Hu and Gallo BMC Genomics 2010, 11:49 http://www.biomedcentral.com/1471-2164/11/49 tissue TF pairs from liver tissue. A closer examination shows that liver-tissue TF pairs from our prediction are enriched with CEBP, HNF3, and HNF4, and that liver- specific TF pairs from Yu et al are enriched with HNF1 and HNF4. All these TFs are known liver-specific TFs such as HNF3 [32], which initiates the liver transcrip- tional event, and HNF1 [33], which interacts with other important TFs to establish transcriptional hierarchy in liver tissues. These results demonstrate that different methods were able to identify interacting TFs from dif- ferent angles. Therefore, the findings from our study provide new insight into the mechanism controlling tis- sue gene expression. Filtering TF pairs of housekeeping genes from those of tissue-expressed genes is an important step to eliminate TF pairs which play general but not tissue-specific roles in individual tissues. The filtering process reduced the number of predicted TF pairs from 3024 to 2549 (15.7%) for all 79 tissues. This reduction for TF pairs was, however, significantly larger when individual tissues were concerned (39% to 59%), indicating that a large number of overlapping TF pairs had ubiquitous roles among different tissues. The remaining interacting TFs in each tissue were more tissue-specific, which was best evidenced by the result that the predicted TF pairs from liver tissue contained the same number of known liver- specific interacting TFs before and after the filtering. The relative small number of tissue-unique TF pairs out of all tissue TF pairs and the findings from conservation analysis for the functions of tissue TF pairs between tis- sues of two muscle groups from this study also indicate that tissue TF pair with identical 2-TF combination might play different functional roles in different tissues. Our findings show that tissue gene expression is con- trolled by a variety of interacting TFs either on the pro- moter of a gene or through TF-TF interaction networks. These identified TF interactions may constitute a large part of interacting TFs in each tissue but is not a com- plete list. To fully understand the mechanisms control- ling tissue gene expression requires additional study, which has been best evidenced from the comparison of interacting TFs in liver tissue between Yu et al. [19] and ours. Other than the prediction methods, the target gene selection can contribute greatly to tissue TF identi- fication. Our prediction picked up 8 of the 27 known liver-specific interacting TFs in liver tissues. A couple factors might be responsible for not identifying the other known liver-specific interacting TFs. First, these known liver-specific TF interactions were discovered from broad spatial and temporal conditions. The selected liver genes in this study however represented only one of many conditions under which liver-specific TFs play their roles. This was exemplified by known liver-specific interacting TFs in tissue TF pairs from Page 11 of 15 liver and fetal liver tissues from our prediction. Whereas tissue TF pairs from liver tissue contained 8 known live- specific interacting TFs, fetal liver contained 3 known live-specific interacting TFs with 2 common to those in liver, demonstrating the impact of temporal conditions on tissue TF discoveries. Second, it is unlikely for the top 300 tissue-expressed genes from a single condition to all have information for tissue interacting TF predic- tion. The choice of the top 300 tissue-expressed genes was based on the report of Pennacchio et al. [18] who have successfully used them to predict tissue-specific enhancers. Increasing the size of genes however would increase the chance of bringing noise to the prediction. Therefore, other than different computational approaches, selecting a proper list of tissue-expressed genes would have a great impact on the prediction of tissue TF pairs. One of the goals of this study was to find interacting TFs controlling tissue gene expression in a broad spatial and temporal manner. We performed analysis to identify tissue-type TF pairs for 11 selected tissue-type groups. While, as described above, each specific tissue may reflect only a small portion of all spatial and temporal conditions where tissue TF pairs play their regulation roles, tissue-type TF interactions provide a general view of their roles in multiple conditions. The analysis pro- cess has also led to other findings that the same type of tissues may have significant differences in both the con- tents of tissue TF pairs and the TF functional roles, which has been demonstrated by the conservation analy- sis of tissue TF pairs and their functions from muscle tissues. Tissue-type TF-TF interaction networks have provided not only lines of information on how tissue transcriptional programs are constructed but also new findings of potential roles for tissue-specific TFs in TF- TF interaction networks from liver tissue. Conclusions In this study, we successfully employed our previously developed function conservation approach [22], to pre- dict functional TF pairs from tissue-expressed genes in 79 human tissues. Based on the predictions, tissue TF pairs were identified. Our analyses led to the discovery of 2549 unique tissue TF pairs for the 79 human tissues. The validity of the discovered tissue TF pairs has been demonstrated by both known interacting and liver-spe- cific TFs. We also extended our study to find interacting TFs controlling gene expression in a broad temporal and spatial manner and identified 379 tissue-type TF pairs from 11 tissue-type groups, from which tissue-type TF-TF interaction networks have been built. The results also indicated that tissue-specific TFs may play an important role in recruiting non-tissue-specific TFs to the TF-TF interaction network, offering the potential for