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Bioinformatics Advance Access originally published online on July 14, 2005
Bioinformatics 2005 21(18):3645-3650; doi:10.1093/bioinformatics/bti581
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions{at}oupjournals.org

Integration of metabolic networks and gene expression in virtual reality

Yuting Yang 1,4, Levent Engin 1,4, Eve Syrkin Wurtele 2,4, Carolina Cruz-Neira 1,3,4 and Julie A. Dickerson 1,4,*

1Department of Electrical and Computer Engineering Iowa State University, Ames, IA 50011, USA
2Department of Genetics, Development and Cell Biology Iowa State University, Ames, IA 50011, USA
3Department of Industrial and Manufacturing Systems Engineering Iowa State University, Ames, IA 50011, USA
4Virtual Reality Applications Center Iowa State University, Ames, IA 50011, USA

*To whom correspondence should be addressed.

Motivation: Metabolic networks combine metabolism and regulation. These complex networks are difficult to understand and visualize due to the amount and diverse types of information that need to be represented. For example, pathway information gives indications of interactions. Experimental data, such as transcriptomics, proteomics and metabolomics data, give snapshots of the system state. Stereoscopic virtual environments provide a true three-dimensional representation of metabolic networks, which can be intuitively manipulated, and may help to manage the data complexity.

Results: MetNet3D, a 3D virtual reality system, allows a user to explore gene expression and metabolic pathway data simultaneously. Normalized gene expression data are processed in R and visualized as a 3D plot. Users can find a particular gene of interest or a cluster of genes that behave similarly and see how these genes function in metabolic networks from MetNetDB, a database of Arabidopsis metabolic networks, using animated network graphs. Interactive virtual reality, with its enhanced ability to display more information, makes such integration more effective by abstracting key relationships.

Availability: MetNet3D and some sample datasets are available at http://www.vrac.iastate.edu/research/sites/metnet/Download/Download.htm

Contact: julied{at}iastate.edu

Supplementary information: Color snapshots and movies are available at http://www.vrac.iastate.edu/research/sites/metnet/Bioinformatics/SupplementaryInformation.htm

Received on June 22, 2004; revised on April 29, 2005; accepted on July 11, 2005

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