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GOAnnotator: linking protein GO annotations to evidence text.

Francisco M Couto1, Mário J Silva, Vivian Lee

  • 1Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Portugal. fcouto@di.fc.ul.pt

Journal of Biomedical Discovery and Collaboration
|December 22, 2006
PubMed
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This study introduces GOAnnotator, a tool that links uncurated protein annotations to relevant literature text, achieving 93% precision. This enhances the Gene Ontology (GO) annotation process for UniProt proteins.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Protein annotation with Gene Ontology (GO) terms is complex and time-consuming.
  • Manual GO annotation is precise but slow, leading to reliance on less accurate, automatically generated uncurated annotations.
  • Existing text-mining systems for automatic annotation lack the quality required by curators.

Purpose of the Study:

  • To develop an automated approach for linking uncurated GO annotations to supporting literature text.
  • To improve the quality and efficiency of protein GO annotation for UniProt proteins.
  • To provide a tool that assists human curators in the GO annotation process.

Main Methods:

  • An approach was developed to link uncurated GO annotations to literature text based on term similarity.

Related Experiment Videos

  • Text selection prioritized literature excerpts semantically similar to the uncurated annotation terms.
  • The Gene Ontology (GO) hierarchy was utilized to enhance the precision of the annotation selection process.

    • Main Results:

    • The developed approach successfully links uncurated annotations to relevant literature.
    • Extracted texts not only support existing uncurated annotations but also suggest novel annotations.
    • The GOAnnotator tool, integrating this approach, achieved 93% precision in providing correct evidence text for GO annotations.

    Conclusions:

    • GOAnnotator significantly improves the precision of automated GO annotation, reaching 93% accuracy.
    • The use of the GO hierarchy is key to achieving high precision by filtering terms similar to those in the GO Annotation (GOA) database.
    • GOAnnotator is the first approach to deliver high precision, offering efficient support for GO curators and is available as a free web tool.