Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

This article is part of the supplement: Symposium of Computations in Bioinformatics and Bioscience (SCBB07)

Open Access Research

BioGraphE: high-performance bionetwork analysis using the Biological Graph Environment

George Chin1*, Daniel G Chavarria1, Grant C Nakamura2 and Heidi J Sofia3

Author Affiliations

1 High Performance Computing Group, Computational Sciences and Mathematics Division, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington, USA

2 Information Analytics Department, Computational and Statistical Analytics Division, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington, USA

3 Computational Biology and Bioinformatics Department, Computational Sciences and Mathematics Division, Pacific Northwest National Laboratory, P.O. Box 999, Richland, Washington, USA

For all author emails, please log on.

BMC Bioinformatics 2008, 9(Suppl 6):S6  doi:10.1186/1471-2105-9-S6-S6

Published: 28 May 2008

Abstract

Background

Graphs and networks are common analysis representations for biological systems. Many traditional graph algorithms such as k-clique, k-coloring, and subgraph matching have great potential as analysis techniques for newly available data in biology. Yet, as the amount of genomic and bionetwork information rapidly grows, scientists need advanced new computational strategies and tools for dealing with the complexities of the bionetwork analysis and the volume of the data.

Results

We introduce a computational framework for graph analysis called the Biological Graph Environment (BioGraphE), which provides a general, scalable integration platform for connecting graph problems in biology to optimized computational solvers and high-performance systems. This framework enables biology researchers and computational scientists to identify and deploy network analysis applications and to easily connect them to efficient and powerful computational software and hardware that are specifically designed and tuned to solve complex graph problems. In our particular application of BioGraphE to support network analysis in genome biology, we investigate the use of a Boolean satisfiability solver known as Survey Propagation as a core computational solver executing on standard high-performance parallel systems, as well as multi-threaded architectures.

Conclusion

In our application of BioGraphE to conduct bionetwork analysis of homology networks, we found that BioGraphE and a custom, parallel implementation of the Survey Propagation SAT solver were capable of solving very large bionetwork problems at high rates of execution on different high-performance computing platforms.