simona: a comprehensive R package for semantic similarity analysis on bio-ontologies

Background Bio-ontologies are keys in structuring complex biological information for effective data integration and knowledge representation. Semantic similarity analysis on bio-ontologies quantitatively assesses the degree of similarity between biological concepts based on the semantics encoded in...

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Vydané v:BMC genomics Ročník 25; číslo 1; s. 869 - 15
Hlavný autor: Gu, Zuguang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London BioMed Central 16.09.2024
BioMed Central Ltd
Springer Nature B.V
BMC
Predmet:
Animal Genetics and Genomics
Annotations
Bio-ontology
Bioconductor
Biological effects
Biological Ontologies
Biology
Biomedical and Life Sciences
Computational Biology - methods
Data analysis
Data integration
Data structures
Ecosystem biology
Genes
Information processing
Knowledge representation
Life Sciences
Methods
Microarrays
Microbial Genetics and Genomics
Ontology
Plant Genetics and Genomics
Proteomics
R (Programming language)
R package
Random variables
Robustness
Semantic similarity
Semantics
Similarity
Software
Topology
Visualization (Computers)
Germany
Semantic similarity
Bioconductor
Bio-ontology
R package
ISSN:1471-2164, 1471-2164
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Shrnutí:Background Bio-ontologies are keys in structuring complex biological information for effective data integration and knowledge representation. Semantic similarity analysis on bio-ontologies quantitatively assesses the degree of similarity between biological concepts based on the semantics encoded in ontologies. It plays an important role in structured and meaningful interpretations and integration of complex data from multiple biological domains. Results We present simona , a novel R package for semantic similarity analysis on general bio-ontologies. Simona implements infrastructures for ontology analysis by offering efficient data structures, fast ontology traversal methods, and elegant visualizations. Moreover, it provides a robust toolbox supporting over 70 methods for semantic similarity analysis. With simona , we conducted a benchmark against current semantic similarity methods. The results demonstrate methods are clustered based on their mathematical methodologies, thus guiding researchers in the selection of appropriate methods. Additionally, we explored annotation-based versus topology-based methods, revealing that semantic similarities solely based on ontology topology can efficiently reveal semantic similarity structures, facilitating analysis on less-studied organisms and other ontologies. Conclusions Simona offers a versatile interface and efficient implementation for processing, visualization, and semantic similarity analysis on bio-ontologies. We believe that simona will serve as a robust tool for uncovering relationships and enhancing the interoperability of biological knowledge systems.
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ISSN:1471-2164
1471-2164
DOI:10.1186/s12864-024-10759-4