LucidDraw: Efficiently visualizing complex biochemical networks within MATLAB
1 The Key Laboratory of Industrial Biotechnology, Ministry of Education, Jiangnan University, Wuxi 214122, China
2 School of Biotechnology, Jiangnan University, Wuxi 214122, China
3 School of Computer Science, Jiangsu Teachers University of Technology, Changzhou 213001, China
BMC Bioinformatics 2010, 11:31 doi:10.1186/1471-2105-11-31Published: 15 January 2010
Biochemical networks play an essential role in systems biology. Rapidly growing network data and versatile research activities call for convenient visualization tools to aid intuitively perceiving abstract structures of networks and gaining insights into the functional implications of networks. There are various kinds of network visualization software, but they are usually not adequate for visual analysis of complex biological networks mainly because of the two reasons: 1) most existing drawing methods suitable for biochemical networks have high computation loads and can hardly achieve near real-time visualization; 2) available network visualization tools are designed for working in certain network modeling platforms, so they are not convenient for general analyses due to lack of broader range of readily accessible numerical utilities.
We present LucidDraw as a visual analysis tool, which features (a) speed: typical biological networks with several hundreds of nodes can be drawn in a few seconds through a new layout algorithm; (b) ease of use: working within MATLAB makes it convenient to manipulate and analyze the network data using a broad spectrum of sophisticated numerical functions; (c) flexibility: layout styles and incorporation of other available information about functional modules can be controlled by users with little effort, and the output drawings are interactively modifiable.
Equipped with a new grid layout algorithm proposed here, LucidDraw serves as an auxiliary network analysis tool capable of visualizing complex biological networks in near real-time with controllable layout styles and drawing details. The framework of the algorithm enables easy incorporation of extra biological information, if available, to influence the output layouts with predefined node grouping features.