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Bioinformatics Advance Access originally published online on March 16, 2009
Bioinformatics 2009 25(9):1178-1184; doi:10.1093/bioinformatics/btp128
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© 2009 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.

GS2: an efficiently computable measure of GO-based similarity of gene sets

Troy Ruths *, Derek Ruths and Luay Nakhleh

Department of Computer Science, Rice University, 6100 Main Street, MS 132, Houston, TX, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: The growing availability of genome-scale datasets has attracted increasing attention to the development of computational methods for automated inference of functional similarities among genes and their products. One class of such methods measures the functional similarity of genes based on their distance in the Gene Ontology (GO). To measure the functional relatedness of a gene set, these measures consider every pair of genes in the set, and the average of all pairwise distances is calculated. However, as more data becomes available and gene sets used for analysis become larger, such pair-based calculation becomes prohibitive.

Results: In this article, we propose GS2 (GO-based similarity of gene sets), a novel GO-based measure of gene set similarity that is computable in linear time in the size of the gene set. The measure quantifies the similarity of the GO annotations among a set of genes by averaging the contribution of each gene's GO terms and their ancestor terms with respect to the GO vocabulary graph. To study the performance of our method, we compared our measure with an established pair-based measure when run on gene sets with varying degrees of functional similarities. In addition to a significant speed improvement, our method produced comparable similarity scores to the established method. Our method is available as a web-based tool and an open-source Python library.

Availability: The web-based tools and Python code are available at: http://bioserver.cs.rice.edu/gs2.

Contact: troy.ruths{at}rice.edu

Associate Editor: Limsoon Wong

Received on December 8, 2008; revised on February 9, 2009; accepted on March 2, 2009

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