A predictive model for regulatory sequences directing liver-specific transcription.
The identification and interpretation of the regulatory signals within the human genome remain among the greatest goals and most difficult challenges in genome analysis. The ability to predict the temporal and spatial control of transcription is likely to require a combination of methods to address the contribution of sequence-specific signals, protein-protein interactions and chromatin structure. We present here a new procedure to identify clusters of transcription factor binding sites characteristic of sequence modules experimentally verified to direct transcription selectively to liver cells. This algorithm is sufficiently specific to identify known regulatory sequences in genes selectively expressed in liver, promising acceleration of experimental promoter analysis. In combination with phylogenetic footprinting, this improvement in the specificity of predictions is sufficient to motivate a scan of the human genome. Potential regulatory modules were identified in orthologous human and rodent genomic sequences containing both known and uncharacterized genes.
Krivan
W
W
Bioinformatics Unit, Center for Genomics and Bioinformatics, Karolinska Institutet, 17177 Stockholm, Sweden.
Wasserman
W W
WW
D000818:Animals; D019295:Computational Biology / Q000379:methods; D018983:DNA Footprinting / Q000379:methods; D006801:Humans; D008099:Liver / Q000378:metabolism*; D051379:Mice; D008957:Models, Genetic; D009928:Organ Specificity / Q000235:genetics; D010802:Phylogeny; D051381:Rats; D012045:Regulatory Sequences, Nucleic Acid / Q000235:genetics*; D014158:Transcription, Genetic / Q000235:genetics*