GO2MSIG generates collections of gene sets in MSigDB format based on the Gene Ontology (GO) project hierarchy and gene association data, for use with the Gene Set Enrichment Analysis (GSEA) implementation available at the Broad Institute. This enables rapid creation of gene set collections for multiple species.
Syntren (Synthetic Transcriptional Regulatory Networks) is a network generator that creates synthetic transcriptional regulatory networks and produces simulated gene expression data that approximates experimental data. Network topologies are generated by selecting subnetworks from previously described regulatory networks. Interaction kinetics are modeled by equations based on Michaelis-Menten and Hill kinetics. Our results show that the statistical properties of these topologies more closely approximate those of genuine biological networks than do those of different types of random graph models. Several user-definable parameters adjust the complexity of the resulting data set with respect to the structure learning algorithms.
RENCO (REgulatory Network generator with COmbinatorial control) is a C++ based software for automatic generation of ordinary differential equations for gene and protein expression dynamics in artificial regulatory networks.
Generator (GENElist Research Aimed Theme-discovery executOR) is a tool to evaluate and group incoherently annotated genes into subsets according to their gene ontology (GO) terms.