Journal Article

Total ancestry measure: quantifying the similarity in tree-like classification, with genomic applications

Haiyuan Yu, Ronald Jansen, Gustavo Stolovitzky and Mark Gerstein

in Bioinformatics

Volume 23, issue 16, pages 2163-2173
Published in print August 2007 | ISSN: 1367-4803
Published online May 2007 | e-ISSN: 1460-2059 | DOI: https://dx.doi.org/10.1093/bioinformatics/btm291
Total ancestry measure: quantifying the similarity in tree-like classification, with genomic applications

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Motivation: Many classifications of protein function such as Gene Ontology (GO) are organized in directed acyclic graph (DAG) structures. In these classifications, the proteins are terminal leaf nodes; the categories ‘above’ them are functional annotations at various levels of specialization and the computation of a numerical measure of relatedness between two arbitrary proteins is an important proteomics problem. Moreover, analogous problems are important in other contexts in large-scale information organization—e.g. the Wikipedia online encyclopedia and the Yahoo and DMOZ web page classification schemes.

Results: Here we develop a simple probabilistic approach for computing this relatedness quantity, which we call the total ancestry method. Our measure is based on counting the number of leaf nodes that share exactly the same set of ‘higher up’ category nodes in comparison to the total number of classified pairs (i.e. the chance for the same total ancestry). We show such a measure is associated with a power-law distribution, allowing for the quick assessment of the statistical significance of shared functional annotations. We formally compare it with other quantitative functional similarity measures (such as, shortest path within a DAG, lowest common ancestor shared and Azuaje's information-theoretic similarity) and provide concrete metrics to assess differences. Finally, we provide a practical implementation for our total ancestry measure for GO and the MIPS functional catalog and give two applications of it in specific functional genomics contexts.

Availability: The implementations and results are available through our supplementary website at: http://gersteinlab.org/proj/funcsim

Contact: mark.gerstein@yale.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  6281 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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