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Bioinformatics Advance Access published online on May 31, 2007

Bioinformatics, doi:10.1093/bioinformatics/btm291
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Developing a similarity measure in biological function space

Haiyuan Yu 1,4,5,*, Ronald Jansen 6,* and Mark Gerstein 1,2,3,{dagger}

Department of Molecular Biophysics & Biochemistry1 and Computer Science2 Program in Computational Biology and Bioinformatics3 266 Whitney Avenue, Yale University PO Box 208114, New Haven, CT 06520 (203) 432-6105, FAX (360) 838-7861
Department of Genetics4 Harvard University
Department of Cancer Biology5 Dana-Farber Cancer Institute 1 Jimmy Fund Way Boston, MA 02
Computational Biology Center, Memorial Sloan-Kettering Cancer Center6 307 East 63rd Street, 2nd floor New York, NY 10021

*To whom correspondence should be addressed. Mark Gerstein, E-mail: mark.gerstein{at}yale.edu


   Abstract

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 level 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.

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 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.

Associate Editor: Dr. Alex Bateman


Received on December 31, 2006; revised on May 2, 2007; accepted on May 22, 2007

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