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Related Experiment Videos

Gotrees: predicting go associations from protein domain composition using decision trees.

Boris Hayete1, Jadwiga R Bienkowska

  • 1Serono Reproductive Biology Institute, Rockland, MA 02370, USA.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|March 12, 2005
PubMed
Summary

This study introduces a novel method for protein annotation using Gene Ontology (GO) terms, improving sensitivity over existing tools. The approach models proteins by their full functional domain content for more accurate biological role prediction.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • The Gene Ontology (GO) provides a standardized vocabulary for protein function, but manual annotation struggles to keep pace with data generation.
  • Existing automated methods, like InterPro2GO/PFAM2GO, rely on single protein domains, potentially leading to false positives and limited sensitivity for less studied proteins.

Purpose of the Study:

  • To develop a more sensitive and precise automated method for assigning Gene Ontology terms to proteins.
  • To improve upon existing homology-based annotation tools by considering the entire functional domain content of proteins.

Main Methods:

  • Proteins were modeled using their complete set of functional domains.
  • Individual decision tree classifiers were trained for each GO term based on known protein assignments.

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  • The novel method's performance was compared against the InterPro2GO approach.

    • Main Results:

    • The developed method demonstrated high sensitivity, specificity, and precision, with robustness to sparse data.
    • Compared to InterPro2GO, the new approach significantly improved sensitivity for Molecular Function (22%), Biological Process (27%), and Cellular (50%) GO terms.
    • A slight decrease in precision was observed, but overall performance was enhanced.

    Conclusions:

    • Modeling proteins by their entire functional domain content offers a more sensitive approach to Gene Ontology annotation.
    • This method provides a valuable tool for annotating less studied proteins and addressing the limitations of single-domain-based predictions.
    • The improved sensitivity enhances the comprehensiveness of protein function databases.