Hu and Gallo BMC Genomics 2010, 11:49 http://www.biomedcentral.com/1471-2164/11/49 Page 6 of 15 Table 1: Summary of the identified tissue and tissue-unique TF pairs as well as top 5 tissue TF pairs in the 79 human tissues. (Continued) Skin Trachea 156 146 20 12 CEBPA:CEBPGAMMA**, HNF4ALPHA:PPARA, HMGIY:OCT4, CEBPGAMMA:PLZF, [41] AP2ALPHA:CP2 CEBPA:CEBPGAMMA**, CEBP:PAX4, CETS1P54:PPARA, AP2:XVENT1, [41] AP2ALPHA:TST1 Top 5 tissue TF pairs are those with the most significant correlation between enriched TFBS pairs and enriched overlapping orthologous genes; **experimentally proven interacting TFs with literature support; * at least one TF is tissue-specific based on tissue-specific single TFs from TRANSFAC11.4 database [37] specific single TFs. Out of 162 tissue TF pairs from liver tissue, we were able to obtain 30 (Binomial test; p = 2.3 × 10-14) where both TFs were liver-specific. For the 27 liver-specific known interacting TFs, we found 8 (30%) in both the predicted TF pairs (Binomial test; p = 3.6 × 10-9) and tissue TF pairs (Binomial test; p = 2.9 × 10-11) from liver tissue. These include HNF4ALPHA:HNF4AL- PHA, NF1:COUP_DR1, CEBPGAMMA:HNF4ALPHA, CEBPA:HNF3B, HNF3:HNF4ALPHA, HNF3:PPARA, CEPBA:GATA4, and HNF1:OCT1. All of them are key elements in liver specific transcriptional regulation. GO enrichment analyses indicated that genes whose promo- ters contained the predicted liver-specific TFBS pairs were mainly involved in liver specific functions [27,28], including oxidation reduction, acute-phase response, gluconegnesis, and lipoprotein & lipid metabolic pro- cesses (Additional File 2). Further analysis of the binding sites on the promoter sequence of individual genes indi- cated that we were able to reliably identify interacting TFs similar to those previously reported. One of the examples was the APOA1 gene, which was well-charac- terized to be synergistically bound by HNF3 and HNF4 [8]. Our prediction was able to successfully identify the HNF3:HNF4 binding sites on its promoter. A closer examination shows that our predicted HNF3 and HNF4 binding sites for the APOA1 gene are exactly those experimentally proven, liver tissue-specific HNF3 and HNF4 binding site combinations [8], which are highly conserved between human and mouse in regards to both nucleotide sequence and spacing between each binding site (Figure 3). It is important to note that the 79 human tissues represent only part of the temporal and spatial condi- tions from which the 105 known interacting TFs were discovered, and therefore it is unlikely to have all known interacting TFs in our predicted list. Neverthe- less, our results indicate that the use of function conser- vation approach and tissue-expressed genes was able to reliably identify to a great extent known interacting TFs, thus presenting very strong evidence for the validity of the identified tissue TF pairs. These results also indicate that filtering the TF pairs of housekeeping genes from those in each tissue is an important step to eliminate TFs playing a ubiquitous role, thereby the resulting TF pairs are more tissue-specific. Identification of tissue-type TF pairs One of the goals of this study is to find interacting TFs controlling gene expression in a broad spatial and tem- poral manner such as interacting TFs common to the same type of tissues. This can be achieved by searching tissue TF pairs common across all tissues of the same type such as the 7 muscle tissues. However, the use of all tissues may reduce the power for tissue-type TF pair identification, since the contents of tissue TF pairs and even the function of a common tissue TF pair may be dif- ferent between tissues of the same type. Therefore, we sought to first classify tissues into smaller but more clo- sely related groups based on tissue TF pairs, from which representative tissues for the same tissue type could be obtained. Accordingly, we used hierarchical clustering to group tissues, as no a priori knowledge was available for the number of groups for each tissue type. The results are shown in Figure 4, where tissues of the same type are generally grouped together such as testis, liver, pancreas, and brain. There are, however, exceptions for other tissue types which are grouped into either distinct groups or into groups with other types of tissues such as muscle and immune systems. While the muscle tissues are classi- fied into two distinct groups, of which one contains ske- letal muscle, heart, and tongue and the other contains smooth muscle and cardiac myocytes, tissues for immune systems are classified into a few groups, one of which dis- plays tighten link with a few cancer tissues. We extended our analysis to investigate conservation for tissue TF pairs between tissues of the two muscle groups. We computed overlap for both tissue TF pairs and their biological functions between tissues using hypergeometric distribution. We found little or no over- lap for both tissue TF pairs and their functions among tissues between these two groups, which was especially true for the function of tissue TF pairs (data not shown). On the other hand, both tissue TF pairs and their functions showed significant overlap between tis- sues within the same group (Figure 5). These results not only demonstrate the validity of our tissue classification but also indicated that tissues from the same type (here