Experimental data of a single promoter can be used for in silico detectionof genes with related regulation in the absence of sequence similarity

Gene expression is presently a major focus in genome analysis, and the expe rimental data on regulatory mechanisms and functional transcription factor binding sites are steadily growing. However, the annotation of transcriptio nal regulation of sequences cannot keep pace with the exponential growth of sequence databases. Employing detailed experimental data of a single promo ter or enhancer to predict genes with similar regulation would provide a po werful method to link the literature about transcriptional regulation and s equence databases. To this end, we used information on individual functiona l transcription factor binding sites to compose in silico promoter and enha ncer models of muscle-specific genes and to analyze the rodents section of EMBL with these models. Exhaustive evaluation of all hits revealed every se cond to third match to be a muscle-associated gene. Moreover, functionally related regulatory regions were detected by our model-based approach even i n the absence of sequence similarity. We believe that this new approach is a substanial extension to database analysis by BLAST or FASTA, which are re stricted to sequence similarity.