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Updated: May 21, 2026

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A Web Tool for Generating High Quality Machine-readable Biological Pathways
Published on: February 8, 2017
GO-Elite: a flexible solution for pathway and ontology over-representation
Alexander C Zambon1, Stan Gaj, Isaac Ho
1Departments of Pharmacology and Medicine, University of California at San Diego, La Jolla, CA 92093, USA.
Bioinformatics (Oxford, England)
|June 30, 2012
Summary
GO-Elite is a versatile pathway analysis tool supporting numerous species and biological IDs. It performs over-representation analysis on various ontologies and databases, presenting minimal, non-overlapping terms for clear visualization.
Area of Science:
- Bioinformatics
- Computational Biology
- Systems Biology
Background:
- Pathway analysis is crucial for interpreting high-throughput biological data.
- Existing tools often have limitations in species, identifier, or pathway support.
- Gene Ontology (GO) and other structured ontologies are fundamental for biological interpretation.
Purpose of the Study:
- To introduce GO-Elite, a flexible and powerful pathway analysis tool.
- To enable over-representation analysis across diverse species, identifiers, pathways, and gene sets.
- To provide a user-friendly platform for biological data interpretation.
Main Methods:
- GO-Elite performs over-representation analysis on user-provided gene lists against various biological databases.
- It leverages structured ontologies to identify a minimal set of non-overlapping terms.
- The tool supports over 60 species and 50 ID systems, including genes, diseases, phenotypes, and pathways.
Main Results:
- GO-Elite successfully performs pathway and ontology analysis for a wide range of biological data.
- It provides a minimal set of relevant terms, facilitating interpretation.
- Results can be visualized on WikiPathways or as networks.
Conclusions:
- GO-Elite offers a flexible and comprehensive solution for pathway and ontology analysis.
- Its broad support for species and ID systems makes it applicable to diverse research areas.
- The tool enhances biological data interpretation through efficient and visualized results.
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