Evaluation of GO-based functional similarity measures using S. cerevisiae protein interaction and expression profile data

doi:10.1186/1471-2105-9-472

BMC Bioinformatics
Volume 9

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Research article

Evaluation of GO-based functional similarity measures using S. cerevisiae protein interaction and expression profile data

Tao Xu¹^,2 , LinFang Du² and Yan Zhou³^,1

¹Shanghai-MOST Key Laboratory of Health and Disease Genomics, Chinese National Human Genome Center at Shanghai, Shanghai 201203, PR China

²College of Life Sciences, Sichuan University, Chengdu 610064, PR China

³Department of Microbiology, School of Life Sciences, Fudan University, Shanghai 200433, PR China

author email corresponding author email

BMC Bioinformatics 2008, 9:472doi:10.1186/1471-2105-9-472


Published:	6 November 2008

Abstract

Background

Researchers interested in analysing the expression patterns of functionally related genes usually hope to improve the accuracy of their results beyond the boundaries of currently available experimental data. Gene ontology (GO) data provides a novel way to measure the functional relationship between gene products. Many approaches have been reported for calculating the similarities between two GO terms, known as semantic similarities. However, biologists are more interested in the relationship between gene products than in the scores linking the GO terms. To highlight the relationships among genes, recent studies have focused on functional similarities.

Results

In this study, we evaluated five functional similarity methods using both protein-protein interaction (PPI) and expression data of S. cerevisiae. The receiver operating characteristics (ROC) and correlation coefficient analysis of these methods showed that the maximum method outperformed the other methods. Statistical comparison of multiple- and single-term annotated proteins in biological process ontology indicated that genes with multiple GO terms may be more reliable for separating true positives from noise.

Conclusion

This study demonstrated the reliability of current approaches that elevate the similarity of GO terms to the similarity of proteins. Suggestions for further improvements in functional similarity analysis are also provided.