GNLabComputational Pipeline for Large-Scale Gene Network Analysis | |
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GNLab Tags
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GNLab Description
The GNLab name stands for Gene Network virtual Laboratory. GNLab was developed to be a novel bioinformatics tool for the large-scale analysis of gene regulatory networks (GRNs). This C++ command-line tool supports the analysis of both the static structure and dynamic behaviour of a GRN. GNLab is developed to support the design and implementation of large-scale repeatable computational experimentations on GRNs. GNLab consists of separable components for network generation, simulation, analysis, visualization, comparison and inference. Through the use of a user-defined script, these components can be piped together to construct a pipeline of GRN analysis. Each component can be invoked by a command-line option. Main features: Network Generation: Three network growth models are available in GNLab. A random network can be generated by the Erdos-Renyi model (-r), the Scale-free model (-f), or the Charleston-Ho model (-g). The Charleston-Ho model is a newly proposed network growth model that is based on well-know processes in genome evolution. This model is shown to capture the detailed topological structure of the real GRNs (Ho and Charleston, in preparation). Network Visualization:GNLab can produce input files for GraphViz, GEOMI and Cytoscape. This functionality can be invoked by the command-line option -v. Network Simulation: Using the Hill's kinetics, the gene expression pattern of a GRN can be simulated either deterministically or stochastically. Data for the time-series gene expression profile can be generated by invoking command-line option -t. The simulated data is stored in a text file with a ".data" extension. Three types of microarray datasets can be simulated by GNLab: time-series, gene perturbation, and condition-specific datasets. The command-line option -s is used to invoked a simulation of microarray data. Network Analysis: The static structure of a GRN can be quantified by a collection of network topological features. A set of 11 topological features is calculated in GNLab. Network Comparison : The topological similarity between two networks with the same number of nodes can be calculated in GNLab. A network comparison is invoked through the command-line option -c. A one-line summary of topological differences between the two networks is outputed to the console. Network Inference : GNLab does not perform network inference directly. However, it allows the microarray dataset it generates to be converted into the input format of ARACNe and Banjo. This process is invoked by the command-line option -d.
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