  Understanding the three-dimensional (3D) structure of the genome is essential
for elucidating vital biological processes and their links to human disease. To
determine how the genome folds within the nucleus, chromosome conformation
capture methods such as HiC have recently been employed. However, computational
methods that exploit the resulting high-throughput, high-resolution data are
still suffering from important limitations. In this work, we explore the idea
of manifold learning for the 3D chromatin structure inference and present a
novel method, REcurrent Autoencoders for CHromatin 3D structure prediction
(REACH-3D). Our framework employs autoencoders with recurrent neural units to
reconstruct the chromatin structure. In comparison to existing methods,
REACH-3D makes no transfer function assumption and permits dynamic analysis.
Evaluating REACH-3D on synthetic data indicated high agreement with the ground
truth. When tested on real experimental HiC data, REACH-3D recovered most
faithfully the expected biological properties and obtained the highest
correlation coefficient with microscopy measurements. Last, REACH-3D was
applied to dynamic HiC data, where it successfully modeled chromatin
conformation during the cell cycle.
