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Bioinformatics. 2016 Apr 15;32(8):1185-94. doi: 10.1093/bioinformatics/btv712. Epub 2015 Dec 7.

Extending gene ontology with gene association networks.

Author information

1
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China, Department of Energy Plant Research Laboratory, Michigan State University, East Lansing, MI 48824, USA.
2
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
3
School of Software, Harbin Institute of Technology, Harbin, China and.
4
Department of Energy Plant Research Laboratory, Michigan State University, East Lansing, MI 48824, USA, Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.

Abstract

MOTIVATION:

Gene ontology (GO) is a widely used resource to describe the attributes for gene products. However, automatic GO maintenance remains to be difficult because of the complex logical reasoning and the need of biological knowledge that are not explicitly represented in the GO. The existing studies either construct whole GO based on network data or only infer the relations between existing GO terms. None is purposed to add new terms automatically to the existing GO.

RESULTS:

We proposed a new algorithm 'GOExtender' to efficiently identify all the connected gene pairs labeled by the same parent GO terms. GOExtender is used to predict new GO terms with biological network data, and connect them to the existing GO. Evaluation tests on biological process and cellular component categories of different GO releases showed that GOExtender can extend new GO terms automatically based on the biological network. Furthermore, we applied GOExtender to the recent release of GO and discovered new GO terms with strong support from literature.

AVAILABILITY AND IMPLEMENTATION:

Software and supplementary document are available at www.msu.edu/%7Ejinchen/GOExtender

CONTACT:

jinchen@msu.edu or ydwang@hit.edu.cn

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
26644414
DOI:
10.1093/bioinformatics/btv712
[Indexed for MEDLINE]

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