-based analysis of Gene Ontology structure over 15 years.
- Examination of specific restructuring events, particularly the 2019 Cellular Component branch reorganization.
- Comparison of GO's modularization with emerging semantic frameworks.
Main Results:
- Gene Ontology evolution is characterized by both incremental growth and significant curator-driven restructurings.
- A major 2019 reorganization of the Cellular Component branch modularized terms into anatomical entities and protein complexes.
- This restructuring impacts similarity metrics and aligns GO with multi-layer semantic network frameworks.
Conclusions:
- Gene Ontology should be treated as a dynamic, multi-layer semantic network, not a static resource.
- Version-aware and multi-layer models are essential for ensuring reproducibility and interpretability in computational analyses.
- Understanding GO's evolution is key to accurately representing biological function across various dimensions.
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