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Background: The analysis of differential gene expression is a fundamental tool to relate gene regulation with specific biological processes. Differential binding of transcription factors (TFs) can drive differential gene expression. While DNase-seq data can provide global snapshots of TF binding, tools for detecting differential binding from pairs of DNase-seq data sets are lacking., Results: In order to link expression changes with changes in TF binding we introduce the concept of differential footprinting alongside a computational tool. We demonstrate that differential footprinting is associated with differential gene expression and can be used to define cell types by their specific TF occupancy patterns., Conclusions: Our new tool, Wellington-bootstrap, will enable the detection of differential TF binding facilitating the study of gene regulatory systems.

Background: Reverse-engineering approaches such as Bayesian network inference, ordinary differential equations (ODEs) and information theory are widely applied to deriving causal relationships among different elements such as genes, proteins, metabolites, neurons, brain areas and so on, based upon multi-dimensional spatial and temporal data. There are several well-established reverse-engineering approaches to explore causal relationships in a dynamic network, such as ordinary differential equations (ODE), Bayesian networks, information theory and Granger Causality., Results: Here we focused on Granger causality both in the time and frequency domain and in local and global networks, and applied our approach to experimental data (genes and proteins). For a small gene network, Granger causality outperformed all the other three approaches mentioned above. A global protein network of 812 proteins was reconstructed, using a novel approach. The obtained results fitted well with known experimental findings and predicted many experimentally testable results. In addition to interactions in the time domain, interactions in the frequency domain were also recovered., Conclusions: The results on the proteomic data and gene data confirm that Granger causality is a simple and accurate approach to recover the network structure. Our approach is general and can be easily applied to other types of temporal data.