Using this definition leads to the observed continuum of chromatin sensitivities across TFs.
Thanks to all authors and all the helpful input from lab members and members of the computational biology platform. Looking forward to feedback.

The framework quantifies chromatin sensitivity as the degree to which binding is determined by the motif sequence, i.e. the motif affinity, versus the flanking sequence around the motif, likely indicating the presence of additional motifs bound by other TFs.

To circumvent this problem, we developed a deep-learning-based framework which assesses chromatin sensitivity via an evaluation of predicted binding at a balanced set of in silico sequence constructs.

However, as we show, genomic motifs are biased in terms of their occurrence relative to chromatin features, making it difficult to quantitatively assess chromatin sensitivity from binding profiles at genomic motifs.

We started by characterizing TF binding at genomic motifs and found a large variety in binding behaviours, indicating both differential sensitivity to chromatin for different TFs and a general dependence of chromatin sensitivity on motif affinity.

Key finding is that chromatin sensitivity is not binary, but a continuum across TFs, clearly arguing against a purely hierarchical model involving chromatin-insensitive TFs (pioneers) that open the chromatin for other chromatin-sensitive TFs to bind.

Demonstrates how a combination of opportunistic binding and context-specific chromatin-opening underpin TF specificity, driving differentiation trajectories.

Great colleagues in a vibrant and supportive community. We are looking forward to your application !!!

congrats Pia. Well deserved