A systematic phylogenetic footprinting approach was performed to identify conserved transcription factor binding sites (TFBSs) in mammalian promoter regions using human, mouse and rat sequence alignments. We found that the score distributions of most binding site models did not follow the Gaussian distribution required by many statistical methods. Therefore, we performed an empirical test to establish the optimal threshold for each model. We gauged our computational predictions by comparing with previously known TFBSs in the PCK1 gene promoter of the cytosolic isoform of phosphoenolpyruvate carboxykinase, and achieved a sensitivity of 75% and a specificity of approximately 32%. Almost all known sites overlapped with predicted sites, and several new putative TFBSs were also identified. We validated a predicted SP1 binding site in the control of PCK1 transcription using gel shift and reporter assays. Finally, we applied our computational approach to the prediction of putative TFBSs within the promoter regions of all available RefSeq genes. Our full set of TFBS predictions is freely available at http://bfgl.anri.barc.usda.gov/tfbsConsSites.

A systematic phylogenetic footprinting approach was performed to identify conserved transcription factor binding sites (TFBSs) in mammalian promoter regions using human, mouse and rat sequence alignments. We found that the score distributions of most binding site models did not follow the Gaussian distribution required by many statistical methods. Therefore, we performed an empirical test to establish the optimal threshold for each model. We gauged our computational predictions by comparing with previously known TFBSs in the PCK1 gene promoter of the cytosolic isoform of phosphoenolpyruvate carboxykinase, and achieved a sensitivity of 75% and a specificity of approximately 32%. Almost all known sites overlapped with predicted sites, and several new putative TFBSs were also identified. We validated a predicted SP1 binding site in the control of PCK1 transcription using gel shift and reporter assays. Finally, we applied our computational approach to the prediction of putative TFBSs within the promoter regions of all available RefSeq genes. Our full set of TFBS predictions is freely available at http://bfgl.anri.barc.usda.gov/tfbsConsSites.
Algorithms ; Amino Acid Sequence ; Animals ; Base Sequence ; Binding Sites ; genetics ; Cell Line, Tumor ; Computational Biology ; methods ; Conserved Sequence ; Electrophoretic Mobility Shift Assay ; Humans ; Intracellular Signaling Peptides and Proteins ; genetics ; Luciferases ; genetics ; metabolism ; Mice ; Normal Distribution ; Oligonucleotides ; genetics ; metabolism ; Phosphoenolpyruvate Carboxykinase (GTP) ; genetics ; Promoter Regions, Genetic ; genetics ; Protein Binding ; Rats ; Recombinant Fusion Proteins ; genetics ; metabolism ; Regulatory Sequences, Nucleic Acid ; genetics ; Reproducibility of Results ; Sp1 Transcription Factor ; genetics ; metabolism ; Transcription Factors ; metabolism ; Transfection