Because of computational complexity of the problem, Gibbs sampling is often used to identify binding positions (18) . In this paper, we present a new strategy that clusters Gibbs sampling results at each input nucleotide-a technique we term positional clustering-to improve accuracy of predicted TF binding. We evaluate the efficacy of our approach using known examples of binding and regulation in yeast and experimentally testing predicted TF-binding sites upstream of the subunit genes coding for the heteromeric mammalian neurotransmitter receptor system, the type A g-aminobutyric acid receptor (GABA A R).