ReTRN: a retriever of real transcriptional regulatory network and expression data for evaluating structure learning...

One of the important goals in systems biology is to infer transcription network based on gene expression data. Validation of the reconstructed network often requires benchmark datasets, e.g. gene expression data, which are usually unattainable. Synthetic datasets are therefore often needed to test the structure learning algorithms in a fast and reproducible manner. However, due to the lack of knowledge about the gene expression profiles, synthetic datasets may not resemble the biological reality. Here we present a computational tool, namely, ReTRN (Real Transcriptional Regulatory Networks) for extracting subnetworks from known transcription network and for generating corresponding gene expression data. By comparing with other implementations, we demonstrate that the network generated by ReTRN possesses scale free property, which resembles the biological reality. Moreover, ReTRN simultaneously generates gene expression data reflecting the temporal relationship in gene expression. We conclude that ReTRN provides a valid alternative to existing implementation and can be widely used in systems biology research.