Gene Regulatory Modular Network of asthma
Gene regulatory networks provide insight into the mechanisms of differential gene expression at a system level.
However, the methods for inference, functional analysis and visualization of regulatory modules and networks
require the user to collect heterogeneous data from many sources using numerous bioinformatics tools.
This makes the analysis expensive and time-consuming.
In this work, the BiologicalNetworks application -the data integration and network visualization environment - was extended with tools for inference and analysis of gene regulatory modules and networks.
The backend database of the application integrates public data on gene expression, pathways, transcription factor
binding sites, gene and protein sequences, and functional annotations.
Thus, all data essential for the analysis can be mined publicly. In addition, the user's data can either be integrated in the database and become public, or kept private within the application.
The capabilities to analyze multiple gene expression experiments are also provided.
The generated network, regulatory modules and binding sites can be visualized and further analyzed within this same application.
In this use case, to build a regulatory module network, we used published microarray data (Novershtern et al. 2008) and compared the modules inferred by BiologicalNetworks with the modules inferred by the authors using Module Networks software (Segal et al. 2003). In Study 1, the input was all mouse genes listed in 61 modules inferred by Novershtern and others (the modules were inferred for 8086 mouse genes). To run the analysis on so many genes (6890 genes, as not all genes were included in the modules), 10GB RAM was required. Therefore, we also conducted Study 2 on a much smaller set of 16 genes from one module.
