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Total ancestry measure: quantifying the similarity in tree-like classification, with genomic applications
Haiyuan Yu1, Ronald Jansen, Gustavo Stolovitzky
1Department of Molecular Biophysics & Biochemistry, Yale University, PO Box 208114, New Haven, CT 06520, USA.
Bioinformatics (Oxford, England)
|June 2, 2007
Summary
We introduce the total ancestry method, a new probabilistic approach to quantify protein function similarity using Gene Ontology (GO) classifications. This method allows for rapid assessment of shared functional annotations and their statistical significance.
Area of Science:
- Bioinformatics
- Computational Biology
- Proteomics
Background:
- Protein function classifications like Gene Ontology (GO) use directed acyclic graph (DAG) structures.
- Calculating numerical relatedness between proteins is crucial for proteomics and information organization.
Purpose of the Study:
- To develop a novel probabilistic method for computing protein functional relatedness.
- To enable efficient assessment of statistical significance for shared functional annotations.
Main Methods:
- Developed the 'total ancestry method' based on shared higher-level category nodes.
- Compared the method with existing quantitative functional similarity measures.
- Implemented the measure for GO and MIPS functional catalogs.
Main Results:
- The total ancestry measure follows a power-law distribution for quick statistical significance assessment.
- Formal comparisons with other methods (shortest path, LCA, information-theoretic) were conducted.
- Practical applications in functional genomics contexts were demonstrated.
Conclusions:
- The total ancestry method provides a robust and efficient way to measure protein functional similarity.
- This approach has implications for large-scale information organization beyond proteomics.
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