GNLab Computational Pipeline for Large-Scale Gene Network Analysis
Download

GNLab Ranking & Summary

GNLab Tags

analysis SSIS pipeline structure computational performance computational efficiency pipeline computational geometry computational custom pipeline Computational Model large network Automatic Pipeline large scale deployments computational finance Computational Component Computational Biolog computational astronomy daisy pipeline Penelope Cruz wallpaper computational chemistry gene analysis generate large-scale cloud microarray gene analysis gene expression analysis computational problem computational power pipeline component tester test pipeline component decode pipeline computational neuroscience pipeline fashion gene-reaction-protein network computational approach create gene network gene network generator protein-coding gene analysis analysis pipeline gene data analysis generate computational artwork simulate gene network growing generate gene network gene frequency data analysis large phylogenies analysis Computational Pipeline Large-scale Analysis Static Structure Computational Intelligence segmentation pipeline modifier gene analysis gene expression data analysis processing pipeline large-scale avg free firewall avipreview ares .snd to text fruity loops dj blaze hdtv driver auto parts software download video mixer alif baa quran touch keyboard v1.0 iball pen drive file explorer 2690 ssh server wind

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.

GNLab Related Software