Dense neural networks for predicting chromatin conformation.

ABSTRACT:

Background

DNA inside eukaryotic cells wraps around histones to form the 11nm chromatin fiber that can further fold into higher-order DNA loops, which may depend on the binding of architectural factors. Predicting how the DNA will fold given a distribution of bound factors, here viewed as a type of sequence, is currently an unsolved problem and several heterogeneous polymer models have shown that many features of the measured structure can be reproduced from simulations. However a model that determines the optimal connection between sequence and structure and that can rapidly assess the effects of varying either one is still lacking.

Results

Here we train a dense neural network to solve for the local folding of chromatin, connecting structure, represented as a contact map, t

SUBMITTER: Farre P 

PROVIDER: S-EPMC6186068 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature