To reduce the false positive rates of forward engineering method, Yu et al proposed a combinatorial inferring method that integrates forward engineering with reverse engineering of which relationships between TFs and targets are inferred based on expressional correlation. Compared with other networks, TRN has advantages in properties of reflecting regulatory relationship, dynamics and scale-free topological structure. TRN depicts the transcriptional regulation of TFs on SC 144 hydrochloride target genes which is an important regulatory mechanism of gene expression. Neph S et al studied TRN of 41 diverse cell and tissue types using DNase I footprinting technology and found that human TF networks are highly cell selective. TRN is a scale-free network, in which the number of nodes that make a large number of connections with other nodes is much lower than the number of nodes with few connections, whereby hubs play a central role in directing the cellular response to a specific stimulus. All these features make TRN an irreplaceable tool in disease research. In 2012, Zeng et al found hepatocellular TASP 0390325 carcinoma metastasis related TF-regulated modules by comparing regulatory network between metastatic and non-metastatic liver cancer. With the development of high-throughput technology, especially the flourish of SNP microarray, combined analysis of genome and transcriptome is becoming increasingly popular, and has greatly promoted our understanding of complex diseases. Copy number variation, an important kind of genomic variation, has gained increasing attention in recent years mainly due to SNP microarray technology which has made studying whole genome fast and economical. The importance of CNVs to occurrence and development of disease has been confirmed in many studies. Until now, most studies of CNVs are focused on CNVs�� impact on expression of genes located in verified regions, like eQTL, a linear-regression based method. Others may combine CNV with network method, like co-expression network to analyze CNVs�� impact on not just genes inside CNV regions but also outside CNV regions that are co-expressed. But there is little work about interpreting influence of genomic variation on expression through its disturbance to TRN. Mutation in TFs can cause huge cascade effects as a TF targets a large amount of genes involving many biological processes. For example, TP53, a well-known tumor suppressor transcription factor, its mutation has been reported associated with cell migration and invasion. In 2012, David et al detailed three mutated transcriptional factors NKX2-5, GATA4, and TBX5 and their affected pathways in congenital heart disease.