Modeling protein–DNA binding via high-throughput in vitro technologies

Protein–DNA binding plays a central role in gene regulation and by that in all processes in the living cell. Novel experimental and computational approaches facilitate better understanding of protein–DNA binding preferences via high-throughput measurement of protein binding to a large number of DNA sequences and inference of binding models from them. Here we review the state of the art in measuring protein–DNA binding in vitro, emphasizing the advantages and limitations of different technologies. In addition, we describe models for representing protein–DNA binding preferences and key computational approaches to learn those from high-throughput data. Using large experimental data sets, we test the performance of different models based on different measuring techniques. We conclude with pertinent open problems.