Determining how transcriptional regulatory signals are encoded in vertebrate 
genomes is essential for understanding the origins of multicellular complexity; 
yet the genetic code of vertebrate gene regulation remains poorly understood. In 
an attempt to elucidate this code, we synergistically combined genome-wide 
gene-expression profiling, vertebrate genome comparisons, and transcription 
factor binding-site analysis to define sequence signatures characteristic of 
candidate tissue-specific enhancers in the human genome. We applied this 
strategy to microarray-based gene expression profiles from 79 human tissues and 
identified 7187 candidate enhancers that defined their flanking gene expression, 
the majority of which were located outside of known promoters. We 
cross-validated this method for its ability to de novo predict tissue-specific 
gene expression and confirmed its reliability in 57 of the 79 available human 
tissues, with an average precision in enhancer recognition ranging from 32% to 
63% and a sensitivity of 47%. We used the sequence signatures identified by this 
approach to successfully assign tissue-specific predictions to approximately 
328,000 human-mouse conserved noncoding elements in the human genome. By 
overlapping these genome-wide predictions with a data set of enhancers validated 
in vivo, in transgenic mice, we were able to confirm our results with a 28% 
sensitivity and 50% precision. These results indicate the power of combining 
complementary genomic data sets as an initial computational foray into a global 
view of tissue-specific gene regulation in vertebrates.