Tools for in-Silico Reconstruction and Visualization of Gene Regulatory Networks (GRN)

Plethora of software tools are available for inference and visualization of Gene Regulatory Networks (GRN) with their relative strengths and weaknesses. System and computational biologists quite often find it difficult to select a candidate tool for their experimentation. In this paper we present a comprehensive study on some of the promising and influential software tools developed so far for in-silico reconstruction and visualization of GRN. We discuss the features of each tool, the underlying technology used along with their relative merits and limitations. Researchers normally use synthetic gene expression data for evaluation and validation of GRN methods. We also discuss various synthetic data generators and tools that support benchmarking against gold standards like DREAM challenge data. Finally, we suggest few important issues that may be helpful for the development of effective inference and visualization softwares.