Hu and Gallo BMC Genomics 2010, 11:49 http://www.biomedcentral.com/1471-2164/11/49 Page 7 of 15 Figure 2 Statistical significance for the occurrence of known interacting TFs. Validation results (-log10(p-values)) for predicted TF pairs (orange) and tissue TF pairs (green) in the 79 human tissues. Dash line indicates significant p-value cutoff which is < 0.05 above the dash line. Known interacting TFs display enrichment more in the tissue TF pairs than in predicted TF pairs for both the number of tissues (37 vs. 9) and the degree of enrichment (Binomial test: p = 3.2 × 10-2 to 6.8 × 10-6 vs. p = 4 × 10-2 to 3.4 × 10-4). 5 muscle tissues) may have great difference in both the contents of tissue TF pairs and TF functional roles. Based on the clustering results, we selected 11 tissue- type groups, each having 2 to 16 tissues, for tissue-type TF pair discovery. A cutoff threshold of tissue TF pairs common in at least 50% tissues from the same group was set up for searching tissue-type TF pairs. In addi- tion to the TF level, we also searched for tissue-type TF pairs based on their function using the same criteria of > 50% tissues in the same group. To this end, we were able to identify tissue-type pairs for all tissue groups as listed in Table 2. Whereas the number of tissue-type TF pairs ranges from 17 for immune/cancer group to 74 for testis, those at the functional level have relatively smaller numbers, ranging from 3 for thyroid to 40 for testis. All (379) tissue-type TF pairs as well as their corresponding functions for the 11 tissue-type groups are listed in Additional File 3. Reconstruction of tissue-type TF-TF interaction networks In an effort to reveal TF relationships in controlling tis- sue gene expression, we performed analysis to recon- struct TF-TF interaction networks. Using tissue-type TF pairs, we first looked for those with one shared TF between each other in the same tissue, from which TF- TF interaction networks were built by joining 2 or more TF pairs (Figure 1c). TF-TF interaction networks with the same topology in at least 2 tissues from the same tissue-type group were then selected as tissue-type TF- TF interaction networks, which are multi-input network motifs consisting of at least 3 TFs that bind to a set of gene promoters. A total of 84 tissue-type TF-TF interac- tion networks were identified for the 11 tissue-type groups, ranging from 1 for immune/cancer to 22 for tes- tis (Additional File 4). Sixty two of these tissue-type TF- TF interaction networks have a linear relationship between TFs with 1 to 4 internal TFs (i.e. TF connecting to 2 other TFs), indicating that the majority of the TF- TF regulatory networks have simple TF relationships for controlling tissue gene expression. Figure 6a shows a multi-input network motif from liver tissues, in which FOXJ2, HNF1, and TTF1 regulate 6 genes in a combina- torial manner by either 2 or 3 TFs. The remaining 22 tissue-type TF-TF interaction networks display more complex interacting structures with some of the internal TFs connecting to 3 or more TFs. Unlike the tissue TF pairs, we did not find any common tissue-type TF-TF interaction networks between differ- ent tissue types. In light of this, we performed a search to see if any single TFs played central roles in control- ling tissue gene expression across different tissues, and looked for internal TFs in multiple tissue-type TF-TF interaction networks. To this end, we found that AP2, PPARA, PAX4, FAC1, ZIC3, and SPZ1 served as inter- nal TFs in 8, 8, 8, 6, 5, and 4 tissue-type TF-TF interac- tion networks, respectively, suggesting their role as central hubs in tissue-type TF-TF interaction networks. Whereas FAC1 acts as the internal TF in 6 tissue-type TF-TF interaction networks from immune systems and cancer, SPZ1 mainly serves as the internal TF in tissue- type TF-TF interaction networks from testis, and the rest in 5 to 6 tissue-type TF-TF interaction networks from different tissue types. These results indicate that