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BMC Bioinformatics. 2013;14 Suppl 2:S15. doi: 10.1186/1471-2105-14-S2-S15. Epub 2013 Jan 21.

Identifying cross-category relations in gene ontology and constructing genome-specific term association networks.

Author information

1
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.

Abstract

BACKGROUND:

Gene Ontology (GO) has been widely used in biological databases, annotation projects, and computational analyses. Although the three GO categories are structured as independent ontologies, the biological relationships across the categories are not negligible for biological reasoning and knowledge integration. However, the existing cross-category ontology term similarity measures are either developed by utilizing the GO data only or based on manually curated term name similarities, ignoring the fact that GO is evolving quickly and the gene annotations are far from complete.

RESULTS:

In this paper we introduce a new cross-category similarity measurement called CroGO by incorporating genome-specific gene co-function network data. The performance study showed that our measurement outperforms the existing algorithms. We also generated genome-specific term association networks for yeast and human. An enrichment based test showed our networks are better than those generated by the other measures.

CONCLUSIONS:

The genome-specific term association networks constructed using CroGO provided a platform to enable a more consistent use of GO. In the networks, the frequently occurred MF-centered hub indicates that a molecular function may be shared by different genes in multiple biological processes, or a set of genes with the same functions may participate in distinct biological processes. And common subgraphs in multiple organisms also revealed conserved GO term relationships. Software and data are available online at http://www.msu.edu/~jinchen/CroGO.

PMID:
23368677
PMCID:
PMC3549802
DOI:
10.1186/1471-2105-14-S2-S15
[Indexed for MEDLINE]
Free PMC Article

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