Mapping protein-DNA interactions across biological scales: principles, scalability and applications.
TL;DR
A comparative overview of major PDI technologies organized according to the biological scale at which they operate is provided, including sequence- and chromatin-based binding prediction, multi-omics integration, and regulatory network inference.
Abstract
Mapping protein-DNA interactions (PDIs) is essential for understanding transcriptional regulation and chromatin organization. Experimental approaches now range from in vitro assays that characterize intrinsic DNA-binding specificity to chromatin-based methods that capture protein occupancy in native genomes, as well as single-cell and single-molecule technologies that reveal regulatory heterogeneity across cells and individual chromatin fibers. These methods differ in resolution, sensitivity, input requirements, and their ability to preserve chromatin context, giving each approach distinct strengths and limitations. Here, we provide a comparative overview of major PDI technologies organized according to the biological scale at which they operate. We discuss their underlying principles, quantitative features, throughput, and key considerations for experimental design and method selection. We also review computational approaches for PDI analysis, including sequence- and chromatin-based binding prediction, multi-omics integration, and regulatory network inference. In addition, we discuss current challenges, such as platform-specific biases, sparse signals in single-cell datasets, and the lack of standardized benchmarking, and highlight future directions for improving PDI mapping and interpretation.