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Open Access Software

GO-2D: identifying 2-dimensional cellular-localized functional modules in Gene Ontology

Jing Zhu1, Jing Wang1, Zheng Guo12*, Min Zhang1, Da Yang1, Yanhui Li1, Dong Wang1 and Guohua Xiao1

Author Affiliations

1 Department of Bioinformatics, Harbin Medical University, Harbin 150086, China

2 Department of Pharmacology and Bio-pharmaceutical Key Laboratory of Heilongjiang Province and State, Harbin Medical University, Harbin 150086, China

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BMC Genomics 2007, 8:30  doi:10.1186/1471-2164-8-30

Published: 24 January 2007

Abstract

Background

Rapid progress in high-throughput biotechnologies (e.g. microarrays) and exponential accumulation of gene functional knowledge make it promising for systematic understanding of complex human diseases at functional modules level. Based on Gene Ontology, a large number of automatic tools have been developed for the functional analysis and biological interpretation of the high-throughput microarray data.

Results

Different from the existing tools such as Onto-Express and FatiGO, we develop a tool named GO-2D for identifying 2-dimensional functional modules based on combined GO categories. For example, it refines biological process categories by sorting their genes into different cellular component categories, and then extracts those combined categories enriched with the interesting genes (e.g., the differentially expressed genes) for identifying the cellular-localized functional modules. Applications of GO-2D to the analyses of two human cancer datasets show that very specific disease-relevant processes can be identified by using cellular location information.

Conclusion

For studying complex human diseases, GO-2D can extract functionally compact and detailed modules such as the cellular-localized ones, characterizing disease-relevant modules in terms of both biological processes and cellular locations. The application results clearly demonstrate that 2-dimensional approach complementary to current 1-dimensional approach is powerful for finding modules highly relevant to diseases.