Connecting chromatin sequence to chromatin structure

Synopsis

The DNA packaged inside a nucleus shows complex structures stabilized by a host of DNA-bound factors. This combination of DNA and bound factors is known as chromatin. Both the distribution of bound factors and the contacts between different locations of the DNA can be now measured on a genome-wide scale. Nevertheless, to what extent is the likelihood of contact between sites in the genome encoded by the spatial sequence of bound factors? 

Current approaches at addressing this question primarily use simulations of heterogeneous polymers to generate structures using the locations of bound factors. In contrast, in this talk I will present a number of statistical methods for modelling the probabilities of contact between distant genomic regions based on concepts ranging from statistical physics-archive, Bayesian modelling and state-of-the-art machine learning through dense neural networks. Our methods not only provide biologically meaningful predictions, they also highlighting key features of the mechanisms through which the three-dimensional conformation of DNA is coordinated by the interactions between DNA-bound factors.